Search Results (657)

Surface-enhanced Raman spectroscopy (SERS) has rapidly emerged as a powerful analytical technique for the sensitive and selective detection of organophosphorus pesticides (OPPs) in complex food matrices. This review summarizes recent advances in substrate engineering, emphasizing structure–performance relationships between nanomaterial design and molecular enhancement mechanisms. Functional groups such as P=O, P=S, and aromatic rings are highlighted as key determinants of Raman activity through combined chemical and electromagnetic effects. State-of-the-art substrates, including noble metals, carbon-based materials, bimetallic hybrids, MOF-derived systems, and emerging liquid metals, are critically evaluated with respect to sensitivity, stability, and applicability in typical matrices such as fruit and vegetable surfaces, juices, grains, and agricultural waters. Reported performance commonly achieves sub-μg L⁻¹ to low μg L⁻¹ detection limits in liquids and 10⁻³ to 10 μg cm⁻² on surfaces, with reproducibility often in the 5–15% RSD range under optimized conditions. Persistent challenges are also emphasized, including substrate variability, quantitative accuracy under matrix interference, and limited portability for real-world applications. Structure–response correlation models and data-driven strategies are discussed as tools to improve substrate predictability. Although AI and machine learning show promise for automated spectral interpretation and high-throughput screening, current applications remain primarily proof-of-concept rather than routine workflows. Future priorities include standardized fabrication protocols, portable detection systems, and computation-guided multidimensional designs to accelerate translation from laboratory research to practical deployment in food safety and environmental surveillance. Full article

The ongoing challenge of doping in sports has triggered the adoption of advanced scientific strategies for the detection and prevention of doping abuse. This review examines the potential of integrating metabolomics aided by artificial intelligence (AI) and machine learning (ML) for profiling small-molecule metabolites across biological systems to advance anti-doping efforts. While traditional targeted detection methods serve a primarily forensic role—providing legally defensible evidence by directly identifying prohibited substances—metabolomics offers complementary insights by revealing both exogenous compounds and endogenous physiological alterations that may persist beyond direct drug detection windows, rather than serving as an alternative to routine forensic testing. High-throughput platforms such as UHPLC-HRMS and NMR, coupled with targeted and untargeted metabolomic workflows, can provide comprehensive datasets that help discriminate between doped and clean athlete profiles. However, the complexity and dimensionality of these datasets necessitate sophisticated computational tools. ML algorithms, including supervised models like XGBoost and multi-layer perceptrons, and unsupervised methods such as clustering and dimensionality reduction, enable robust pattern recognition, classification, and anomaly detection. These approaches enhance both the sensitivity and specificity of diagnostic screening and optimize resource allocation. Case studies illustrate the value of integrating metabolomics and ML—for example, detecting recombinant human erythropoietin (r-HuEPO) use via indirect blood markers and uncovering testosterone and corticosteroid abuse with extended detection windows. Future progress will rely on interdisciplinary collaboration, open-access data infrastructure, and continuous methodological innovation to fully realize the complementary role of these technologies in supporting fair play and athlete well-being. Full article

The intestinal microbiota plays a crucial role in maintaining epithelial barrier integrity, while its impairment and the resulting inflammation contribute to numerous human pathologies. To preserve intestinal homeostasis, various probiotics are being developed; however, their selection and validation require accessible yet physiologically relevant models. We recently established a high-throughput Gut-on-Chip model comprising human epithelial (Caco-2) cells and peripheral blood mononuclear cells (PBMCs), demonstrating epithelial barrier disruption and pro-inflammatory cytokine secretion upon inflammation induction. The present study aimed to evaluate the feasibility of co-culturing anaerobic members of the human intestinal microbiota within this model and to assess their effects on inflammation-induced epithelial damage. We successfully co-cultured five intestinal anaerobic bacterial species in direct contact with the epithelial monolayer for two days. As proof of concept, we demonstrate that live Bacteroides thetaiotaomicron and its supernatant preserve epithelial barrier integrity and attenuate CCL2 secretion by Caco-2 cells. In contrast, Clostridium scindens did not prevent epithelial damage but suppressed CCL20 secretion, revealing a promising target for future studies. By recapitulating some of the key aspects of intestinal inflammation, we suggest that the current Gut-on-Chip model has potential as an easy-to-use platform for screening next-generation probiotics and live biotherapeutics with homeostatic and immunomodulatory properties. Full article

