Search Results (31,104)

A nanoliter-scale fabrication method was applied to construct a colorimetric lateral flow strip for urea detection (Urea-CLFS). The device involves two main papers: a nitrocellulose membrane (NC-Mb) for urease enzyme immobilization and chromatography paper (CH-PP) containing a phenol red indicator. Urea-CLFS is a tool for detecting urea that is based on enzyme catalysis and the change in color of phenol red when urea is present. The Urea-CLFS fabrication was made possible by the minimal amount of nanoliters used in reagent consumption. The use of small arrays of phenol red dots provides a higher response result compared to single dots applied on CH-PP. To find the most effective design, it analyzed how urease was aligned on NC-Mb horizontally and vertically. According to our findings, the vertical alignment of the urease enzyme on NC-Mb leads to a prolonged reaction time, which leads to higher product production. The optimization process included optimizing various parameters, including the layer number of phenol red on CH-PP, phenol red concentration, urease concentration, reaction time, and sample volume. Under optimal conditions, the Urea-CLFS provided a linear range of 0.25–8.0 mmol L⁻¹ with an LOD of 0.34 mmol L⁻¹, which is sufficient for human health diagnostics. The accuracy of the Urea-CLFS was demonstrated by the recovery of the human urine sample between 95 ± 3% and 103 ± 3% (n = 3). Full article

With the widespread adoption of mobile devices in daily life, efficiently capturing and digitizing documentation has emerged as a critical research question. The acquisition of documents via mobile devices is often compromised by shadow interference and geometric distortions, which degrade image quality and adversely affect both OCR accuracy and readability. To address this, we propose a novel method that utilizes control points and illumination prediction to effectively rectify distortions and eliminate shadows in captured document images. Spatial attention is employed to guide the interpolation between control points and reference points, effectively eliminating geometric distortions in the captured document images. Following geometric unwarping, an illumination correction model is applied to remove shadows and enhance surface clarity, improving both human readability and OCR accuracy. Our method demonstrates robust performance in effectively rectifying document distortions across diverse scenarios. Evaluation on the DocUNet benchmark dataset shows that our approach achieves competitive results compared with state-of-the-art techniques. Full article

Herein, we propose an ultrasensitive electrochemical immunosensor based on glucose oxidase labeling and enzyme-catalyzed Au staining. In brief, the primary antibody (Ab₁), bovine serum albumin, an antigen and then a bionanocomposite that contains a second antibody (Ab₂), poly(3-anilineboronic acid) (PABA), Au nanoparticles (AuNPs) and glucose oxidase (GOx) are modified on a glassy carbon electrode coated with multiwalled carbon nanotubes, yielding a corresponding sandwich-type immunoelectrode. In the presence of glucose, a chemical reduction of NaAuCl₄ by enzymatically generated H₂O₂ can precipitate a lot of gold on the Ab₂-PABA-AuNPs-GOx immobilized immunoelectrode. In situ anodic stripping voltammetry (ASV) detection of gold in 8 μL 1.0 M aqueous HBr-Br₂ is conducted for the antigen assay, and the ASV detection process takes approximately 6 min. This method is employed for the assay of human immunoglobulin G (IgG) and human carbohydrate antigen 125 (CA125), which demonstrates exceptional sensitivity, high selectivity and fewer required reagents/samples. The achieved limits of detection (S/N = 3) by the method are 0.25 fg mL⁻¹ for IgG (approximately equivalent to containing 1 IgG molecule in the 1 microlitre of the analytical solution) and 0.1 nU mL⁻¹ for CA125, which outperforms many previously reported results. Full article

