BackgroundCognitive impairment is a primary contributor to disability among older adults, with growing evidence identifying central adiposity as an adjustable risk factor for neurodegeneration, but comprehensive predictive models integrating central adiposity indices for mild cognitive impairment (MCI) in aging individuals remain underexplored.ObjectiveThis study aimed to establish and verify a model for predicting the risk of MCI in elderly population by incorporating anthropometric indices.MethodsWe calculated five central adiposity indices using anthropometric measurements from 2464 United States adults aged 60 years or older (National Health and Nutrition Examination Survey, 2011-2014). Cognitive performance was assessed using three standardized neuropsychological tests. A random assignment placed participants into either a training (n = 1725) or a validation (n = 739) set. Furthermore, the data from participants in the 2011 wave of the China Health and Retirement Longitudinal Study served as an external validation cohort (n = 536). LASSO-selected predictors were employed to inform multivariable logistic regression modeling.ResultsPositive linear relationships were found between three anthropometric indices-A Body Shape Index (ABSI), Conicity Index (CoI) and Weight-Adjusted-Waist Index (WWI)-with MCI risk (p < 0.05, p for nonlinearity > 0.05). The nomogram incorporating ABSI demonstrated strong discriminative capacity (training AUC = 0.861; internal validation AUC = 0.826; external validation AUC = 0.798), precise calibration, and good clinical utility.ConclusionsThe risk of MCI was independently linked to central adiposity indices (ABSI, WWI, and CoI). The nomogram incorporating ABSI provided a validated, clinically applicable prediction model for initial screening of MCI in older populations.

The limited efficacy and slow onset of current antidepressants underscore the urgent need for novel therapeutic strategies. Here, we established a novel zebrafish inflammation-based screening model and identified 18β-glycyrrhetinic acid (18β-GA) as a potent anti-inflammatory candidate. In a chronic social defeat stress (CSDS) mouse model, 18β-GA demonstrated significant antidepressant effects, which were associated with attenuated neuroinflammation and a shift in microglial polarization toward an anti-inflammatory phenotype. Mechanistically, 18β-GA inhibited the mTOR/p70S6K signaling pathway, leading to the restoration of autophagy and subsequent suppression of NLRP3 inflammasome activation in microglia. Using a transwell co-culture system, we further confirmed that 18β-GA protects neurons from microglia-mediated inflammatory injury. To overcome pharmacokinetic limitations, we developed a nanoliposomal formulation (Nano 18β-GA) that achieved rapid brain accumulation within 0.5h, as visualized by time-dependent in vivo imaging. Remarkably, a single administration of Nano 18β-GA produced significant antidepressant effects, maintained the original mechanism of action, and exhibited a favorable biosafety profile. Together, our work delineates a translational pipeline from natural product discovery to nano-enabled therapy, offering a rapidly acting strategy with substantial translational potential for depressive disorder.

Numerous studies have demonstrated a strong association between gut microbiota dysbiosis and the progression of colitis, potentially mediated by disruptions in microbial tryptophan metabolism. Targeting commensal bacterial tryptophan metabolism may offer a promising therapeutic strategy. However, a broader spectrum of active substances is necessary to diversify engineered probiotics and enhance the efficacy and precision of treatments. In this study, using a dextran sulfate sodium (DSS)-induced murine colitis model for active substance screening, we observed a significant alteration of the gut microbiota structure concurrent with a pronounced decrease in the level of the microbial-derived tryptophan metabolite, 5-hydroxyindoleacetic acid (5-HIAA). Exogenous supplementation with 5-HIAA restored intestinal epithelial barrier integrity and alleviated colitis symptoms. Mechanistically, 5-HIAA activates the aryl hydrocarbon receptor (AhR), leading to enhanced transcription of IL-10 and subsequent modulation of the NF-κB p65/MLCK/pMLC signaling pathway in intestinal epithelial cells. This cascade promotes increased expression of tight junction proteins, thereby improving intestinal barrier function and attenuating colitis. Furthermore, we engineered a facultative anaerobic commensal bacterium capable of delivering 5-HIAA directly to the gut and evaluated its efficacy in a mouse model of colitis. Our findings indicate that targeted modulation of the key microbial metabolite 5-HIAA effectively suppresses the onset and progression of colitis. The use of engineered bacteria for site-specific 5-HIAA delivery represents a novel and promising therapeutic approach for inflammatory bowel disease (IBD).