As synthetic biology advances toward precise design, the construction of high-quality mutant libraries has become essential for large-scale functional screening. Traditional approaches, such as random and saturation mutagenesis, often suffer from low accuracy, high bias, and limited coverage. An ideal method should offer controlled mutagenesis, comprehensive coverage, high throughput, operational simplicity, and controllable outcomes, enabling effective large-scale screening. Here, we developed a high-throughput, precisely controlled method for constructing a mutagenesis library based on chip-based oligonucleotide synthesis. Using PSMD10 as a model, we constructed a full-length amber codon scanning mutagenesis library with 93.75% mutation coverage. Among the five polymerases evaluated, KAPA HiFi HotStart, Platinum SuperFi II and Hot-Start Pfu DNA Polymerase demonstrated higher amplification efficiency and lower chimera formation rates, making them preferred enzymes for optimized library construction. Analysis of unmapped reads highlighted key technical factors, such as oligonucleotide synthesis errors and chimeric sequence formation caused by incomplete extension of DNA polymerase or synthesis across discontinuous templates during PCR. To improve efficiency and fidelity, we recommend refining PCR conditions and strengthening oligo synthesis quality control. We establish an efficient, scalable, precisely controlled mutagenesis library construction strategy tailored for high-throughput functional research and recommend using a high-fidelity, low-bias polymerase to ensure quality. Full article

Clinical application of ex vivo lung perfusion (EVLP) has increased marginal donor lung utilization. It has been developed as a platform for donor lung reconditioning. However, many of the current repair strategies are limited by a maximum reliable EVLP circuit duration of 12 h. Past studies have successfully extended EVLP through nutrient supplementation, but the exact components and respective mechanisms by which EVLP is extended remains unknown. As such, the focus of this study was to systematically evaluate the effects of nutritional supplements in EVLP perfusates on cell apoptosis, viability, confluence, and migration. To test this, we developed a high-throughput human lung endothelial cell culture platform where experimental perfusates with various combinations of GlutaMAX (a glutamine dipeptide), Travasol (amino acids), Intralipid (lipids), Multi-12 (vitamins), cysteine, and glycine were tested using the Incucyte Live imaging system. GlutaMAX supplementation alone significantly reduced apoptosis, improved viability and cell migration beyond all other supplements and further outperformed standard endothelial cell culture medium. Travasol offered short-term benefits, while Intralipid offered minimal functional support. Multi-12 improved viability and apoptosis independently and in combination with other supplements. The best experimental perfusate targeted the glutathione synthesis pathway, combining GlutaMAX, cysteine and glycine and further reduced apoptosis compared with GlutaMAX alone. Collectively, these results suggest that nutrient selection during EVLP is critical and highlights the need to systematically evaluate perfusate modifications as opposed to broad-spectrum nutrient delivery. This in vitro model provides a cost-effective platform for preclinical screening of perfusate modifications to enhance organ viability during EVLP. Full article

