Search Results (104)

Natural product research has experienced substantial growth over the past two decades, driven by a renewed appreciation for the structural complexity and biological relevance of compounds derived from nature. Technological advances in separation science, spectroscopic characterization, and high-sensitivity bioassays have collectively restored natural products to a position of prominence in modern drug discovery efforts. Nature remains the most prolific source of bioactive molecular diversity, drawing from microorganisms, plants, and marine life to offer a vast reservoir of structurally novel scaffolds whose pharmacological potential remains largely unexplored. Effective extraction and isolation remain foundational to natural product research, as the quality and purity of isolated compounds directly govern the reliability of downstream biological evaluation. Recent years have witnessed remarkable innovation in this space, spanning green and designer solvent systems, pressurized and ultrasound-assisted extraction platforms, supercritical fluid techniques, and integrated purification workflows that dramatically reduce processing time while improving compound recovery and analytical throughput. Particularly noteworthy is the growing application of artificial intelligence and machine learning tools for solvent selection, extraction optimization, and metabolite dereplication, which in combination with advanced phase-separation strategies and informatic platforms have substantially expanded the scope of detectable and characterizable metabolites within complex biological matrices. This review summarizes recent progress in extraction and isolation methodologies supporting natural product research, with particular emphasis on combinatorial extraction strategies, next-generation solvent systems, and AI-driven applications that have collectively improved operational efficiency, selectivity, and analytical output over the past five years. Full article

Background: Extracellular vesicles (EVs) are increasingly explored as nanocarriers in drug delivery; however, selecting an appropriate loading strategy for a given small-molecule cargo still relies largely on empirical, resource-intensive parallel screening within EV formulation workflows. Despite the widespread application of passive incubation, electroporation, saponin-mediated permeabilization, freeze–thaw cycling, and sonication, there is currently no mechanistically grounded, descriptor-informed framework that enables rational prioritization of loading methods during the early design stage of EV-based dosage forms, leading to inefficient trial-and-error experimentation. Methods: We assembled a chemically diverse dataset of 21 compounds with experimentally determined loading efficiencies across five EV loading methods and calculated seven mechanistically motivated physicochemical descriptors (LogP, molecular weight, aqueous solubility, hydrogen bond donors/acceptors, polar surface area, and formal charge) for each drug. Separate Elastic Net regression models were trained for each loading strategy. Model performance was evaluated using leave-one-out cross-validation, a predefined external validation set (n = 4), and 50 repeated random train–test splits. The analysis emphasized decision-level ranking of loading methods rather than the precise prediction of absolute efficiencies. The applicability domain was assessed via leverage analysis to define the supported chemical space for prospective implementation in EV-based formulation development. Results: As anticipated for biologically heterogeneous EV systems, continuous regression performance remained modest (LOOCV R² = 0.06–0.41). In contrast, decision-level accuracy for identifying the experimentally optimal loading method was consistently high across validation schemes (internal: 76.5%; predefined external: 75%; repeated random validation: 80.5 ± 16.8%). Mechanical disruption methods (freeze–thaw and sonication) demonstrated comparatively greater predictive stability, while misclassification patterns suggested potential nonlinear behavior for highly polar, ionizable cargos. All compounds resided within the leverage-defined applicability domain, confirming adequate descriptor-space representation. Conclusions: This study establishes a mechanistically interpretable, descriptor-based decision-support framework capable of reliably prioritizing EV loading strategies for small-molecule cargos beyond empirical chance without altering standard protocols. By reframing the modeling objective from high-precision efficiency prediction to robust ranking of candidate methods, the approach offers a practical tool to triage between commonly used techniques, thereby reducing experimental burden in early-stage EV formulation development. The framework provides a quantitative basis for integrating molecular-descriptor-guided method selection into rational EV-based drug delivery design and can be expanded with membrane-specific descriptors and larger datasets. Full article

