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Surface plasmon resonance (SPR) is a widely adopted and highly informative biophysical technique in fragment-based drug discovery, enabling the detection and characterization of weak and transient molecular interactions that initiate the progression from fragments to lead compounds. Owing to its sensitivity, low sample consumption, and compatibility with both direct-binding and functional assay formats, SPR plays a central role within integrated fragment discovery workflows, supporting affinity estimation, hit validation, and mechanistic interrogation when applied with appropriate experimental design. Recent advances in sensor-chip chemistries, buffer and solvent optimization, clean-screening strategies, and high-throughput instrumentation have further enhanced the robustness and reliability of SPR-based fragment assays. When combined with complementary biophysical and structural approaches, including thermal shift analysis, calorimetry, mass spectrometry, thermophoresis, NMR spectroscopy, X-ray crystallography, and cryo-electron microscopy, SPR contributes to a multidimensional validation framework that enables informed hit triage, structural interpretation, and structure-guided optimization. This review provides a practical guide to the use of SPR in fragment-based discovery, covering conceptual principles, experimental design, data analysis strategies, and best practices for minimizing artifacts and improving data quality. Beyond technical considerations, SPR illustrates a broader principle of molecular discovery: even weak interactions, when measured rigorously and interpreted in context, can yield insights that guide the transformation of fragment hits into biologically meaningful and therapeutically relevant molecules.

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disorder characterized by unpredictable flares and variable clinical quiescence. Despite validated clinical indices like the British Isles Lupus Assessment Group (BILAG) score, reliable molecular biomarkers for monitoring disease activity remain limited, particularly in underrepresented South Asian populations. Weaimed to identify arobust molecular framework to distinguish SLE flares from remission in an Indian cohort. We conducted a discovery-phase study in an Indian cohort (n=16) stratified by Easy-BILAG scoring. Plasma proteomic profiling via LC-MS/MS was integrated with targeted cytokine quantification using the Olink Proximity Extension Assay (PEA). Differential expression and network analyses delineated immune-regulatory, hypoxic-vascular, and myeloid-activation pathways. A Random Forest classifier was trained on selected biomarkers and evaluated using leave-one-out cross-validation (LOOCV), permutation testing, and bootstrapped AUROC confidence intervals, with model interpretability assessed by SHAP values. Data are available via ProteomeXchange with identifier PXD075349. Proteomic comparison identified a compact panel of proteins distinguishing flare from remission, characterized by a molecular polarization; flare states exhibited upregulation of COL18A1 and CSF1 (vascular and myeloid activation), while remission showed sustained expression of cytoskeletal scaffolding and immunoregulatory components, including FLNA, SH3BGRL3, and IGHG4. Cytokine analyses identified coordinated chemokine modules (CXCL9, CCL2, CCL3, and CCL13) preferentially upregulated during flare. The machine-learning model achieved robust internal discrimination with a mean AUROC of 0.96. Notably, a COL18A1 normalized protein expression cut-off yielded 100% specificity and 87.5% sensitivity, acting as an objective 'rule-in' adjunct for active disease. Normalized protein expression (NPX) cut-off yielded 100% specificity and 87.5% sensitivity, acting as an objective "rule-in" adjunct for active disease. This study establishes a parsimonious 5-protein biosignature of candidate leads (COL18A1, HYOU1, IGHG4, FLNA, and SH3BGRL3) that effectively captures the multifactorial pathophysiology of SLE flare. By anchoring discovery in a systematically under sampled Indian population, this work enhances global diversity in lupus biomarker research and establishes a scalable, AI-driven framework for precision assessment of disease activity.

The advent of single-cell RNA sequencing (scRNA-seq) has enhanced our ability to study cellular heterogeneity. Accurately identifying distinct subpopulations and their defining markers is critical for understanding tissue diversity. We introduce CORTADO, a hill-climbing optimization framework for marker discovery and clustering refinement. CORTADO maximizes differential expression, minimizes redundancy via cosine similarity, and enforces sparsity for interpretability. By using CORTADO-selected markers to inform the cell-type identification process, an iterative refinement approach markedly increases the Adjusted Rand Index (ARI), a metric that quantifies how well the clustering assignments align with gold-standard cell-type annotations. Benchmarking across brain, immune, spatial, and cancer datasets confirms that CORTADO delivers biologically relevant markers and consistently outperforms state-of-the-art methods in both marker discovery and clustering accuracy.

