Search Results (3,374)

Background/Objectives: Gram staining is an essential diagnostic technique used for the rapid identification of bacterial and fungal infections, playing a pivotal role in clinical decision-making, especially in point-of-care (POC) settings. Manual staining, while effective, is labor-intensive and prone to variability, relying heavily on the skill of laboratory personnel. Current automated Gram-staining systems are primarily designed for high-throughput laboratory environments, limiting their feasibility in decentralized healthcare settings such as emergency departments and rural clinics. This study aims to introduce and evaluate the Point-of-Care Gram Stainer (PoCGS^®), a compact, automated device engineered for single-slide processing, addressing challenges related to portability, standardization, and efficiency in POC applications. Methods: The PoCGS^® device was developed to emulate expert manual staining techniques through features such as methanol fixation and programmable reagent application. A comparative evaluation was performed using 40 urine samples, which included both clinical and artificial specimens. These samples were processed using PoCGS^®, manual staining by skilled experts, and manual staining by unskilled personnel. The outcomes were assessed based on microbial identification concordance, the staining uniformity, presence of artifacts, and agreement with the culture results. Statistical analyses, including agreement rates and quality scoring, were conducted to compare the performance of PoCGS^® against manual staining methods. Results: PoCGS^® achieved a 100% concordance rate with expert manual staining in terms of microbial identification, confirming its diagnostic accuracy. However, staining quality parameters such as the uniformity and presence of artifacts showed statistically significant differences when compared to skilled and unskilled personnel. Despite these limitations, PoCGS^® demonstrated a comparable performance regarding artifact reduction and agreement with the culture results, indicating its potential utility in POC environments. Challenges such as fixed processing times and limited adaptability to varying specimen characteristics were identified as areas for further improvement. Conclusions: The study findings suggest that PoCGS^® is a reliable and valuable tool for microbial identification in POC settings, with a performance comparable to skilled manual staining. Its compact design, automation, and ease of use make it particularly beneficial for resource-limited environments. Although improvements in staining uniformity and background clarity are required, PoCGS^® has the potential to standardize Gram staining protocols and improve diagnostic turnaround times. Future developments will focus on optimizing staining parameters and expanding its application to other clinical sample types, ensuring robustness and broader usability in diverse healthcare settings. Full article

This review focuses on the experimental methods and technologies of cytogenomics and how they can be combined in the process of chromosomic diagnostics and research. It is stressed that no cytogenomic methods can be comprehensive on their own. The strengths and weaknesses of each method have to be considered. This is especially important in a time where the main stream of human genetics diagnostics is actively proclaiming that high throughput methods are able to replace all other established tests. Full article

Precision medicine stems from a new approach to the prevention, diagnosis and treatment of patients, due to the shift in focus away from pathology and towards the uniqueness of the individual, personalising the diagnostic–therapeutic pathway. This paradigm shift has been made possible by the emergence of new high-throughput technologies capable of generating large amounts of data on multiple levels of a biological system, identifying pathology-related genes, transcripts, proteins and metabolites. Metabolomics plays a primary role in this context, providing, through non-invasive sampling, a very close image of the phenotype of the organism being studied by detecting metabolites, end products downstream of gene transcription, present in cells, tissues, organs and biological fluids. The enormous amount of data that these modern technologies make available, together with the need to elucidate the complex interplay of the various biological levels by combining data from distinct omics, has led to the need to employ advanced informatics techniques, among which artificial intelligence has recently emerged. These innovations are of great interest in the field of perinatology, representing an attempt to optimise the diagnostic timeline for the most critical newborns. In addition, they may contribute to the improvement of prevention strategies available to date. All these contributions prove to be crucial at very vulnerable life stages, allowing crucial intervention opportunities. In this review, we have analysed studies that have integrated metabolomics with at least one other omics in the perinatal field, attempting to highlight the usefulness of multiomics integration and the different methods employed. Full article