Monitoring peptide and protein self-association is essential for understanding biological function, formulation stability, and aggregation mechanisms. While size-exclusion chromatography (SEC) is routinely used to quantify protein-size variants under native conditions, its hyphenation to high-resolution mass spectrometry (HRMS) for simultaneous structural characterization remains limited. Here, we report the development and validation of a robust SEC-UV/HRMS method optimized for native-like analysis of bovine serum albumin (BSA) monomers and higher-order oligomers using standard-flow electrospray ionization. Systematic evaluation of source parameters, mobile-phase composition, and chromatographic conditions enabled retention of native BSA structure, minimized in-source unfolding, and enhanced MS sensitivity, allowing detection of oligomers up to the heptamer. A short, narrow-bore 200 Å UHPLC SEC separation column was used. Low-flow separations (~0.05 mL/min) enabled efficient ionization and 10 min run times. An accelerated 60 °C stress-testing protocol demonstrated that SEC-MS can semi-quantitatively monitor oligomerization dynamics, complementing UV-based quantification and revealing transient species not resolved by UV alone. The method showed acceptable linearity, precision, and sample stability, and comparison with SEC-RALS/LALS confirmed molecular-weight trends across aggregation states. Overall, the developed SEC-UV/HRMS workflow provides a rapid, sensitive, and widely accessible approach for UV-based quantification of monomer- and HRMS-based characterizing protein aggregation in research and quality control in pharmaceutical laboratories. Full article

Circulating tumor cells (CTCs) represent a powerful, minimally invasive window into tumor biology and disease evolution. Technological progress over the past decade has markedly improved the ability to isolate, preserve, and interrogate viable CTCs, transforming them from simple prognostic markers to functional tools for precision oncology. Advances in microfluidic platforms, immunomagnetic enrichment, aptamer-based capture, and nanostructured interfaces have expanded the efficiency and fidelity of CTC recovery, enabling comprehensive molecular profiling and ex vivo analysis. These innovations have paved the way for the development of CTC-derived preclinical models, including xenografts, organoids, and chorioallantoic membrane assays, which recapitulate patient-specific tumor heterogeneity and support individualized drug-sensitivity testing. In this review, we summarize current technologies for CTC isolation, outline recent achievements in functional and pharmacological characterization, and discuss the translational impact of CTC-derived models. We further identify persistent challenges and emerging opportunities, highlighting how integration of multi-omics platforms, artificial intelligence, and standardized workflows may accelerate the clinical implementation of CTC-guided personalized therapy. Full article

Conventional topical therapy for corneal and anterior segment diseases is limited by rapid tear clearance and multilayer corneal barriers, resulting in low bioavailability and the need for frequent dosing. Artificial intelligence (AI) is emerging as a complementary approach that learns quantitative relationships between molecular structure, formulation variables, and ocular performance. In corneal drug delivery, machine learning models have been used to optimize multicomponent formulations and processing conditions; predict key quality attributes such as particle size, zeta potential, encapsulation efficiency and release kinetics; and estimate corneal permeability, retention and ocular irritation risk, thereby reducing experimental burden and guiding safer design. AI can also be coupled with mechanistic ocular pharmacokinetic/pharmacodynamic models to translate formulation attributes into predicted tissue exposure. Finally, inverse design approaches enable the discovery of new carriers and devices, illustrated by machine learning-guided peptide carriers and smart contact lens platforms that combine sensing with on-demand drug release. Despite these advances, current datasets remain small and heterogeneous, external validation and benchmarking against conventional workflows are limited, and uncertainty quantification and interpretability must be addressed to enable clinical translation. This review summarizes corneal barriers and delivery platforms, critically evaluates where AI provides measurable value across design, characterization and performance and highlights data and validation priorities needed for trustworthy AI-enabled corneal therapeutics. Full article

Objective: Although Hu sheep are renowned for their high fecundity, the multi-tissue regulatory networks governing spermatogenesis, particularly within the hypothalamic–pituitary–testicular (HPT) axis, remain poorly understood. This study aimed to elucidate these mechanisms by performing a comparative proteomic analysis of the HPT axis in Hu sheep and three other breeds. Methods: We utilized data-independent acquisition (DIA) proteomics to analyze hypothalamic, pituitary, and testis tissues from 36 samples across four breeds. The experimental workflow included protein extraction, enzymatic digestion, LC-MS/MS, and subsequent bioinformatic analyses, complemented by histological examination. Results: Hu sheep exhibited accelerated testicular development and an earlier onset of spermatogenesis. Comprehensive proteomic profiling identified a total of 10,528 proteins, with 771 differentially expressed proteins (DEPs) detected in the testis. These testicular DEPs were significantly enriched in pathways related to spermatogenesis, the blood–testis barrier, and steroid hormone biosynthesis. Notably, the cAMP signaling pathway was consistently enriched across all three tissues, underscoring its pivotal role in regulating spermatogenesis. Protein–protein interaction (PPI) network analysis further highlighted hub proteins, such as MET, suggesting their potential involvement in somatic cell functions and the spermatogenic microenvironment. Key findings were validated by Western blot analysis. Conclusion: This study is the first multi-tissue proteomic investigation proposing a model in which the high reproductive performance of Hu sheep is potentially linked to the efficient, coordinated regulation of spermatogenesis-related proteins and signaling pathways—particularly in the testis. These findings offer novel insights into the molecular mechanisms of male reproduction in sheep and identify potential targets for future research and breeding applications. Full article