Background: The emergence of pan-drug-resistant (PDR) Klebsiella pneumoniae has compromised the efficacy of last-line agents, leaving few therapeutic options. Repurposing zidovudine, an FDA-approved thymidine analog with antibacterial activity, may enhance existing therapies, but pharmacodynamic data under clinically relevant conditions are scarce. This study addresses this gap using static and dynamic in vitro models. Materials/methods: A PDR strain of Klebsiella pneumoniae harboring bla_NDM-1, bla_CMY-6, bla_CTX-M-15, bla_SHV-2, and disrupted mgrB was used in this study. Minimum inhibitory concentrations (MICs) followed by static time-kills were performed to investigate the synergistic interplay between zidovudine and last-line antibiotics (ceftazidime/avibactam, polymyxin B). To simulate human pharmacokinetics, a hollow-fiber infection model (HFIM) was employed using steady-state concentrations of zidovudine (4 mg/L), polymyxin B (4 mg/L), and avibactam (22 mg/L). Structural and morphological effects on bacterial cells were examined via fluorescence microscopy following glutaraldehyde fixation. Results: In this study, the PDR K. pneumoniae showed a ~5-fold reduction in polymyxin MIC when combined with zidovudine (from >4 µg/mL to 0.25 µg/mL). Time-kill assays demonstrated ≥2.5 log₁₀ CFU/mL bacterial reduction with zidovudine-based combinations, whereas monotherapies failed to inhibit bacterial growth. In the HFIM, the triple combination achieved rapid bactericidal activity (>3 log₁₀ CFU/mL reduction within 4 h) and sustained killing (>5–6 log₁₀ reduction maintained through 216 h), with bacterial counts remaining below 1 CFU/mL. In contrast, dual combinations initially reduced bacterial burden (1–3 log₁₀ reduction) but failed to maintain suppression, with significant regrowth (>10¹⁰ CFU/mL) observed by 168 h. Microscopy corroborated these findings, revealing extensive cellular damage in the zidovudine-containing treatment arms. These HFIM results underscore the potential of zidovudine-based triple therapy in overcoming resistance to last-line antibiotics in K. pneumoniae. Conclusions: Our results provide promising unprecedented insight into novel zidovudine-based combination therapies against difficult-to-treat MBL Gram-negatives. The observed synergy in MIC reduction, rapid killing in time-kill assays, and near-complete eradication in the HFIM underscore the therapeutic potential of this triple combination. Future studies will focus on broadening the application of these novel combinations to other ‘superbugs’, such as highly resistant strains of Acinetobacter baumannii and Pseudomonas aeruginosa. Full article

Background/Objectives: Selenoproteins play indispensable roles in embryonic development, with their dysregulation linked to various metabolic and neurological disorders. This study aims to systematically quantify the mRNA expression levels of all 24 selenoprotein genes in murine heart, brain, liver, and kidney tissues across embryonic (E8.5, E12.5, E18.5) and postnatal (P7, P30, P90) developmental stages, in order to elucidate the regulatory landscape of selenium metabolism during development. Methods: We collected tissues from mice at six developmental stages and performed RNA extraction followed by quantitative real-time PCR (qPCR) to measure the expression of all 24 selenoprotein genes. Data were normalized using the geometric mean of ActB and Gapdh, and statistical analyses were conducted using one-way ANOVA with Duncan’s post hoc test. Results: Our analysis reveals three principal findings: (1) Distinct expression patterns emerge among selenoprotein families—deiodinases (Dio1-3) and thioredoxin reductases (Txnrd1-3) exhibit limited embryonic expression (<20-fold changes), while glutathione peroxidases (Gpx1, Gpx3, Gpx4) and biosynthesis-related genes (Selenop, Msrb1) show substantial postnatal upregulation (up to 600-fold increases); (2) Selenoproteins essential for embryonic survival (Gpx4, Txnrd1, Txnrd2, Selenoi, Selenot) display expression profiles concordant with their essential developmental functions; (3) Selenop and Msrb1, involved in selenium transport and redox regulation, demonstrate early embryonic upregulation with further increases during postnatal development. Conclusions: These spatiotemporal expression patterns elucidate the regulatory landscape of selenium metabolism during development and provide mechanistic insights into the phenotypes associated with selenium deficiency. The findings offer valuable implications for human nutritional interventions and developmental health. Full article