Femoral hernias are relatively uncommon in clinical practice but are of considerable clinical significance because they are associated with a higher risk of strangulation happening. Even rarer is the discovery of the appendix within the hernia sac, a condition known as De Garengeot hernia, and within this already highly unlikely scenario, having a tumor within the hernia is exceedingly rare. Among such cases, Goblet cell carcinoma (GCC) represents an incredibly rare occurrence. We present the case of an 83-year-old man who had undergone transabdominal preperitoneal (TAPP) repair for an incarcerated femoral hernia. During the operation, the incarcerated part was found to be the tip of the appendix, which made it necessary to perform a laparoscopic appendectomy (LA). Subsequent histopathological examination revealed GCC invading the muscularis propria, and given the patient's advanced age, significant comorbidities, and the family's stated preference, a right hemicolectomy was not performed. At the 1-year follow-up, computed tomography (CT) and tumor markers indicated no sign of recurrence. For older individuals with co-existing health conditions and those at the early stage of GCC, a cautious management approach backed by active monitoring might be a sensible option following a thorough evaluation of risks and benefits, and this instance provides a realistic view on dealing with such rare and medically complex scenarios.

Tuberculosis (TB) persists as a formidable global health challenge, particularly due to the emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) variants that limit the effectiveness of existing therapies. These variants limit therapeutic options, prolong treatment duration and increase the risk of treatment failure. Additionally, the antitubercular drug discovery remains relatively limited; moreover, the rise in drug-resistant strains necessitates the need to identify and develop novel drug candidates that can overcome these resistance patterns. This study aims to design novel InhA inhibitors that can bind to and inhibit InhA and circumvent the resistance pathway in the KatG enzyme of Mycobacterium tuberculosis. We sourced an extensive library of 276 518 natural products from the LOTUS database and screened the compounds through a series of rigorous computational approaches such as drug-likeness filtration, molecular docking, MM-GBSA analysis, and ADMET prediction. We developed a machine learning model using a Message Passing Neural Network (MPNN). The MPNN was trained to predict bioactivity profiles of 31 597 natural products against the InhA enzyme. Molecular dynamics simulations further confirmed the stability of these interactions over 100-nanoseconds. Out of the screened compounds, four novel drug candidates exhibited strong binding affinities with binding energies of -12.204 kcal/mol, -11.926 kcal/mol, -11.624 kcal/mol, and -11.548 kcal/mol, respectively, surpassing the co-crystallized ligand (-8.895 kcal/mol), and the standard drug, Isoniazid (-12.204 kcal/mol). The top hit molecules demonstrated high and considerable structural stability during molecular dynamics simulations. Additionally, the pharmacokinetic profile of LTS0161715 exhibited low toxicity. positioning LTS0161715 for further investigation. These research findings elucidate the potential of direct InhA inhibitors, particularly LTS0161715, as a promising drug candidate for anti-tubercular drug development, while also highlighting the need for further optimization to enhance safety and efficacy.

Covering: up to Oct, 2025Cinnamoyl-containing non-ribosomal peptides (CCNPs) are characterized by a cinnamoyl group, which is substituted at the ortho position with an alkyl side chain and linked to a peptide scaffold, representing a structurally unique and pharmacologically promising family of microbial natural products. Here, the current knowledge of their chemical diversity, biological activities, and intricate biosynthetic mechanisms is systematically summarized. Their characteristic biosynthetic logic centers on the conserved coupling of a highly reducing type II polyketide synthase (hrPKS II) with a non-ribosomal peptide synthetase (NRPS). Particular emphasis is placed on two fundamental principles: first, the conserved pathway for cinnamoyl benzene ring formation, initiated by an isomerase and proceeding via 6π-electrocyclization catalyzed by three distinct enzymes classes, namely, YsfF (4-hydroxybenzoyl-CoA thioesterase (4-HBT)-like enzymes), Kcn17-19 (DsrE family enzyme components) and YssX/YsfX, thereby providing a reliable signature for genome mining. Second, the remarkable diversity of cinnamoyl tailoring, peptide modification, and NRPS assembly collectively offers substantial opportunities for structural diversification through combinatorial biosynthesis strategies. Elucidating these biosynthetic features has shifted the research paradigm from discovery to rational engineering. However, this shift has not yet translated into comprehensive pharmacological evaluation, as most reported activities have been only briefly assessed, and extensive studies are still needed to fully realize their therapeutic potential. This review not only consolidates recent advances but also provides a strategic framework for future research aimed at unlocking the full potential of these fascinating natural products.