Rhizosphere microorganisms effectively exploit nutrient resources within the rhizosphere, while growth-promoting bacteria in this environment play a vital role in regulating soil fertility and enhancing plant health. In this study, we utilized a comprehensive approach that included the isolation, purification, and identification of dominant microorganisms, alongside high-throughput sequencing technology. This methodology was employed to analyze the primary microbial groups and their diversity within the rhizosphere soil of Helichrysum arenarium (L.) Moench in Altay, Xinjiang, China. By isolating bacterial strains from the rhizosphere soil using a dilution coating method, we successfully obtained 43 distinct strains. Subsequently, selective media were employed to screen for growth-promoting characteristics among these isolated strains derived from the rhizosphere soil of H. arenarium (L.) Moench. The results, obtained through high-throughput amplification sequencing, revealed diverse bacterial communities belonging to 35 phyla, 93 orders, 215 families, 324 genera, and 231 species associated with H. arenarium (L.) Moench, as well as fungal communities comprising 14 phyla, 47 orders, 96 families, 204 genera, and 571 species present in the rhizosphere soil. Among these identified communities, Actinobacteriota emerged as the predominant bacterial phylum while Ascomycetes and Mortieromycetes were recognized as the principal fungal phyla found in the rhizospheric soil of H. arenarium (L.) Moench. Analysis of culturable bacteria’s promotion activity within this rhizospheric environment indicated that three strains—S16, S31, and S29—exhibited the highest solubility index for inorganic phosphorus; additionally, the screened strains S7 and S10 demonstrated nitrogen-fixing capabilities. Furthermore, ten strains exhibiting excellent iron-bearing capacities were identified; notably, strain S16 displayed the highest D/d value indicating, its superior iron-bearing capacity. The growth-promoting bacteria were identified as Kocuria rosea, Priestia megaterium, Bacillus mobilis, Bacillus bataviensis, three variants of Bacillus mycoides, Bacillus paramobilis, Bacillus sonorensis, and Alcaligenes faecalis. This study provides a foundational understanding of how microorganisms in the rhizosphere of H. arenarium (L.) Moench influence soil nutrient release and offers valuable insights into enhancing yield and quality cultivation by isolating, screening, and identifying growth-promoting bacteria from rhizosphere soil. Full article

The extraction of adherent cells, such as B16 murine melanoma cells, from Petri dish cultures is critical in biomedical applications, including cell reprogramming, transplantation, and regenerative medicine. Traditional detachment methods—enzymatic, mechanical, or chemical—often compromise cell viability by altering membrane integrity and disrupting adhesion proteins. To address these challenges, this study investigated sensor-based detection of the pre-detachment phase in a MPWM (Microfluidic Pulse Width Modulation) extraction system. Our approach integrates a micromechatronic system with a microfluidic suction circuit, real-time CCD imaging, and computational analysis to detect and characterize the pre-detachment moment before full extraction. A precisely controlled hydrodynamic force field progressively disrupts adhesion in multiple stages, reducing mechanical stress and preserving cell integrity. Real-time video analysis enables continuous monitoring of positional dynamics and oscillatory responses. Image processing and deep learning algorithms determine object center coordinates, allowing the MPWM system to dynamically adjust suction parameters. This optimizes detachment while minimizing liquid absorption and reflux volume, ensuring efficient extraction. By combining microfluidics, sensor detection, and AI-driven image processing, this study established a non-invasive method for optimizing adherent cell detachment. These findings have significant implications for single-cell research, regenerative medicine, and high-throughput biotechnology, ensuring maximal viability and minimal perturbation. Full article

Years of employing the “one-shot” fertilization practice have led to low nutrient utilization efficiency and the degradation of soil structure in the black soil region during crop cultivation. Replacing a portion of chemical fertilizers with microbial agents can effectively solve these issues. In this study, we conducted a field plot experiment comparing eight different treatment combinations to investigate the effects of combining microbial agents with varying amounts of chemical fertilizers on black soil nutrients, soil ecology, and maize yield. The high-throughput absolute quantification 16S rRNA sequencing method was utilized to further investigate the effect of the various treatments on soil bacterial community structure and elucidate the interactions between environmental factors and microbial communities. The results showed that MC80 increased maize yield by 5.76% compared to RC, with an input–output ratio of 1:1.58. Additionally, soil nutrient levels in MC80 were higher than those in RC, increasing nutrient utilization efficiency, activating soil nutrients, and enhancing soil fertility. Meanwhile, the absolute quantification data of bacteria also indicated the highest bacterial abundance and diversity in MC80 samples. Among these, Acidobacteria was the main contributor to the changes in the bacterial community, showing significant enrichment in MC80. RDA and Spearman correlation analyses indicated that soil nutrients are the key factors influencing the bacterial community in this ecosystem, while the microbial community plays a crucial role in nutrient transformation processes. Principal component analysis (PCA) was used for comprehensive evaluation and ranking. Overall, the soil under the MC80 treatment was most conducive to microbial survival and maize growth. This study provides a high-yield and sustainable fertilization method for maize and offers a theoretical basis for applying microbial agents in sustainable agriculture. Full article

