Biosensors doi: 10.3390/bios16110654

Authors: Umisha Siwakoti Oshima Shah Manoj Pandit Kyle Rugg Mark A. DeCoster Steven Amos Jones Elisa Castagnola

Nitric oxide (NO) regulates neuronal signaling and neurovascular function, but its rapid diffusion, short lifetime, and reactivity complicate real-time detection in tissue. We developed flexible platinum microelectrode arrays (MEAs) modified with nanostructured platinum (nano-Pt) and a permselective poly(m-phenylenediamine) (mPD) coating for sensitive, selective NO monitoring. Nano-Pt electrodeposition reduced impedance approximately eightfold at 10 Hz and increased charge-storage capacity approximately 7.4-fold without increasing electrode dimensions. Simultaneous amperometric recordings from multiple electrode channels demonstrated the multisite sensing proof of concept of the flexible MEA platform. Amperometric calibration showed linear responses from 50 to 250 nM (R2 > 0.999), with a sensitivity of 243.16 ± 6.12 pA/µM and an operational detection limit of 50 nM. The mPD coating attenuated responses to ascorbic acid, uric acid, and dopamine. Sensors retained NO sensitivity (180 ± 8.6 pA/µM) in endothelial cell culture medium and detected transients following exposure of primary brain microvascular endothelial cells to copper-containing high-aspect-ratio structures (CuHARS), corresponding to peak NO-equivalent concentrations of 320 ± 50 nM. In mouse hippocampus, local NMDA/glycine administration evoked responses corresponding to estimated NO concentrations of 154 ± 31 nM. Finite-element simulations predicted detectable NO concentration gradients across neighboring electrode sites. These results support flexible nano-Pt/mPD MEAs for NO monitoring in complex biological environments and motivate experimental validation of spatially resolved measurements.

Biosensors doi: 10.3390/bios16110653

Authors: Arkady S. Abdurashitov Roman A. Verkhovskii Oleg V. Grishin Ekaterina S. Prikhozhdenko Olga A. Inozemtseva Evgeny V. Shashkov Valery V. Tuchin Daniil N. Bratashov

Photoacoustic (PA) techniques including imaging, spectroscopy, and cytometry have demonstrated high biological and clinical potential for solving biomedical problems. Early diagnoses of cancer, infections, and cardiovascular disorders can be made through the detection of rare disease markers with a high PA contrast in static and dynamic conditions. As new promising analytical tools, PA techniques require specific calibration procedures. Here, we introduce a novel dynamic PA phantom—a spinning disk with light-absorbing targets in media, simulating a blood background covered by a liquid layer for acoustic coupling. The performance of calibration tools mimicking circulating tumor cells (CTCs) in the blood flow was demonstrated during the testing of the high-speed PA flow cytometry (PAFC) system. The obtained data suggest that a robust, easy-to-use, and low-cost dynamic PA phantom can be promising for quick validation of the sensitivity, resolution, response time, and reproducibility of clinically relevant PAFC and potentially other flow cytometry diagnostic platforms (for instance, fluorescent and Raman) for the detection of fast-moving circulating biomarkers including CTCs, bacteria, and clots.

Biosensors doi: 10.3390/bios16110651

Authors: Fernando Lahoz Sergio de Armas-Rillo Beatriz Abdul-Jalbar Diego Luis-Ravelo Tomás González-Hernández Francisco Bauzá Miguel Sotés Pierpaolo Bruscolini David Íñiguez

Random Lasing (RL) is based on the interaction of an optical amplifying system and a highly scattering medium that produces the optical feedback necessary for laser emission. RL has demonstrated its potential as a diagnostic tool in biomedicine when biological tissues are used as the scattering source. Growing interest now focuses on the development of non-invasive techniques for the early detection of neurodegenerative diseases. In this study, we evaluated the early diagnostic potential of RL in a mouse model (R6/1 mice) of Huntington’s disease (HD), a neurodegenerative disorder caused by a CAG trinucleotide repeat expansion in the huntingtin gene. RL spectra were obtained from blood samples of 8-week-old R6/1 mice, prior to the onset of overt motor symptoms, and from their wild-type littermates. Multivariate statistical classification of the RL data yielded a moderate discrimination performance (~66% accuracy). However, after mathematical treatment of RL spectra and the application of machine-learning algorithms, the classification accuracy increased to ~90%. These findings support further investigation of RL signals from blood combined with machine-learning-based data as a potential non-invasive approach for the early detection of HD.

Biosensors doi: 10.3390/bios16110652

Authors: Satheesh Natarajan Everardo Cobos Subhash C. Chauhan Murali M. Yallapu

Liquid biopsy offers a minimally invasive approach to cancer detection and monitoring, but the analysis of rare, fragmented tumor-derived nucleic acids remains challenging because of their low abundance and interference from wild-type sequences. Peptide nucleic acids (PNAs), with their high-affinity hybridization, nuclease resistance, and single-base mismatch discrimination, provide a promising molecular recognition strategy for cancer diagnostics. This review critically examines PNA-based biosensors, PNA-containing nanostructures and emerging PNA-origami-inspired architectures for precision oncology. Unlike recent reviews that discuss only PNA chemistry, utilized in biosensing the individual biomarkers and DNA-origami technologies, this review uniquely focuses on PNA-origami nanoarchitectures for cancer diagnostics in nanoscale-level detection. It critically examines how the chemical modification, programmable nanoscale arrangement, and spatial arrangement of probes and reporters may enable multiplexed detection of cancer-associated mutations, DNA methylation signatures, gene-fusion junctions and circulating microRNAs in liquid-biopsy samples. Optical, plasmonic, colorimetric, electrochemical, and nanopore-based detection strategies are discussed alongside established PNA-clamping and biosensing approaches. Emphasis is placed on analytical performance, structural characterization, matrix compatibility, reproducibility, scalability, and benchmarking against digital PCR, next-generation sequencing, and CRISPR-based diagnostics. Key translation barriers include scalable production, reproducible assembly, matrix compatibility, analytical benchmarking and validation using patient-derived specimens. Currently, PNA origami should be considered a preclinical, origami-inspired design framework for spatially organized and multiplexed cancer biomarker detection, rather than a clinically implemented diagnostic technology.

Biosensors doi: 10.3390/bios16110650

Authors: Jiaxi Li Rui Qin Yibo Yang Xunyu Yuan Lei Nie Guanglan Liao Yaping Xie

Sepsis remains a major cause of mortality and healthcare burden worldwide, and delays in identifying infectious etiologies and clinically actionable diagnostic signals continue to compromise the timely initiation of appropriate antimicrobial therapy and patient outcomes. Conventional blood culture methods are limited by prolonged turnaround times, while recent advances in molecular diagnostic technologies have significantly improved the speed and sensitivity of pathogen detection. These advances highlight the potential of rapid and accurate molecular diagnostics to support timely diagnosis and targeted treatment. However, as analytical performance continues to improve, the major bottlenecks have increasingly extended beyond individual assay performance to workflow integration, operational complexity, and clinical implementation. Current platforms still face inherent trade-offs among diagnostic performance, cost, automation, and usability, limiting their widespread clinical adoption. To address these challenges, we propose the SPICE framework, encompassing Speed, Precision, Intelligence, Cost, and Ease of Use, as a unified perspective for evaluating existing technologies and guiding the design of next-generation molecular diagnostic systems. Based on these five interconnected dimensions, this review synthesizes recent advances in nucleic acid amplification, sequencing, multiplex detection, data-driven analysis, and sample-to-answer diagnostic platforms within a unified system-level framework, and compares representative approaches according to their SPICE-based capability profiles, major strengths, and limitations. These analyses further identify key barriers to clinical translation and the system-level requirements for the development and clinical implementation of intelligent, integrated sample-to-answer molecular diagnostic platforms for sepsis.

Biosensors doi: 10.3390/bios16110649

Authors: Mengdie Xue Weihua Fan Lin Zhou Lei Yin

Biosensors have become indispensable tools in clinical diagnostics, transforming how diseases are detected, monitored, and managed. In this review, we survey six key emerging platform classes: engineered biorecognition biosensors (CRISPR-Cas, aptamer, molecularly imprinted polymers); electrochemical and electrical biosensors (graphene/MXene/quantum-dot nanomaterial transducers, field-effect, electrochemical impedance spectroscopy, photoelectrochemical sensing); optical and plasmonic biosensors (SPR, LSPR, SERS); lateral flow and paper-based assays; artificial intelligence-integrated smart diagnostics; and lab-on-chip microfluidic systems. For each, we analyze the recognition chemistry, transduction strategy, and reported clinical performance. We then map these technologies onto high-impact clinical domains: infectious disease, oncology, cardiovascular medicine, diabetes and metabolic monitoring, critical care and sepsis, and neurodegenerative disease—highlighting representative and quantitative performance metrics and explicitly distinguishing analytical performance from clinical validation status using a six-level validation framework. Finally, we discuss the translational barriers (reproducibility, matrix complexity, manufacturing scalability, regulatory pathways, and biocompatibility) and the opportunities that health-economic evidence for microfluidic platforms, AI integration, and wearable and implantable form factors offer for closing the gap between analytical performance demonstrated in laboratory settings and real-world clinical adoption.

Biosensors doi: 10.3390/bios16110648

Authors: Xiuting Li Huan Liu

Semiconductor–noble metal hybrids have emerged as attractive surface-enhanced Raman scattering (SERS) platforms for pharmaceutical analysis because they integrate interfacial chemical interactions with plasmon-mediated electromagnetic enhancement. Herein, a semiconductor–noble metal hybrid SERS substrate was constructed by sputtering Ag nanoparticles (NPs) onto exfoliated MoS2 nanosheets (NSs) and was applied to the quantitative detection of ibrutinib. Within this hybrid architecture, accessible surfaces for analyte adsorption were provided by the exfoliated MoS2 NSs, while plasmonically coupled electromagnetic “hotspots” were generated by the Ag NPs, as further supported by finite-difference time-domain (FDTD) simulations. The optimized substrate enabled quantitative SERS detection of ibrutinib over 10−6–10−2 mg mL−1 (R2 = 0.991), with a detection limit of 3.73 × 10−8 mg mL−1 and a relative standard deviation of 5.81%. These results demonstrate the potential of Ag-decorated MoS2 NSs for sensitive SERS detection of ibrutinib in standard solutions.

Biosensors doi: 10.3390/bios16110647

Authors: Fangfang He Zehui Bai Pansong Zhang

DNA adenine methyltransferase (Dam MTase) is a key epigenetic biomarker linked to bacterial pathogenicity and tumor progression, holding great value for disease diagnosis and drug discovery. Conventional Dam MTase detection approaches suffer from complex workflows, long assay times, limited sensitivity and unsatisfactory anti-interference ability, severely hindering their real-world implementation. Herein, a facile fluorescent biosensor was constructed for rapid Dam MTase activity detection by integrating a hairpin DNA probe, endonuclease cleavage inhibition and isothermal cyclic amplification. In the absence of Dam MTase, Nt.AlwI specifically cleaves the engineered hairpin probe to trigger amplification and generate intense fluorescence. Conversely, Dam-induced DNA methylation prevents enzymatic cleavage, suppresses amplification and reduces fluorescence output, where fluorescence intensity varies with Dam MTase activity in a concentration-dependent fashion. Experimental validation confirms high specificity and favorable anti-interference performance of this sensor under complex biological conditions. It enables inhibitor screening and reliable measurement of endogenous Dam MTase activity in bacterial samples. With simple configuration, easy manipulation and low cost, the biosensor addresses major drawbacks of classic assays and provides a robust analytical platform for epigenetic studies, clinical bacterial testing and high-throughput targeted-inhibitor screening.

Biosensors doi: 10.3390/bios16110646

Authors: Dania O. Sarieva Elena V. Alieva Valery N. Konopsky Dmitry V. Basmanov

The morphology of the functional coating (thickness, density, homogeneity) determines the accuracy and reproducibility of label-free biosensor analysis, yet the quality of the coating is usually assessed indirectly, outside the conditions of the analysis. We describe an in situ method for functional assessment of the coating by the biosensor’s own response, based on surface waves in a one-dimensional photonic crystal with two-dimensional imaging. In one cycle of glutaraldehyde activation and protein immobilization, we determine sorption capacity, interchannel homogeneity, flow stability and surface density. At a manyfold excess of reactive centers, the saturated binding signal is insensitive to the local density of functional groups. On a series of 3-aminopropyltriethoxysilane (APTES) coatings, the sorption capacity and homogeneity depend critically on the solvent composition, and the solution-front delay maps separate the coating nonuniformity from mass transfer. For the optimized coating, formed in a 40% solution of acetic acid in ethanol, where the low water content and acidity suppress the self-condensation of APTES, the surface concentration of bovine serum albumin reaches 1.98 mg/m2, a dense side-on monolayer with a coefficient of variation of 1.2%. The results are confirmed by IgG immobilization and streptavidin–biotin binding. The approach enables noninvasive control of biofunctionalization.

Biosensors doi: 10.3390/bios16110645

Authors: Ying Zhang Jiajia Zhang Xinyi Liu Jikai Yu Chenyang Li Zhongze Gu

Low-intensity pulsed ultrasound (LIPUS) has antitumor potential, but its effects on tumor cell mechanics remain poorly understood. In this study, photonic crystal cellular force microscopy (PCCFM) enabled time-resolved mechanical measurements of colorectal cancer cells after LIPUS treatment. Serial PCCFM structural-color and corresponding bright-field images were acquired to reconstruct the vertical stress and substrate deformation in cell-associated regions; integration of the reconstructed stress over defined regions yielded quantitative collective and single-cell vertical force measurements LIPUS altered the time course of collective cellular force after seeding exhibiting distinct collective response patterns. Single-cell analysis further showed marked intercellular heterogeneity in force magnitude and temporal profiles. These mechanical changes were accompanied by altered F-actin organization, fewer peripheral protrusions, and reduced migration and invasion indices, indicating concordant changes in cellular force and motility-related structural and functional phenotypes. In vivo, xenografts derived from LIPUS-pretreated cells had reduced final xenograft volume and tumor weight. Piezo1 immunofluorescence and Ca2+ imaging suggested an association between mechanosensitive-channel signaling and the observed mechanical changes. Overall, PCCFM resolved temporal vertical force responses to LIPUS and revealed associations among LIPUS treatment, cellular mechanics, and motility- and growth-related phenotypes. Therefore, PCCFM provides a label-free, visual, and quantitative cellular-force sensing approach for assessing the dynamic responses of tumor cells to external mechanical stimulation.

Biosensors doi: 10.3390/bios16110644

Authors: Yusuke Hirata Keisuke Shigetoh Rie Hirao Toru Onishi Jiaju Ma Nobuhiko Muramoto Nobuhiro Ishida

Detecting airborne viruses is important for preventing infectious disease outbreaks, but conventional workflows require aerosol collection and downstream processing. Here, we developed a proof-of-concept spiral microchannel chip integrating aerosol capture and on-chip nucleic acid amplification for localized virus detection. Peripheral microwells along the outer turns served as capture sites and reaction reservoirs, while an upstream humidification pool reduced evaporation under airflow. During aspiration, curvature-induced centrifugal bias and Dean-vortex secondary flow promoted aerosol transport toward the wells. Numerical simulations and fluorescent bead experiments provided complementary evidence of particle transport and deposition near the peripheral wells. At an airflow rate of 50 mL/min, the humidification pool extended the approximate drying time from 10 to 30 min. Amplification-accessible virus-derived DNA was detected directly in the wells using recombinase polymerase amplification (RPA) with an exonuclease-cleavable fluorescent probe. End-to-end operation was demonstrated using nebulized bacteriophage T2 aerosols, with localized fluorescence observed in multiple wells after incubation at 40 °C for 30 min, without intermediate sample transfer or nucleic acid extraction. By integrating capture, humidification, and amplification on a single chip, this approach reduces operational complexity and reagent handling. These findings demonstrate the feasibility of compact, extraction-free aerosol detection and support further development toward source-proximal and distributed airborne-virus monitoring.

Biosensors doi: 10.3390/bios16110643

Authors: Ben Wu Bin-Sheng He

Subcellular fluorescent reporters are used to select compounds and investigate drug mechanisms, but treatment can change reporter delivery, chemistry and biological effects as well as the intended measurand. This critical narrative review examines how those alternatives affect decisions in intracellular biosensing assays. We synthesize representative methodological and pharmacological studies across six cellular compartments and compare the scope of existing reviews of probe design, drug visualization and enzyme-specific validation. Published examples connect oxidation-product identification, calibration-model dependence and state-dependent localization to the conclusions each measurement can support. A decision matrix links competing explanations to informative countertests and specifies when a result supports only an optical phenotype, a compartmental measurement or a functional response. Drug-by-probe-by-illumination comparisons and independent functional endpoints are used to separate apparent optical rescue from biological recovery. Screening applications require an additional test of whether reporter features improve prediction beyond cell-count or viability baselines. The proposed workflow integrates established controls for a defined pharmacological decision; it is not a new validated method or a reporter ranking. Its practical purpose is to make the interpretation of discordant measurements explicit and to identify which additional experiment could change compound prioritization.

Biosensors doi: 10.3390/bios16110642

Authors: Zhuoan Jin Xiaodong Li Jing Zhang Ling Yang

Drug-metabolizing enzymes (DMEs), which are crucial for xenobiotic biotransformation, profoundly influence therapeutic efficacy, drug safety, and physiological homeostasis. However, conventional analytical methods such as LC-MS/MS and fluorescence-based assays face limitations in background interference, spatiotemporal resolution, and in vivo imaging, while the structurally diverse catalytic cavities and overlapping substrate preferences of DMEs complicate isoform-specific activity assessment. In this context, chemiluminescence (CL) probes, particularly programmable Schaap-type 1,2-dioxetanes that operate without external light excitation, offer near-zero background and high sensitivity for monitoring DME activity. Here, cavity-guided CL probes are defined as probes whose recognition triggers, steric dimensions, electronic properties, and linker architectures are selected or optimized according to the substrate-binding and catalytic features of a target enzyme’s catalytic cavity. This review summarizes reported DME-targeted CL probes and discusses how active-pocket architecture and substrate-recognition rules can guide probe design for oxidases, hydrolases, and reductases. Representative applications in drug-metabolism analysis, drug–drug interaction assessment, disease-related activity imaging, and translational studies are highlighted. On this basis, we propose a design framework that matches active-pocket properties, substrate archetypes, and CL triggering mechanisms. A cross-case comparison now summarizes detection limits, linear ranges, emission wavelengths, signal enhancement, response characteristics, selectivity, precision, or reproducibility where reported, and biological applications; unreported parameters are explicitly identified as evidence gaps.