Avocado (Persea americana Mill.) is one of the most widely consumed fruits worldwide. The tree species is traditionally classified into three botanical races: Mexican, Guatemalan, and West Indian (with a potentially distinct Colombian genepool). However, previous studies using molecular markers, such as AFLPs, microsatellites (SSRs), and GBS-derived SNP markers, have only partially resolved this racial divergence, especially in the hyper agrobiodiverse region of northwest South America. Therefore, in order to confirm genetic identity and origin of “criollo” avocado cultivars in the region, as well as to improve their traceability as rootstocks for the Hass variety, we performed low-coverage whole genome resequencing (lcWGS) on 205 ex situ conserved tree samples, comprising 42 commercial varieties and 163 “criollo” trees from various provinces in Colombia. This characterization yielded a total of 64,310,961 SNPs at an average coverage of 4.69×. Population structure analysis using principal component analysis (PCA) and ADMIXTURE retrieved at least five genetic clusters (K = 5), partly confirmed by Bayesian phylogenetic inference. Three clusters matched the recognized Mesoamerican botanical races (Mexican, Guatemalan, and West Indian), and two clusters reinforced the distinctness of two novel Andean and Caribbean Colombian genetic groups. Finally, in order to retrieve high-quality SNP markers for racial screening, a second genomic dataset was filtered, consisting of 68 avocado tree samples exhibiting more than 80% ancestry to a given racial cluster, and 9826 SNPs with a minimum allele frequency (maf) of 5%, a minimum sequencing depth (SD) of 10× per position, and missing data per variant not exceeding 20% (i.e., variants with genotypes present in at least 80% of the samples). This racially segregating high-quality subset was analyzed against the racial substructure using linear mixed models (LMMs), enabling the identification of 254 SNP markers associated with the five avocado genetic races. The previous candidate SNPs may be leveraged by nurseries and producers through a high-throughput SNP screening system for the racial traceability of seedling donor trees, saplings, and rootstocks. These genomic resources will support the selection of regionally adapted elite rootstocks and represent a landmark in Colombian horticulture as the first large-scale lcWGS-based characterization of a local avocado germplasm collection. Full article

High-grade gliomas (HGGs) are aggressive brain tumors with limited treatment options and poor survival outcomes. Variants including isocitrate dehydrogenase (IDH)-wildtype, IDH-mutant, and histone 3 lysine to methionine substitution (H3K27M)-mutant subtypes demonstrate considerable tumor heterogeneity at the genetic, cellular, and microenvironmental levels. This presents a major barrier to the development of reliable models that recapitulate tumor heterogeneity, allowing for the development of effective therapies. Glioma tumor organoids (GTOs) have emerged as a promising model, offering a balance between biological relevance and practical scalability for precision medicine. In this study, we present a refined methodology for generating three-dimensional, multiregional, patient-derived GTOs across a spectrum of glioma subtypes (including primary and recurrent tumors) while preserving the transcriptomic and phenotypic heterogeneity of their source tumors. We demonstrate the feasibility of a high-throughput drug-screening platform to nominate multi-drug regimens, finding marked variability in drug response, not only between patients and tumor types, but also across regions within the tumor. These findings underscore the critical impact of spatial heterogeneity on therapeutic sensitivity and suggest that multiregional sampling is critical for adequate glioma model development and drug discovery. Finally, regional differential drug responses suggest that multi-agent drug therapy may provide better comprehensive oncologic control and highlight the potential of multiregional GTOs as a clinically actionable tool for personalized treatment strategies in HGG. Full article

High variability in stem cell research is a well-known limiting phenomenon, with technical variation across experiments and laboratories often surpassing variation caused by genotypic effects of induced pluripotent stem cell (iPSC) lines. Evaluation of kidney organoid protocols and culture conditions across laboratories remains scarce in the literature. We used the original air-medium interface protocol to evaluate kidney organoid success rate and reproducibility with several human iPSC lines, including a novel patient-derived GRACILE syndrome iPSC line. Organoid morphology was assessed with light microscopy and immunofluorescence-stained maturing glomerular and tubular structures. The protocol was further adapted to four microplate-based high-throughput approaches utilizing spheroid culture steps. Quantitative high-content screening analysis of the nephrin-positive podocytes and ECAD-positive tubular cells revealed that the choice of approach and culture conditions were significantly associated with structure development. The culture approach, iPSC line, experimental replication, and initial cell number explained 35–77% of the variability in the logit-transformed proportion of nephrin and ECAD-positive area, when fitted into multiple linear models. Our study highlights the benefits of high-throughput culture and multivariate techniques to better distinguish sources of technical and biological variation in morphological analysis of organoids. Our microplate-based high-throughput approach is easily adaptable for other laboratories to combat organoid size variability. Full article