Background/Objectives: Marine traditional Chinese materia medica (TCMM)—including sulfated polysaccharides (for example, fucoidan), triterpenoid saponins (for example, frondoside A), bioactive peptides, and nacre/pearl matrices—occupies a chemical space distinct from terrestrial remedies. Growing oncologic interest requires a clear link between traditional efficacy claims and modern mechanistic and translational standards. A growing number of studies have reported antitumor activities of marine TCMMs in vitro, in vivo, and in early clinical settings. This review synthesizes these anticancer data within a framework spanning mechanism → multi-omics → quality (critical quality attributes, CQAs) → clinical, and uses fucoidan, frondoside A, and nacre as anchor exemplars for vertical translation along this continuum. Methods: We performed a narrative, mechanism-anchored review of marine TCMMs in oncology, focusing on four major chemotypes: sulfated polysaccharides, triterpenoid saponins, peptides/matrix proteins, and mineral–organic composites. Evidence extraction covered active-constituent characterization by LC/HPSEC-MALS/MS/NMR, high-content phenotypic screening (HCS), network pharmacology and in-silico modelling, and in-vivo efficacy studies with pharmacokinetics/pharmacodynamics (PK/PD) and bioanalytics. We then organized multi-omics readouts—transcriptomics, proteomics/phosphoproteomics, metabolomics/lipidomics, epigenomics/post-translational modifications, and immunomics/tumor microenvironment profiling—into mechanism-oriented “minimal biomarker sets” that link CQAs, exposure, pathway engagement, and preclinical or early clinical endpoints. Results: Marine TCMMs converged on four core anticancer mechanisms. First, triterpenoid saponins such as frondoside A promoted mitochondrial p53–caspase apoptosis, restrained pro-survival autophagy, and in some settings also pushed cells toward ferroptosis, with key nodes including SLC7A11, GPX4, ACSL4, FSP1, FDX1, and redox-sensitive actin networks. Second, several agents reshaped the tumor immune microenvironment (TIME): fucoidan increased natural killer cell cytotoxicity in humans, whereas trabectedin and lurbinectedin depleted monocytes and tumor-associated macrophages and restored T-cell activity, converging on STAT3, NF-κB, and prostaglandin EP signaling as shared immune hubs. Third, many studies reported reduced HIF-1α and VEGF signaling, lower matrix metalloproteinase expression, and inhibition of epithelial–mesenchymal transition, linking regulated cell death, immunity, and metabolism through the angiogenesis–invasion axis. Finally, multi-omics datasets supported compact biomarker panels that can track pathway engagement, capture exposure–response relationships, and guide mechanism-based trial design. Conclusions: Marine TCMMs couple polypharmacology with omics tractability. Convergent evidence supports mechanism-anchored development using defined CQAs, exposure measurements, and biomarker endpoints, particularly along ferroptosis and tumor-microenvironment axes and in hypoxia/angiogenesis–invasion biology. Key gaps include material heterogeneity, incomplete exposure–response characterization, and a limited number of randomized clinical trials with standardized products. Addressing these gaps through harmonized analytics, transparent reporting, and cautious, biomarker-driven trials may enable reliable integration of marine TCMMs into precision oncology.

In retinoblastoma (RB), the search for newer therapeutics is a thrust area for better disease management, and there is a lack of in vitro models that recapitulate the clinical scenario. Hence, we aim to establish RB patient-derived spheroid culture and evaluate its utility as an in vitro drug screening model. Initially we employed different media compositions to optimize the spheroid culture establishment. Sanger sequencing and Multiplex ligation dependent probe amplification were done to confirm the RB1 genotype of the spheroids. Drug responsiveness was assessed using CellTiter-Glo® luminescence-based cytotoxicity assay and correlated with patients clinicopathological details. Spheroids were successfully cultured using DMEM-F12 media with defined growth factors in nine tumors and one vitreous sample. RB1 genotyping of spheroids showed the similar mutational landscape as the tumor samples. Based on the in vitro drug responsiveness to carboplatin, five cultures were segregated as chemoresistant in which four of them were clinically known high-risk RB. One of the spheroids was further expanded and used to evaluate the in vitro efficacy of an FDA-approved drug to overcome resistance. Therefore, these RB patient-derived spheroids closely mirroring the clinicopathological features of the patient samples, represent a promising tool for drug screening and personalized RB therapy development.

Microfluidic technologies have emerged as powerful tools in pharmaceutical sciences, offering precise control over fluid handling, mixing, and mass transfer at the microscale. These unique characteristics have enabled significant advances in controlled pharmaceutical formulation and drug screening, addressing key limitations of conventional bulk-scale methods, such as poor reproducibility, high material consumption, and limited physiological relevance. This review provides a comprehensive overview of recent progress in microfluidics for pharmaceutical applications, with a focus on formulation control, nanocarrier and advanced drug delivery system fabrication, controlled release, and stability enhancement. The role of microfluidic platforms in high-throughput and physiologically relevant drug screening, including cell-based and organon-chip models, is critically discussed. Furthermore, the integration of microfluidics with emerging technologies such as automation, artificial intelligence, digital microfluidics, and advanced analytical tools is highlighted as a driver of datadriven and continuous pharmaceutical development. Key challenges related to scalability, standardization, regulatory acceptance, and ethical considerations are also examined. Finally, future perspectives emphasize the growing translational potential of microfluidics in continuous manufacturing, personalized medicine, and precision therapeutics. Overall, this review underscores the transformative impact of microfluidic technologies on modern pharmaceutical formulation and drug screening, positioning them as integral components of next-generation drug development pipelines.