Biosensors doi: 10.3390/bios16110641

Authors: Kyeonggu Lee Hyeji Yu Minsu Kim Hongjin Kim Seohyeon Kim Jonggyun Kim Ki-Young Jung Seung Min Lee Chang-Hwan Im

An eyeglass-type electroencephalography (EEG) device has emerged as a promising platform for continuous cognitive state monitoring owing to its seamless integration into daily life and greater usability compared with conventional headset-style EEG systems. However, the feasibility of EEG-based reduced-vigilance monitoring using this form factor remains insufficiently examined. To address this issue, we conducted a two-phase experiment involving 21 participants to establish a mental fatigue-sensitive index and validate a monitoring algorithm for reduced vigilance associated with drowsiness or mental fatigue using a custom-made eyeglass-type EEG device. In the first phase, a long-term sustained attention task (d2 test) was employed to induce mental fatigue, and the ratio of low-alpha to high-beta power was identified as a potential biomarker of mental-fatigue-related vigilance decline. In the second phase, system performance was evaluated through a prolonged, monotonous video-watching experiment designed to elicit reduced vigilance. The algorithm combined the drowsiness detection index (theta-plus-alpha power) from the TP9 and TP10 channels with the candidate fatigue-sensitive index from the differential channel. Subject-specific calibration-based thresholds were used to generate index-specific segment-level decisions, which were combined using a logical OR rule. In the pseudo-real-time evaluation of 16 participants, the final fusion model achieved a mean accuracy of 70.31%, a false-positive rate of 10.40%, a recall of 49.67%, and a precision of 81.80%, calculated from participant-level metrics. Detection performance depended partly on the inclusion of segments with prolonged eye closure, with lower recall after their exclusion. Nevertheless, these findings provide preliminary evidence for the feasibility of detecting behaviorally defined reduced vigilance using an eyeglass-type EEG device.

Biosensors doi: 10.3390/bios16110640

Authors: Kudratjon Zohirov Mirjakhon Temirov Adhamjon Zohirov Asror Abduvaliyev Farrux Qodirov Janibek Kurbanov Zavqiddin Temirov Rashid Nasimov

Wearable surface electromyography (sEMG) biosensors enable non-invasive monitoring of hand and forearm activity and are promising for sign language recognition (SLR) in digital health applications. However, signals acquired with wearable sEMG devices are affected by noise and motion artifacts, and the preprocessing filter strongly shapes the extracted features, yet filtering methods are rarely compared under identical conditions. Here, eight preprocessing methods (Bessel, Butterworth, Chebyshev Type I and II, Elliptic, Equiripple FIR, Discrete Wavelet Transform (DWT), and Modified Wiener) were compared under controlled laboratory conditions using four-channel sEMG recorded with a wireless wearable acquisition system from 20 participants performing 10 sign classes. All methods shared the same segmentation, feature extraction, and classification pipeline with KNN, LDA, LR, RF, and SVM classifiers, evaluated under random split, cross-session, and subject-independent protocols. Butterworth filtering with KNN achieved the highest random split performance (Accuracy 96.66%, F1-score 96.12%) and the highest subject-independent accuracy (88.81%). Filter effectiveness depended on the classifier and validation scenario, and a higher SNR did not guarantee higher accuracy. LR and LDA required approximately 0.1 ms per sample for prediction in the evaluated computational environment. However, these measurements do not establish real-time performance on wearable hardware. The findings provide comparative evidence for selecting sEMG preprocessing methods under controlled offline conditions, while their applicability to real-time wearable systems requires further validation.

Biosensors doi: 10.3390/bios16110639

Authors: Kexun Tang Junhao Zheng Yuxin Zhou Dongzi Zhang Chao Ren Meijie Chen Feidan Yu Yi Ruan Qiang Lin Xiao Liang

Reliable assessment of liver iron concentration (LIC) is important for diagnosing and monitoring iron overload disorders. We used a spin exchange relaxation free (SERF) atomic magnetometer to record magnetic relaxation signals from resected human liver tissue. A total of 23 specimens from 20 patients, including normal, fatty, cirrhotic, and hepatocellular carcinoma (HCC) liver, were polarized with a Helmholtz coil inside a magnetic shield, and the field decay after switch off was recorded. For each specimen, a water-referenced, mass-normalized relaxation amplitude ΔB (pT/g) was derived. ΔB was compared with the HISTO-derived water transverse relaxation rates, R2water, from the patients’ clinical MRI. For HCC, SERF measurements were made on resected tumor tissue, whereas MRI sampled non-cancerous liver parenchyma in the same patient. Within each category, ΔB increased with R2water and rank order agreed fully in three of the four categories. Slope differences between categories were significant with conventional standard errors but not with heteroscedasticity-consistent (HC3) standard errors and are therefore regarded as exploratory. These measurements establish a SERF workflow for comparing post-polarization magnetic relaxation amplitudes across resected human liver specimens.

Biosensors doi: 10.3390/bios16110638

Authors: Solpa Lee Seoyeon Baek Yongwoo Jang

In biosensor design, molecularly imprinted polymers (MIPs) function as synthetic recognition matrices that create structurally selective binding sites through template-directed imprinting and can incorporate or interface with signal-generating units to convert binding events into measurable outputs. Although MIP-based sensors have traditionally focused on single-analyte detection, many applications in biomedical diagnostics, food safety, environmental monitoring, and point-of-care testing require simultaneous discrimination of multiple chemically related targets in complex samples. Conventional multiplexed MIP sensing relies primarily on spatially separated sensor elements or multi-template imprinting. Although these approaches have enabled substantial progress, they often require additional sensing compartments, electrodes, readout pathways, or spectrally resolved signals as the number of targets increases. Accordingly, a key challenge is to generate multiple analyte-dependent responses within a compact sensing format, such as a single well, sensing region, or integrated array, without expanding the number of physical channels. Cross-reactive MIP arrays offer a complementary strategy by employing partially selective receptors to generate multidimensional response patterns that can be computationally decoded. In this context, multiplexing capacity depends not only on imprinting chemistry but also on signal-generating materials that translate MIP–analyte interactions into measurable features. This review examines optical probes, electrochemical materials, plasmonic nanoparticles, porous frameworks, and two-dimensional materials as the candidate signal-generating components of cross-reactive MIP arrays. Such arrays have so far been directly demonstrated only with colorimetric, structural-color, and carbon-dot fluorescent readouts and with conducting-polymer electronic tongues, whereas the discussion of plasmonic, porous-framework, and two-dimensional materials rests on single-analyte demonstrations and is presented as a prospective design opportunity. It also discusses chemometric, machine learning, and artificial intelligence-based decoding strategies, which are increasingly important for extracting analyte-specific information from nonlinear, high-dimensional, and partially overlapping signals. Future advances will require the co-design of MIP receptors, signal-generating materials, compact array architectures, and drift-aware AI models for robust real-world multiplexed sensing.

Biosensors doi: 10.3390/bios16110637

Authors: Tomohiko Suemori Takashi Yamada Yoko Haramura Masaaki Satoh

Pulmonary circulatory abnormalities are common in congenital heart disease and are central to perioperative management. We evaluated multisite time-resolved near-infrared spectroscopy (TR-NIRS) for simultaneous assessment of TR-NIRS-derived estimated regional pulmonary blood volume index (PBVi) and cerebral blood volume (CBV) in children undergoing ventricular septal defect surgery. This retrospective study included 87 surgical episodes in 72 children undergoing pulmonary artery banding (PAB, n = 28), primary intracardiac repair (ICR, n = 42), or second-stage ICR (n = 17). PBVi and CBV indices were measured before and after surgery. Perioperative changes were analyzed using mixed-effects models, and associations with conventional hemodynamic indices were assessed using Spearman correlation. Preoperative PBVi/CBV ratios were 1.91 in the PAB group, 1.19 in the primary ICR group, and 0.94 in the second-stage ICR group. PBVi/CBV ratio correlated with Qp/Qs (r = 0.41, p = 0.002), while PBVi correlated with pulmonary-artery index (r = 0.48, p = 0.001) and left ventricular end-diastolic volume index (r = 0.30, p = 0.029). Multisite TR-NIRS may provide complementary physiological information regarding regional pulmonary and cerebral blood-volume distribution in children with congenital heart disease, although further validation of thoracic TR-NIRS measurements is required.

Biosensors doi: 10.3390/bios16110636

Authors: Lin Zha Xiaonan Pang Zhongxuan Gui Haotian Wang Xiaofeng Jin Fan Wang

Programmed death ligand-1 (PD-L1) is an essential immune checkpoint protein that plays a pivotal role in tumor immune evasion and has emerged as an important biomarker for cancer diagnosis, prognosis, and immunotherapy monitoring. Aptamers have attracted increasing attention as promising molecular recognition elements owing to their high specificity, excellent chemical stability, and facile synthesis. In this study, we developed an in silico-guided aptamer screening strategy that integrates virtual library construction, molecular docking, secondary structure prediction, and structural interaction analysis to identify high-affinity PD-L1-binding aptamers, yielding a dissociation constant (Kd) of 20.73 nM. Based on the optimized aptamer, a reduced graphene oxide (rGO)-modified screen-printed electrode was fabricated to construct a label-free electrochemical aptasensor. The developed aptasensor exhibited a wide linear detection range from 0.01 to 100 nM, with a detection limit of 0.0074 nM. Moreover, the biosensor demonstrated excellent selectivity, satisfactory reproducibility, and good storage stability. The combination of computational aptamer screening with electrochemical sensing provides an efficient strategy for developing high-performance aptamer biosensors and offers considerable potential for rapid PD-L1 determination in clinical liquid biopsy and cancer immunotherapy monitoring.

Biosensors doi: 10.3390/bios16110635

Authors: Jinchao You Zhiqiang Chen Xiaohao Gan Tian Shen Lina Zheng Chen Zhang Ye He

Iron is an important parameter in environmental water analysis, where its concentration strongly influences water quality and environmental behavior, thus necessitating its detection in aqueous samples. Herein, we report a simple, label-free fluorescent sensing platform based on copper-doped dual-emission carbon dots (CuCDs) for the rapid and quantitative detection of Fe3+ in aqueous media. CuCDs were synthesized via a one-step hydrothermal method using Cu (NO3)2·3H2O and tryptamine (C10H12N2) as precursors at 180 °C for 6 h. The resulting CuCDs exhibited two characteristic emission peaks at 336 nm and 395 nm under 290 nm excitation, and their fluorescence was selectively quenched by Fe3+. Under optimized conditions (MES buffer, pH 5, 60 °C, 10 min reaction time), the fluorescence intensity ratio (F0/F) showed a linear correlation with Fe3+ concentration in the range of 20–700 μM at both emission wavelengths, yielding limits of detection of 11.68 μM (336 nm) and 10.07 μM (395 nm). The sensor demonstrated good selectivity against eleven common coexisting metal ions at 100-fold concentrations. Practical applicability was validated through spiked recovery assays in Min River water samples, achieving recoveries of 84.4–119.5% with results consistent with those obtained via standard atomic absorption spectrometry. The method offers low cost, a 10 min response time, and operational simplicity, providing a proof-of-concept basis for environmental water screening and future portable implementation.

Biosensors doi: 10.3390/bios16110634

Authors: Mengyuan Li Yiyan Xie Xinlong Zhou Ragini Singh Wilfried Blanc Rui Min Xian Zhou Bingyuan Zhang Santosh Kumar

Cronobacter sakazakii (C. sakazakii) is a food pathogen that has drawn attention in the area of food safety, especially in relation to infant formula and other food matrices. However, the typical methods for detecting C. sakazakii can be tedious, time-consuming, and need complex tools and equipment, which restricts their use for quick detection. As a result, sensitive and quick detection techniques are essential. In the present study, a new type of optical fiber biosensor was developed, which combines CeCoMOF@Ag@MoS2 nanomaterials and gold nanorods (AuNRs). The sensor adopts a multimode fiber (MMF)–lanthanum-doped fiber (LDF)–MMF (MLM) fiber structure, and the integration of CeCoMOF, Ag@MoS2, and AuNRs combines the structural advantages of the porous framework with the plasmonic response of the metallic nanostructures, providing a composite sensing interface for optical fiber LSPR detection and measuring the refractive-index sensitivity of different concentrations of C. sakazakii. The sensing interface was designed by integrating CeCoMOF@Ag@MoS2 and AuNRs on the probe surface to optimize the LSPR response and provide a suitable platform for antibody functionalization. The probe was subsequently functionalized with monoclonal antibodies against C. sakazakii to facilitate target recognition. According to the experimental findings, the developed sensor’s detection range is 1 × 100–1 × 107 CFU/mL, the sensitivity is 5.1447 nm/[CFU/mL], and the detection limit is 0.78 CFU/mL. Moreover, the sensor has good repeatability, stability, selectivity, and reusability. The performance of the suggested sensor was analyzed in terms of sensing capabilities with starch-based matrix under the conditions tested. Note that the enhanced performance is not just due to La doping but due to the combined effect of the multi-tapered MLM structure, La doping and the nanomaterial–antibody interaction.

Biosensors doi: 10.3390/bios16110633

Authors: Sandra Costanzo Giovanni Buonanno Antonio Curcio

A combined approach of microwave sensor and machine learning procedure is proposed in this paper to realize accurate prediction of blood glucose concentration in terms of classification/regression model, by exploiting the full information (energy) content coming from a reduced set of scattering data provided by a compact fractal-shaped microwave sensor working in the transmission mode. Starting from an experimental baseline of six complex permittivity curves measured from blood-mimicking phantoms in the physiological spectrum 72 ÷ 216 mg/dL, six full-wave simulated transmission S21 curves are derived and adopted as starting points for the training process in the machine learning algorithm. Then, a frequency-dependent quadratic fitting model coupled with non-stationary stochastic noise perturbation is adopted to properly enrich the dataset, thus avoiding the need for a huge number of measurements. The proposed methodology is evaluated in-silico and further validated on an independent experimental scenario including 15 VNA-measured S21 transmission spectral responses. These are acquired from blood-mimicking gel phantoms on a text fixture (5 measurement curves used for training set augmentation, and 10 adopted for test validation). The discussed results demonstrate the preliminary effectiveness of the proposed approach as a promising proof-of-concept for non-invasive blood glucose monitoring, offering promising perspectives for future clinical investigations.

Biosensors doi: 10.3390/bios16110632

Authors: Vladimir I. Makarov Natalia A. Rozanova Yulia K. Komleva Elizaveta S. Perepelitsa Alexey S. Skobeltsin Mariia A. Lebedeva Anakha Satish Pavel P. Tregub Igor D. Romanishkin Victoria I. Zhdankina Anton S. Averchuk Tatiana I. Baranich Yulia A. Timoshina Nataliya A. Kolotyeva Alla B. Salmina

Vascular dysfunction at the level of cerebral endothelial cells is an early component of Alzheimer’s disease pathogenesis. Fluorescence lifetime imaging microscopy (FLIM) with phasor analysis enables label-free assessment of cellular metabolism based on NAD(P)H autofluorescence. This proof-of-concept study aimed to evaluate the potential of FLIM with fit-derived phasor analysis to compare cerebral endothelial cells (CECs) derived from a healthy donor and a patient with Alzheimer’s disease (AD) and to characterize their responses to metabolic modulators. CECs were differentiated from induced pluripotent stem cells obtained from a healthy donor (PO2) and a patient with AD (Alz9L, APP V717I mutation). Two-photon NAD(P)H FLIM was performed under basal conditions and following treatment with 3-chloro-5-hydroxybenzoic acid (3CLBA) and sodium lactate. The analysis included mean lifetime (τmean), phase lifetime (τφ), biexponential fitting parameters, and phasor coordinates reconstructed from these parameters. Under basal conditions, the phasor distributions of PO2 and Alz9L cells differed significantly despite similar mean NAD(P)H lifetimes. Alz9L cells exhibited a lower τφ and a higher lifetime of the short-lived component, τ1. Following sodium lactate treatment, Alz9L cells exhibited a higher a1/a2 ratio and lower τmean and τ1 values than PO2 cells. Comparison of the basal and post-lactate states revealed pronounced decreases in τmean and τ1 in Alz9L cells. Fit-derived phasor analysis jointly visualizes fitted NAD(P)H decay parameters and illustrates differences between the two studied CEC lines despite similar basal mean lifetimes. These proof-of-concept observations require validation in additional cell lines from independent donors.

Biosensors doi: 10.3390/bios16110631

Authors: Anastasia V. Bartosh Lyubov V. Barshevskaya Anatoly V. Zherdev Chuanlai Xu Boris B. Dzantiev

Rapid and productive monitoring of pesticide residues in food is essential for effective food safety. In this study, a lateral flow immunoassay (LFIA) was developed for the simultaneous detection of two triazole fungicides, cyproconazole and difenoconazole, in fruits and berries. The assay was realized using an indirect competitive format and two types of gold nanoparticles, namely nanospheres and nanoflowers, as labels. The chosen nanoflowers-based assay, which was chosen for its greater sensitivity, achieved visual and instrumental limits of detection of 0.6 ng/mL and 0.01–0.02 ng/mL for cyproconazole and 4 ng/mL and 0.20–0.75 ng/mL for difenoconazole, respectively. The assay was successfully applied to plum, cranberry, and cherry extracts, with recoveries ranging from 91% to 113% for cyproconazole and from 92% to 119% for difenoconazole.

Biosensors doi: 10.3390/bios16110630

Authors: Puyang Hou Zhi Qiao Qian Wang Jinjin Liu Kaihui Song Song Fang Ming Li

Improper use of vanillin as a food flavor additive presents a potential threat, and its effective supervision is therefore essential. Rapid biosensing is a beneficial supplementary or alternative strategy for current detection methods. Two modes of immunochromatographic assay (ICA) strips, based on gold nanoparticles (GNP) and fluorescent microparticles (FMP) labeled with a monoclonal antibody against vanillin, were separately assembled to obtain a GICA strip and an FICA strip for the detection of vanillin. The colorimetric/fluorescence signals of the strips were directly judged for semi-quantification by the naked eye and then digitally quantified by strip readers. The GICA demonstrated a linear range of 15.6–500 ng/mL, having a limit of detection (LOD) of 10.3 ng/mL. For the FICA strip, the enhanced sensitivity achieved an LOD value as low as 2.04 ng/mL and showed a linear range between 3.12 ng/mL and 200 ng/mL, representing a 5-fold improvement over the GICA strip. The two modes of ICA strips all had high specificity, stability, accuracy and precision for on-site vanillin detection within 10 min. For vanillin levels in authentic food product samples, the results were well correlated with the measurements obtained via the referenced ELISA and HPLC methods. The results indicated that two modes of ICA strips were capable of rapid, sensitive, on-site, semi-quantitative and quantitative detection of vanillin in food products, ultimately guaranteeing food safety and consumer health.

