Search Results (2,377)

Early diagnosis is critical for the effective management of neurodegenerative disorders, and retinal alterations have emerged as promising early biomarkers due to the retina’s close developmental and functional link to the brain. The zebrafish (Danio rerio), with its rapid development, transparent embryos, and evolutionarily conserved visual system, represents a powerful and versatile model for studying retinal degeneration. This review discusses a range of behavioral assays—including visual adaptation, motion detection, and color discrimination—that are employed to evaluate retinal function in zebrafish. These methods enable the detection of subtle visual deficits that may precede overt anatomical damage, providing a non-invasive, efficient strategy for early diagnosis and high-throughput drug screening. Importantly, these behavioral tests also serve as sensitive functional readouts to evaluate the efficacy of pharmacological treatments over time. Compared to traditional murine models, zebrafish offer advantages such as lower maintenance costs, faster development, optical transparency for live imaging, and ethical benefits due to reduced use of higher vertebrates. However, variability in experimental protocols highlights the need for standardization to ensure reliability and reproducibility. Full article

Hydrogen is widely recognized as a key enabler of the clean energy transition, but the lack of safe, efficient, and scalable storage technologies continues to hinder its broad deployment. Conventional hydrogen storage approaches, such as compressed hydrogen storage, cryo-compressed hydrogen storage, and liquid hydrogen storage, face limitations, including high energy consumption, elevated cost, weight, and safety concerns. In contrast, solid-state hydrogen storage using carbon-based adsorbents has gained growing attention due to their chemical tunability, low cost, and potential for modular integration into energy systems. This review provides a comprehensive evaluation of hydrogen storage using carbon-based materials, covering fundamental adsorption mechanisms, classical materials, emerging architectures, and recent advances in computationally AI-guided material design. We first discuss the physicochemical principles driving hydrogen physisorption, chemisorption, Kubas interaction, and spillover effects on carbon surfaces. Classical adsorbents, such as activated carbon, carbon nanotubes, graphene, carbon dots, and biochar, are evaluated in terms of pore structure, dopant effects, and uptake capacity. The review then highlights recent progress in advanced carbon architectures, such as MXenes, three-dimensional architectures, and 3D-printed carbon platforms, with emphasis on their gravimetric and volumetric performance under practical conditions. Importantly, this review introduces a forward-looking perspective on the application of artificial intelligence and machine learning tools for data-driven sorbent design. These methods enable high-throughput screening of materials, prediction of performance metrics, and identification of structure–property relationships. By combining experimental insights with computational advances, carbon-based hydrogen storage platforms are expected to play a pivotal role in the next generation of energy storage systems. The paper concludes with a discussion on remaining challenges, utilization scenarios, and the need for interdisciplinary efforts to realize practical applications. Full article

Antibiotic resistance is one of the most significant public health issues today. As a consequence, there is an urgent need for novel classes of antibiotics. This necessitates the development of highly efficient screening methods for the rapid identification of antibiotic-producing bacteria. Here, we describe a new method for high-throughput screening of antimicrobial compounds (AMC) producing halophilic bacteria. Our methodology used a newly designed 3D-printed Petri plate replicator used for drop deposition and colony replication. We employed this device in combination with a modified agar overlay assay to screen more than 7400 bacterial colonies. A total of 54 potential AMC producers were discovered at a success rate of 0.7%. Although 40% of them lost their antibacterial activity during the secondary screening, 22 strains retained inhibitory activity and were able to suppress the growth of one or more safe relatives of the ESKAPE group pathogens. The ethyl acetate extract from the most potent strain, Virgibacillus salarius POTR191, demonstrated moderate antibacterial activity against Enterococcus faecalis, Acinetobacter baumanii, and Staphylococcus epidermidis with minimal inhibitory concentrations of 128 μg/mL, 128 μg/mL, and 512 μg/mL, respectively. We propose that our replica plate assay could be used for target-based antimicrobial screening of various extremophilic bacteria. Full article