The COVID-19 pandemic underscored the urgent need for rapid, reliable, and safe laboratory workflows that ensure both diagnostic accuracy and biosafety for laboratory personnel. We developed a comprehensive approach for SARS-CoV-2 detection using saliva-based RT-qPCR that spans the entire process from sample transfer to final disposal. This workflow integrates biosafety principles with efficient diagnostic procedures, ensuring safe handling, minimized exposure risks, and reliable molecular testing. Critical components included biosecurity, standardized protocols for sample receipt, secure transfer, safe processing, and environmentally responsible disposal. By applying a holistic safety framework, we not only protected laboratory staff during the pandemic but also established a model that can inform preparedness for future emerging infectious disease threats. This approach demonstrates how laboratory safety and diagnostic efficiency can be simultaneously achieved, offering a reference for institutions seeking to balance biosafety and public health needs in outbreak situations. Full article

Accurate genotyping of small insertions and deletions (InDels; <5 bp) remains technically challenging in routine molecular breeding, largely due to the limited resolution of agarose gel electrophoresis and the labor-intensive nature of polyacrylamide-based assays. Here, we present the Tri-Primer Amplification Refractory Mutation System (TP-ARMS), a simple and cost-effective PCR-based strategy that enables high-resolution genotyping of small InDels using standard agarose gels. The TP-ARMS employs a universal reverse primer in combination with two allele-specific forward primers targeting insertion and deletion alleles, respectively. This design allows clear discrimination of homozygous and heterozygous genotypes using a two-tube PCR workflow. The method showed complete concordance with Sanger sequencing in detecting 1–5 bp InDels across multiple crop species, including rice (Oryza sativa) and quinoa (Chenopodium quinoa). In addition, using a TP-ARMS reduced experimental time by approximately 90% compared with PAGE-based approaches and avoided the high equipment and DNA quality requirements of fluorescence-based assays. The practical applicability of the TP-ARMS was demonstrated in breeding populations, including efficient genotyping of a 3-bp InDel in OsNRAMP5 associated with cadmium accumulation and a 6-bp promoter InDel in OsSPL10 underlying natural variation in rice trichome density across 370 accessions. Collectively, the TP-ARMS provides a robust, scalable, and low-cost solution for precise small InDel genotyping, with broad applicability in marker-assisted breeding and functional genetic studies. Full article

Thrips are cosmopolitan agricultural pests and important vectors of plant viruses, and the increasing coexistence of multiple morphologically similar species has intensified the demand for species-specific molecular identification. However, traditional morphological identification and PCR assays using universal primers are often inadequate for mixed-species samples and field-adaptable application. In this study, we developed a species-specific molecular identification framework targeting a polymorphism-rich region of the mitochondrial cytochrome c oxidase subunit I (COI) gene, which is more time-efficient than sequencing-based COI DNA barcoding, for four economically important thrips species in southern China, including the globally invasive Frankliniella occidentalis. By aligning COI sequences, polymorphism-rich regions were identified and used to design four species-specific primer pairs, each containing a diagnostic 3′-terminal nucleotide. These primers were combined with a PBS-based DNA extraction workflow optimized for single-insect samples that minimizes dependence on column-based purification. The assay achieved a practical detection limit of 1 ng per reaction, demonstrated species-specific amplification, and maintained reproducible amplification at DNA inputs of ≥1 ng per reaction. Notably, PCR inhibition caused by crude extracts was effectively alleviated by fivefold dilution. Although the chemical identities of the inhibitors remain unknown, interspecific variation in inhibition strength was observed, with T. hawaiiensis exhibiting the strongest suppression, possibly due to differences in lysate composition. This integrated framework balances target specificity, operational simplicity, and dilution-mitigated inhibition, providing a field-adaptable tool for thrips species identification and invasive species monitoring. Moreover, it provides a species-specific molecular foundation for downstream integration with visual nucleic acid detection platforms, such as the CRISPR/Cas12a system, thereby facilitating the future development of portable molecular identification workflows for small agricultural pests. Full article