In the lower and middle sections of the Yangtze River Basin Region (YRBR) in China, challenges posed by climate change and delayed harvesting of preceding crops have hindered the timely sowing of wheat, leading to an increasing prevalence of late-sown wheat fields. This trend has emerged as a significant impediment to achieving high and stable production of wheat in this area. During the growing seasons of 2022–2023 and 2023–2024, an unmanned aerial vehicle (UAV)-based multispectral camera was used to monitor different wheat materials at various growth stages under normal sowing treatment (M1) and late sowing with increased plant density (M2). By assessing yield loss, the wheat tolerance to late sowing was quantified and categorized. The correlation between the differential vegetation indices (D-VIs) and late sowing resistance was examined. The findings revealed that the J2-Logistic model demonstrated optimal classification performance. The precision values of stable type, intermediate type, and sensitive type were 0.92, 0.61, and 1.00, respectively. The recall values were 0.61, 0.92, and 1.00. The mean average precision (mAP) of the model was 0.92. This study proposes a high-throughput and low-cost evaluation method for wheat tolerance to late sowing, which can provide a rapid predictive tool for screening suitable varieties for late sowing and facilitating late-sown wheat breeding. Full article

This article reviews the progress made in applying machine learning to predict the osteogenic differentiation of mesenchymal stem cells. Bone defects pose a significant clinical challenge due to limitations of traditional therapies such as autologous bone graft donor shortages, allograft immune risks and so on. Mesenchymal stem cells offer a promising solution for bone regeneration due to their osteogenic differentiation potential, but their clinical utility is hindered by unpredictable differentiation efficiency and heterogeneity. Machine learning has emerged as a powerful tool to address these issues by enabling early, non-invasive prediction of osteogenic differentiation and high-throughput analysis of complex data like morphology and omics. This review systematically summarizes the application of ML in three key areas: early prediction using cellular morphology, omics data analysis for biomarker discovery, and drug/biomaterial screening for enhancing osteogenesis. We compare the performance of multiple ML models like ResNet-50, LASSO, and random forests and highlight their advantages and limitations. Additionally, we discuss challenges in data standardization and model interpretability, and propose future directions for translating ML into clinical practice. This review provides a comprehensive overview of how ML can revolutionize MSC-based bone regeneration by improving prediction accuracy and optimizing therapeutic strategies. Full article

CRISPR (clustered regularly interspaced short palindromic repeats)-Cas (CRISPR-associated protein) nucleases enable precise genome editing, but off-target cleavage remains a critical challenge. Here, we report the development of MAD7_HF, a high-fidelity variant of the MAD7 nuclease engineered through a bacterial screening system leveraging the DNA gyrase-targeting toxic gene ccdB. This system couples survival to efficient on-target cleavage and minimal off-target activity, mimicking the transient action required for high-precision editing. Through iterative selection and sequencing validation, we identified MAD7_HF, harboring three substitutions (R187C, S350T, K1019N) that enhanced discrimination between on- and off-target sites. In Escherichia coli assays, MAD7_HF exhibited a >20-fold reduction in off-target cleavage across multiple mismatch contexts while maintaining on-target efficiency comparable to wild-type MAD7. Structural modeling revealed that these mutations stabilize the guide RNA-DNA hybrid at on-target sites and weaken interactions with mismatched sequences. This work establishes a high-throughput bacterial screening strategy that allows the identification of Cas12a variants with improved specificity at a given target site, providing a useful framework for future efforts to develop precision genome-editing tools. Full article

Fish have become increasingly prominent as experimental models due to their unique capacity to bridge basic biological research with translational applications across diverse scientific disciplines. Their biological traits, such as external fertilization, high fecundity, rapid embryonic development, and optical transparency, facilitate in vivo experimentation and real-time observation, making them ideal for integrative research. Species like zebrafish (Danio rerio) and medaka (Oryzias latipes) have been extensively validated in genetics, toxicology, neuroscience, immunology, and pharmacology, offering robust platforms for modeling human diseases, screening therapeutic compounds, and evaluating environmental risks. This review explores the multidisciplinary utility of fish models, emphasizing their role in connecting molecular mechanisms to clinical and environmental outcomes. We address the main species used, highlight their methodological advantages, and discuss the regulatory and ethical frameworks guiding their use. Additionally, we examine current limitations and future directions, particularly the incorporation of high-throughput omics approaches and real-time imaging technologies. The growing scientific relevance of fish models reinforces their strategic value in advancing cross-disciplinary knowledge and fostering innovation in translational science. Full article