Zebrafish are model organisms for drug screening owing to their transparent bodies, rapid embryonic development, and genetic similarities with humans. However, using standard polystyrene culture plates can limit the oxygen supply, potentially affecting embryo survival and the reliability of assays conducted in zebrafish. In this study, we evaluated the application of a novel, highly oxygen-permeable culture plate (InnoCell^TM) in zebrafish development and drug screening assays. Under both normal and oxygen-restricted conditions, zebrafish embryos cultured on InnoCell^TM plates exhibited significantly improved developmental parameters, including heart rate and body length, compared with those cultured on conventional polystyrene plates. The InnoCell^TM plate enabled a significant reduction in medium volume without compromising zebrafish embryo viability, thereby demonstrating its advantages, particularly in high-throughput 384-well formats. Drug screening tests using antiangiogenic receptor tyrosine kinase inhibitors (TKIs) revealed enhanced sensitivity and more pronounced biological effects in InnoCell^TM plates, as evidenced by the quantification of intersegmental blood vessels and gene expression analysis of the vascular endothelial growth factor receptor (vegfr, also known as kdrl). These results indicate that the InnoCell^TM highly oxygen-permeable plate markedly improves zebrafish-based drug screening efficiency and assay reliability, highlighting its potential for widespread application in biomedical research. Full article

Electroporation-mediated gene delivery is a cornerstone of synthetic biology, offering several advantages over other methods: higher efficiencies, broader applicability, and simpler sample preparation. Yet, electroporation protocols are often challenging to integrate into highly multiplexed workflows, owing to limitations in their scalability and tunability. These challenges ultimately increase the time and cost per transformation. As a result, rapidly screening genetic libraries, exploring combinatorial designs, or optimizing electroporation parameters requires extensive iterations, consuming large quantities of expensive custom-made DNA and cell lines or primary cells. To address these limitations, we have developed a High-Throughput Microfluidic Electroporation (HTME) platform that includes a 384-well electroporation plate (E-Plate) and control electronics capable of rapidly electroporating all wells in under a minute with individual control of each well. Fabricated using scalable and cost-effective printed-circuit-board (PCB) technology, the E-Plate significantly reduces consumable costs and reagent consumption by operating on nano to microliter volumes. Furthermore, individually addressable wells facilitate rapid exploration of large sets of experimental conditions to optimize electroporation for different cell types and plasmid concentrations/types. Use of the standard 384-well footprint makes the platform easily integrable into automated workflows, thereby enabling end-to-end automation. We demonstrate transformation of E. coli with pUC19 to validate the HTME’s core functionality, achieving at least a single colony forming unit in more than 99% of wells and confirming the platform’s ability to rapidly perform hundreds of electroporations with customizable conditions. This work highlights the HTME’s potential to significantly accelerate synthetic biology Design-Build-Test-Learn (DBTL) cycles by mitigating the transformation/transfection bottleneck. Full article

Biodegradation is a green and efficient method for lignin depolymerization and conversion. In order to screen potential bacterial strains for efficient lignin degradation, composts of cow dung and wheat straw were prepared, and the dynamic changes in the predicted bacterial community structure and function in different periods of the composts were investigated. Then, bacteria with an efficient lignin degradation ability were finally screened out from the compost samples. Based on the monitoring results of the physicochemical indexes of the composting process, it was found that the temperature and pH of the compost firstly increased and then decreased with the extension of time, and the water content and C/N gradually decreased. High-throughput sequencing of compost samples from the initial (DA), high-temperature (DB), and cooling (DC) periods revealed that the number of OTUs increased sharply then stabilized around 2000, and the alpha diversity of the bacterial community decreased firstly and then increased. The predominant phyla identified included Proteobacteria, Firmicutes, Chloroflexi, and Bacteroidetes, determined by the relative abundance of beta-diversity-associated species. Functional gene analysis conducted using Tax4Fun revealed that the genes were primarily categorized into Metabolism, Genetic Information Processing, Environmental Information Processing, and Cellular Processes. Based on the decolorization of aniline blue and the degradation efficiency of alkali lignin, eight bacterial strains were isolated from compost samples at the three stages. Cupriavidus sp. F1 showed the highest degradation of alkali lignin with 66.01%. Cupriavidus sp. D8 showed the highest lignin degradation potential with all three enzyme activities significantly higher than the other strains. The results provide a strategy for the lignin degradation and utilization of biomass resources. Full article