This study presents a comprehensive Quantitative Microbial Risk Assessment (QMRA) for Escherichia coli in northeastern Greece’s riverine and deltaic aquatic systems, evaluating potential human health risks from recreational water exposure. The analysis integrates seasonal microbiological monitoring data—E. coli, total coliforms, enterococci, Salmonella spp., Clostridium perfringens (spores and vegetative forms), and physicochemical parameters (e.g., pH, temperature, BOD₅)—across multiple sites. A beta-Poisson dose–response model within a Monte Carlo simulation framework (10,000 iterations) was applied to five exposure scenarios, simulating varying ingestion volumes for different population groups. Median annual infection risks ranged from negligible to high, with several locations (e.g., Mandra River, Konsynthos South, and Delta Evros) surpassing the World Health Organization (WHO)’s benchmark of 10⁻⁴ infections per person per year. A Gradient Boosting Regressor (GBR) model was developed to enhance predictive capacity, demonstrating superior accuracy metrics. Permutation Importance analysis identified enterococci, total coliforms, BOD₅, temperature, pH, and seasons as critical predictors of E. coli concentrations. Additionally, sensitivity analysis highlighted the dominant role of ingestion volume and E. coli levels across all scenarios and sites. These findings support the integration of ML-based tools and probabilistic modelling in water quality risk governance, enabling proactive public health strategies in vulnerable or high-use recreational zones. Full article

Focal cortical dysplasia (FCD) is a major cause of drug-resistant epilepsy, yet its molecular basis remains poorly understood. Numerous studies have analyzed RNA, protein, and microRNA alterations, but results are often inconsistent across subtypes and methodologies. To address this gap, we conducted a systematic review integrating transcriptomic, proteomic, and microRNA data from 117 human studies of FCD subtypes I–III. Differentially expressed factors were extracted, categorized by subtype, and analyzed using pathway enrichment and network approaches. Our integrative analysis revealed convergent dysregulation of neuroinflammatory, synaptic, cytoskeletal, and metabolic pathways across FCD subtypes. Consistently altered genes, including IL1B, TLR4, BDNF, HMGCR, and ROCK2, together with dysregulated microRNAs such as hsa-miR-21-5p, hsa-miR-155-5p, and hsa-miR-132-3p, were linked to PI3K–Akt–mTOR, Toll-like receptor, and GABAergic signaling, emphasizing shared pathogenic mechanisms. Importantly, we identified overlapping transcript–protein patterns and subtype-specific molecular profiles that may refine diagnosis and inform therapeutic strategies. This review provides the first cross-omics molecular framework of FCD, demonstrating how convergent pathways unify heterogeneous findings and offering a roadmap for biomarker discovery and targeted interventions. Full article

To explore the potential of Large Language Models (LLMs) as AI Annotators in the domain of sustainability reporting, this study establishes a systematic evaluation methodology. We use the specific case of European football clubs, quantifying their sustainability reports based on the sport Positive matrix as a benchmark to compare the performance of three state-of-the-art models (i.e., GPT-4o, Qwen-2-72b-instruct, and Llama-3-70b-instruct) against human expert scores. The evaluation is benchmarked on dimensions including accuracy, mean absolute error (MAE), and hallucination rates. The results indicate that GPT-4o is the top performer, yet its average accuracy of approximately 56% shows it cannot fully replace human experts at present. The study also reveals significant issues with overconfidence and factual hallucinations in models like Qwen-2-72b-instructon. Critically, we find that by implementing further data processing, specifically a Chain-of-Verification (CoVe) self-correction method, GPT-4o’s initial hallucination rate is successfully reduced from 16% to 10%, while accuracy improved to 58%. In conclusion, while LLMs demonstrate immense potential to streamline and democratize sustainability ratings, inherent risks like hallucinations remain a primary obstacle. Adopting verification strategies such as CoVe is a crucial pathway to enhancing model reliability and advancing their effective application in this field. Full article