The rapid evolution of CRISPR/Cas technology has transformed genome editing across biological systems in which zebrafish have emerged as a powerful vertebrate model for functional genomics and disease research. Due to its transparency, genetic similarity to humans, and suitability for large-scale screening, zebrafish is an appropriate system for translating molecular discoveries into biomedical and environmental applications. Thereby, this review highlights the recent progress in zebrafish gene editing, targeting innovations in ribonucleoprotein delivery, PAM-flexible Cas variants, and precision editors. These approaches have greatly improved editing accuracy, reduced mosaicism, and enabled efficient F0 phenotyping. In the near future, automated microinjections, optimized guide RNA design, and multi-omics validation pipelines are expected to enhance reproducibility and scalability. Although recent innovations such as ribonucleoprotein delivery, PAM-flexible Cas variants, and precision editors have expanded the zebrafish genome-editing toolkit, their benefits are often incremental and context-dependent. Mosaicism, allele complexity, and variable germline transmission remain common, particularly in F0 embryos. Precision editors enable defined nucleotide changes but typically exhibit modest efficiencies and locus-specific constraints in zebrafish. Consequently, rigorous validation, standardized workflows, and careful interpretation of F0 phenotypes remain essential. This review critically examines both the capabilities and limitations of current zebrafish gene-editing technologies, emphasizing experimental trade-offs, reproducibility challenges, and realistic use cases. Full article

Characterizing and simulating complex reservoirs, particularly unconventional resources with multiscale and non-homogeneous features, presents significant bottlenecks in cost, efficiency, and accuracy for conventional research methods. Consequently, there is an urgent need for the digital and intelligent transformation of the field. To address this challenge, this paper proposes that the core solution lies in the deep integration of physical mechanisms and data intelligence. We systematically review and define a new research paradigm characterized by the trinity of digital cores (geometric foundation), physical simulation (mechanism constraints), and artificial intelligence (efficient reasoning). This review clarifies the core technological path: first, AI technologies such as generative adversarial networks and super-resolution empower digital cores to achieve high-fidelity, multiscale geometric characterization; second, cross-scale physical simulations (e.g., molecular dynamics and the lattice Boltzmann method) provide indispensable constraints and high-fidelity training data. Building on this, the methodology evolves from surrogate models to physics-informed neural networks, and ultimately to neural operators that learn the solution operator. The analysis demonstrates that integrating these techniques into an automated “generation–simulation–inversion” closed-loop system effectively overcomes the limitations of isolated data and the lack of physical interpretability. This closed-loop workflow offers innovative solutions to complex engineering problems such as parameter inversion and history matching. In conclusion, this integration paradigm serves not only as a cornerstone for constructing reservoir digital twins and realizing real-time decision-making but also provides robust technical support for emerging energy industries, including carbon capture, utilization, and sequestration (CCUS), geothermal energy, and underground hydrogen storage. Full article

Accurate potential energy surfaces (PESs) are the prerequisite for precise studies of molecular dynamics and spectroscopy. The permutationally invariant polynomial neural network (PIP-NN) method has proven highly successful in constructing full-dimensional PESs for gas-phase molecular systems. Building upon over a decade of development, we present CQPES v1.0 (ChongQing Potential Energy Surface), an open-source software package designed to automate and accelerate PES construction. CQPES integrates data preparation, PIP basis generation, and model training into a modernized Python-based workflow, while retaining high-efficiency Fortran kernels for processing dynamics interfaces. Key features include GPU-accelerated training via TensorFlow, the robust Levenberg–Marquardt optimizer for high-precision fitting, real time monitoring via Jupyter and Tensorboard, and an active learning module that is built on top of these. We demonstrate the capabilities of CQPES through four representative case studies: CH₄ to benchmark high-symmetry handling, CH₃CN for a typical unimolecular isomerization reaction, OH + CH₃OH to test GPU training acceleration on a large system, and Ar + H₂O to validate the active learning module. Furthermore, CQPES provides direct interfaces with established dynamics software such as Gaussian 16, Polyrate 2017-C, VENUS96C, RPMDRate v2.0, and Caracal v1.1, enabling immediate application in chemical kinetics and dynamics simulations. Full article