The main objective of this study was to reveal the molecular mechanism of the albinism in Schima superba and to identify the related functional genes to provide theoretical support for the optimization of S. superba seedling nursery technology. Combining third-generation SMRT sequencing with second-generation high-throughput sequencing technology, the transcriptomes of normal seedlings and albinism seedlings of S. superba were analyzed and the sequencing data were functionally annotated and deeply resolved. The results showed that 270 differentially expressed transcripts were screened by analyzing second-generation sequencing data. KEGG enrichment analysis of the annotation information revealed that, among the photosynthesis-antenna protein-related pathways, the expression of LHCA3 and LHCB6 was found to be down-regulated in S. superba albinism seedlings, suggesting that the down-regulation of photosynthesis-related proteins may affect the development of chloroplasts in leaves. Down-regulated expression of VDE in the carotenoid biosynthesis leads to impaired chlorophyll cycling. In addition, transcription factors (TFs), such as bHLH, MYB, GLK and NAC, were closely associated with chloroplast development in S. superba seedlings. In summary, the present study systematically explored the transcriptomic features of S. superba albinism seedlings, screened out key genes with significant differential expression and provide a reference for further localization and cloning of the key genes for S. superba albinism, in addition to laying an essential theoretical foundation for an in-depth understanding of the molecular mechanism of the S. superba albinism. The genes identified in this study that are associated with S. superba albinism will be important targets for genetic modification or molecular marker development, which is essential for improving the cultivation efficiency of S. superba. Full article

The growing threat of infectious diseases requires novel therapeutics with different mechanisms of action. Antimicrobial peptides (AMPs), which are crucial for innate immunity, are a promising research area. The medicinal leech (Hirudo medicinalis) is a potential source of bioactive AMPs that are vital while interacting with microorganisms. This study aims to investigate the antimicrobial properties of peptides found in the H. medicinalis genome using a novel high-throughput screening method based on the expression of recombinant AMP genes in Escherichia coli. This approach enables the direct detection of AMP activity within cells, skipping the synthesis and purification steps, while allowing the simultaneous analysis of multiple peptides. The application of this method to the first identified candidate AMPs from H. medicinalis resulted in the discovery of three novel peptides: LBrHM1, NrlHM1 and NrlHM2. These peptides, which belong to the lumbricin and macin families, exhibit significant activity against E. coli. Two fragments of the new LBrHM1 homologue were synthesised and studied: a unique N-terminal fragment (residues 1–23) and a fragment (residues 27–55) coinciding with the active site of lumbricin I. Both fragments exhibited antimicrobial activity in a liquid medium against Bacillus subtilis. Notably, the N-terminal fragment lacks homologues among previously described AMPs. Full article

Introduction: Scattering of kernels due to angular dehiscence is a key bottleneck in mechanized harvesting of oilseed rape. Materials and Methods: In this study, a dual-track “genotype–phenotype” screening strategy was established by innovatively integrating high-throughput KASP molecular marker technology and a standardized random collision phenotyping system for the complex quantitative trait of angular resistance. Results: Through the systematic evaluation of 634 oilseed rape hybrid progenies, it was found that the KASP marker S12.68, targeting the cleavage resistance locus (BnSHP1) on chromosome C9, achieved a 73.34% introgression rate (465/634), which was significantly higher than the traditional breeding efficiency (<40%). Phenotypic characterization screened seven excellent resources with cracking resistance index (SRI) > 0.6, of which four reached the high resistance standard (SRI > 0.8), including the core materials NR21/KL01 (SRI = 1.0) and YuYou342/KL01 (SRI = 0.97). Six breeding intermediate materials (44.7–48.7% oil content, mycosphaerella resistance MR grade or above) were created, combining high resistance to chipping and excellent agronomic traits. For the first time, it was found that local germplasm YuYou342 (non-KL01-derived line) was purely susceptible at the S12.68 locus (SRI = 0.86), but its angiosperm vascular bundles density was significantly increased by 37% compared with that of the susceptible material 0911 (p < 0.01); and the material 187308 (SRI = 0.78), although purely susceptible at S12.68, had a 2.8-fold downregulation in expression of the angiosperm-related gene, BnIND1, and a 2.8-fold downregulation of expression of the angiosperm-related gene, BnIND1. expression was significantly downregulated 2.8-fold (q < 0.05), indicating the existence of a novel resistance mechanism independent of the primary effector locus. Conclusions: The results of this research provide an efficient technical platform and breakthrough germplasm resources for oilseed rape crack angle resistance breeding, which is of great practical significance for promoting the whole mechanized production. Full article

