Search Results (2,446)

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

Viral diseases pose a significant threat to lily (Lilium spp.) cultivation; however, large-scale assessments of virus prevalence and diversity in South Korea are limited. This study combined RT-PCR surveys, high-throughput sequencing (HTS), and analyses of 48 lily hybrid transcriptomes to characterize the lily virome. RT-PCR screening of 100 samples from 13 regions showed that 87% were infected, primarily with lily mottle virus (LMoV, 65%), Plantago asiatica mosaic virus (PlAMV, 34%), cucumber mosaic virus (CMV, 34%), and lily symptomless virus (LSV, 25%). Mixed infections were approximately twice as frequent as single infections and were associated with greater symptom severity, particularly in triple-virus combinations. High-throughput sequencing expanded detection to six viruses, including milk vetch dwarf virus (MDV) and lily virus B (LVB), the latter confirmed as a variant of strawberry latent ringspot virus (SLRSV). Near-complete genomes of several viruses were assembled and validated through RT-PCR. Transcriptome mining identified eight virus species across 26 cultivars; PlAMV was the most common, and viral loads varied significantly among hybrids. Phylogenetic analyses revealed close relationships between Korean and Chinese isolates and host-related clustering in PlAMV. These findings highlight the complexity of lily viromes in South Korea and provide essential resources for diagnostics, disease management, and biosecurity. 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

Organismal biosensing leverages the olfactory acuity of living systems to detect volatile organic compounds (VOCs) associated with cancer, offering a low-cost and non-invasive complement to conventional diagnostics. Early studies demonstrate its feasibility across diverse platforms. In C. elegans, chemotaxis assays on urine samples achieved sensitivities of 87–96% and specificities of 90–95% in case–control cohorts (n up to 242), while calcium imaging of AWC neurons distinguished breast cancer urine with ~97% accuracy in a small pilot cohort (n ≈ 40). Trained canines have identified prostate cancer from urine with sensitivities of ~71% and specificities of 70–76% (n ≈ 50), and AI-augmented canine breath platforms have reported accuracies of ~94–95% across ~1400 participants. Insects such as locusts and honeybees enable ultrafast neural decoding of VOCs, achieving 82–100% classification accuracy within 250 ms in pilot studies (n ≈ 20–30). Collectively, these platforms validate the principle that organismal behavior and neural activity encode cancer-related VOC signatures. However, limitations remain, including small cohorts, methodological heterogeneity, and reliance on binary outputs. This review proposes a Dual-Pathway Framework, where Pathway 1 leverages validated indices (e.g., the Chemotaxis Index) for high-throughput screening, and Pathway 2 applies machine learning to high-dimensional behavioral vectors for cancer subtyping, staging, and monitoring. By integrating these approaches, organismal biosensing could evolve from proof-of-concept assays into clinically scalable precision diagnostics. 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

Mechanical exfoliation remains the most common method for producing high-quality two-dimensional (2D) materials, but its inherently low yield requires screening large numbers of samples to identify usable flakes. Efficient optimization of the exfoliation process demands scalable methods to analyze deposited material across extensive datasets. While machine learning clustering techniques have demonstrated ~95% accuracy in classifying 2D material thicknesses from optical microscopy images, current tools are limited by slow processing speeds and heavy reliance on manual user input. This work presents an open-source, GPU-accelerated software platform that builds upon existing classification methods to enable high-throughput analysis of 2D material samples. By leveraging parallel computation, optimizing core algorithms, and automating preprocessing steps, the software can quantify flake coverage and thickness across uncompressed optical images at scale. Benchmark comparisons show that this implementation processes over 200× more pixel data with a 60× reduction in processing time relative to the original software. Specifically, a full dataset of2916 uncompressed images can be classified in 35 min, compared to an estimated 32 h required by the baseline method using compressed images. This platform enables rapid evaluation of exfoliation results across multiple trials, providing a practical tool for optimizing deposition techniques and improving the yield of high-quality 2D materials. Full article