A spiral microfluidic chip (SMC) and multi-spiral microfluidic chip (MSMC) for lipid nanoparticle (LNP) production were fabricated using a CO₂ laser engraving method, using perfluoropolyether (PFPE) and poly(ethylene glycol) diacrylate as photopolymerizable base materials. The SMC includes a spiral microchannel that enables rapid fluid mixing, thereby facilitating the production of small and uniform LNPs with a size of 72.82 ± 24.14 nm and a PDI of 0.111 ± 0.011. The MSMC integrates multiple parallel SMC structures, which enables high-throughput LNP production without compromising quality and achieves a maximum production capacity of 960 mL per hour. The LNP fabrication technology using SMC and MSMC has potential applications in the pharmaceutical field due to the ease of chip fabrication, the simplicity and cost-effectiveness of the process, and the ability to produce high-quality LNPs. Full article

Background/Objectives: Microfluidic mixing has become the gold standard procedure for manufacturing nucleic acid lipid-based delivery systems, offering precise control over critical process parameters. The choice and design of microfluidic mixers are often seen as a key driving force affecting the critical quality attributes of the resulting lipid nanoparticles (LNPs). Methods: This study aimed to evaluate LNPs manufactured using two low-cost microfluidic mixers alongside manual mixing (pipette mixing (PM)), followed by characterization studies using orthogonal analytics as well as expression studies to establish whether low-cost microfluidic manufacturing methods are suitable for bench-scale and high-throughput research. Results: The results show that all manufacturing methods can produce LNPs with sizes ranging between 95 and 215 nm with high encapsulation (70–100%), and enhanced analytics showed variations between the LNPs produced using the different mixers. Despite these differences, pipette mixing production of LNPs demonstrated its application as a high-throughput screening tool for LNPs, effectively distinguishing between different formulations and predicting consistent expression patterns both in vitro and in vivo. Conclusions: Overall, these results validate the use of low-cost microfluidic mixers without compromising the efficiency and integrity of the resulting LNPs. This study supports the increased accessibility of small-scale LNP manufacturing and high-throughput screening. Full article

We propose a pulsed modulated digital holographic microscopy (PM-DHM) technique for the dynamic measurement of flowing microparticles in microfluidic systems. By digitally tuning the pulse width and the repetition rate of a laser source within a single-frame exposure, this method enables the recording of multiple images of flowing microparticles at different time points within a single hologram, allowing the quantification of velocity and acceleration. We demonstrate the feasibility of PM-DHM by measuring the velocity, acceleration, and forces exerted on PMMA microspheres and red blood cells flowing in microfluidic chips. Compared to traditional frame-sampling-based imaging methods, this technique has a much higher time resolution (in a range of microseconds) that is limited only by the pulse duration. This method demonstrates significant potential for high-throughput label-free flow cytometry detection and offers promising applications in drug development and cell analysis. Full article