Background/Objectives: Substance use disorders, particularly opioid addiction, continue to pose a major global health and toxicological challenge. Morphine dependence represents a significant problem in both clinical practice and preclinical research, particularly in modeling the pharmacodynamics of withdrawal. Rodent models remain indispensable for investigating the neurotoxicological effects of chronic opioid exposure and withdrawal. However, conventional behavioral assessments rely on manual observation, limiting objectivity, reproducibility, and scalability—critical constraints in modern drug toxicity evaluation. This study introduces MWB_Analyzer, an automated and high-throughput system designed to quantitatively and objectively assess morphine withdrawal behaviors in rats. The goal is to enhance toxicological assessments of CNS-active substances through robust, scalable behavioral phenotyping. Methods: MWB_Analyzer integrates optimized multi-angle video capture, real-time signal processing, and machine learning-driven behavioral classification. An improved YOLO-based architecture was developed for the accurate detection and categorization of withdrawal-associated behaviors in video frames, while a parallel pipeline processed audio signals. The system incorporates behavior-specific duration thresholds to isolate pharmacologically and toxicologically relevant behavioral events. Experimental animals were assigned to high-dose, low-dose, and control groups. Withdrawal was induced and monitored under standardized toxicological protocols. Results: MWB_Analyzer achieved over 95% reduction in redundant frame processing, markedly improving computational efficiency. It demonstrated high classification accuracy: >94% for video-based behaviors (93% on edge devices) and >92% for audio-based events. The use of behavioral thresholds enabled sensitive differentiation between dosage groups, revealing clear dose–response relationships and supporting its application in neuropharmacological and neurotoxicological profiling. Conclusions: MWB_Analyzer offers a robust, reproducible, and objective platform for the automated evaluation of opioid withdrawal syndromes in rodent models. It enhances throughput, precision, and standardization in addiction research. Importantly, this tool supports toxicological investigations of CNS drug effects, preclinical pharmacokinetic and pharmacodynamic evaluations, drug safety profiling, and regulatory assessment of novel opioid and CNS-active therapeutics. Full article

Mycotoxins are responsible for a multitude of diseases in both humans and animals, resulting in significant medical and economic burdens worldwide. Conventional detection methods, such as enzyme-linked immunosorbent assay (ELISA), high-performance liquid chromatography (HPLC), and liquid chromatography-tandem mass spectrometry (LC-MS/MS), are highly effective, but they are generally confined to laboratory settings. Consequently, there is a growing demand for point-of-care testing (POCT) solutions that are rapid, sensitive, portable, and cost-effective. Lateral flow assays (LFAs) are a pivotal technology in POCT due to their simplicity, rapidity, and ease of use. This review synthesizes data from 78 peer-reviewed studies published between 2015 and 2024, evaluating advances in nanoparticle-based LFAs for detection of singular or multiplex mycotoxin types. Gold nanoparticles (AuNPs) remain the most widely used, due to their favorable optical and surface chemistry; however, significant progress has also been made with silver nanoparticles (AgNPs), magnetic nanoparticles, quantum dots (QDs), nanozymes, and hybrid nanostructures. The integration of multifunctional nanomaterials has enhanced assay sensitivity, specificity, and operational usability, with innovations including smartphone-based readers, signal amplification strategies, and supplementary technologies such as surface-enhanced Raman spectroscopy (SERS). While most singular LFAs achieved moderate sensitivity (0.001–1 ng/mL), only 6% reached ultra-sensitive detection (<0.001 ng/mL), and no significant improvement was evident over time (ρ = −0.162, p = 0.261). In contrast, multiplex assays demonstrated clear performance gains post-2022 (ρ = −0.357, p = 0.0008), largely driven by system-level optimization and advanced nanomaterials. Importantly, the type of sample matrix (e.g., cereals, dairy, feed) did not significantly influence the analytical sensitivity of singular or multiplex lateral LFAs (Kruskal–Wallis p > 0.05), confirming the matrix-independence of these optimized platforms. While analytical challenges remain for complex targets like fumonisins and deoxynivalenol (DON), ongoing innovations in signal amplification, biorecognition chemistry, and assay standardization are driving LFAs toward becoming reliable, ultra-sensitive, and field-deployable platforms for high-throughput mycotoxin screening in global food safety surveillance. Full article