Background: As a key rice breeding resource, aromatic rice is widely cultivated in agriculture due to its unique aroma. Badh2 mutations cause function loss, enabling rice’s characteristic aroma. Methods: In this study, we analyzed several badh2 mutation types across 8 japonica and 16 indica aromatic rice lines. Based on the 7 bp deletion in badh2-E2 identified in japonica aromatic lines, we developed a multiplex-ready PCR assay for badh2 genotyping. Additionally, leveraging the deletion mutation in badh2-E7 from the indica aromatic line Yexiang, we designed a KASP marker. Results: All 8 japonica aromatic lines carried a 7 bp deletion in badh2-E2, while 12 indica aromatic lines harbored an 8 bp deletion in badh2-E7, and 4 additional indica aromatic lines exhibited an 8 bp deletion in badh2-E2. The multiplex-ready PCR assay was used to screen 200 individual plants from the aromatic rice line Jia 58: 199 plants showed the expected results, while the remaining 1 exhibited two fluorescent signal peaks—suggesting that it may be a heterozygous individual. Using the KASP marker, we performed genotyping analysis on F₇ progeny individuals derived from the cross between Yexiang (aromatic line) and Yuenongsimiao (non-aromatic line). Combined with phenotypic observations, we successfully screened out an elite aromatic line named Zhexiangzhenhe, which not only possesses aroma but also maintains superior agronomic traits. Conclusions: The multiplex-ready PCR assay and KASP markers facilitate high-throughput genotyping in large-scale breeding populations, providing breeders with a rapid and efficient selection tool to accelerate aromatic trait improvement in rice. Full article

The development of effective tools to combat viral diseases remains a major challenge for the aquaculture industry. Infectious pancreatic necrosis virus (IPNv) is one of the most devastating pathogens affecting salmonids, leading to high mortality rates and substantial economic losses worldwide. Here, we present a novel nanobody discovery pipeline based on a Type IIS restriction enzyme-driven library assembly method that enables the rapid generation of highly diverse nanobody repertoires. This streamlined approach not only shortens the time required for nanobody identification but also offers remarkable adaptability, allowing its application to virtually any protein target, including antigens from aquaculture pathogens and beyond. By integrating this strategy with density gradient–based enrichment and high-throughput screening, we successfully identified and validated a nanobody against the VP2 protein of IPNv, a key structural component essential for viral infectivity. These findings highlight the potential of this platform both as a versatile methodological advance in antibody engineering and as a practical foundation for developing innovative diagnostic and therapeutic tools. Ultimately, nanobodies generated through this pipeline could play a pivotal role in improving disease management and enhancing sustainability in aquaculture. 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

To achieve rapid screening and semi-quantitative analysis of pesticide residues in mobile laboratories and on-site tea testing, a novel method based on thermal-assisted plasma ionization–time-of-flight mass spectrometry (TAPI-TOF/MS) has been developed for the detection of 20 pesticide residues, including insecticides and fungicides, in tea. This method eliminates the need for liquid chromatography, or column connections. Instead, it utilizes the high temperature of the sample inlet and stage to fully volatilize and inject the sample. By integrating TAPI-TOF/MS with an automated pesticide residue pretreatment instrument, the entire sample extraction process can be performed automatically. The analysis time for each sample has been reduced to 1.5 min, allowing for the processing of 60 samples per batch. An accurate mass spectrometry database has been established for screening and confirmation purposes. The software automatically matches the mass spectrometry database by analyzing the measured ion mass deviation, ion abundance ratio, and the relative contribution weight of each ion, generating a qualitative score ranging from 0 to 100. The lowest concentration yielding a qualitative score of ≥75 was defined as the screening limit, which ranged from 0.10 to 5.00 mg/kg for the 20 pesticides. Within their respective linear ranges, the method demonstrated good linearity with correlation coefficients (R²) ranging from 0.983 to 0.999. The average recovery rates (n = 5) of the target pesticides ranged from 70.6% to 117.0% at the set standard concentrations, with relative standard deviations (RSD) ranging from 1.7% to 13.1%. Using this method, 15 tea samples purchased from the Rizhao market in China were analyzed. Ten samples were found to contain residues of metalaxyl or pyraclostrobin, yielding a detection rate of 66.7%. This technology provides technical support for the rapid detection and quality control of multiple pesticide residues in tea, meeting the requirements for high-throughput and on-site analysis. Full article