Exoskeleton-assisted bodyweight support training (BWST) has demonstrated enhanced neurorehabilitation outcomes in which joint motion prediction serves as the critical foundation for adaptive human–machine interactive control. However, joint angle prediction under dynamic unloading conditions remains unexplored. This study introduces an adaptive wavelet-denoising fusion (AWDF) model to predict lower-limb joint angles during BWST. Utilizing a custom human-tracking bodyweight support system, time series data of surface electromyography (sEMG), and inertial measurement unit (IMU) from ten adults were collected across graded bodyweight support levels (BWSLs) ranging from 0% to 40%. Systematic comparative experiments evaluated joint angle prediction performance among five models: the sEMG-based model, kinematic fusion model, wavelet-enhanced fusion model, late fusion model, and the proposed AWDF model, tested across prediction time horizons of 30–150 ms and BWSL gradients. Experimental results demonstrate that increasing BWSLs prolonged gait cycle duration and modified muscle activation patterns, with a concomitant decrease in the fractal dimension of sEMG signals. Extended prediction time degraded joint angle estimation accuracy, with 90 ms identified as the optimal tradeoff between system latency and prediction advancement. Crucially, this study reveals an enhancement in prediction performance with increased BWSLs. The proposed AWDF model demonstrated robust cross-condition adaptability for hip and knee angle prediction, achieving average root mean square errors (RMSE) of 1.468° and 2.626°, Pearson correlation coefficients (CC) of 0.983 and 0.973, and adjusted R² values of 0.992 and 0.986, respectively. This work establishes the first computational framework for BWSL-adaptive joint prediction, advancing human–machine interaction in exoskeleton-assisted neurorehabilitation. Full article

Unsymmetrical dimethylhydrazine (1,1-Dimethylhydrazine, UDMH) is widely used as a high-performance liquid rocket propellant for the space industry globally. The release and leakage of UDMH into the environment, especially the soil environment, pose serious threats to ecosystems and human beings. In order to reveal the hazards of UDMH to soil and facilitate subsequent remediation, the adsorption behavior of UDMH in typical soil (yellow-brown soil, red soil, and black soil) matrices was explored, the environmental fate and toxicity of UDMH were presented by simulation calculation, and the phytotoxicity was evaluated by germination assay in the present study. The results showed that the adsorption performance of red soil, yellow-brown soil, and black soil for UDMH increased sequentially by integrating the findings from kinetic and thermodynamic studies. A highly significant correlation between the physicochemical and adsorption parameters for various soil matrices indicated a considerable impact of soil physicochemical properties on the adsorption behavior of UDMH in soils. The environmental fate simulation calculation indicated that UDMH and its transformation products were prone to being dissolved in soil water and migrating; however, once these compounds were present in the surface layer of dry soil, severe ecological and environmental pollution would occur. Based on a thorough evaluation of the toxicity parameters, formaldehyde dimethylhydrazone has been identified as demonstrating the most pronounced environmental toxicity profile, thus warranting prioritized attention. The results of a germination assay demonstrated that more than 100 mg·kg⁻¹ of UDMH in the soil would lead to strong phytotoxicity to plants, and more than 200 mg·kg⁻¹ of UDMH would significantly affect the early germination of seeds. Hence, this research provided helpful insights and theoretical support for the environmental fate and remediation of UDMH. Full article

In many current assembly scenarios, efficient collaboration between humans and robots can improve collaborative efficiency and quality. However, the efficient arrangement of human–robot collaborative (HRC) tasks constitutes a significant challenge. In a collaborative workspace where humans and robots collaborate on assembling a shared product, the determination of task allocation between them is of crucial importance. To address this issue, offline feasible HRC paths are established based on assembly task constraint information. Subsequently, the HRC process is simulated within a virtual environment leveraging these feasible paths. Human assembly intentions are explicitly expressed through human assembly trajectories, and implicitly expressed through simulation results such as assembly time and human–robot resource allocation. Furthermore, a two-stage MLP-LSTM network is employed to train and optimize the assembly simulation database. In the first stage, a sequence generation model is trained using high-quality HRC processes. Then, the network learns human evaluation patterns to score the generated sequences. Ultimately, task allocation for HRC is performed based on the high-scoring generated sequences. The effectiveness of the proposed method is demonstrated through assembly scenarios of two products. Compared with traditional optimization methods like DFS and Greedy, the human collaboration ratio has been optimized by 10%, while the collaborative quality evaluation has been improved by 3%. Full article