Alzheimer's disease (AD) diagnosis requires analysis of diverse data types to capture the heterogeneous factors underlying its development and progression. Magnetic resonance imaging (MRI) and positron emission tomography (PET) noninvasively measure brain structure and neuronal activity, respectively, and can serve as early indicators of AD onset and future progression. We propose V3D-MMoE, an interpretable framework to adaptively integrate incomplete multimodal 3D neuroimaging for AD diagnosis prediction and biomarker discovery. It goes beyond prior approaches by leveraging (1) a sparse mixture-of-experts formulation to account for variation in the importance of different modality combinations, (2) modality alignment to enhance cross-modal learning, and (3) cross-encoders to dynamically handle missing modalities. When applied to MRI and PET scans to predict two-year AD diagnosis, V3D-MMoE outperformed state-of-the-art multimodal 3D neuroimaging methods. Interpretability analyses revealed subject-specific MRI and PET biomarkers consistent with the known biology of AD. Ablation experiments demonstrated the benefit of leveraging multimodal neuroimaging.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal human malignancies, in part due to late diagnosis and the lack of robust molecular biomarkers. Although aberrant DNA methylation is a defining feature of PDAC, most studies rely on single cohorts, limiting reproducibility and biological interpretation. Here, we performed an integrative multi-cohort analysis of genome-wide DNA methylation profiles from four independent PDAC datasets generated on Illumina EPIC and HumanMethylation450 platforms, comprising 364 tumors and 99 normal controls. Using a harmonized preprocessing cross-platform normalization strategy, we identified hundreds of CpG sites that were consistently differentially methylated across all cohorts. Integration with pancreatic chromatin-state annotations, hydroxymethylation profiles, and protein-protein interaction networks was used to contextualize recurrent DNA methylation changes. This analysis showed that hypermethylation preferentially targets promoter- and enhancer-associated regulatory elements linked to neuronal and developmental gene networks. To assess predictive relevance, we trained interpretable and non-linear machine-learning models with strict cross-cohort evaluation, and combined SHAP-based feature attribution with deep neural network saliency analysis. Intersection of statistical, biological, and machine-learning evidence identified a compact 18-CpG candidate signature that stratified tumor and normal samples across the analyzed cohorts. Together, this study demonstrates that PDAC methylation remodeling exhibits consistent and reproducible patterns across cohorts that are biologically interpretable. Furthermore, the study shows that integrating chromatin context, network topology, and interpretable machine learning can help identify candidate epigenetic biomarkers with translational potential.

Fissidens is one of the most diverse genera of mosses in China. Most members of this genus are terrestrial, with a few preferring tree trunks as substrates. Fissidens pokhrensis is newly discovered in China, occurring at the base of a tree in Yunnan Province. Morphological descriptions and photographs of this species are provided. In addition, an updated identification key is presented for Chinese species of Fissidens characterized by semilimbate leaves and papillose or mammillose laminal cells, including F. pokhrensis.