Biosensors doi: 10.3390/bios16110629

Authors: SangHyeok Kim Daejin Park

This study presents an on-device worker-safety framework that retains resolved primary risks as finite contexts for interpreting later worker behavior. Continuous FFH, workload, and BSEC-derived environmental monitoring is integrated with event-triggered No-motion gating and INT8 worker-state recognition. The selected 4.6-s CNN achieved 98.66% macro-F1 in segment-stratified validation, and nRF5340 replay completed the scheduled 1.2-s inference cycle without deadline misses. We distinguish representative literature-derived P1–P4 paradigms from a controlled integrated P4-I/P5 comparison using identical FFH, gas, workload and worker-state inputs, and identical same-episode decision rules. Across 1212 bounded matched event sequences, policy outputs agreed before episode closure; at 1812 post-closure worker-state checkpoints P5 retained prior-risk evidence, yielding 719 monitoring (R1) and 1093 elevated/critical (R2/R3) outputs, whereas P4-I had closed the earlier episode. A separate finite-state audit enumerated 9134 reachable abstract states and 91,861 admissible transitions. These deterministic findings isolate decision semantics rather than field hazard accuracy or subject-independent generalization. Separately, PPK2 profiling on an nRF5340-based Thingy:53 measured 3.319 versus 2.454 mA (periodic CNN versus CNN-skipped), a 26.05% reduction in the nRF5340 SoC mean current measured at the P2 header when inference was skipped.

Biosensors doi: 10.3390/bios16110628

Authors: Jessica Zúñiga-Hernández Francisca Herrera Vielma Fernanda Berrocal-Navarrete Paz Marin-Sanhueza Paula Romero Jenaro García-Huidobro Carlos E. Zambra

Paper-based colorimetric devices could support decentralized screening of cholinesterase inhibition after organophosphate (OP) exposure. We systematically evaluated how substrate identity, concentration and volume, reagent hydration, reading time, glycerol addition, and serum volume affect color development in a two-layer platform comprising a glass-fiber (LF1) reaction layer and a pH-indicator detection layer. Acetylcholine chloride (AChCl), butyrylcholine chloride (BChCl), and butyrylcholine iodide were first screened in solution; AChCl and BChCl were then tested on paper with commercial serum and six individual human sera, and color was quantified as blue-channel intensity from scanned images of the pH-indicator. A working configuration using 8 µL of 60 mM substrate, with readings taken after 20 min, was selected based on the observed colorimetric response. AChCl and BChCl did not differ significantly under these conditions, and larger serum volumes gave no consistent improvement. The selected configuration was 4 µL of serum and 8 µL of 60 mM AChCl containing 5% (v/v) glycerol. Commercial serum-only controls without added substrate shifted the indicator from yellow to green, showing a sample-related contribution to the basal indicator color that may influence the final RGB signal. In a separate exploratory comparison using BChCl, blue-channel intensity was not significantly correlated with pre-treatment reference cholinesterase activity in the six human sera. These results provide a working configuration for further microdevice development and highlight the need to account for sample background.

Biosensors doi: 10.3390/bios16110625

Authors: Yifan Bian Qianbin Zhao Gaozhe Cai Longfei Wu Liangping Ma Sheng Yan Donghui Wang

Periprosthetic joint infection (PJI) is a severe and difficult-to-treat complication following artificial joint replacement, occurring in approximately 1–2% of total joint arthroplasties and normally caused by the adhesion of bacteria to the prosthesis and the formation of biofilms. Since this disease lacks a single definitive diagnostic indicator, a comprehensive judgment needs to be made by combining serum inflammatory indicators, joint synovial fluid tests, microbial cultures, imaging, and intraoperative pathology. Although microbial culture remains the gold standard, it usually requires more than 3 days. Microfluidics breaks through the limitations of traditional cultivation and conventional nucleic acid testing, which are characterized by long time consumption, large sample requirements, complex operations, and bulky equipment. Microfluidics has the irreplaceable advantage in real-time, micro-scale, and multi-indicator synchronous detection of bacterial pathogens, while the serious consequences of PJI require more advanced and rapid detection technologies to enable more effective treatment. In this review, in addition to summarizing the advances of microfluidic technology in the field of bacterial detection over the past five years, we also propose and analyze the microfluidic technology strategies that are likely to develop in the field of PJI detection in the future.

Biosensors doi: 10.3390/bios16110627

Authors: Alessandra Corolea Lorena Dima Carmen-Daniela Neculoiu Mihaela Elena Idomir Alexandra Ripszky Silviu Mirel Pituru Mihaela Badea

Background: Growing research interest has focused on biosensing platforms as rapid, sensitive and economically advantageous tools for bacterial identification and antimicrobial resistance or susceptibility assessment in microbiological diagnostics. Conventional microbiological workflows remain essential reference methods but may require extended incubation and processing times. A wide range of biosensing strategies has therefore been investigated to accelerate bacterial detection and the assessment of antimicrobial response. Methods: This narrative review examines biosensing approaches relevant to bacterial detection and AST, including living-cell and whole-cell biosensors, electrochemical and impedimetric platforms, optical biosensors, bacteriophage-based systems, immunosensors, aptamer-based technologies, microfluidic approaches, and CRISPR-associated molecular detection. Particular attention is given to the distinction between platforms in which living cells function as sensing elements and systems in which bacterial cells or bacterial components represent the analytical target. Results: Reported analytical performance varied considerably according to bacterial species, recognition element, transduction strategy, sample matrix, sample-preparation requirements, and validation design. Selected experimental platforms achieved rapid detection or antimicrobial-response measurements; however, many studies were performed using buffer systems, artificially spiked samples, or relatively limited collections of bacterial isolates. Phenotypic AST platforms, molecular resistance-detection systems, and bacterial-identification technologies provide different types of information and should therefore not be considered interchangeable. Translation into routine microbiology remains limited by challenges related to sample preparation, reproducibility, sensor stability, standardization, validation against reference methods, and integration into clinical workflows. Conclusions: Cell-based and related biosensing approaches show potential to complement conventional microbiological methods, but further standardisation, validation and integration into routine workflows are required before widespread clinical implementation.

Biosensors doi: 10.3390/bios16110626

Authors: Peng Wu Jun Li Weiqi Wang

Tunneling magnetoresistance (TMR) sensors are highly sensitive magnetic field detectors with applications in biomagnetic sensing and human–computer interfaces. However, the systematic optimization of TMR sensor structures and array configurations for magnetic eye tracking remains limited. This study presents a simulation-guided approach to optimize TMR sensor arrays for eye tracking. First, a finite element model of a CoFeB/MgO/CoFeB magnetic tunnel junction was developed in COMSOL Multiphysics to optimize the MgO barrier thickness. Simulations from 0.5 to 4.0 nm showed that a 1.5 nm MgO barrier achieved an MR ratio of 285%, a sensitivity of 0.13 Ω·m/A, and a wide linear sensing range. TMR arrays with three to seven sensors were then evaluated using an NdFeB magnetic marker and a spherical eyeball model. The five-sensor array achieved the highest overall performance. Elliptical (O-shape) and sinusoidal (wave-shape) eye trajectories were further simulated using time-dependent magnetic field analysis with an ALE moving mesh. The maximum value method successfully reconstructed both trajectories with good agreement with the original paths. These results demonstrate the feasibility of TMR-based wearable eye tracking for human–computer interfaces.

Biosensors doi: 10.3390/bios16110624

Authors: Southamany Sisavengsouk Boonnada Pamornpathomkul Tanasait Ngawhirunpat Praneet Opanasopit Phuvamin Suriyaamporn

Microneedle (MN)-based optical biosensors are emerging as minimally invasive platforms for point-of-care testing (POCT) and wearable glucose monitoring by enabling direct access to interstitial fluid (ISF), which closely reflects plasma glucose dynamics. This review summarizes recent advances in MN-based glucose sensing with particular emphasis on optical biosensors. Blood-to-ISF glucose transport, physiological time lag, MN architectures, fabrication strategies, and ISF sampling mechanisms are discussed. Glucose recognition mechanisms are outlined together with optical transduction approaches, including colorimetric, fluorescence, surface-enhanced Raman scattering (SERS), photonic, and plasmonic sensing. Many colorimetric MN platforms currently function as single-use or endpoint POCT systems, whereas reversible fluorescence- and photonic-based sensors provide greater potential for repeated measurements and continuous glucose monitoring. Advanced functional materials, smartphone-assisted readout, and emerging artificial intelligence/machine-learning approaches for signal processing, calibration, and glucose prediction are also examined. Key analytical, mechanical, biocompatibility, and ex vivo/in vivo performance parameters are critically evaluated, together with patents, clinical investigations, and commercialization status. Compared with previous reviews that mainly address ISF sampling, general MN biosensing, glucose monitoring across multiple sensing modalities, or wearable optical sensors separately. This review provides an integrated glucose-specific framework linking ISF physiology, MN-mediated sampling, point-of-care and emerging continuous optical sensing, advanced materials, digital/AI analysis, performance validation, and clinical translation. Finally, current limitations involving biofouling, signal drift, long-term stability, manufacturing scalability, and clinical validation are discussed to highlight future directions toward advanced point-of-care platforms and emerging intelligent, wearable, and continuous glucose-monitoring systems.

Biosensors doi: 10.3390/bios16110623

Authors: Shmuel Burg Meir Cohen Reut Ashkenazy Sophie Terenteva Yaniv Lustig Viki Indenbaum Ravit Koren Tran Mai Hoa Tong Thi Kim Tuyen Ma Thi Huyen Nguyen Minh Hoan Le Thi Hoi Nguyen Vu Trung Eli Schwartz Amos Danielli

Microfluidic fluorescence immunoassays enable sensitive biomarker detection, but performance is limited by inefficient capture on planar surfaces and background fluorescence from unbound labels. Here, we present chip-based optical modulation biosensing (Chip-OMB), combining three-dimensional magnetic bead-based target capture, bead immobilization, internal fluid flushing, and modulated optical background subtraction. Magnetic beads are concentrated and immobilized in the chamber, while controlled on-chip flushing displaces unbound fluorescent labels without requiring user-performed washing after chip loading. Alternating optical interrogation of the bead cluster and surrounding solution enables background subtraction. Using fluorescently labeled magnetic beads, Chip-OMB achieved limits of detection of 0.1 pM in buffer and 22 pM in undiluted plasma, corresponding to 580-fold and 22-fold lower equivalent fluorophore-concentration limits of detection, respectively, than conventional steady-state bulk fluorescence measurements using a plate reader. Measurements correlated with the laboratory optical modulation biosensing system (R2 = 0.98). In an anti-dengue virus serotype 2 (DENV-2) nonstructural protein 1 (NS1) immunoglobulin G (IgG) immunoassay, Chip-OMB achieved a 3 ng/mL limit of detection with a ~4-log dynamic range. Among 56 sera, 29 DENV-2 cases confirmed by reverse transcription quantitative polymerase chain reaction (RT-qPCR) were Panbio IgG-positive and above the exploratory Chip-OMB cutoff, while all 27 study-defined negative controls were below it.

Biosensors doi: 10.3390/bios16110622

Authors: Alice Farsi Luigi La Spada Maria Elena Candela Mehdi Jafari Shahbazzade Mahdiye Eslami

Non-invasive salivary cortisol measurement requires separation of receptor binding from matrix effects and measurement artefacts. We combined a 1550 nanometre dual-graphene optical transducer with an antibody-functionalised cortisol assay and two separately evaluated artificial intelligence workflows. For structural modelling, 8192 scrambled Sobol proposals yielded 7941 accepted geometries. Grouped development selected an extremely randomised tree ensemble, whose sealed-test coefficient of determination was 0.986. Genetic and particle swarm searches were performed after model freeze, and direct transfer-matrix calculations gave a leading candidate sensitivity of 198.60 degrees per refractive-index unit and a resonance width of 0.1790 degrees. These computational candidates were distinct from the nominal cartridge used experimentally. The receptor-functionalised assay had a detection limit of 0.15 nanograms per millilitre and a validated quantitative range of 0.25–8.00 nanograms per millilitre. A gradient-boosted model using complete target and reference angular traces achieved mean absolute errors of 0.25 and 0.50 nanograms per millilitre in 25 blinded artificial-saliva samples and 40 human-saliva samples, respectively; conventional calibration gave corresponding errors of 0.34 and 0.62. Human-saliva measurements were compared with isotope-dilution liquid chromatography–tandem mass spectrometry. Nominal 90% prediction intervals achieved 92% and 90% coverage. The nominal optical advantage over the simpler AZO/graphene reference was incremental, and residual cortisone cross-reactivity remained. These results support a proof-of-concept biosensor requiring further manufacturing and external clinical evaluation.

Biosensors doi: 10.3390/bios16110621

Authors: Saleh I. Alzahrani

Dengue remains a major global public health concern, highlighting the need for rapid and sensitive methods for the early detection of dengue virus biomarkers. In the present study, a finite element model for the detection of dengue nonstructural protein 1 (NS1) using an interdigitated electrode (IDE)-based impedimetric biosensor was developed in COMSOL Multiphysics. The proposed sensor consists of gold IDEs coated with an antibody-based biorecognition layer and immersed in a conductive aqueous sample medium. Antibody–NS1 binding was modeled using the Langmuir adsorption isotherm. Frequency-domain simulations were used to examine the impedance response over a range of NS1 concentrations from 0 to 1000 ng/cm3. The results showed that the simulated impedance magnitude decreased with increasing NS1 concentration and excitation frequency. At 10 MHz, the normalized impedance response increased rapidly at low NS1 concentrations and gradually approached saturation at higher concentrations. A linear fit to three simulated concentration points (1–3 ng/cm3) yielded a normalized response slope of 0.9223%/(ng/cm3) (R2 = 0.9963), providing a preliminary characterization of the model-predicted low-concentration response. These findings demonstrate the potential of computational modeling for characterizing the predicted electrical response of an IDE-based impedimetric NS1 biosensor, providing a basis for sensor optimization and future experimental validation. The reported sensitivity and concentration-dependent responses are simulation-based predictions and have not been experimentally validated.

Biosensors doi: 10.3390/bios16110620

Authors: Mariana Castro-Montano Ling Li Tolu Abifarin Andrea Zuehlsdorf Andy Petros Simon Hannam Panicos A. Kyriacou Meha Qassem

Generalised oedema is characterised by tissue swelling due to excess water accumulation. It is a frequent complication encountered in infants admitted to paediatric and neonatal intensive care. In clinical practice, oedema assessment depends on a combination of weight monitoring, fluid balance calculations and direct visual inspection of the skin. However, all of these lack accuracy and sensitivity, especially in critically ill infants. We report the use of near infrared (NIR) spectroscopy using a portable spectrometer and an optical fibre with a custom attachment allowing continuous recordings for 30 min. Data were collected from 20 neonates and infants admitted to the intensive care unit, with or without oedema, across three consecutive days. Statistically significant differences were found in the 1450 nm features, amplitude (p = 0.009), ratio (p < 0.001) and band area (p < 0.001), and the 970 nm ratio followed the same direction (p = 0.005), all with higher values in the oedema group. A late fusion model, combining spectral data and clinical features, effectively differentiated between oedematous and non-oedematous infants, achieving AUC 0.909 ± 0.016, p = 0.004. This proof-of-concept study suggests that near-infrared spectroscopy can detect oedema in neonates and infants, as assessed against clinical evaluation, laying the groundwork for its potential application as a non-invasive clinical detection and monitoring tool.

Biosensors doi: 10.3390/bios16110619

Authors: Yongtang Tian Chunjie Chen Wujing Cao Xiaojun Wu Xinyu Wu

Multi-terrain adaptability and metabolic reduction are critical for translating load-carrying lower-limb exoskeletons into practical use. However, conventional single-mode sensing cannot synchronously quantify human–exoskeleton interaction forces and physiological metabolic responses across complex terrains. To address this limitation, this study develops a rigid–flexible coupled load-carrying lower-limb exoskeleton and constructs a dual-modal test platform for interaction force and metabolic energy expenditure. Using DYMH-102 diaphragm box load cells and the Cosmed K5 portable gas metabolism analyzer, a standardized outdoor multi-scenario sensing protocol is established. This protocol quantifies exoskeleton assistance performance and human metabolic benefits under a 15 kg load over three representative terrains: hard level ground (HLG), gently uneven grassland (GUG), and gently sloped mountain road (GMR). The experimental results show that terrain complexity directly affects sensor measurement outcomes and exoskeleton assistance performance: under the three terrain conditions, the shoulder load transfer efficiencies of the exoskeleton are 55.97%, 54.76%, and 42.45%, respectively, and the peak impact load reduction rates reach 78.26%, 80.00%, and 66.67%. A descriptive gradual decline in metabolic saving was observed, likely attributable to terrain perturbations, gait distortion, and human–machine interface slippage, with metabolic cost reductions of 16.28%, 11.93%, and 11.48%. Unlike conventional exoskeleton evaluations relying on isolated performance metrics, this work integrates mechanical and physiological dual-modal sensing to create a synchronous quantitative assessment method for wearable devices in complex outdoor environments. The proposed method mitigates the poor terrain adaptability and lack of multi-parameter coupled evaluation that plague current wearable biosensing systems.

Biosensors doi: 10.3390/bios16110618

Authors: Hassan Abdullah Albargy

Biological plausibility has outpaced the measurement and clinical validation of the role of circulating high-mobility group box 1 (HMGB1) in linking tissue injury to inflammation in acute respiratory distress syndrome (ARDS). This is a critical narrative review to determine whether the evidence concerning HMGB1 biology, circulating assays, and emerging biosensors can support the same clinical assertions. Focused appraisal of 33 clinical, biosensor, and analytical reports was facilitated by PubMed, Scopus, and Google Scholar searching and reference-list checking. Investigational trials reveal linkage to injury or poor outcomes, as well as negative results and significant selection criteria, sampling, assays, and endpoints. Reported prognostic thresholds are not applicable across matrices or platforms, and an enhanced panel AUC does not separate the contribution of HMGB1. There were six quantitative biosensor reports that showed detection of the analytes in controlled solutions, cell-derived material, or experimental disease states; however, human ARDS cohort validation was not reported in accessible biosensor reports. Thus, low detection limits cannot determine the accuracy or clinical utility of ARDS diagnosis. Central Gap is the information missing between an analytically created measurement and a prospectively validated clinical choice. There is need for matrix-specific assay verification, evaluation in an independent ARDS cohort, and, then, for testing to determine whether an HMGB1-informed strategy improves decision-making or outcomes.

Biosensors doi: 10.3390/bios16110617

Authors: Guiling Luo Xiaodan Huang Tao Liu Brij Mohan Wei Sun

Marine environmental monitoring is critical for ecological preservation, climate regulation, and the sustainability of the blue economy. However, traditional marine biosensors face severe bottlenecks when deployed in situ, driven by the overwhelming matrix effects of high salinity, aggressive biofouling, and highly fluctuating physical parameters across variable ocean scales. Recently, the convergence of marine biosensing technologies and artificial intelligence (AI) has been proposed as a transformative route toward transcending these long-standing barriers. This narrative review critically surveys recent advances in AI-driven marine biosensors, organizing the evidence through an explicit classification of genuine biosensors, biomimetic sensors, conventional chemical sensors, and AI-assisted environmental monitoring systems, and labeling each technology according to its demonstrated validation environment (seawater, freshwater, laboratory, or proposed integration). We first examine how machine learning and deep learning architectures accelerate the inverse design and optimization of recognition elements and functional nanomaterials. Next, we evaluate core AI algorithms dedicated to signal preprocessing, multi-component deconvolution, and nonlinear quantitative regression, which collectively enable robust data interpretation amidst intense marine noise. Furthermore, we highlight cutting-edge applications of these intelligent sensing platforms in tracking heavy-metal pollution, ecological biosecurity threats, and emerging contaminants, followed by an analysis of their systemic integration onto autonomous marine assets coupled with IoT edge-computing and adaptive anti-biofouling frameworks. Finally, we discuss current challenges—including data scarcity, model interpretability (“black-box” limitations), and domain generalizability—and offer a task-prioritized roadmap linking each research priority to sensing tasks and validation endpoints for the realization of globally networked, self-calibrating, intelligent ocean-monitoring systems.