Despite remarkable progress in cancer immunotherapy, many agents that show efficacy in murine or in vitro models fail to translate clinically. Zebrafish (Danio rerio) have emerged as a powerful complementary model that addresses several limitations of traditional systems. Their optical transparency, genetic tractability, and conserved immune and oncogenic signaling pathways enable high-resolution, real-time imaging of tumor–immune interactions in vivo. Importantly, zebrafish offer a unique opportunity to study the core mechanisms of health and sickness, complementing other models and expanding our understanding of fundamental processes in vivo. This review provides an overview of zebrafish immune system development, highlighting tools for tracking innate and adaptive responses. We discuss their application in modeling immune evasion, checkpoint molecule expression, and tumor microenvironment dynamics using transgenic and xenograft approaches. Platforms for high-throughput drug screening and personalized therapy assessment using patient-derived xenografts (“zAvatars”) are evaluated, alongside limitations, such as temperature sensitivity, immature adaptive immunity in larvae, and interspecies differences in immune responses, tumor complexity, and pharmacokinetics. Emerging frontiers include humanized zebrafish, testing of next-generation immunotherapies, such as CAR T/CAR NK and novel checkpoint inhibitors (LAG-3, TIM-3, and TIGIT). We conclude by outlining the key challenges and future opportunities for integrating zebrafish into the immuno-oncology pipeline to accelerate clinical translation. Full article

Lactate dehydrogenase (LDH) catalyzes the reversible interconversion of pyruvate and lactate, coupled with the redox cycling of NADH and NAD⁺. While LDHA has been extensively studied as a therapeutic target, particularly in cancer, due to its role in the Warburg effect, LDHB remains underexplored, despite its involvement in the metabolic reprogramming of specific cancer types, including breast and lung cancers. Most known LDH inhibitors are designed against the LDHA isoform and act competitively at the active site. In contrast, LDHB exhibits distinct kinetic properties, substrate preferences, and structural features, warranting isoform-specific screening strategies. In this study, 115 natural compounds previously reported as LDHA inhibitors were systematically evaluated for LDHB inhibition using an integrated in silico and in vitro approach. Virtual screening identified 16 lead phytochemicals, among which luteolin and quercetin exhibited uncompetitive inhibition of LDHB, as demonstrated by enzyme kinetic assays. These findings were strongly supported by molecular docking analyses, which revealed that both compounds bind at an allosteric site located at the dimer interface, closely resembling the binding mode of the established LDHB uncompetitive inhibitor AXKO-0046. In contrast, comparative docking against LDHA confirmed their active-site binding and competitive inhibition, underscoring their isoform-specific behavior. Our findings highlight the necessity of assay conditions tailored to LDHB’s physiological role and demonstrate the application of a previously validated colorimetric assay for high-throughput screening. This work lays the foundation for the rational design of selective LDHB inhibitors from natural product libraries. Full article

Human Interleukin-6 (hIL-6) is a pro inflammatory cytokine that binds to its receptor, IL-6Rα followed by binding to gp130 and subsequent dimerization to form a hexamer signaling complex. As a critical inflammation mediator, hIL-6 is associated with a diverse range of diseases and monoclonal antibodies in clinical use that either target IL-6Rα or hIL-6 to inhibit signaling. Here, we perform high-throughput structure-based computational screening using ensemble docking for small-molecule antagonists for which the target conformations were taken from 600 ns long molecular dynamics simulations of the apo protein. Prior knowledge of the contact sites from binary complex studies and experimental work was incorporated into the docking studies. The top 20 scoring ligands from the in silico studies after post analysis were subjected to in vitro functional assays. Among these compounds, the ligand with the second-highest calculated binding affinity experimentally showed an ~84% inhibitory effect on IL6-induced STAT3 reporter activity at 10 μM concentration. This finding may pave the way for designing small-molecule inhibitors of hIL-6 of therapeutic significance. Full article

Deep learning has great potential in the field of functional peptide prediction. This study combines metagenomics and deep learning to efficiently discover potential umami peptides in fermented sausages. A candidate peptide library was generated using metagenomic data from fermented sausages, an integrated deep learning model was constructed for prediction, and SHAP (SHapley Additive exPlanations) interpretability analysis was performed to elucidate the key amino acid features and contributions of the model in predicting umami peptides, screening the top ten peptides with the highest predicted probability. Subsequently, molecular docking was performed to assess the binding stability of these peptides with the umami receptor T1R1/T1R3, selecting the three peptides DDSMAATGL, DGEEDASM, and DEEEVDI with the most stable binding for further study. Docking analysis revealed the important roles of the key receptor residues Glu301, Arg277, Lys328, and His71 in hydrogen bond formation. Molecular dynamics simulations validated the robust integrity of the peptide–receptor associations. Finally, sensory evaluation demonstrated that these three peptides possessed significant umami characteristics, with low umami thresholds (0.11, 0.37, and 0.44 mg/mL, respectively). This study, based on metagenomics and deep learning, provides a high-throughput strategy for the discovery and validation of functional peptides. Full article