Syringe Service Programs (SSPs) have proven effective in reducing HIV transmission among people who inject drugs (PWID). However, early HIV detection remains an underutilized opportunity for preventing transmission and curbing outbreaks. This paper emphasizes the impact of implementing routine, opt-out HIV screening within SSPs as a strategy to enhance early detection and prevention efforts. In 2022, the IDEA Exchange Tampa, Florida's second legally sanctioned SSP, transitioned from an opt-in to a routine opt-out screening model. This shift identified six anonymous participants with acute and untreated HIV, including three confirmed seroconversions (within 59 days of a prior negative test), two individuals diagnosed during their first HIV screening at the SSP with 'unknown infection duration', and one previously diagnosed individual who had been out of care. Through a robust linkage-to-care framework, all six participants were linked to care within an average of 7.5 days (100% linkage rate). Clinical counseling sessions revealed that these individuals were part of an interconnected 26-person risk network involving needle-sharing and sex exchange behaviors. These findings underscore the critical role of systematic opt-out screening in SSPs for accelerating case detection and facilitating rapid linkage-to-care. Expanding routine, opt-out HIV screening at SSPs is essential to improving detection, curbing outbreaks, and advancing the goals of the Ending the HIV Epidemic (EHE) initiative.

AI-enhanced non-invasive diagnosis of chronic kidney disease using LIBS of fingernail biomarkers.

Developing a stable and reliable universal model for different sample characteristics is a complex undertaking. During spectral calibration, variable shifts arising from sample characteristics and environmental conditions can compromise algorithmic accuracy. To address substantial errors in sample detection for universal models, a wavelength screening algorithm is incorporated into the pre-processing stage of model transfer. This removes wavelengths associated with irrelevant variables and information correction, retaining only effective variables as the final feature subset. This facilitates spectral data correction by model transfer algorithms, thereby realising universal modelling. Selecting CARS and RF, along with their combined wavelength screening methods, we constructed universal models for different maize traits in conjunction with the DS method. Experimental validation demonstrated that the combined wavelength screening methods outperformed single-wavelength screening models across all evaluation metrics in the universal models. Among 12 spectral datasets for distinct traits, the CARS-RF-DS model constructed by integrating features extracted from outlier sample sets—demonstrated optimal performance. Its correlation coefficient R² reached 0.9603, RMSEP was 0.0102, with an MAE of 0.0080. For the ear grain sample set, R² exceeded 0.93 and RPD surpassed 3.8, indicating that the CARS-RF-DS model possesses a degree of universality and generalisability.

Objectives Aerobic gymnastics involves repeated high-impact technical elements, yet discipline-specific evidence linking modifiable physical fitness and training exposures to injury risk remains fragmented. Thus, the aim of this systematic review was to synthesize evidence on physical fitness attributes and training-related factors associated with injury risk in aerobic gymnasts, and to appraise study designs, injury definitions, and measurement approaches. Methods A systematic search was conducted in PubMed, Scopus, and Web of Science Core Collection. It were eligible studies including aerobic gymnasts that reported quantitative associations between modifiable fitness and/or training exposures and injury outcomes. Risk of bias was executed with QUIPS. Results Twelve studies were deemed eligible. Injury/pain burden was substantial and typically involved the lower limbs and, in some samples, the lumbar region. Greater exposure (training volume/competition density) was consistently associated with injury occurrence in some studies, and lower-limb alignment/loading-distribution measures differed between injured and uninjured aerobic gymnasts, although screening models showed limited sensitivity. Psychosocial factors and limited prevention/healthcare uptake were also reported. Conclusion Current evidence suggests injury risk in aerobic gymnastics is multifactorial, with exposure and selected alignment/loading measures as recurrent correlates. Prospective, standardized surveillance and robust analytic approaches are needed. Systematic review registration https://osf.io/d6pmj .

Development and validation of a machine learning model for frailty screening using claims data in Japan: the Longevity Improvement & Fair Evidence Study.

Larval zebrafish as a translational model for neurotoxicity screening of emerging psychoactive substances.

The Intersection of Social Determinants of Health and Informatics: A Review of Strategies to Improve Care Delivery.

Based on the umami-transformer machine learning model: Screening of umami peptides from ultrasound-assisted enzymatic hydrolysis of shiitake mushrooms and study on their flavor mechanism.

Chemometric screening models to verify the geographical origin of traditional Brazilian cheese

Design and evaluation of DNA aptamers with "stem-loop-chain" structures for spectrum neutralization of ricin and abrin.

Study on the dissolution and recovery mechanism of bacterial cellulose by green ionic liquid designed based on COSMO-RS.

Computationally guided amino acid-based deep eutectic solvents for one-pot nitrogen functionalization of lignin.

A functionally relevant model for interrogating brain tumor-endothelial cell interactions.

Tailoring poly(2-oxazoline)s for drug delivery: predicting API compatibility via COSMO-RS.