Biosensors doi: 10.3390/bios16110616

Authors: Pengyu Lou Cheng Xu Kaiyang Yin Enhui Guo Xiaoying Lü Zhigong Wang

Parkinson’s disease (PD) is a rapidly progressive neurodegenerative disorder whose core motor symptoms—tremor, bradykinesia, and rigidity—severely impair patients’ quality of life. Conventional clinical rating scales are inherently subjective and exhibit limited inter-rater reliability, underscoring the urgent need for objective and quantitative assessment tools. This article presents a novel proof-of-concept study that lays the foundation for objective tremor quantification based on inertial measurement unit (IMU) signals. The framework consists of three stages: first, a robust Mahalanobis distance algorithm based on the minimum covariance determinant (MCD) estimator eliminates multivariate outliers from raw IMU data; second, acceleration in the sensor coordinate system is converted to the geographic coordinate system via quaternion rotation, the gravitational component is removed, and 3D tremor trajectories are reconstructed using Simpson’s numerical integration; third, Delaunay triangulation partitions the reconstructed point cloud, constructs pentahedral voxels, and calculates the enclosed volume, which serves as an intuitive metric for tremor energy and spatial activity range. The method was evaluated using a BWT61CL IMU sensor (sampling frequency 100 Hz) to collect hand tremor data from four PD rest tremor (RT) patients and four matched healthy control (HC) participants under standardized rest conditions. To assess the ability of the method to distinguish pathological tremor from physiological motion, the control group was expanded to include four Essential tremor (ET) patients. This revealed a measurable reduction in tremor volume post-treatment compared to baseline, consistent with the expected direction of clinical observations. These preliminary findings suggest that the proposed IMU-based volume method, combining robust Mahalanobis distance and Delaunay triangulation, has potential for tracking the spatiotemporal evolution of tremor symptoms in PD, offering a proof-of-concept analytical framework for quantifying tremor spatial amplitude that warrants validation in larger cohorts.

Biosensors doi: 10.3390/bios16110615

Authors: Soren M. Lipman Angel S. Zavala-Custodio Samantha N. Garcia Xinqi Zhou Evan W. Miller

Voltage-sensitive fluorescent dyes enable optical measurements of transmembrane potential but can be limited by poor aqueous solubility, aggregation, and incomplete cellular membrane loading. Here, we evaluated supramolecular complexation as an approach to improve the imaging performance of the voltage-sensitive fluorophore Berkeley Red Sensor of Transmembrane potential (BeRST). BeRST was complexed with bovine serum albumin (BSA) and structurally distinct cyclodextrins (CDs), and its solution-phase photophysical properties and live-cell fluorescence intensity were assessed. Both host identity and concentration influenced the local environment of the BeRST chromophore, producing spectral changes consistent with reduced aggregation. In HEK 293T cells, the effects of complexation on mean cellular fluorescence intensity were likewise host- and concentration-dependent, with all hosts evaluated significantly enhancing mean cellular fluorescence intensity under at least one condition. The voltage sensitivity of complexed BeRST in live cells remains unchanged from noncomplexed BeRST, as determined by voltage imaging. Together, these findings show that CD complexation can tune the photophysical and cellular imaging properties of BeRST while preserving its voltage sensitivity, supporting supramolecular complexation as a strategy to improve the delivery and cellular brightness of lipophilic voltage-sensitive fluorescent dyes.

Biosensors doi: 10.3390/bios16110614

Authors: Suhail Asghar Qureshi Zuhairiah Zainal Abidin Gameel Saleh

Rapid and continuous monitoring of blood hematocrit (HCT) levels is critical for detecting hemoconcentration and plasma leakage associated with dengue virus progression. This paper presents a label-free, metamaterial-based microwave sensor designed to detect dielectric variations corresponding to changes in HCT. The proposed structural design utilizes a keyhole-shaped resonator. The sensor’s transmission coefficients (S21) were numerically evaluated using simulated dielectric properties representing an extended modelled HCT range of 0% to 80%. The simulated analysis demonstrates a systematic response, linear in resonance frequency (R2 = 0.996) and curved in magnitude (R2 = 0.812), achieving a resonance shift sensitivity of 4.5 MHz/%HCT and a magnitude sensitivity of 0.0599 dB/%HCT. Based on these simulations, a 20% relative rise from a patient’s baseline HCT (assuming = 50%) corresponds to a 45 MHz resonance shift and a −0.599 dB magnitude deviation. As an initial proof of concept, the prototype was tested with alcohol–water mixtures. Over 0–40% alcohol the resonance moved to lower frequencies, whereas above 60%, it reversed. This compact microwave sensor is a candidate framework for point-of-care HCT monitoring, subject to validation on blood.

Biosensors doi: 10.3390/bios16110613

Authors: Edgar Francisco Arcos Hurtado Leonardo Antonio Bermeo Varón Santiago Vásquez Salazar Luis David Delgado Giraldo

Spinal cord injuries (SCIs) are a major cause of upper limb impairment. In individuals with incomplete SCI, limitations of traditional myoelectric control have motivated artificial intelligence-based pattern recognition to better detect movement intent. This study aimed to develop and evaluate a system for the acquisition and classification of 8 hand gestures from myoelectric signals (MESs) in three participants with incomplete SCI. The 4-channel system captured myoelectric signals using individual sensors, from which 22 time-domain and frequency-domain features were extracted. Three feature selection techniques were applied: ReliefF, sequential feature selection (SFS), and genetic algorithms (GA). Eight machine learning models were trained using cross-validation: random forest (RF), support vector machines (SVM), multilayer perceptron (MLP), k-nearest neighbors (KNN), linear discriminant analysis (LDA), logistic regression (LR), decision trees (DT), and Gaussian Naïve Bayes (GNB). Statistical analysis confirmed RF as the best-performing classifier (p < 0.05), while KNN and MLP showed equivalent performance across subjects. Offline accuracies ranged from 71.78% to 81.60%. Real-time evaluation of RF-SFS yielded 76.67% accuracy with a mean end-to-end processing time of 330.42 ± 6.65 ms. Given the small and heterogeneous cohort, these findings should be interpreted as preliminary, participant-specific evidence of the feasibility of myoelectric hand gesture classification in individuals with incomplete SCI rather than as population-level estimates of performance.

Biosensors doi: 10.3390/bios16110612

Authors: Lyazzat Mukhangaliyeva Aigerim Omirzakova Marzhan Nurlankyzy Ramazan Ashirkhan Bakdaulet Yendibay Aizhan Issatayeva Zere Adilova Wilfried Blanc Christabel K. L. Tan Daniele Tosi Zhannat Ashikbayeva

Breast cancer remains a major cause of cancer-related mortality in women, and its substantial molecular heterogeneity highlights the need for methods capable of characterizing tumor cells at the cellular level. CD44, a transmembrane glycoprotein associated with breast cancer progression, metastasis, and cancer stem-like phenotypes, is a promising biomarker for selective recognition of aggressive, basal-like cell populations. However, conventional approaches to evaluating CD44 expression including immunohistochemistry, immunofluorescence, and flow cytometry require labeling and multi-step sample preparation. In this work, a gold-coated, polydimethylsiloxane (PDMS)-based extrinsic semi-distributed interferometric (SDI) optical fiber biosensor functionalized with anti-CD44 antibodies was developed for label-free, selective detection of CD44-expressing HCC1806 breast cancer cells. The functionalized sensor exhibited a concentration-dependent response with a limit of detection (LoD) of 2.51 cells/mL, while the antibody-free control sensor exposed to HCC1806 cells and the anti-CD44-functionalized sensor exposed to CD44-low HEK293 cells showed only minimal responses, supporting selective detection under the tested conditions. These results support the potential of the antibody-functionalized ESDI platform for label-free detection of CD44-expressing cells, providing a groundwork for its validation in clinical samples as a step toward tumor-cell diagnostics.

Biosensors doi: 10.3390/bios16110611

Authors: Chuangjie Zheng Yun Lu Qingwu Ma Mei Ouyang Youjun Zeng Bowei Ma

Surface plasmon resonance imaging (SPRi) is a powerful label-free technique for biomolecular interaction analysis, yet conventional intensity-based SPRi systems suffer from limited refractive index resolution and rely on costly light sources. Here, we present a high-throughput, low-cost SPRi biosensor based on a differential intensity detection algorithm with standard LED illumination. By probing the opposite reflectance variations on the two flanks of the resonance dip at 730 and 780 nm and taking their difference, the scheme cancels common-mode noise while enhancing the sensitivity by up to 2.6-fold. Specifically, the differential signal is computed pixel-wise as the difference between the two reflectance images acquired under the 730 and 780 nm LEDs, ID = I730 − I780, so that the opposite-signed responses on the two flanks are added while common-mode intensity fluctuations are subtracted. The system employs only three standard LEDs, a six-channel flow-through cell, and a single CMOS camera, with a light-source cost below 200 USD. A sensitivity of 1.03 × 104 grayscale values per refractive index unit (RIU) with excellent linearity (R2 = 0.9989) and a resolution of 1.9 × 10−6 RIU were achieved. As a proof of concept, the biosensor was applied to label-free monitoring of autophagy-related LC3B accumulation in A549 lung cancer cell lysates using the small-molecule inhibitor DC-LC3in-D5 as the probe. The SPRi responses showed a dose-dependent trend fully consistent with flow cytometry and Western blotting. Benefiting from its high-throughput capacity, low cost, and antibody-free readout, this platform holds potential for evaluating autophagy-mediated therapeutic resistance upon anticancer drug treatment.

Biosensors doi: 10.3390/bios16110610

Authors: Wenjun Jia Min Zhang Hailong Zhang Weiqi Sun Yongqin Wang

Flexible piezoelectric sensors are highly desirable for kinematic evaluation in home-based rehabilitation; however, conventional piezoelectric sensors often suffer from poor output stability, low sensitivity, and insufficient dynamic response. To address these limitations, this study employs electrospinning technology to synthesize PVDF-TrFE/MXene/MgO composite nanofibers, with PVDF-TrFE serving as the polymer matrix and MXene and MgO acting as dual-functional fillers. Benefiting from the synergistic interplay between MXene and MgO, the crystalline phase structure and interfacial polarization behavior of the composite are effectively modulated, promoting the polar β-phase in PVDF-TrFE and significantly boosting its piezoelectric output. The optimized flexible piezoelectric sensor, after optimization, delivers a maximum output voltage of 26.5 V under a 30 N external force (approximately 4.5 times that of the pristine PVDF-TrFE device) while maintaining stable operation over 5000 cyclic loading tests, along with excellent response and recovery characteristics. Based on robotic multi-joint motion tests serving as a preliminary human-motion surrogate, the as-prepared composite flexible piezoelectric sensor collectively shows considerable promise for wearable motion tracking, home-based rehabilitation evaluation, and intelligent health monitoring.

Biosensors doi: 10.3390/bios16110609

Authors: Nairo Brilhante-da-Silva Sibele Andrade Roberto Nidiane Dantas Reis Prado Eliza Lima dos Santos Laura Rosilene Soares de Souza Leila Mendonça-Lima Carla Freire Celedonio Fernandes Soraya dos Santos Pereira

Snakebite envenoming remains an important global public health problem, and Brazil ranks among the countries with the highest notification rates, especially in the Northern region. Therapeutic intervention is decisive in snakebite cases, as early administration of antivenom reduces the severity of systemic and local manifestations. However, in the absence of a diagnostic test, identification of the snake responsible for envenoming remains restricted to clinical and epidemiological assessment. In this context, this study demonstrates the potential for developing a diagnostic assay for snakebite envenoming using nanobioconjugates composed of antivenom VHHs against rattlesnakes and jararacas, medically important snakes in the country. In dot blot assays, the anti-Bothrops nanobioconjugates showed visible reactivity against Bothrops jararacussu venom down to 10 ng/µL and more consistent reactivity at concentrations ≥ 50 ng/µL. The anti-Crotalus nanobioconjugates detected Crotalus durissus terrificus venom at concentrations as low as 400 ng/µL and also showed reactivity toward three additional Crotalus subspecies venoms. In control assays, no cross-reactivity was observed with the heterologous venoms evaluated. These results provide initial evidence for the potential use of VHH-based nanobioconjugates for venom detection, while indicating that further optimization of analytical sensitivity is required before clinical application.

Biosensors doi: 10.3390/bios16110608

Authors: Quentin Bruxelles Lisa Treboux Geneviève Hamel-Côté René C.-Gaudreault Francine Durocher Caroline Diorio Sébastien Fortin

The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor involved in xenobiotic metabolism, immune regulation, and cellular homeostasis. AhR-ligand interactions have traditionally been investigated using radioligand binding assays and, more recently, by microscale thermophoresis (MST) applied to purified recombinant AhR-aryl hydrocarbon receptor nuclear translocator (AhR-ARNT) complexes. Although these approaches enable characterization of ligand binding, they require either radiolabeled ligands or purified recombinant receptor systems and therefore do not fully reproduce the molecular complexity surrounding the receptor in cells. Here we develop a lysate-based MST assay to characterize ligand-dependent AhR responses within a cell-derived molecular environment without requiring receptor purification. An AhR-green fluorescent protein (AhR-GFP) rescue cell line was generated in an AHR knockout background, allowing the fluorescently tagged AhR to serve simultaneously as the target protein and intrinsic fluorescent tracer. Functional validation confirmed ligand-induced nuclear translocation of AhR-GFP and cytochrome P450 family 1 subfamily A member 1 (CYP1A1) induction with sensitivity comparable to that of wild-type cells. MST measurements performed directly in cellular lysates revealed concentration-dependent responses to the AhR agonists 6-formylindolo[3,2-b]carbazole (FICZ), 2-(4-amino-3-methylphenyl)-5-fluorobenzothiazole (5F 203), β-naphthoflavone, and 1-hydroxyphenazine, as well as the antagonists CH223191 and BAY 2416964, enabling determination of apparent half-maximal effective concentration (EC50app). Collectively, these findings establish a purification-free MST assay for probing ligand-dependent AhR responses within a complex cell-derived molecular environment.

Biosensors doi: 10.3390/bios16110607

Authors: Yu Bai Wei Gan Feiyu Chen Ziqin Liao Yujie Wu Junwei Zheng Kunjie Wang Rui Lin Yuzhu Wang Xiangzeng Kong Xuan Wei

Volatile organic compounds (VOCs) act as vital early indicators of biotic or abiotic stress in plants, and their accurate, timely detection is significant for early crop health warning. To address the need for field-deployable sensors with high selectivity, operational stability, and practical usability, we developed a resistive VOC sensor with a sensing layer of 4-fluorothiophenol (FTP)-functionalized silver nanowires embedded in a poly(vinyl alcohol)/poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) matrix. Reversible halogen bonding between FTP ligands and aldehyde/ketone groups enables analytes to induce swelling of the PVA matrix, producing significant signal amplification by altering resistance, while aldehydes and ketones are differentiated by polarity and steric hindrance, with optimal responsiveness to aldehydes. The sensor achieves a linear response to aldehydes over 25–200 ppm (R2 = 0.99), an estimated detection limit of 11.6 ppb, and response and recovery times of ~30 s, with reliable resistance to humidity and temperature variations. Combining stomatal observation with real-time sensor recordings of mechanical damage and pre-symptomatic early blight, we monitored plant stress multidimensionally, detecting disease signals three days before visible symptoms. Principal component analysis establishes stress-type fingerprints, and long short-term memory forecasts VOC emission trends, providing diagnostic classification and predictive intelligence for early intervention and a low-cost, non-destructive pathway for plant health monitoring.

Biosensors doi: 10.3390/bios16110606

Authors: Shixuan Zhang Fengli Gao Yaoxing Mao Ruohan Wang Xinyao Yi Ning Xia Lin Liu

Protein biomarkers play an indispensable role in the early diagnosis, prognosis assessment, therapeutic monitoring, and treatment evaluation of diseases. Optical biosensors have emerged as promising alternatives for protein detection due to their label-free, real-time, and highly sensitive characteristics. Molecularly imprinted polymers (MIPs), known as “plastic antibodies,” can provide chemically stable, low-cost, and tailorable recognition elements through the formation of target-complementary cavities in polymer matrices, making them highly attractive for integration with optical sensing platforms. In this work, we systematically summarize the advances in MIP-based optical biosensors for protein detection. The fundamental principles of biosensors and recognition elements are introduced, followed by a brief overview of the key components of imprinting technology, such as imprinting materials, functional monomers, cross-linking agents, polymerization mechanisms, template fixation strategies, and template removal conditions. Then, we comprehensively evaluate the integration of MIPs with various optical transduction techniques, including fluorescence, chemiluminescence, electrochemiluminescence, surface-enhanced Raman scattering, colorimetry, and surface plasmon resonance. Finally, the major challenges are discussed, and future development directions are proposed. This review aims to provide systematic theoretical references and useful insights for the rational design, performance improvement, and clinical translation of MIP-based optical biosensors for protein detection.

Biosensors doi: 10.3390/bios16110605

Authors: Sandra Friedrich Lisa-Christin Rottbauer Joachim Wegener

Cell-based biosensors are valuable tools in ecotoxicology, as they provide sensitive alternatives to conventional chemical sensors. In combination with holistic transducers, they report on any impact on cell physiology as a whole, instead of quantifying just one chemical species. In environmental analysis, label-free and time-resolved whole-cell biosensors are particularly advantageous, as conventional endpoint assays provide limited information on response kinetics. We applied two complementary biosensor platforms to assess pesticide-induced effects on Kc167 cells derived from Drosophila melanogaster. Electric cell-substrate impedance sensing monitors pesticide-induced effects on cell adhesion through time-resolved capacitance measurements, while imaging microphysiometry (IMP) monitors cellular respiration by measuring oxygen consumption of cells adherent to an oxygen-sensitive optrode. Six commercial pesticides were studied in concentration-dependent exposures and compared, using a conventional resazurin-based viability assay. All three biosensors detected distinct, dose-dependent phenotypic responses to pesticide exposure, yielding pesticide-specific phenotypic scorecards, highlighting the value of multiparametric measurements. Furthermore, ECIS and IMP were integrated into a common sensor platform, enabling simultaneous monitoring of cell adhesion and respiration for the same cell population. The multimodal approach provides complementary, time-resolved information on pesticide-induced, phenotypic effects on insect cells as a basis for multiparametric screening of crop protection products.