Ultrafine particles (UFPs) containing polycyclic aromatic hydrocarbons (PAHs) benzo[a]pyrene (BaP), are linked to pollution-induced health concerns, with skin being highly susceptible to contamination. Understanding the metabolic fate of these environmental pollutants in the skin is crucial. Moreover, traditional in vitro models often lack metabolic competency, while animal testing raises ethical concerns. This study introduces a novel approach combining stable isotope labeling (SIL) and liquid chromatography–high-resolution mass spectrometry (LC-HRMS) to investigate BaP metabolism. The physiologically relevant 3D reconstructed human epidermis (RHE) model was used. RHE models were exposed to BaP and deuterium-labeled BaP (BaP-d12). These analyses, followed by data analysis incorporating stable isotope filtering, revealed the presence of five distinct BaP phase I metabolites, including mono-hydroxylated, dihydroxylated, and quinone derivatives. This study demonstrates the power of coupling stable isotope labeling with LC-HRMS for the comprehensive characterization of BaP metabolic pathways in human skin. The identification of specific metabolites enhances our understanding of BaP detoxification mechanisms and their potential adverse effects. This analytical approach holds promise for investigating the metabolic fate of various other environmental pollutants. Full article

We aimed to assess the seroprevalence of anti-Neospora caninum and anti-Toxoplasma gondii antibodies in cattle intended for human consumption in Paraíba, Brazil, and the associated risk factors. A total of 110 serum samples from slaughtered cattle were analyzed using the Indirect Fluorescence Antibody Test (IFAT), with cut-off points of 1:200 for N. caninum and 1:64 for T. gondii. Seroprevalence was 8.2% (9/110) for N. caninum (titers 1:200–1:6400) and 18.2% (20/110) for T. gondii (titers 1:64–1:512). Risk factor analysis revealed that the variable female sex (cows) and the extensive farming system were statistically significantly associated with seroprevalence for N. caninum. Whereas for T. gondii, extensive farming, frequent animal purchase, and the lack of separation between calves and adult cattle were statistically significant. These findings demonstrate the circulation of these parasites in herds, with implications for animal and public health, indicating a potential risk of transmission to definitive hosts and humans through the consumption of raw or undercooked infected meat. Full article

The transition toward Industry 4.0 and the emerging concept of Industry 5.0 demand intelligent tools that integrate efficiency, adaptability, and human-centered design. This paper presents a Computer Vision-based framework for automated motion classification in Methods-Time Measurement 2 (MTM-2), with the aim of supporting industrial time studies and ergonomic risk assessment. The system uses a Convolutional Neural Network (CNN) for pose estimation and derives angular kinematic features of key joints to characterize upper limb movements. A two-stage experimental design was conducted: first, three lightweight classifiers—K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), and a Shallow Neural Network (SNN)—were compared, with KNN demonstrating the best trade-off between accuracy and efficiency; second, KNN was tested under noisy conditions to assess robustness. The results show near-perfect accuracy (≈100%) on 8919 motion instances, with an average inference time of 1 microsecond per sample, reducing the analysis time compared to manual transcription. Beyond efficiency, the framework addresses ergonomic risks such as wrist hyperextension, offering a scalable and cost-effective solution for Small and Medium-sized Enterprises. It also facilitates integration with Manufacturing Execution Systems and Digital Twins, and is therefore aligned with Industry 5.0 goals. Full article

Pre-trial detention is intended to be a measure of last resort, yet it is excessively applied across jurisdictions worldwide. This paper examines its use, with particular emphasis on its application to women and its incompatibility with international human rights law, standards, and norms. We demonstrate that the inappropriate and widespread use of custodial remand violates fundamental human rights, while exposing the gendered and intersectional barriers that impede women’s access to bail. We further underscore the far-reaching social, economic, and emotional consequences of women’s incarceration. Drawing on a limited but expanding body of research, we argue that pre-trial detention operates as a form of gendered punishment that reflects and reinforces structural inequalities, producing enduring harms for women, their families, and communities. The paper concludes by calling for investment in gender-sensitive, non-custodial, and community-based alternatives that advance women’s decarceration. These measures must be underpinned by reforms that give practical effect to human rights law, standards, and norms, while also addressing the structural conditions that lead to women’s involvement in the criminal-legal system, and ending the unnecessary imprisonment of those who are legally innocent. Full article