Integrating the advantages of machine learning with the rapidly accumulating high-throughput sequencing data facilitates our capacity for biological discovery and the advancement of molecular medicine. In recent years, bulk RNA-seq technology has established itself as a cost-effective and widely used method for obtaining complete transcriptome profiles of test samples, enabling the identification of key cancer-associated expression patterns. Various machine learning algorithms, in turn, enable the development of informative diagnostic and prognostic models, ensuring the efficient processing of high-dimensional RNA-Seq data. The convergence of these methods shows great promise for oncology. In this narrative review, we describe bulk RNA-Seq-based ML models in oncology as a complete workflow from data preprocessing to model validation. We provide practical recommendations for algorithm selection and study design, and discuss bulk RNA-Seq deconvolution as a cost-effective alternative to single-cell RNA-Seq for analyzing tumor cellular composition. These insights offer a practical guide for developing reproducible diagnostic and prognostic models with translational potential. Full article

Background: Rheumatoid arthritis (RA) is a systemic chronic inflammatory autoimmune disease causing progressive joint destruction, resulting in significant morbidity and increased mortality. Despite advances in treatment, current pharmacological options, including NSAIDs, DMARDs, and biological agents, have limitations in tissue repair and can lead to severe side effects. Objectives: This study aims to explore drug repurposing as a viable approach to identify novel therapeutic agents for RA by utilizing existing FDA-approved drugs. Methods: We applied an integrated computational strategy that uniquely combines network pharmacology with molecular docking and dynamics simulations. The process began with the construction of a protein–protein interaction (PPI) network from 2723 RA-associated genes, which identified five central targets: TNF-α, IL-6, IL-1β, STAT3, and AKT1. We then built protein–drug interaction (PDI) networks by screening 2637 FDA-approved drugs against these targets. Critically, the top candidates from this network analysis were not just docked but were further validated using 100 ns molecular dynamics simulations to thoroughly evaluate binding affinity, complex stability, and interaction dynamics. Results: This multi-tiered computational workflow identified Rifampicin, Telmisartan, Danazol, and Pimozide as the most promising repurposing candidates. They demonstrated strong binding affinities and, importantly, formed stable complexes with TNF-α, IL-6, IL-1β, and STAT3, respectively, in dynamic simulations. The key innovation of this study is this sequential funnel approach, which integrates large-scale network data with atomic-level simulation to prioritize high-confidence drug candidates for RA. Conclusions: In conclusion, this study highlights the potential of repurposing FDA-approved drugs to target key proteins involved in RA, offering a cost-effective and time-efficient strategy to discover new therapies. Full article

Background: Autism spectrum disorder (ASD) and related neurodevelopmental conditions are a significant public health concern, with diagnostic delays hindering timely intervention. Traditional assessments often lead to waiting times exceeding a year. Advances in artificial intelligence (AI) and biomarker-based screening offer objective, efficient alternatives for early identification. Objective: This review synthesizes the latest evidence for AI-enabled technologies aimed at improving early ASD identification. Modalities covered include eye-tracking, acoustic analysis, video- and sensor-based behavioral screening, neuroimaging, molecular/genetic assays, electronic health record prediction, and home-based digital applications or apps. This manuscript critically evaluates their diagnostic accuracy, clinical feasibility, scalability, and implementation hurdles, while highlighting regulatory and ethical considerations. Findings: Across modalities, machine learning approaches demonstrate strong accuracy and specificity in ASD detection. Eye-tracking and voice-acoustic classifiers reliably differentiate for autistic children, while home-video analysis and Electronic Health Record (EHR)-based algorithms show promise for scalable screening. Multimodal integration significantly enhances predictive power. Several tools have received Food and Drug Administration clearance, signaling momentum for wider clinical deployment. Issues persist regarding equity, data privacy, algorithmic bias, and real-world performance. Conclusions: AI-enabled screeners and diagnostic aids have the potential to transform ASD detection and access to early intervention. Integrating these technologies into clinical workflows must safeguard equity, privacy, and clinician oversight. Ongoing longitudinal research and robust regulatory frameworks are essential to ensure these advances benefit diverse populations and deliver meaningful outcomes for children and families. Full article