Efficient production of high-quality cubical crushed stone is critical for road construction and concrete manufacturing. Conventional vibrating screens suffer from low cubicality and high energy consumption, limiting their applicability. We developed a novel spiral vibrating screen featuring a helical screening surface and adjustable oscillation parameters. Experimental studies were conducted on granite aggregates (5–20 mm) at vibration frequencies of 16–26 Hz and amplitudes of 1.5–4.0 mm to evaluate cubicality, screening efficiency, throughput, and energy consumption. Under optimal operating conditions (22 Hz, 3.0 mm amplitude), the prototype achieved 84–86% cubical particles, 93–95% screening efficiency, and specific energy consumption of 1.20 ± 0.05 kWh/t. Compared with conventional flat and drum screens, cubicality improved by 8–12 percentage points, while energy consumption decreased by up to 12%. The developed screen offers a scalable solution for producing high-quality cubical aggregates with lower energy demand and reduced clogging risks. These findings provide practical guidance for improving aggregate processing technologies. Full article

The Fraser Valley of British Columbia, Canada is among the top ten blueberry producing regions globally. Viral diseases are established in the region and significantly reduce average yields. While testing for two viruses is routine, characterization of all the viruses present in the region is incomplete. We used high-throughput sequencing to obtain an unbiased overview of RNA viruses present in 97 plants collected across the region. In addition to known viruses, we identified four luteoviruses previously unidentified in the region. Two of them matched the blueberry virus L (BlVL) and blueberry virus M (BlVM). recently found in the USA, while the third constitutes a new major variant of BlVM (BlVM-2), and the fourth a new luteovirus, which we named blueberry virus N (BlVN). The genome sequences were ~5 kbp long and contained four open-reading frames similar to other luteoviruses. PCR screening revealed that these luteoviruses are widespread in the region, and that plants typically harbour more than one of these luteoviruses. While luteoviruses are typically vectored by aphids, they were also present in nursery stock, indicating that spread also occurs via vegetative propagation. Full article

The growing threat of Antimicrobial Resistance (AMR) demands innovative drug discovery, yet conventional 2D cell cultures fail to accurately mimic in vivo conditions, leading to high failure rates in preclinical studies. This review addresses the critical need for more physiologically relevant platforms by exploring recent advancements in bioengineered 3D tissue models for studying bacterial pathogenesis and antimicrobial drug discovery. We conducted a systematic search of peer-reviewed articles from 2015 to 2025 across PubMed, Scopus, and Web of Science, focusing on studies that used 3D models to investigate host–pathogen interactions or antimicrobial screening. Data on model types, biomaterials, fabrication techniques, and key findings were systematically charted to provide a comprehensive overview. Our findings reveal that a diverse range of biomaterials, including biopolymers and synthetic polymers, combined with advanced techniques like 3D bioprinting, are effectively used to create sophisticated tissue scaffolds. While these 3D models demonstrate clear superiority in mimicking biofilm properties and complex host–pathogen dynamics, our analysis identified a significant research gap: very few studies directly integrate these advanced bioengineered 3D models for high-throughput antimicrobial drug discovery. In conclusion, this review highlights the urgent need to bridge this disparity through increased research, standardization, and scalability in this critical interdisciplinary field, with the ultimate goal of accelerating the development of new therapeutics to combat AMR. Full article