Caenorhabditis elegans is one of the most extensively studied model organisms in biology. Its advantageous features, including genetic homology with humans, conservation of disease pathways, transparency, short lifespan, small size and ease of maintenance have established it as a powerful system for research in aging, genetics, molecular biology, disease modeling and drug discovery. However, traditional methods for worm handling, culturing, scoring and imaging are labor-intensive, low throughput, time consuming, susceptible to operator variability and environmental influences. Addressing these challenges, recent years have seen rapid innovation spanning microfluidics, robotics, imaging platforms and AI-driven analysis in C. elegans-based research. Advances include micromanipulation devices, robotic microinjection systems, automated worm assays and high-throughput screening platforms. In this review, we first summarize foundational developments prior to 2020 that shaped the field, then highlight breakthroughs from the past five years that address key limitations in throughput, reproducibility and scalability. Finally, we discuss ongoing challenges and future directions for integrating these technologies into next-generation automated C. elegans research. Full article

The female reproductive system represents a highly complex regulatory network governing critical physiological functions, encompassing reproductive capacity and endocrine regulation that maintains female physiological homeostasis. The in vitro simulation system provides a novel tool for biomedical research and can be used as physiological and pathological models to study the female reproductive system. Recent advances in this technology have evolved from 2D and 3D printing to organ-on-a-chip (OOC) and microfluidic systems, which has emerged as a transformative platform for modeling the female reproductive system. These microphysiological systems integrate microfluidics, 3D cell culture, and biomimetic scaffolds to replicate key functional aspects of reproductive organs and tissues. They have enabled precise simulation of hormonal regulation, embryo-endometrium interactions, and disease mechanisms such as endometriosis and gynecologic cancers. This review highlights the current state of female reproductive OOCs, including ovary-, uterus-, and fallopian tube-on-a-chip system, their applications in assisted reproduction and disease modeling, and the technological hurdles to their widespread application. Though significant barriers remain in scaling OOCs for high-throughput drug screening, standardizing protocols for clinical applications, and validating their predictive value against human patient outcomes, OOCs have emerged as a transformative platform to model complex pathologies, offering unprecedented insights into disease mechanisms and personalized therapeutic interventions. Future directions, including multi-organ integration for systemic reproductive modeling, incorporation of microbiome interactions, and clinical translation for mechanisms of drug action, will facilitate unprecedented insights into reproductive physiology and pathology. Full article

Perovskites (ABX₃) exhibit remarkable potential in optoelectronic conversion, catalysis, and diverse energy-related fields. However, the tunability of A, B, and X-site compositions renders conventional screening methods labor-intensive and inefficient. This review systematically synthesizes the roles of physical simulations and machine learning (ML) in accelerating perovskite discovery. By harnessing existing experimental datasets and high-throughput computational results, ML models elucidate structure-property relationships and predict performance metrics for solar cells, (photo)electrocatalysts, oxygen carriers, and energy-storage materials, with experimental validation confirming their predictive reliability. While data scarcity and heterogeneity inherently limit ML-based prediction of material property, integrating high-throughput computational methods as external mechanistic constraints—supplementing standardized, large-scale training data and imposing loss penalties—can improve accuracy and efficiency in bandgap prediction and defect engineering. Moreover, although embedding high-throughput simulations into ML architectures remains nascent, physics-embedded approaches (e.g., symmetry-aware networks) show increasing promise for enhancing physical consistency. This dual-driven paradigm, integrating data and physics, provides a versatile framework for perovskite design, achieving both high predictive accuracy and interpretability—key milestones toward a rational design strategy for functional materials discovery. Full article