Biosensors doi: 10.3390/bios16110604

Authors: Murat Kavruk Esra Menfaatli Arif Kösemen Sadullah Öztürk Veli Cengiz Ozalp

The COVID-19 pandemic exposed profound vulnerabilities in global diagnostic infrastructures, highlighting the urgent need for rapid, sensitive, and field-deployable biosensors to manage future outbreaks (Disease X). Ionic liquids (ILs), characterized by their tunable solvation, high ionic conductivity, and exceptional thermal stability, are emerging as transformative materials in sensor engineering. This review critically evaluates the integration of ILs across diverse biosensor platforms, moving beyond their conventional roles to assess their specific utility in pandemic preparedness. We analyze global research trends, demonstrating the potential of IL-based pathogen detection. Furthermore, we explore how ILs facilitate not only the direct electrochemical and optical detection of primary pathogens but also the monitoring of pandemic-associated secondary health parameters, such as cytokine storms (e.g., IL-6), metabolic distress, and hematological or organ-level complications. By bridging the gap between sample preparation, such as rapid, cold-chain-free nucleic acid extraction, and ultra-sensitive signal transduction, IL-based platforms offer a promising route toward decentralized surveillance. We deliberately layer this evidence: mechanistic support for IL-mediated gains in electron transfer and bioreceptor stabilization is strong, analytical support for low detection limits in defined matrices is substantial, but deployment evidence remains limited, as most systems are still laboratory prototypes. Finally, we discuss current translational challenges, including biocompatibility and standardization, outlining a roadmap for deploying IL-integrated diagnostics in resource-limited settings.

Biosensors doi: 10.3390/bios16110603

Authors: Hatice Metin Selim Buyukkurt Mustafa Unal

Fluorescence background is a persistent limitation in quantitative Raman spectroscopy of biological tissue, and it is pronounced in the pregnant cervix, where weak Raman bands sit on a strong and variable baseline. Baseline correction is a routine step, yet its effect on the final result is rarely tested in a controlled way. We examined how the choice of correction strategy affects spectral metrics and the biological difference inferred between two gestational stages. Ex vivo cervical spectra from a rat model at gestational days 15 and 20 were processed with fixed-degree polynomial fitting from the second to seventh degrees, five-point piecewise linear correction, an automated polynomial routine, and asymmetric least squares. Correction quality was assessed by the ratio of Raman-active to -inactive regions and by whether each method preserved the direction of gestational change. The choice of method altered the quantitative output and, for some methods, reversed the inferred direction, because the automated high degree of fit over-suppressed the spectrum and inflated the metric. Moderate-degree polynomial fitting and asymmetric least squares gave the most coherent and biologically consistent results. Baseline correction therefore determines cervical Raman outputs and should be selected and reported transparently.

Biosensors doi: 10.3390/bios16110602

Authors: Alexis Chugá-Portilla Oswaldo Menéndez-Granizo

Crop-monitoring systems require sustainable, self-powered sensing solutions. Leaf-based Electric-Field Energy Harvesters (Leaf-EFEHs) offer a potential approach; however, the relationship between foliar moisture content (FMC) and their electrical response remains insufficiently understood. This work characterizes this relationship in Brassica rapa pekinensis and Spinacia oleracea Leaf-EFEHs under controlled dehydration and exploratorily evaluates whether synthetic data can provide incremental improvements in the prediction of open-circuit voltage (VOC) and short-circuit current (ISC). Fifty leaves per species were electrically characterized at eight dehydration stages, producing two species-specific datasets of 400 observations each. Five regression models and five synthetic-data generation methods were evaluated using measurement-level partitioning and 20 repeated leaf-level partitions. The electrical response varied nonlinearly during dehydration, with an initial increase followed by a decline in Brassica rapa pekinensis and an overall decline followed by stabilization in Spinacia oleracea. Under measurement-level partitioning for Brassica rapa pekinensis, Random Forest achieved the highest baseline performance, with R2=0.865 for VOC and R2=0.859 for ISC. Gaussian noise produced the smallest distributional discrepancies, while Mixup best preserved the correlation structure. Synthetic-data augmentation did not consistently improve predictive performance. Under leaf-level validation, temporal interpolation produced the largest gain for the Brassica rapa pekinensis MLP predicting VOC (ΔR2=+0.0619), whereas Gaussian noise produced the largest Spinacia oleracea gain for the MLP predicting ISC (+0.0330). Overall, augmentation utility was model-, strategy-, and species-dependent, emphasizing the need to evaluate synthetic data through predictive utility, preservation of the dehydration process, and generalization to unseen leaves.

Biosensors doi: 10.3390/bios16110601

Authors: Haekang Ji Jiwon Lee Jikang Bae Geupil Jang Youngdae Yoon

MobR, a MarR family transcriptional repressor from Comamonas testosteroni, responds to hydroxybenzoic acid derivatives and represents a promising regulatory component for transcription factor (TF)-based whole-cell biosensors. In this study, we developed a MobR-based whole-cell biosensor in Escherichia coli by coupling the native mobA promoter with an enhanced green fluorescent protein (eGFP) reporter. Although the wild-type biosensor responded to 3-hydroxybenzoic acid (3-HBA), 2,3-dihydroxybenzoic acid (2,3-DHBA), and 3,4-dihydroxybenzoic acid (3,4-DHBA), its fluorescence output and dynamic range were limited. To improve biosensor performance, we independently engineered both the promoter architecture and the MobR transcription factor. Promoter variants were generated through modification of regulatory sequences to enhance transcriptional output, whereas structure-guided mutagenesis targeting predicted ligand-interacting residues was employed to modulate ligand recognition. Combinatorial integration of the optimized promoter and MobR variants produced biosensors exhibiting a 2–3-fold increase in fluorescence output while maintaining ligand-dependent regulation. Engineered biosensors enabled reliable quantitative detection of hydroxybenzoic acid derivatives, with promoter engineering primarily enhancing transcriptional output and structure-guided MobR engineering enabling modulation of ligand responsiveness. Collectively, this work demonstrates that promoter architecture and transcription factor function can be engineered as complementary design parameters and subsequently integrated to rationally optimize TF-based whole-cell biosensors. This modular strategy provides a practical framework for enhancing transcriptional output while tuning ligand response profiles.

Biosensors doi: 10.3390/bios16110600

Authors: Yifei Feng Hengcong Gong Tianjiao Min Jing Zhang Di Meng Luyao Yu Ying He

Ballistocardiography (BCG) enables unobtrusive physiological monitoring, but its signal quality is sensitive to body–sensor coupling and posture. This study proposes a dual-channel quality assessment framework for seated BCG and measurement-configuration evaluation. A chair-integrated Mach–Zehnder interferometer fiber-optic sensing system recorded back and hip BCG signals from 11 participants at five backrest angles (100°, 120°, 140°, 160°, and 180°). Thirty candidate features were extracted from 15 s windows, and channel-specific three-class quality models were developed using selected interpretable features and a bagged-trees classifier under repeated subject-wise evaluation. Classification probabilities were mapped to continuous quality scores and integrated with JJ-interval consistency and cross-channel delay stability to construct a dual-channel quality index. The models distinguished low-, medium-, and high-quality windows, while the joint index characterized differences in dual-channel analyzability across measurement configurations. Angle-related differences were not statistically significant when all windows were included but became significant after excluding low-quality windows. Within the tested cohort and experimental configuration, 140° showed the highest descriptive overall quality, followed by 160°, whereas 100° performed worst. The angle ranking was largely stable across alternative weighting schemes. However, cross-channel consistency does not constitute independent physiological validation, and improvements in downstream parameter-estimation accuracy remain unverified. The proposed framework supports quality-guided evaluation of seated BCG acquisition configurations and warrants further validation against independent physiological references.

Biosensors doi: 10.3390/bios16110599

Authors: Zhiqiang Chen Kangyao Zhang

Bisphenol A (BPA) and mercury are two types of important food contaminants that also cause severe and irreversible damage to ecosystems and human health. Therefore, it is necessary to provide a comprehensive summary of their industrial applications, toxicity hazards, restrictive measures and detection methods. This review introduces the wide range of applications of BPA and mercury in industrial production and daily life, as well as the accompanying environmental pollution problems. The toxicity and mechanisms of action of BPA and mercury are herein presented. Although countries around the world have taken relevant measures to ensure the safe use of products and safe food consumption, the risk of their poisoning still exists. Focused on subsequent technical prevention, this review summarizes several common sample pretreatment methods, enabling the determination of the true BPA or mercury content and eventually improving detection accuracy. Four types of functional nanomaterial (FNM)-enhanced DNA biosensors are primarily discussed in this review for the highly selective and sensitive determination of BPA and mercury using mainstream colorimetric, electrochemical, fluorescence and surface-enhanced Raman scattering (SERS) methods, as well as other methods. This review also analyzes the existing issues in the development of sample pretreatment and detection methods based on FNM-enhanced DNA biosensors and discusses future direction prospects with the help of the latest cutting-edge technologies such as wearable smart devices, artificial intelligence and big data.

Biosensors doi: 10.3390/bios16110598

Authors: Jianming Zhu Qirui Yang Huadang Meng Jinghui Lu Hong Ling Zhencheng Chen Baoli Heng

Current clinical indicators for diabetes management are limited by the invasiveness of conventional measurements and their inability to capture metabolic memory, highlighting the need for reliable biomarkers for long-term metabolic status assessment. Advanced glycation end products (AGEs) accumulate progressively in corneal tissues. In this study, a portable AGE detection system based on corneal autofluorescence was developed. To enhance the acquisition capability of weak fluorescence signals, the system integrated a silicon photomultiplier (SiPM) with a dual-phase lock-in amplification technique. Multiple regression models were established using clinical data from 300 participants, with dietary factors incorporated as additional input features. Among them, serum AGE measurements from 104 participants were used as reference values. The Random Forest Regression model exhibited the best predictive performance, with approximately 85% of samples showing absolute relative prediction errors below 5%, a mean absolute percentage error of 3.8%, and a maximum relative error of 12.01%. Longitudinal monitoring further demonstrated that prediction errors were below 7% at three of the four time points, while the error at Day 0 was 10.77%. These findings suggest that the proposed system can provide relatively accurate and stable estimates of serum AGE concentrations and may have potential for supporting for long-term diabetes monitoring and early screening.

Biosensors doi: 10.3390/bios16110597

Authors: Yaqian Han Jiayi Shi Ye Sun

Accurate quantification of trace metal ions in complex biological matrices remains challenging because of severe matrix interference and sluggish metal–ligand coordination at ultralow concentrations. Herein, we report an active coordination sensitization strategy for surface-enhanced Raman scattering (SERS)-based metal-ion analysis by modulating molecular recognition kinetics and interfacial chemical equilibria. Unlike conventional approaches relying primarily on plasmonic substrate engineering, this strategy directly tunes coordination chemistry through temperature and pH control. For Cu2+ detection, thermal activation at 50 °C accelerates tetrakis(4-carboxyphenyl) porphyrin (TCPP) metalation, yielding limits of detection (LODs) of 2.94 × 10−11 M in water and 2.01 × 10−10 M in serum, representing 28- and 12-fold improvements, respectively. For Zn2+ sensing, pH regulation promotes thiol-functionalized terpyridine (TPY)–Zn2+ coordination, giving LODs of 2.86 × 10−13 M in water and 1.0 × 10−11 M in serum, with 35- and 18-fold improvements, respectively. Density functional theory (DFT) calculations provide insights into temperature- and protonation-dependent coordination behaviors. Integrated with a microfluidic SERS platform, the method achieved serum recoveries of 97.9–102.1% with relative standard deviations (RSDs) below 4.20%, demonstrating accurate and reproducible analysis while advancing SERS sensing from passive plasmonic signal amplification toward active regulation of molecular recognition chemistry.

Biosensors doi: 10.3390/bios16110596

Authors: Zixuan Ma Mengdong Liu Hongbo Zhang Xitao Li Huiqin Wang Taifeng Lin Ping Zhang

Ensuring the microbiological safety of drinking water is critical for public health and food production. However, the conventional plate counting method is time-consuming and poorly suited for rapid on-site microbial detection. Here, we propose a filtration-enhanced dual-mode surface-enhanced Raman scattering (SERS) biosensor for the rapid, culture-free detection of waterborne pathogens, which can be directly integrated into household faucets. The biosensor combines a porous filter membrane with Au nanoparticles (AuNPs), achieving simultaneous enrichment of waterborne bacteria and significant amplification of SERS signals. The dual-mode design provides a visible colorimetric response, in which the substrate color gradually changes with increasing bacterial load, enabling preliminary semi-quantitative assessment of water contamination. In addition, the biosensor achieved the lowest experimentally detectable bacterial load of 103 CFU/platform. Support Vector Machine (SVM) algorithm was further applied to discriminate different types of pathogens, yielding a classification accuracy of 96%. Practical tests on household water purifiers revealed that microbial contamination accumulated progressively with service time, which was highly consistent with the results obtained by traditional plate counting. The developed biosensing platform provides a rapid (within 10 min), sensitive, and user-friendly tool for on-site monitoring of drinking water safety in household scenarios and food-related environments.

Biosensors doi: 10.3390/bios16110595

Authors: Stefan Wagner Maxime Gavage Bastian Breiner Daniel Kainz Jan Lüddecke Riccardo Marega André Zimmermann

Food allergies pose a serious and increasing public health challenge as many allergens can trigger potential life-threatening reactions in affected individuals. Reliable detection of allergens such as soy proteins is therefore a critical and essential tool to detect cross-contaminations, particularly in food processing and production environments. To enable monitoring directly at these sites, portable biosensing platforms are required to provide rapid point-of-need detection. However, the development of such systems requires the combination of biochemistry, material science, and precision engineering. In this article we present a centrifugal microfluidic platform integrating automated sample preparation, clean-up, and immunological detection in a single-use disposable cartridge. Targeting soybean protein as a model allergen, the system combines optimized immunoassay chemistry with advanced polymer microfabrication. Regioselective antibody coupling strategies were investigated for stable and oriented bead conjugation. N-glycan click chemistry was used to produce monodisperse, highly functional fluorescent microbeads. The microfluidic LabDisk was first manufactured by thermoforming and later transferred to injection molding with integrated optical elements to increase sensitivity. The fully integrated LabDisk detected soy protein at the lowest concentrations tested, namely 1 ppm in extraction buffer and 4 ppm in incurred complex matrices. This work shows the important steps from biochemical concept to scalable manufacturing, paving the way for mass-produced, portable allergen biosensors.

Biosensors doi: 10.3390/bios16110594

Authors: Jinan Sabsabi Andressa Adame Bianyun Yu Francis Vanier Christian Padioleau Nii Patterson Allan Feurtado Samira Selmani Mohamad Sabsabi François Vidal

Rapid quantitative characterization of protein-rich agricultural materials remains challenging due to the molecular and elemental complexity of plant-derived matrices. In this study, external-cavity quantum cascade laser-based mid-infrared (QCL-MIR) spectroscopy and laser-induced breakdown spectroscopy (LIBS) were investigated as complementary approaches for predicting protein and elemental composition in pea flour (Pisum sativum L.) samples. QCL-MIR spectroscopy primarily probed molecular vibrational information, whereas LIBS directly monitored elemental composition through plasma-generated atomic and ionic emission signatures. To address the high dimensionality and nonlinear relationships present in both spectroscopic datasets, advanced chemometric strategies combining Uninformative Variable Elimination (UVE), Partial Least Squares (PLS) regression, and Extreme Learning Machine (ELM) modeling were implemented. Given the limited dataset size (N = 20), predictive performance was assessed using a concentration-stratified five-fold prediction strategy. The optimized UVE–PLS–ELM approach improved predictive performance compared with conventional linear PLS models for both techniques. QCL-MIR spectroscopy showed strong predictive performance within the present dataset for total protein content (R2 = 0.97) and also provided promising predictions of phosphorus and potassium through indirect correlations with sample composition. LIBS also demonstrated strong calibration and predictive capability for total protein content (R2 = 0.93) and good predictive performance for Na, Mg, Ca, Mn, and Fe. However, LIBS performance decreased at concentration extremes, particularly for high-concentration elements (K and P) and trace elements such as Zn and Cu. These results demonstrate the complementary strengths of QCL-MIR and LIBS and highlight the potential of nonlinear chemometric approaches for accurate compositional analysis of protein-rich agricultural materials within the present limited dataset.

Biosensors doi: 10.3390/bios16110592

Authors: Yun Gyo Seo In Jung Kim

Metal ions such as iron and copper can affect the quality and stability of beverages, necessitating biosensor development. In this study, we evaluated the applicability of the fluorescent protein tKeima as a fluorescence-quenching platform for screening Fe2+, Fe3+, and Cu2+ in beverage matrices. tKeima showed concentration-dependent fluorescence quenching toward all three metal ions in soft drinks, ion drinks, vegetable juices, matcha, and milk. Analysis of the matrix effects of these beverages revealed that tKeima retained approximately 72–82% of the quenching response observed under buffer conditions, indicating that metal-responsive fluorescence was largely retained. Spike recovery analysis demonstrated that added Fe2+, Fe3+, and Cu2+ could be detected in the beverage matrices, with recovery values generally ranging from approximately 70 to 84% at higher spike concentrations. Overall, these findings suggest that tKeima retains measurable metal-responsive fluorescence quenching in complex beverage matrices and provide a basis for its use as a simple fluorescence-based platform for first-line screening of iron and copper ions in beverage samples. This study provides a first proof-of-concept evaluation of fluorescent protein-based metal-responsive sensing of real food matrices using pretreated commercial beverages.

Biosensors doi: 10.3390/bios16110593

Authors: Kristina Jones Lin Pan Fuming Zhang Vitor Pomin Chunyu Wang

The global resurgence of mpox has established monkeypox virus (MPXV), comprising Clades I and II, as a significant public health threat. Studies of homologous vaccinia virus proteins indicate that cell-surface glycosaminoglycans (GAGs), including heparan sulfate (HS) and chondroitin sulfate (CS), facilitate viral attachment and entry. However, direct interactions between GAGs and MPXV surface proteins A29L/E8L remain poorly characterized. Using surface plasmon resonance (SPR), we quantified the apparent binding affinities of A29L (Clade I KD = 0.8 ± 0.3 nM) and E8L (Clade I KD = 1.2 ± 0.2 nM; Clade II KD = 1.6 ± 0.1 nM) for heparin, a common HS analog. We also quantified E8L apparent binding affinities to CS-A (Clade I KD = 0.4 ± 0.2 nM; Clade II KD = 0.65 ± 0.06 nM) and CS-E (KD = 0.6 ± 0.1 nM; Clade II KD = 0.70 ± 0.08 nM). We further determined that Clade I A29L and E8L preferentially recognized long-chain fully sulfated or N-sulfated heparin. Lastly, we determined a small library of marine-derived sulfated glycans (MSGs) as effective inhibitors for the Clade I E8L and A29L protein-GAG interface. These findings establish previously uncharacterized binding interactions and identify MSGs as potential candidates for anti-mpox drug development.