Background/Objectives: Alginate and its oligosaccharides (AOS) are widely used in the food industry all over the world. However, how they are fermented by the human gut microbiota has not been fully elucidated. Here, we aim to explore the structure–property relationships of the fermentation of these carbohydrates by the human gut microbiota. Methods: High-performance liquid chromatography, 16S rRNA gene amplicon high-throughput sequencing, whole genome sequencing, and metabolome analysis were used to study the fermentation of alginate and AOS by the human gut microbiota. Results and Conclusions: Low-molecular-weight alginate and AOS were more fermentable than alginate. Moreover, fermentation of AOS with a molecular weight (Mw) of 0.8 kDa produced higher amounts of acetate and butyrate than that with a Mw of 0.3 kDa. B. xylanisolvens was a keystone species responsible for the fermentation. Additionally, each B. xylanisolvens strain was characterized with a unique capability for AOS fermentation. Specifically, B. xylanisolvens P19-10, a bacterium isolated from healthy human colon, exhibited the best fermentation capacity. Genomic analysis suggested that B. xylanisolvens P19-10 was armed with a plethora of carbohydrate-active enzymes. Additionally, the polysaccharide lyase family 6_1 was identified as a candidate enzyme responsible for the utilization of AOS. Moreover, fermentation of AOS by B. xylanisolvens P19-10 was associated with significant changes in bacterial metabolites and metabolic pathways. Future perspectives: Our study provides novel mechanistic insights into the fermentation of alginate and AOS by human gut microbiota, which has applications for the development of new carbohydrate-based nutraceuticals and foods. Full article

This study established a dual analytical workflow integrating thermal desorption–electrospray ionization–tandem mass spectrometry (TD-ESI-MS/MS) for rapid qualitative screening and ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) for confirmatory quantification of 17 psychoactive substances and metabolites across six classes (opioids, amphetamine-type stimulants, cocaine, ketamine-type drugs, cannabinoids, and etomidate analogs) in hair matrices. Validation of the TD-ESI-MS/MS method demonstrated its sensitivity (limits of detection: 0.1–0.2 ng/mg) and precision (<19.3%), with matrix effects controlled to <19.6%. The TD-ESI-MS/MS method achieved an analysis time of 1 min per sample, enabling high-throughput screening with a sensitivity >85.7% and a specificity >89.7% for the 17 analytes. UPLC-MS/MS confirmation validated the screening results with accuracy rates of 89.7–99.8%. An analysis of specimens confirmed positive identified etomidate analogs as the predominant psychoactive substances (73.6%), with a lower prevalence of amphetamine-type stimulants (12.5%), ketamine-type drugs (9.0%), and opioids (2.8%). The polydrug use patterns identified concurrent etomidate–amphetamine consumption (n = 5) and complex analog combinations (etomidate–isopropoxate–metomidate, n = 13), suggesting evolving abuse trends. Despite limitations in the temporal resolution and representativeness of the cohort, this study demonstrated the viability of TD-ESI-MS/MS for bridging forensic and public health priorities. Future work should focus on optimizing the durability of the ion source for TD-ESI and validating this method across diverse populations to enhance its generalizability. Full article

Without using any optical lenses, lensless digital holography (LDH) records the hologram of a sample and numerically retrieves the amplitude and phase of the sample from the hologram. Such lensless imaging designs have enabled high-resolution and high-throughput imaging of specimens using compact, portable, and cost-effective devices to potentially address various point-of-care-, global health-, and telemedicine-related challenges. However, in lensless digital holography, the reconstruction results are severely affected by zero-order noise and twin images as only the hologram intensity can be recorded. To mitigate such interference and enhance image quality, extensive efforts have been made. In recent years, deep learning (DL)-based approaches have made significant advancements in the field of LDH reconstruction. It is well known that most deep learning networks are often regarded as black-box models, which poses challenges in terms of interpretability. Here, we present a deep unfolding network, dubbed the ISTAHolo-Net, for LDH reconstruction. The ISTAHolo-Net replaces the traditional iterative update steps with a fixed number of sub-networks and the regularization weights with learnable parameters. Every sub-network consists of two modules, which are the gradient descent module (GDM) and the proximal mapping module (PMM), respectively. The ISTAHolo-Net incorporates the sparsity-constrained inverse problem model into the neural network and hence combines the interpretability of traditional iterative algorithms with the learning capabilities of neural networks. Simulation and real experiments were conducted to verify the effectiveness of the proposed reconstruction method. The performance of the proposed method was compared with the angular spectrum method (ASM), the HRNet, the Y-Net, and the DH-GAN. The results show that the DL-based reconstruction algorithms can effectively reduce the interference of twin images, thereby improving image reconstruction quality, and the proposed ISTAHolo-Net performs best on our dataset. Full article