The emergence of multidrug-resistant Staphylococcus aureus (MDR-S. aureus) demands innovative strategies to identify robust microbial producers of potent antibiotics. This study characterizes Streptomyces virginiae THA-960, a soil-derived actinomycete, as a producer of the known anti-MDR-S. aureus antibiotic. Phenotypic screening showed THA-960's efficacy against clinical MDR-S. aureus isolates, with MICs as low as 0.08 mg/mL, outperforming conventional antibiotic-producing Streptomyces. Time-kill assays and SEM confirmed rapid bactericidal action via cell wall disruption. Complete genome sequencing revealed a rich biosynthetic potential, housing 31 specialized metabolite gene clusters. Phylogenomic analysis of 521 Streptomyces genomes delineated S. virginiae into distinct groups and showed that the amycomicin biosynthetic gene cluster is conserved within a specific taxonomic group, providing a genomic roadmap for targeted strain selection. Crucially, Ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Qtof-MS) analysis detected a metabolite feature putatively assigned as amycomicin in THA-960. This integrated multi-omics study provides a phylogenomic framework for the targeted exploitation of Streptomyces strains against antimicrobial resistance.

The expansion of biomedical ontologies with relevant, high utility concepts remains a significant challenge in biomedical knowledge representation, particularly for rapidly evolving fields like Environmental Determinants of Health (EnDOH). In this work, we evaluate the effectiveness of using LLMs in support of ontology expansion, comparing Retrieval-Augmented Generation (RAG) with non-RAG concept extraction from the medical literature. Candidate concepts were generated across 15 targeted topics using category-specific prompts. The quality of candidate concepts was assessed through semantic similarity to existing EnDOH concepts and sub-hierarchies. This design enables both a comparative analysis of RAG versus non-RAG concept extraction approaches and the identification of topic-level concept alignment with the ontology. Our results quantify the comparative strengths and weaknesses of RAG vs non-RAG concept extraction and offer a replicable methodology for effectively extracting potentially useful candidate concepts from the literature for the purpose of inclusion in biomedical ontologies.

Antimicrobial peptides (AMPs), distinguished by their broad-spectrum antibacterial activity and low propensity to induce antibiotic resistance, show the promising potential for infectious diseases. However, the direct development of AMPs into therapeutics remains challenging due to their poor proteolytic stability and limited in vivo antibacterial activity. This study employed our pioneering solid-phase and side-chain guanidyl-construction (SSG) stapling technique to systematically screen and modify the natural AMP Aurein1.2, resulting in a series of guanidyl-stapled peptides A1 to A9. The optimal guanidyl-stapled peptide A4 exhibited the significantly improved α-helical content and proteolytic stability than the linear counterpart, as well as potent in vitro antibacterial activity and acceptable hemolysis. Importantly, A4 demonstrated markedly enhanced therapeutic performance for the treatment of the Staphylococcus aureus infected wound on mice, highlighting its strong potential as the next-generation antimicrobial agent.

This study aimed to evaluate the bioactive compounds identified in fenugreek-paitan-turmeric (FPT) by liquid chromatography-high resolution mass spectrometry analysis and their interactions with dipeptidyl peptidase-4 (DPP-4) and sodium-glucose cotransporter-2 (SGLT2), which are associated with glucose regulation. Computational analyses were conducted, including molecular docking to screen potential compounds and molecular dynamics simulations, where 250 ns trajectories were evaluated to assess complex stability, and further validated using molecular mechanics Poisson-Boltzmann surface area (MM/PBSA), dynamic cross-correlation matrix (DCCM), and principal component analysis (PCA). Molecular docking identified (+)-ar-turmerone and β-carboline-3-carboxylic acid as compounds with moderate binding affinities toward DPP-4 (-6.9 kcal/mol) and SGLT2 (-8.6 kcal/mol), respectively. Molecular dynamics simulations indicated a more favorable interaction with SGLT2 than DPP-4, although structural fluctuations were observed, particularly after 100 ns. MM/PBSA analysis identified a favorable binding free energy for SGLT2 of -42.135 kJ/mol, whereas DPP-4 had a positive value of +53.907 kJ/mol, suggesting unfavorable binding. These findings were supported by DCCM analyses and PCA, which indicated more constrained motions in the SGLT2 complex relative to DPP-4. In this study, preliminary computational insights were obtained into the interactions of FPT-derived compounds with SGLT2 and DPP-4. The results suggested a more favorable interaction with SGLT2, whereas the binding to DPP-4 appeared less stable. Further experimental validations, including in vitro and in vivo studies, are needed to validate their efficacy in the management and pathogenesis of diabetes. Image 1.