Biosensors doi: 10.3390/bios16100591

Authors: Yi-Jia Shih Yu-Sheng Chen Chun-Ping Jen

Sensitive label-free detection of low-abundance proteins requires both effective on-chip preconcentration and a compact, low-noise optical readout for use outside conventional laboratories. Here, we present a portable UV-LED dual-Nafion microfluidic platform that integrates a 280 nm UV-LED, a chromium-coated slot collimator, a dual-Nafion ion concentration polarization (ICP) preconcentrator, and a miniature spectrometer within a 3D-printed enclosure. The symmetric dual-Nafion geometry on a UV-transparent quartz substrate confines the ICP enrichment zone to a fixed 120 × 500 µm interrogation window, enabling spatially stable, label-free on-chip absorbance monitoring at 280 nm. Using bovine serum albumin (BSA) as a model protein, the platform showed a linear absorbance response over 0–60 µM (R2 = 0.9933) and a static-loading limit of detection of 0.91 µM. Under a 30 V bias in 0.3 mM phosphate-buffered saline, representative 60 min experiments produced apparent enrichment factors of approximately 41, 4.5 × 103, and 1.6 × 104 at initial BSA concentrations of 1 µM, 10 nM, and 1 nM, respectively. At 10 pM, a reproducible, voltage-dependent absorbance increase was detected with three independent chips, whereas the 0 V and BSA-free controls remained below the detection threshold. The platform provides a practical approach for label-free trace-protein analysis and a potential upstream module for biomarker-specific assays.

Biosensors doi: 10.3390/bios16100590

Authors: Yu-Chi Wu Kuo-Yin Lee Hao-Pu Lin Chin-Chuan Han Yuan-Yang Cheng

Continuous monitoring of neuromuscular and kinematic behaviors is crucial for effective upper-limb rehabilitation. This study presents a biosignal-driven, wrist-worn multimodal sensing system that combines discrete 12-class functional hand-gesture recognition with an exploratory multimodal physical interpretation of motion deviations. The platform integrates a surface electromyography (sEMG) envelope sensor for macroscopic motor unit recruitment, dual force-sensitive resistors (FSR) for end-effector contact force modulation, and an inertial measurement unit (IMU) for spatial–temporal biokinematics. Data collected from 20 healthy adults (12,000 one-second trials, each containing 100 synchronized time points at 100 Hz) demonstrated that a one-dimensional convolutional neural network (1D-CNN) achieves 81.23% ± 16.74% accuracy under subject-independent leave-one-subject-out (LOSO) cross-validation, capturing natural inter-individual neuromuscular variations. For baseline within-dataset comparison, a class-stratified random split yielded a macro F1-score of 96.59%. To bridge the gap between classification and clinical interpretation, we implemented a multimodal Dynamic Time Warping (DTW) pipeline. This approach successfully identified maximal-difference segments (Hotzones) across the aligned biosignals, enabling a qualitative visualization of candidate multimodal physical deviations (sEMG envelope and IMU/FSR kinematic discrepancies) during gesture execution. By establishing gesture-specific reference distributions from healthy subjects, this cost-effective, low-latency IoT biosensor platform demonstrates preliminary technical feasibility. It lays the groundwork for prospective clinical validation in stroke survivors, aiming to capture compensatory mechanisms and support home-based precision rehabilitation.

Biosensors doi: 10.3390/bios16100589

Authors: Nidhi Menon Erdi Kara Prava Sharma Adrian Crutchfield Ayesha Azimuddin Cianna Chung Brianna Wronko-Stevens

Luteinizing hormone (LH) is a critical biomarker for ovulation prediction and reproductive health monitoring, yet most commercially available at-home LH tests rely on qualitative or semi-quantitative visual interpretation, limiting their clinical utility. We present HueFertility Ovulation, a smartphone-enabled quantitative LH measurement system comprising a lateral flow immunoassay dipstick and a mobile application that integrates proprietary color correction algorithms with a deep learning regression model to deliver objective LH concentration estimates from strip images captured under variable real-world conditions. Color correction using the HueCard reference system substantially reduced inter-image color variability, decreasing mean ΔE00 from 11.19 to 4.02 across diverse lighting environments. When evaluated against known LH concentrations in a separately acquired independent dataset, the regression model achieved R2=0.92 and a mean absolute percentage error (MAPE) of 17.4%. The experimental limits of blank, detection, and quantitation were 0.64, 3.18, and 10.00 mIU/mL, respectively. Multi-site precision testing demonstrated %CVs within 25% across three independent locations. At a per-test cost of $0.60 with no external hardware required, HueFertility offers a low-cost, accessible platform for quantitative LH monitoring, with utility in fertility planning, hormonal health tracking, and resource-limited settings. Ultimately, the study highlights the opportunity to extend the technology to other low-cost lateral flow tests and clinical chemistry applications.

Biosensors doi: 10.3390/bios16100588

Authors: Wenjing Zhang Yuru Liu Jingjing Liu Feng Long Hejie Li Xinyu Zhang Yuxiang Shi Chunsheng Li

Clarithromycin (CLA), a widely used macrolide antibiotic and contaminant of emerging concern (CEC), is frequently detected in aquatic environments. Its residues can induce bacterial antibiotic resistance, threatening public health and ecosystems. In this study, we constructed an evanescent wave fluorescence immunoassay for the rapid detection of clarithromycin. It detects CLA in water quickly, sensitively, and in a portable format. The sensor combines evanescent wave fluorescence with an indirect competitive immunoassay. It uses a CLA–bovine serum albumin (BSA) conjugate on an optical fiber bioprobe, along with a Cy5.5-labeled anti-clarithromycin monoclonal antibody (Ka = 7.40 × 109 L/mol) as the signal reporter. After optimization, the assay gave a linear range of 7.50–77.45 μg/L. The LOD was 1.67 μg/L, and the LOQ was 7.50 μg/L. The functionalized fiber bioprobe also held up well in repeated use. It stayed stable for over 200 regeneration cycles, with an RSD of 5.63%. The entire automated detection workflow was completed within 20 min without manual intervention. Real-sample validation was performed using tap water and river water spiked at 10, 30, and 60 μg/L, yielding recoveries ranging from 87.13% to 104.84%, with relative standard deviations below 13.08%. These results were corroborated by ELISA. These attributes, combined with the instrument’s portability and the probe’s regenerability, render this biosensor a promising analytical platform for on-site environmental monitoring of clarithromycin residues.

Biosensors doi: 10.3390/bios16100587

Authors: Cem Celebi Stefan Ludwig Afif Siddiki

We analyse, as a design study, a cryogenic sensor that images the electrostatic stray field of a vitrified biological cell with an array of gate-defined quantum dots in gapped bilayer graphene. No device has been fabricated and no transport data are reported. The sensor is a Coulomb-blockade electrometer: each dot converts the offset charge induced on it by the sample into a shift in its resonance, and the steepness of that resonance—a property of the discrete density of states rather than of the device area—sets the gain, so that sensitivity is not lost as the element is made smaller. For a 50 nm dot at 4.2 K we estimate a charge sensitivity of order 10−2 e/√Hz for a charge 50 nm away, limited by an assumed 1/f offset-charge noise; this is a calculated, not a measured, figure. Because a vitrified sample is electroneutral beyond the Debye length, the sensor does not see net charge but the frozen interfacial dipole layers, whose dipole density equals the permittivity times the potential step across each layer. The measured quantity is therefore a band-limited map of lateral variations in the interfacial potential within about 100 nm of the sensor; a laterally uniform layer is invisible. In this map the membrane potential enters with a weight β = Cmt/εwε0 ≈ 0.07 relative to the surface potential of the glycocalyx, so the sensor is primarily a surface-potential imager and only secondarily, through a calibration we specify, a membrane-potential probe. A 10 mV surface-potential contrast at a period of 250 nm exceeds the 1 ms detection limit by a factor of about 4–13, and the 8 × 8 array at 250 nm pitch resolves periods of 250–300 nm. We give the device geometry, the plunge-freezing protocol, the controls and the inversion procedure, and list what remains to be measured.

Biosensors doi: 10.3390/bios16100586

Authors: Masashiro Sugino Toru Kuboi Yoko Tadatomo Nana Kawaguchi Takaaki Sadamura Akiko Nakano Kosuke Koyano Shinji Nakamura Ken Takahashi Takashi Kusaka

Heart rate variability (HRV) is a non-invasive measure reflecting cardiac autonomic regulation and has been used to investigate developmental changes in preterm infants. Short-term HRV recordings have demonstrated developmental changes with advancing postmenstrual age (PMA), but are highly susceptible to transient physiological conditions, limiting reproducibility. We prospectively performed serial 24 h electrocardiographic recordings in 50 preterm infants born at 24–35 weeks’ gestation from postnatal life until before 37 weeks’ PMA to characterize longitudinal HRV trajectories. A total of 879 recordings were analyzed using a 24 h single-window approach, and developmental trajectories were evaluated using linear mixed-effects models. All HRV indices increased progressively with advancing PMA. Infants born at ≥32 weeks’ gestation exhibited significantly higher HRV values after approximately 33 weeks’ PMA than those born at <32 weeks, indicating distinct maturational trajectories according to gestational age. PMA remained independently associated with multiple HRV indices after adjustment for major clinical factors, whereas body weight z-score, respiratory support, oxygen supplementation, and caffeine exposure showed little or no independent association after adjustment. These findings characterize longitudinal 24 h HRV trajectories in preterm infants and suggest that serial 24 h HRV monitoring may provide a framework for assessing developmental changes in cardiac autonomic regulation during neonatal intensive care.

Biosensors doi: 10.3390/bios16100585

Authors: Junyao Wang Qinglin Jia Xinqiang Zhu

Accurate recognition of gait characteristics under coupled body weight support and weight-bearing conditions is the core foundation for gravity adaptation strategy development.To solve this problem, this paper proposed a domain-adaptive gradient boosting machine (DAGBM) algorithm. The DAGBM algorithm added a cross-individual domain-adaptive regularization module to the standard GBM framework. It minimized both classification loss and maximum mean discrepancy (MMD) to align feature spaces across different subjects. It also worked with an individual–environment feature decoupling model to further improve generalization. Comparative experiments were conducted on a three-point suspension low-gravity test bed. A plantar pressure sensor with 48 points was used to collect gait data. This paper also extracted peak pressure, pressure–time integral, regional pressure ratio, and whole-foot center of pressure (CoP) trajectory standard deviation as features. DAGBM achieves 96.8% accuracy for seven body weight support levels, 97.4% accuracy for four weight-bearing levels, and 94.3% accuracy for all 28 fine-grained coupled conditions. DAGBM significantly outperforms both SGBoost and XGBoost algorithms, which exhibit favorable robustness under varied mechanical loading conditions and serve as a methodological reference for gait recognition research under aerospace-mimicking mechanical conditions.

Biosensors doi: 10.3390/bios16100584

Authors: Shi Ye Zihao Xia Boyi Li Donghao Wu Chi Liu Liyong Ma Shuzheng Shi

Wearable inertial sensors offer a portable means of assessing gait abnormalities associated with neurological and orthopedic disorders. However, intersubject variability, overlapping gait patterns, and class imbalance complicate multiclass recognition. This study presents the Temporal Structural Dual Graph Network (TSDGNet) for pathological gait classification using inertial measurement unit (IMU) signals. Four IMUs positioned on the head, lower back, and both feet provide acceleration and angular velocity signals during walking and turning. The framework combines multiscale temporal feature extraction with graph-based modeling of sequence-level relationships among sensor representations. Two fixed prior graphs encode anatomical and symbiotic relationships, while trainable graph attention and context-guided gated residual refinement adapt sensor-node representations. Class-balanced optimization is incorporated to reduce training bias toward majority classes. Subject-independent evaluation was performed on a public clinical dataset comprising healthy controls and seven neurological or orthopedic disease groups. Comparisons with representative baseline and advanced models showed competitive classification performance. Ablation analyses examined the contributions of temporal modeling, structural relationship learning, and class-balanced optimization. These findings support the potential of combining wearable inertial sensing with temporal and graph-based learning for objective classification of pathological gait.

Biosensors doi: 10.3390/bios16100583

Authors: Anas Mohammed Alhakami Diyaa Abdul Rauf Algazwi Majdi Habib Al-Awjami Walaa Hamdy Elsayed Ali M. Alshami

Background: The physiological differences between deep dry needling (DDN) and superficial dry needling (SDN) for myofascial trigger points (MTrPs) remain unclear. This randomized clinical trial compared the impact of DDN and SDN on ultrasonographic, surface electromyographic (sEMG), and clinical outcomes in patients with non-specific neck pain and upper trapezius MTrPs. Methods: Forty-four participants were randomly allocated to DDN (50 mm) or SDN (13 mm), with 22 participants in each group. Participants received four treatment sessions over 2 weeks. Ultrasonographic outcomes included upper trapezius stiffness, thickness, and blood-flow parameters. sEMG outcomes included maximum voluntary isometric contraction (MVIC), root mean square (RMS), and median frequency (MDF). Pain intensity was measured using the Visual Analogue Scale (VAS) and disability using the Neck Disability Index (NDI). Outcomes were assessed at baseline, following 2 weeks of intervention, and during the 6-week follow-up. Results: No statistically significant between-group differences were detected for the ultrasonographic outcomes. For sEMG, the time-specific contrast for active RMS at the 6-week follow-up was nominally significant (DDN minus SDN: 1.09, 95% CI 0.06–2.13; p = 0.039), whereas the remaining between-group sEMG comparisons were not statistically significant. A small within-group reduction in muscle stiffness was observed in the SDN group at 2 weeks (−0.09, 95% CI −0.17 to −0.001). MVIC increased in both groups at 6 weeks, and active RMS decreased within the SDN group. Both groups showed reductions in pain and disability; adjusted VAS changes ranged from −3.04 to −3.35 cm and adjusted NDI changes ranged from −4.5 to −6.5 points across groups and follow-up assessments. The isolated p = 0.039 finding is interpreted cautiously because multiple outcomes were examined without formal multiplicity adjustment. Conclusions: Both DDN and SDN were associated with improvements in pain and disability, with no statistically significant between-group differences detected for these clinical outcomes in this sample. Most ultrasonographic and sEMG between-group comparisons were also non-significant, although a nominal difference in active RMS was observed at 6 weeks. Because this trial was not designed as an equivalence or non-inferiority study, the findings should not be interpreted as establishing therapeutic equivalence. The absence of consistent ultrasonographic changes suggests that mechanisms beyond measurable local structural or vascular changes may contribute to the observed clinical improvements; such mechanisms were not directly measured in this study.

Biosensors doi: 10.3390/bios16100582

Authors: Yunjuan Wang Yuhang Guo Qing Liu Yeli Xiao Zhuo Xiang Shunhong Xue Xiaobing Kong Nuo Shang Zhifan Jiang Jinjin Wang Wenjing Xu

Acupuncture needles, conventionally employed as therapeutic tools in Chinese medicine, have emerged as compelling substrates for constructing minimally invasive sensing platforms. Acupuncture needles’ unique attributes, including small size, sharp tip geometry, mechanical robustness, biocompatibility, and ease of insertion into tissue, render them attractive for in vivo, real-time monitoring applications. This review provides an overview of the recent advances in acupuncture needle-based biosensors (ANBSs), focusing on three interconnected aspects: interface engineering approaches, sensing strategies, and representative applications. We demonstrate electrochemical and surface-enhanced Raman spectroscopy (SERS)-based platforms as two principal classes of acupuncture needle-based biosensors, and survey their applications in neurotransmitter monitoring, disease biomarker detection, therapeutic drug monitoring, metabolite profiling, acupoint initiation investigation, as well as environmental contaminant screening and food safety assessment. Finally, we also discuss current challenges, including long-term instability, inadequate active sites, biofouling, signal drift, and insufficient reproducibility, while offering perspectives on future directions such as optimization of materials, miniaturization and multifunction, intelligent sensing strategies and clinical translation proposal. By bridging traditional acupuncture needles and advanced analytical science, this review not only provides a coherent framework for researchers in biosensors but also inspires the next generation of minimally invasive point-of-care diagnostic tools.

Biosensors doi: 10.3390/bios16100581

Authors: Xudong Liu Bo Zhang Kefei Zou Ruikai Wan Qiangyuan Zhu

Single-cell RNA sequencing has supplied cell atlases and artificial intelligence (AI)-enabled virtual-cell models with scalable, gene-level descriptions of cellular state. Yet prevalent poly(A)-based counting assays capture only part of the RNA landscape. Here, “single-cell whole-transcriptome detection” denotes sequencing-based efforts to detect a broader range of RNA biotypes than conventional poly(A)-based gene counting; no assay detects or quantifies every cellular RNA species without bias. We compare RNA-capture scope, transcript-structure resolution, temporal labeling, spatial context, and additional modalities as distinct measurement dimensions. From a biosensing perspective, analytical sensitivity, transcript coverage, throughput, and assay bias determine which signals are available to computational models. We then examine cell-state representations, regulatory networks, dynamics, perturbation prediction, and foundation models. Total-RNA assays reveal additional non-coding RNAs, long-read methods inform transcript structure, and temporal assays constrain RNA kinetics. Whether these signals improve prediction beyond conventional gene-count inputs requires matched, task-specific model comparisons. We distinguish molecular observability, statistical association, predictive performance, and causal validation, which requires appropriate interventions. We propose benchmarks controlling sequencing depth, cost, model architecture, and held-out samples while testing feature ablations, out-of-distribution performance, uncertainty calibration, and experimental outcomes. Together, high-throughput poly(A)-based profiling and broader RNA measurements provide complementary cellular breadth and regulatory depth.

Biosensors doi: 10.3390/bios16100580

Authors: Nélia Alberto Maria Fátima Domingues Nunzio Cennamo Adriana Borriello

The interaction of light with biological matter offers a unique combination of analytical capabilities, making photonics a powerful platform for sensing, imaging, and monitoring across a broad range of bioapplications [...]

Biosensors doi: 10.3390/bios16100578

Authors: Suxin Huo Yinchu Zhu Congyun Ji Fu Fan Ruiping Lin Weicheng Ye Yuan Fu Jionggang Hua Gaole Feng Liu Chen Tao Yun Cun Zhang Zhuanli Wu Xing Xu Yue Wu Zheng Ni

Brucellosis is an important zoonotic disease worldwide, and Brucella canis (B. canis) is an emerging concern in companion animal health. Rapid and reliable detection of B. canis in dogs is essential for disease control. Herein, we developed a one-pot MIRA CRISPR/Cas12b fluorescence biosensing platform targeting the conserved omp31 gene. The platform integrates MIRA mediated isothermal amplification with CRISPR/Cas12b-based target recognition and collateral cleavage. After optimization, the assay achieved a detection limit of 5 copies/reaction within 20 min, comparable to qPCR. No cross reactivity was observed with seven common canine pathogens. Among 128 clinical blood samples, the biosensor showed complete agreement with qPCR, achieving 100% sensitivity and 100% specificity. These findings demonstrate that the biosensor provides a rapid, sensitive, and specific approach for B. canis detection, with potential for decentralized veterinary testing through its endpoint visual fluorescence readout and applications in One Health surveillance.