Natural products play a crucial role in drug discovery because of their structural diversity and biological activity. However, identifying their molecular targets remains a challenge. Traditional target identification approaches such as affinity-based protein profiling and activity-based protein profiling are limited by the need for chemical modification or reactive groups in natural products. The emergence of label-free techniques offers a powerful alternative for studying drug–target engagement in a physiological context. In particular, the cellular thermal shift assay (CETSA) exploits ligand-induced protein stabilization—a phenomenon where ligand binding enhances a protein’s thermal stability by reducing conformational flexibility—to assess drug binding without requiring chemical modifications. CETSA’s integration with advanced mass spectrometry and high-throughput platforms has dramatically expanded proteome coverage and sensitivity, enabling the simultaneous quantification of thousands of proteins and the identification of low-abundance targets in native cellular environments. This review highlights the application of key CETSA-based methods to target identification in natural products including Western blot-based CETSA, isothermal dose–response CETSA, mass spectrometry-based CETSA, and high-throughput CETSA. Case studies are presented that demonstrate their effectiveness in uncovering the mechanisms of action of different drugs. The current limitations of CETSA-based strategies are also explored, and future improvements to optimize their potential for drug discovery are discussed. Integrating CETSA with complementary approaches can enhance the target identification accuracy and efficiency for natural products and ultimately advance development of therapeutic applications. Full article

Inorganic nanomaterials are pivotal foundational materials driving traditional industries’ transformation and emerging sectors’ evolution. However, their industrial application is hindered by the limitations of conventional synthesis methods, including poor batch stability, scaling challenges, and complex quality control requirements. This review systematically examines strategies for constructing automated synthesis systems to enhance the production efficiency of inorganic nanomaterials. Methodologies encompassing hardware architecture design, software algorithm optimization, and artificial intelligence (AI)-enabled intelligent process control are analyzed. Case studies on quantum dots and gold nanoparticles demonstrate the enhanced efficiency of closed-loop synthesis systems and their machine learning-enabled autonomous optimization of process parameters. The study highlights the critical role of automation, intelligent technologies, and human–machine collaboration in elucidating synthesis mechanisms. Current challenges in cross-scale mechanistic modeling, high-throughput experimental integration, and standardized database development are discussed. Finally, the prospects of AI-driven synthesis systems are envisioned, emphasizing their potential to accelerate novel material discovery and revolutionize nanomanufacturing paradigms within the framework of AI-plus initiatives. Full article

Bacterial diseases cause high mortality and considerable losses in aquaculture. The rapid expansion of intensive aquaculture has further increased the risk of large-scale outbreaks. However, the emergence of drug-resistant bacteria, food safety concerns, and environmental regulations have severely limited the availability of antimicrobial. Compared to traditional antibiotics, antimicrobial peptides (AMPs) offer broad spectrum activity, physicochemical stability, and lower resistance development. However, their low natural yield and high extraction costs along with the time-consuming and expensive nature of traditional drug discovery, pose a challenge. In this study, we applied a machine-learning macro-model to predict AMPs from three macrogenomes in the water column of South American white shrimp aquaculture ponds. The AMP content per megabase in the traditional earthen pond (TC1) was 1.8 times higher than in the biofloc pond (ZA1) and 63% higher than in the elevated pond (ZP11). A total of 1033 potential AMPs were predicted, including 6 anionic linear peptides, 616 cationic linear peptides, and 411 cationic cysteine-containing peptides. After screening based on structural, and physio-chemical properties, we selected 10 candidate peptides. Using a rapid high-throughput cell-free protein expression system, we identified nine peptides with antimicrobial activity against aquatic pathogens. Three were further validated through chemical synthesis. The three antimicrobial peptides (K-5, K-58, K-61) showed some inhibitory effects on all four pathogenic bacteria. The MIC of K-5 against Vibrio alginolyticus was 25 μM, the cell viability of the three peptides was higher than 70% at low concentrations (≤12.5 μM), and the hemolysis rate of K-5 and K-58 was lower than 5% at 200 μM. This study highlights the benefits of machine learning in AMP discovery, demonstrates the potential of cell-free protein synthesis systems for peptide screening, and provides an efficient method for high-throughput AMP identification for aquatic applications. Full article