Biosensors doi: 10.3390/bios16100579

Authors: Ruimiao Chang Jingjing Wang Yong Li Shuai Jia Liping Li Ruiping Zhang

The sensitive detection of tumor markers is of great significance for clinical diagnosis. Due to the extremely low abundance of early tumor biomarkers and severe background interference from biological matrices, traditional single-modal atomolar biological testing generally has limitations such as high false positives and narrow linear ranges, making it difficult to meet the clinical needs of precise diagnosis and treatment. This has driven the iterative development of biosensing technology from a single signal readout mode to multi-modal collaborative analysis, significantly improving the sensitivity and accuracy of complex biological sample detection. Dual-modal sensors integrate two complementary sensing mechanisms, whereas multi-modal sensors produce three or more independent signal outputs. By combining such mechanisms, for example electrochemistry and optics, these platforms can establish a signal cross-validation and self-calibration detection system. This multidimensional analysis strategy can mitigate interference caused by environmental factors and human operational errors to a certain extent, lowering detection limits and broadening the linear range, thereby potentially providing higher-reliability data support for early tumor diagnosis. This work systematically reviews the technological evolution and performance differences in tumor biomarker sensors from single-mode to multi-mode in recent years, with a focus on summarizing the cutting-edge research progress in this field. It provides a comprehensive reference for researchers and facilitates further applications of multi-modal sensors in point-of-care detection, and artificial intelligence-assisted analysis.

Biosensors doi: 10.3390/bios16100577

Authors: Muhammad Awais Younggue Kim Byeong Ha Lee

Non-contact detection of weak, transient acoustic signals is essential for photoacoustic and laser-ultrasound (LUS) bioimaging, including the assessment of corneal deformation and thickness variation. Passive 3 × 3 fiber-coupler interferometers provide phase-diverse sensing without external modulation; however, conventional ellipse-based calibration becomes unreliable when the measured phase excursion covers only a small fraction of the characteristic ellipse, as in nanometer-scale corneal responses. This study presents a physics-guided temporal convolutional network (PG-TCN) that directly maps two nonideal interferometric outputs to a continuous dynamic phase signal without ellipse fitting or separate phase unwrapping during inference. The model combines dilated one-dimensional convolutions with interferometric-consistency and temporal-regularization losses. Tests using PZT-modulated calibration signals acquired under several static chamber pressures of 10, 15, 20, and 25 kPa showed lower mean absolute error, higher normalized cross-correlation, and lower displacement RMSE than a CNN-LSTM baseline. In corneal-phantom LUS measurements, the first detected echo shifted from 4.632 to 4.488 µs between 10 and 20 kPa, corresponding to a relative thickness reduction of approximately 7.8 µm/kPa. The PG-TCN required 210 ± 18 µs per sequence while using approximately half the trainable parameters of the CNN-LSTM baseline. These results demonstrate a fast physics-guided optical detection framework for non-contact sensing of weak acoustic responses, supporting the use of 3 × 3 optical interferometry as a biosensing approach for LUS and photoacoustic applications.

Biosensors doi: 10.3390/bios16100576

Authors: Liping Yang Nairui Sun Tengchao Sun Yiming Chen Yao Xiao Shu Li Wei Jia Shaoshuai Luo Wei Wang

Multimodal medical image fusion aims to integrate information from different imaging techniques to generate a single fused image that is comprehensive and rich in detail. Structural and functional imaging provide high-resolution anatomical details and tissue metabolic information, respectively. Complementary fusion improves quantitative fusion quality and preserves structural and functional information in one view, which aids clinical interpretation. Currently, convolutional neural network-based methods struggle to efficiently capture global features, whereas Transformer-based models excel at global modeling but incur relatively high computational costs. To address the above issues, we propose MSMFusion, a multiscale state-space modeling method for complementary structural–functional medical image fusion. A multiscale feature extraction module first extracts modality-specific multiscale structural features; subsequently, a dual-path MSMamba establishes long-range dependencies with linear complexity: the full-resolution scanning path preserves fine boundaries and textures, while the downsampled scanning path models global context with a more compact sequence; finally, a gated adaptive fusion module estimates spatial–channel weights based on local content to selectively integrate anatomical structure and functional metabolic information. On the Harvard medical image dataset, MSMFusion achieves a mutual information of 2.2181, a peak signal-to-noise ratio of 32.9390, and a structural similarity of 0.7616 on the MRI–PET task, and also outperforms various representative methods based on CNN, GAN, Transformer, and Mamba on the MRI–CT and MRI–SPECT tasks. Compared with the Transformer backbone, MSMamba reduces the number of parameters from 7.201M to 4.154M and the FLOPs from 476.738G to 159.239G, achieving superior fusion quality at lower computational cost while simultaneously preserving fine anatomical structures and key functional information.

Biosensors doi: 10.3390/bios16100575

Authors: Yang Jun Kang

Red blood cell (RBC) aggregation is a sensitive indicator of hemorheological alterations, but conventional methods generally rely on intermittent or end-point measurements. This study presents a flow-stabilized closed-loop microfluidic platform for continuous measurement of the RBC aggregation index (AI) during controlled circulation and thermal exposure. The system integrates a peristaltic pump, hydraulic flow stabilizer (FS), microfluidic aggregation sensor, and thermal mixer to suppress flow pulsation and RBC sedimentation. The FS increases the characteristic flow-response time by approximately 17.6-fold, markedly attenuating pump-induced fluctuations. Under stabilized conditions, flow rates in the main channel and test chamber remain highly linear with the imposed flow rate, while stable AI measurements are maintained for at least 30 min. The platform also resolves dextran-dependent changes in RBC aggregation during continuous circulation. During thermal exposure from 25 to 47 °C, main-channel image intensity (Imc) and AI remain nearly constant up to 43 °C, whereas exposure at ≥45 °C causes a progressive increase in Imc and a marked decrease in AI. These results demonstrate continuous detection of thermally induced changes in RBC aggregation at a fixed measurement temperature of 25 °C without interrupting circulation and provide a versatile framework for investigating progressive changes in aggregation behavior under controlled and reproducible in vitro conditions.

Biosensors doi: 10.3390/bios16100574

Authors: Ceyhun E. Kırımlı Elçim Elgün Selin Yağmur Tuğtağ

Liquid-phase quartz crystal microbalance (QCM) biosensors are commonly quantified using scalar readouts such as frequency shift, dissipation, or bandwidth. Although useful, these descriptors discard resonance line-shape morphology that carries additional information for quantitative inference. Here, we present a machine-learning-assisted impedance line-shape workflow for temporally robust quantitative 10 MHz QCM biosensing. A passive microfluidic mixer generated controlled glycerol–water concentration gradients under constant total flow while complex impedance spectra were acquired in real time, and each sweep was parameterized using constrained Gaussian/Lorentzian models to obtain 52 physically interpretable descriptors spanning resistance, reactance, impedance magnitude, admittance components, and phase. The pipeline combines consensus outlier handling, training-fold mRMR feature ranking, and regression models evaluated under both shuffled five-fold cross-validation and a stricter temporally blocked protocol in which contiguous portions of the experiment are held out. The workflow achieved R2=0.948 under shuffled validation and R2=0.823 under seven-block temporal validation, the latter reducing prediction error by 48.4% relative to a row-matched Kanazawa frequency-shift baseline evaluated on the same sweeps. Feature-set ablation indicated that phase and conductance line-shape descriptors carry particularly strong temporally generalizable information. Applied to an experimental DNA hybridization assay in 1× PBS, the workflow reduced mean absolute percentage concentration error from 21.02% to 10.73%, a 48.9% improvement over conventional Δf calibration. Retaining resonance morphology therefore improves quantitative QCM biosensing beyond scalar frequency-shift calibration and provides an interpretable basis for AI-assisted electronic biosensors.

Biosensors doi: 10.3390/bios16100573

Authors: Diogo Serrano Paulo Zoio Bernardo B. Carvalho Luís P. Fonseca Cecília R. C. Calado

Chronic kidney disease (CKD) is commonly assessed using blood biomarkers such as creatinine, urea and albumin. This study compared a portable six-channel near-infrared (NIR) analyzer (miniNIR; 1300–2100 nm) with a benchtop Fourier-transform near-infrared (FT-NIR) instrument for the discrimination and quantification of fifty-four independent concentration-defined profiles in composed fetal bovine serum solutions spanning six simulated KDIGO categories. SVM-RBF, Random Forest, XGBoost, Logistic regression and partial least-squares discriminant analysis (PLS-DA) were evaluated. For miniNIR binary ‘healthy/disease’ profile discrimination, SVM-RBF yielded a validation AUC of 1.00, accuracy of 0.91, sensitivity of 1.00 and specificity of 0.83. The six-class profile assignment was substantially more difficult: PLS-DA was selected by grouped nested cross-validation, but validation accuracy was 0.18, macro-F1 was 0.11 and macro-AUC was 0.77 across the evaluable classes. For miniNIR regression, validation R2 values were 0.48 for creatinine, 0.30 for urea and 0.10 for albumin. The strongest FT-NIR validation results were R2 = 0.64 for creatinine, 0.79 for urea and 0.89 for albumin, depending on the spectral window. For FT-NIR binary ‘healthy/disease’ profile discrimination, SVM-RBF yielded a validation AUC of 0.99, accuracy of 0.71 and a specificity of 0.47. Although the high binary AUC in this controlled concentration design for the miniNIR, it should not be interpreted as evidence of CKD-specific recognition or clinical performance. The comparison quantifies the information retained and lost when continuous FT-NIR sampling is reduced to six broad discrete channels; validation in clinically characterized human samples is required before any screening application can be considered.

Biosensors doi: 10.3390/bios16100572

Authors: Ziyi Yang Yangzhi Zhang Xingyu Wang Tongzhou Tang Jiajia Wang Xiaohui Huang Zhenhong Jia

To address the difficulty of simultaneously achieving low cost, high sensitivity, and rapid response in the detection of isocarbophos residues in agricultural products and environmental water samples, a portable fluorescence sensing platform integrating a porous silicon Bragg mirror (PSBM) and upconversion nanoparticles (UCNPs) was constructed in this work. First, a PSBM was fabricated by electrochemical etching and then functionalized. Owing to the photonic bandgap effect of the PSBM, which enhances the local optical field, the enhancement effect of the PSBM on upconversion fluorescence was significantly superior to that of single-layer porous silicon. Subsequently, the isocarbophos aptamer was immobilized on the inner walls of the pores of the PSBM, and UCNPs were conjugated with the isocarbophos complementary strand to prepare fluorescent probes. Then, the probe solution was dropped onto the inner walls of the PSBM pores, and through specific binding between the isocarbophos aptamer and the complementary strand, the UCNPs and the complementary strand were indirectly immobilized on the inner walls of the PSBM pores. After that, isocarbophos standard solutions of different concentrations were added. Because isocarbophos molecules have stronger specific binding affinity for the isocarbophos aptamer, the UCNPs and complementary strand originally immobilized on the inner walls of the PSBM pores were displaced by isocarbophos molecules, resulting in a decrease in the number of UCNPs on the inner walls of the PSBM pores and thereby attenuating the upconversion fluorescence. Finally, the surface of the porous silicon sample was irradiated with a 980 nm laser to excite the UCNPs on the inner walls of the pores to generate upconversion fluorescence. Fluorescence images before and after the reaction were recorded by an image acquisition device, and the change in mean grayscale value was calculated using image processing software to determine the isocarbophos concentration. The limit of detection of this method was 0.27 μg/L. Under identical conditions, the signal change caused by 100 μg/L chlorpyrifos, a structurally similar organophosphate pesticide, was negligible, indicating that the sensor has good selectivity for ICP. These results demonstrate that the proposed platform is a promising low-cost, highly sensitive, and rapid tool for pesticide detection. The anti-interference experiment against structurally similar organophosphate pesticides confirmed the selectivity of the aptamer sensing mechanism, while systematic evaluation of matrix effects in real samples remains an important direction for future research.

Biosensors doi: 10.3390/bios16100571

Authors: Tianci Wang Lingyun Li Yunxiang Wang Yongjun Han Teng Li Chaoran Yu Yufeng Lou Zhen Rong Kai Lin Shengqi Wang

Screening for high-risk human papillomavirus (HR-HPV) is a pivotal strategy for the prevention of invasive cervical cancer, particularly in resource-constrained settings. In this study, we developed a CRISPR-based compact automated real-time detection system, termed Ccard, for rapid testing of HPV16, HPV18, and the internal reference gene GAPDH through a streamlined workflow. A simple room-temperature treatment efficiently releases nucleic acids directly from swab samples, bypassing the need for extraction. The mixture was driven through the channels and loaded into the reaction chambers on the microfluidic chip by a vacuum pump. The lyophilized reagents in the reaction zone of the microfluidic chip were redissolved. The system subsequently performs a single-step RPA-CRISPR/Cas13a reaction, with real-time detection enabled by an Internet of Things device. The assay achieved detection within 30 min, with a limit of detection (LOD) of 5 copies/µL for both HPV16 and HPV18. Validation with clinical samples demonstrated high sensitivity (96.8% for HPV16 and 100% for HPV18) and 100% specificity. These results highlight the reliability, precision, and strong potential of the Ccard system for rapid testing of HR-HPVs.

Biosensors doi: 10.3390/bios16100570

Authors: Jun Liu Yichen Lin Yinuo Yao Yu Zhu Haoran Zhang Xiangyang Wang Jianquan Sun Bo Yuan Yue Ma

Multi-terrain load carriage changes knee motion and muscular demand, requiring assistance that adapts to the current locomotion task. This feasibility study evaluated an integrated pipeline in which five inertial measurement units (IMUs) and a quaternion-based dynamic temporal pooling Transformer (QDTPformer) selected task-specific dynamic movement primitive (DMP) references for powered knee-exoskeleton assistance. Six participants took part across the offline recognition and human-assistance evaluations. For level walking (Flat), ramp ascent (Ramp), and stair ascent (Stairs), QDTPformer achieved 95.74% offline test accuracy and 89.54%±1.07% mean window-level accuracy in leave-one-subject-out evaluation. Online recognition selected the corresponding DMP reference and assistance parameters. Phase-aligned DMP–knee correlations were 0.967, 0.944, and 0.777 for Flat, Ramp, and Stairs, respectively. The equally weighted mean surface electromyography (sEMG) root mean square (RMS) reduction across two muscles and three tasks was 8.42%, with task- and wearer-dependent responses. The main contribution is an end-to-end recognition-to-assistance pipeline evaluated across three terrains using both kinematic and muscle-activity outcomes.

Biosensors doi: 10.3390/bios16100569

Authors: Chia-Chun Lee Wei-Ting Hsueh Chin-Chung Tseng Lung-Ming Fu

Strand displacement amplification (SDA) provides a versatile isothermal framework for chemical and biosensing, coupling polymerase-driven strand displacement with programmable nucleic-acid signal generation. Its analytical scope now extends beyond direct nucleic-acid detection because aptamers, DNAzymes, antibodies, and responsive probes can convert diverse recognition events into amplifiable DNA triggers. This review develops a unified view of SDA from molecular design to practical implementation, with particular emphasis on programmable amplicon generation, recognition-to-DNA transduction, interfacial confinement, cascaded amplification, and CRISPR coupling. The review follows SDA across biomedical, food, and environmental applications, including RNA biomarkers, liquid biopsy, genetic and epigenetic targets, proteins, enzymes, small molecules, and viruses. It also considers how these assays are being transferred into microfluidic, paper-based, lateral-flow, self-powered, regenerative, and intelligent systems. The important distinction is that analytical sensitivity does not necessarily predict practical performance. Extremely low detection limits often depend on cascaded processing beyond SDA itself, whereas real samples expose weaknesses in matrix tolerance, background suppression, reagent stability, fluid control, assay duration, and manufacturing reproducibility. Comparison with commercially established isothermal methods further shows that wider adoption will depend on converting flexible molecular chemistry into robust, standardized, sample-to-answer systems.

Biosensors doi: 10.3390/bios16100568

Authors: Leonardo Pinto de Magalhães Ana Carolina de Sousa Silva Tamara Maria Gomes Mary Ingrid Alves e Santos Ernane José Xavier Costa

Groundwater plays an important role in food production. However, it faces environmental pressures from various factors, and the presence of contaminants in these waters—whether emerging or traditional—is a growing cause for concern. Nevertheless, there are few reviews available that address the analysis of biosensors for the detection of these specific contaminants in groundwater. Therefore, the objective of this study was to conduct a review to determine which types of biosensors, which contaminants, and other factors are most frequently highlighted in studies on the detection of contaminants in groundwater. In line with this objective, the findings indicate a predominance of electrochemical biosensors, with a particular emphasis on the use of bacteria as the biological component. The detection of pesticides has been a focus since the earliest publications, yet there are still significant gaps regarding the detection of multiple substances, such as the co-occurrence of arsenic and other pollutants. Thus, this review contributes to a broad overview of the technologies used, offering important insights for future research and mapping the current state of the art in the detection of contaminants in groundwater.

Biosensors doi: 10.3390/bios16100567

Authors: Rolla Mohamed Abbas Sabahi Somayeh Fardindoost Mina Hoorfar

The growing prevalence of chronic wound infections presents a major global healthcare challenge due to delayed healing, prolonged hospitalization, and increased rates of limb amputation. Conventional diagnostic approaches, including clinical assessment, microbiological culture, and laboratory-based analyses, are often limited by subjective interpretation, delayed turnaround times, invasive sampling, and an inability to continuously monitor the dynamic wound microenvironment. Because wound infection involves changes in both host physiology and microbial activity, reliance on a single biomarker may not adequately characterize wound status, highlighting the potential of multimodal point-of-care diagnostic platforms. Recent advances in wearable biosensors, surface-enhanced Raman spectroscopy (SERS), nanomaterials, and artificial intelligence (AI) have supported the development of multimodal approaches for monitoring complementary host- and pathogen-derived biomarkers to facilitate rapid, non-invasive, and continuous wound infection assessment. This review examines selected multimodal biosensing approaches for wound infection detection, with a focus on electrochemical biosensors, SERS, and AI-assisted data analytics within point-of-care diagnostic frameworks. The review discusses (1) key wound infection biomarkers and the fundamental principles of electrochemical and SERS-based sensing; (2) recent developments in multimodal biosensing platforms, advanced plasmonic materials, and AI-assisted data interpretation and multimodal data fusion; and (3) current challenges and future opportunities for the clinical translation of point-of-care wound diagnostics. By combining complementary sensing modalities with AI-driven data analytics, these approaches may support earlier infection detection, continuous non-invasive monitoring, personalized wound management, and more informed clinical decision-making.

Biosensors doi: 10.3390/bios16100566

Authors: Feng-Lei Guo Hao-Yi Yang Jiu-Sheng Li

We developed a terahertz broadband sensing chip based on the synergistic enhancement effect of localized surface plasmon resonances (LSPRs) and electromagnetically induced transparency (EIT). This chip consists of a periodic array formed by cross-shaped metasurface elements of varying sizes and E-shaped metasurface elements, enabling broadband biochemical substance detection within the 0.50–2.50 THz frequency range while maintaining high sensitivity and broad spectral fingerprint resolution capability. Through analysis of electric field distribution, surface current distribution, and multipole decomposition theory, the physical mechanisms underlying near-field localization enhancement and far-field scattering regulation were systematically revealed. Experimental results demonstrate that the sensor achieves the lowest experimentally detected concentration of 5 μg/mL for lycopene. The characteristic peak sensitivities, derived from the envelope variation rate, are 10.44 × 10−3 a.u./(μg/mL) and 2.67 × 10−3 a.u./(μg/mL), respectively. This chip exhibits significant advantages in trace biochemical substance detection and broadband fingerprint recognition, offering new insights for the in-depth application of metasurface technology in the biosensing field.

Biosensors doi: 10.3390/bios16100565

Authors: Shu Xie Xueming Wen

Pulse oximetry is an optical biosensor for peripheral oxygen-saturation monitoring, but its agreement with arterial co-oximetry during exercise remains uncertain. We searched three databases through 30 August 2026 and conducted supplementary Embase searching in September. Sixteen studies yielded 46 device- or condition-level outcomes. Across four peak/maximal cohorts, mean bias ranged from −0.88 to +2.63 percentage points. Three reported paired SDs ranged from 2.75 to 4.60 points; a fourth was conditionally reconstructed. Primary synthesis was structured and descriptive. Post hoc random-effects mean-bias analyses of three reported-SD cohorts and a conditional fourth were imprecise and clinically heterogeneous, and did not characterize individual agreement. A descriptive technology audit found complete device identification in 14 studies and sensor-site reporting in 15, but no study fully reported numerical emitter wavelengths. Averaging/update behavior was fully reported in two studies, algorithm/post-processing detail in three, motion handling in six, and failure/missingness in seven. The author-agreed appraisal was interpreted descriptively without validated aggregate risk or certainty grades. These data do not establish a universal correction factor or a transportable substitution rule. No prospective registration, incomplete supplementary-search export reconciliation and reference-construct ambiguity limit interpretation. More complete sensor, reference-method and agreement reporting is needed.

Biosensors doi: 10.3390/bios16100564

Authors: Sandro Spagnolo Sára Mikuličová Katharina Davoudian Kiran Sontakke Brian De La Franier Michael Thompson Tibor Hianik

Myelin basic protein (MBP) is released into cerebrospinal fluid during demyelination and is therefore a promising biomarker for the detection of neurodegenerative diseases such as multiple sclerosis. For the label-free detection of MBP, we developed an antifouling aptasensor based on thickness shear mode sensing. DNA aptamers were immobilized on gold-coated piezoelectric crystals functionalized with an engineered antifouling self-assembled monolayer based on 3-(2-mercaptoethoxy)propanoic acid (HS-MEG-COOH) combined with either 2-(2-mercaptoethoxy)ethanol (HS-MEG-OH) or dithiothreitol (DTT) as spacers. The binding of recombinant human MBP to the aptamer was confirmed with a corresponding decrease in resonance frequency in both phosphate-buffered saline (PBS) and artificial cerebrospinal fluid (aCSF). The aptasensor achieved a limit of detection of 11 ng mL−1 in PBS and 2 ng mL−1 in aCSF. PBS measurements were primarily applied to determine the aptasensor functionality, whereas experiments in aCSF evaluated MBP detection at concentrations relevant to reported levels in biological CSF. Selectivity was assessed with aptamer-free surfaces, cytochrome c, and human serum albumin (HSA) with much lower responses, while concentration-dependent MBP detection was maintained in aCSF despite the increased complexity of the matrix. The antifouling performance of the modified sensing surface was further demonstrated in aCSF by the significantly reduced frequency and dissipation responses compared to bare gold. With future validation in human cerebrospinal fluid (CSF) samples, the developed biosensor has the potential to enable sensitive, label-free detection of MBP in CSF for the identification and monitoring of neurodegenerative diseases.

Biosensors doi: 10.3390/bios16100563

Authors: Fine Handrick Vincent O. Imieje Vivien Engel Esther Abiodun Odigie Ewelukwa Ebube Chukwueloka Lilian Nneoma Amafili Onome Keturah Evi Nadja Engel

Metabolic endpoint assays enable rapid screening but provide limited information on treatment kinetics, heterogeneity, and recovery. Here, we established a multistep workflow combining MTS-based metabolic screening with electric cell–substrate impedance sensing (ECIS), a label-free cell-based biosensor, to functionally prioritize complex plant extracts in head and neck squamous cell carcinoma (HNSCC) models. Seventeen soluble extracts were screened at 50 µg/mL for 24 h in four HNSCC cell lines and human adipose-derived stem cells. Selected candidates were characterized by real-time impedance monitoring, multifrequency analysis, and model-derived barrier resistance. Extract 16 showed the most favorable metabolic selectivity profile. ECIS resolved no growth-inhibitory effects, sustained impedance suppression, heterogeneous responses, and transient suppression followed by partial recovery. Recovery was incomplete (CAL-33) or absent (Detroit 562); modeled barrier resistance became resolvable late (FaDu, PE/CA-PJ15) or not at all (CAL-33). Comparison with the PI3Kα inhibitor Inavolisib revealed partially overlapping but non-identical metabolic response profiles and no consistent enhancement by combination treatment. Extract 16 was associated with junctional redistribution, F-actin remodeling, and cell line-dependent PARP cleavage, whereas caspase 3/7 activation could not be detected. Integrating metabolic endpoint screening with multiparametric ECIS monitoring provides greater functional resolution and supports prioritizing complex bioactive samples for chemical and mechanistic investigation.

Biosensors doi: 10.3390/bios16100562

Authors: Shuai Zhang Chao Zhang Jingwei Zhang Yuran Wang Yingze Sun Yijiang Zhou Fengnian Zhao

As a key biomarker for regulating stress levels, timely detection of cortisol can provide an immediate indication of a person’s current stress status and serve as a valuable reference for preventing related diseases. In this study, a laser-induced graphene (LIG)-based electrochemical sensor for rapid cortisol detection was developed. The prepared LIG/Au/Nafion interface exhibits higher electrocatalytic activity toward cortisol than the LIG/Au interface, attributed to the sulfonate-rich interfacial microenvironment of Nafion, which facilitates the interfacial conduction and removal of the protons liberated during cortisol dehydrogenation. Under optimal conditions, the obtained sensor shows a good linear relationship for detecting cortisol solutions over a wide linear range (20–2000 nM) and a low detection limit (1.02 nM). Furthermore, the sensor exhibited a good linear relationship with artificial sweat samples, and the recovery rate for cortisol in actual sweat samples ranged from 96.76% to 113.14%, offering a promising new technical pathway for rapid detection of cortisol in physiological media.

Biosensors doi: 10.3390/bios16100561

Authors: Hamdi Ben Halima Nadia Zine Joan Bausells Abdelhamid Errachid Nicole Jaffrezic-Renault

Cortisol is a key biomarker involved in the regulation of the hypothalamic–pituitary–adrenal axis and is widely used for assessing physiological stress and endocrine disorders. However, its small molecular size and low charge make its direct detection using field-effect transistor (FET)-based biosensors challenging. In this work, we present a competitive ImmunoFET biosensing platform combining a Si3N4-gated ion-sensitive field-effect transistor (ISFET) with anti-cortisol polyclonal antibody-functionalized magnetic nanoparticles (MNPs) for label-free cortisol detection. The sensing strategy relies on a competitive immunoassay in which cortisol in the sample competes with cortisol immobilized on the ISFET surface for antibody binding sites on the MNPs. Following a 45 min pre-incubation, magnetic separation, and redispersion, the MNPs were brought into contact with the cortisol-functionalized FET and analyzed by electrochemical impedance spectroscopy (EIS). The charge-transfer resistance decreased from 4.012 MΩ to 2.599 MΩ as the cortisol concentration increased from 0.02 to 0.6 ng/mL, confirming the expected competitive behavior. The sensor exhibited a concentration-dependent response over the 0.02–0.6 ng/mL cortisol range, with a limit of detection of 0.006 ng/mL. The platform also showed a stronger response toward cortisol than the investigated non-target biomarkers, including NT-proBNP, TNF-α, and IL-10. Its applicability was further demonstrated in real saliva using the standard addition method, yielding a cortisol concentration of 11.6 ng/mL, compared with an expected value of 10.2 ng/mL, corresponding to a 113.7% recovery. These preliminary results demonstrate the feasibility of the proposed MNP-assisted competitive FET for cortisol detection in biological samples and provide a basis for further development toward point-of-care applications.

Biosensors doi: 10.3390/bios16100560

Authors: Rizal Maulana Ádám Rák Sándor Földi György Cserey

The arterial blood pressure (ABP) waveform contains essential information concerning the hemodynamic status and its relation to the diagnosis of cardiovascular conditions. Therefore, the continuous monitoring of the ABP waveform has become important. The arterial cannula, considered the gold standard for this monitoring, carries a risk of vascular trauma due to its invasive nature. Consequently, a measurement system that offers a non-invasive approach and maintains high accuracy in detecting morphological details of the ABP waveform is needed. In this study, we introduce a flexible piezoresistive yarn (FPY) pressure sensor for continuous non-invasive ABP waveform measurement. The sensor was experimentally validated using two scenarios: measurements on a hardware simulator and at a radial artery site. The simulator measurement aimed to assess the sensor’s performance in detecting consistent waveforms, while the radial artery site measurements evaluated the sensor’s performance in measuring ABP waveforms corresponding to the cardiovascular cycle. The simulator measurement results showed high repeatability, reproducibility, and accuracy relative to the reference signal at the upper limit of realistic force magnitude, as measured by the Pearson correlation coefficients: 0.9999 ± 0.0002 (10 measurements), 0.9994 ± 0.0002 (3 measurements), and 0.9731 ± 0.0027 (10 measurements), respectively. The correlation value derived from measurements at the radial artery site compared with measurements using a 3D force sensor as a reference sensor was 0.9880 ± 0.0035 (10 measurements from a single subject). Based on our results, the FPY pressure sensor can be used as an innovative solution for monitoring ABP waveforms and has further potential to be developed as a diagnostic device for evaluating cardiovascular conditions.

Biosensors doi: 10.3390/bios16100559

Authors: Elena Serea Codrin Donciu Marinel Costel Temneanu

Smartphone-based colorimetric biosensor readout is strongly influenced by illumination, yet lighting conditions are often characterized mainly by illuminance rather than by their spectral composition. This study investigates the spectral, photometric, colorimetric, and energetic effects of programmable LED illumination on smartphone colorimetric readout under controlled acquisition conditions. Twenty lighting scenarios were generated using three independently controlled color-capable LED luminaires and characterized by spectral irradiance, illuminance, chromaticity, color rendering, and electrical power. A Samsung Galaxy S20 FE was used to acquire JPEG and RAW images of a standardized 24-patch ColorChecker target used as a controlled colorimetric reference for evaluating the optical readout methodology, and colorimetric deviation was quantified using CIEDE2000. Mean ΔE00 ranged from 11.73 to 42.33 across the investigated scenarios. Two conditions differing by less than 1% in illuminance produced a 37% difference in mean colorimetric error, demonstrating that illuminance alone does not determine readout accuracy. An illuminance-only regression model yielded R2 = 0.819, whereas inclusion of a relative spectral descriptor increased R2 to 0.919. High-output conditions also revealed substantial JPEG clipping despite negligible RAW saturation. These results show that reliable smartphone colorimetric readout requires joint consideration of illumination intensity, spectral composition, electrical demand and image-saturation constraints.

Biosensors doi: 10.3390/bios16100557

Authors: Xiaoyue Chen Yubin Xiong Hanbing Zhu Yinhong Su Leyi Zheng Mu Ku Chen Meng Zhang Lip Ket Chin Wu Zhang

Tapered optical fibers reshape optical fields in confined spaces, enabling versatile microparticle trapping. This study establishes a continuous framework spanning near-field confinement, far-field extension, and environmental modulation by comparing flame-tapered and machine-pulled fibers. Flame tapering produces sharp tips with micrometer-scale foci; taper angles of 24°, 40°, and 60° yield focal positions of 0.2, 5.1, and 7.0 µm, respectively. A 980 nm laser is used, and stable trapping and probe-guided transport of 20 µm polystyrene microspheres were achieved at a threshold of 84 mW, with dual-particle trapping demonstrated via microlens-induced secondary potential wells. In contrast, machine-assisted tapering generates smooth end faces and long transition regions, extending working distances to 67, 122, and 163 µm. A long-transition probe further produced dual axial potential wells that stably confined 5 µm microspheres at 143 and 286 µm at powers above 500 mW. Environmental modulation revealed that increasing the external refractive index from 1.33 to 1.36 shifted the simulated focal positions from 40 to 55 µm, while ethanol–water evaporation experimentally reduced the trapping distances from 353 to 236 µm. These results show that fiber geometry defines the trapping range, whereas refractive index tunes the precise trapping position, offering a compact platform for multiscale manipulation with potential applications in cell positioning and sorting in liquid environments.

Biosensors doi: 10.3390/bios16100558

Authors: Andrian Ragandang Hyunjin Cho Miae Ko Seongwon Seo

Detecting feeding behavior in cattle is important but can be cumbersome when done manually. This study aimed to validate two wearable biosensors─RumiWatch noseband (RW) and Nedap SmartTag Neck (ND)—for monitoring feeding behavior in Hanwoo cattle. Specifically, we assessed the agreement between feeding behaviors recorded by RW and ND and those obtained manually through video observation (VO), focusing on eating time (ET) and ruminating time (RT). Three Hanwoo cows (540 ± 24.12 kg) were fitted with both sensors and continuously video-recorded for three consecutive days. Data from both 1 h (n = 216) and 24 h (n = 9) summaries were used to compare RW with VO, while ND data were analyzed only at the 24 h level. The RW showed strong agreement with VO for RT, with concordance correlation coefficients (CCC) of 0.98 (1 h) and 0.95 (24 h). In contrast, RT from ND showed poor agreement with VO (CCC = 0.12 for 24 h). Least squares means for RT (min/24 h) did not differ between VO (460 min) and RW (461 min), but ND significantly underestimated RT (211 min; p < 0.001). Both RW and ND performed poorly in detecting ET (CCC = 0.05 for RW and −0.15 for ND at 24 h). However, when time spent on both eating and other jaw movements was combined in VO, the combined time did not differ significantly from ET reported by RW (441 min vs. 454 min; p > 0.05), with improved agreement (CCC = 0.62). Cow, day, and their interaction had no significant effect on the deviation between VO and RW for RT (p > 0.05). Overall, these results indicate that RW accurately detects RT but tends to overestimate ET, whereas ND significantly underestimates both ET and RT with poor concordance in Hanwoo cows.

Biosensors doi: 10.3390/bios16100556

Authors: Yuhuan Wei Qibin Wu Hui Zhang Jiayao Lin Zhen Su Sirui Yi Kang Yang Xiaomeng Jia Shanwen Hu

Catalytic hairpin assembly (CHA), as an enzyme-free isothermal nucleic acid amplification technique, offers significant advantages in biosensing owing to its exponential signal gain. Integrating CHA with dual-modal signal output holds promise for further improving the sensitivity and reliability of pathogen detection in complex matrices through cross-validation of multi-dimensional information. However, when two signals are generated within the same reaction system, they are prone to crosstalk and competition, while the interfacial reaction is inherently constrained by steric hindrance and limited mass transfer. To address these issues, we report herein a CHA-based dual-modal detection strategy for Escherichia coli nucleic acid. In this design, the electrochemiluminescence (ECL) signal is localized at the electrode interface, whereas the fluorescence signal is released in the bulk solution, thereby circumventing energy competition and channel crosstalk between the two signals. Concurrently, a three-dimensional scaffold is constructed using the porous structure of phase-transformed chitosan, which provides a high specific surface area to enhance the interfacial loading of hairpin probes and offers unobstructed channels for efficient mass transfer of co-reactants and target molecules, thus alleviating steric hindrance and diffusion limitations. Upon recognition of the target E. coli nucleic acid, the CHA cascade is triggered, driving a programmed conformational transition of the hairpin probes and simultaneously modulating the fluorescence resonance energy transfer (FRET) between FAM-BHQ pairs as well as the electrochemiluminescence resonance energy transfer (ECL-RET) between CdS quantum dots and AuNPs, enabling synchronous output and mutual validation of the dual-modal signals. Under optimized conditions, the proposed strategy exhibits satisfactory sensitivity, stability, and selectivity. This work provides a feasible approach for the deep integration of CHA amplification with dual-modal sensing, offering a reliable tool for rapid and accurate screening of pathogenic microorganisms.

Biosensors doi: 10.3390/bios16100555

Authors: Xianyue Ji Yi Luo Haozhang Ma Xingyu Tao Shuangshuang Wan

Extracellular vesicles (EVs) have emerged as key mediators of intercellular communication and hold great promise for liquid biopsy and therapeutic applications. Their molecular composition and abundance dynamically reflect the pathophysiological state of their parental cells, endowing EVs with significant value for biomarker discovery and precision medicine. However, inefficient biogenesis, limited production yield, and considerable heterogeneity remain core bottlenecks to their clinical translation. This review systematically surveys recent engineering strategies aimed at enhancing EV production, encompassing diverse physical stimuli and biochemical interventions, with a particular focus on the underlying molecular mechanisms that govern EV quantity, composition, and functionality. Despite their varied modes of action, these approaches share a convergent regulatory principle—remodeling the fate of multivesicular bodies (MVBs) by promoting MVB biogenesis, intracellular trafficking, and plasma membrane fusion, while suppressing their diversion toward lysosomal degradation. Importantly, yield-enhancing strategies not only facilitate the scalable manufacturing of therapeutic EVs, but also enable the functional activation of EV-producing cells, enriching disease-relevant molecular signatures and directing the cargo composition for improved diagnostic performance. Crucially, the synergy between yield-enhancing strategies and advanced biosensing platforms is reshaping the entire EV research workflow. Emerging integrated analytical systems—encompassing microfluidics, electrochemical sensors, and acoustic detectors—now enable real-time monitoring of EV secretion kinetics, providing dynamic feedback to optimize stimulation parameters and harvest windows. Moreover, lab-on-a-chip platforms are beginning to merge EV production, in-line purification, and multiplexed detection into a single automated workflow, facilitating simultaneous quality control and functional characterization. Highly sensitive biosensing technologies further allow precise assessment of EV cargo composition, surface marker profiles, and biological activity, ensuring product consistency and potency. Looking forward, the deep integration of EV biogenesis engineering with these smart detection platforms holds the potential to overcome current translational bottlenecks, accelerating the development of scalable EV therapeutics and next-generation liquid biopsy systems for early diagnosis, dynamic disease monitoring, and precision medicine.