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Daily briefing: The rise of evidence-based medicine
Remaining useful life prediction of electronic power components based on stacked denoising autoencoders and temporal fusion networks
Screening psychobiotic bacteria in the human colonic microbiota under high perceived stress
Abstract Psychobiotic bacteria hold promise for modulating the gut-brain axis, particularly under stress-induced dysbiosis. In this study, nine psychobiotic formulations were evaluated using a novel simplified batch version (M-batches) of the Simulator of the Human Intestinal Microbial Ecosystem, including the mucosal compartment (M-SHIME ® ), inoculated with fecal samples from highly stressed donors. Several treatments, particularly those containing Heyndrickxia coagulans [ATB-BCS-042] with either Levilactobacillus (Lv.) brevis [THT-030-201] or Lactiplantibacillus plantarum [THT-030-702], led to significant increases in Bifidobacterium and Akkermansia muciniphila . Modulations in butyrate-producing taxa were observed with H. coagulans + Lactobacillus ( L. ) gasseri [THT-031-301] and Enterococcus faecium [ATB-EFM-030] + Lactobacillus helveticus [THT-031-102]. Combinations of H. coagulans with either Lv . brevis , L. gasseri , or Lactobacillus johnsonii [THT-032-401] facilitated lactobacilli colonization. Dopamine levels increased with E. faecium + Lacticaseibacillus ( Lc. ) paracasei [THT-031-901] and H. coagulans + L. johnsonii , whereas other metabolites, such as Short Chain Fatty Acids (SCFA) and ammonia, remained largely unchanged across treatments. Metabolic outputs also included aryl hydrocarbon receptor (AhR)-activating metabolites, with the strongest effect seen for H. coagulans + L. gasseri , suggesting involvement with stress-related host signaling pathways. Among all probiotics, cocktails containing H. coagulans with either Lv. brevis or L. gasseri produced the most consistent and multifaceted effects. These findings underscore psychobiotic formulations that enhance gut microbiota resilience and boost metabolites potentially influencing host pathways under stress. Using fecal microbiota from highly stressed donors offers a promising in vitro approach that better reflects stress-related gut ecosystems for translational microbiome-brain axis research.
The role of medium-intensity training in promoting functional independence and cognitive function in older adults: a randomized controlled trial
Behavioral and psychological factors associated with pre-frailty and frailty in adults aged 40–65 years: a cross-sectional study
Abstract This study identifies psychosocial and lifestyle factors associated with pre-frailty in adults (40–65) and examines the indirect associations between ageing perceptions and frailty status through the potential explanatory mechanism of psychological distress. A cross-sectional study was conducted with 196 community-dwelling volunteers. Data were collected via online surveys using the FRAIL Scale, AAQ-SF, PHQ-4, and B-PSQI-3. Statistical analyses included hierarchical logistic regression with prespecified sociodemographic, clinical, lifestyle, and psychosocial covariates, model diagnostics, and Hayes’ PROCESS Macro (Model 4). More than half of the participants (57.1%) were pre-frail or frail, with pre-frailty observed in 44.9% of this sample. In the fully adjusted model including education and marital status, poor sleep quality (OR = 1.27, p = 0.011), high psychological distress (OR = 1.28, p = 0.003), and physical ageing perception (OR = 0.86, p = 0.003) were independently associated with higher odds of frailty. Final-model diagnostics showed no problematic multicollinearity (maximum VIF = 1.47), acceptable calibration (Hosmer-Lemeshow p = 0.762), and good discrimination (AUC = 0.852). Mediation analysis revealed that perceiving ageing as a ‘psychosocial loss’ was indirectly associated with frailty status through its statistical co-occurrence with psychological distress severity (Indirect Effect: 0.035, 95% CI [0.019, 0.055]). Subjective ageing perceptions and mental health markers show critical concurrent risk associations with pre-frailty in middle age, even among individuals with high educational attainment. Preventive strategies may benefit from integrating sleep hygiene and psychological support alongside lifestyle modifications.
Medium-adaptive wideband near-field antenna for microwave detection of internal cavities in plant stems
Abstract This paper presents a novel medium-adaptive wideband near-field antenna for early detection of internal cavities in plant stems, including branches and small trunks. Unlike conventional antennas designed for free-space operation, the proposed antenna is explicitly engineered to operate in close proximity to a lossy, anisotropic, and dispersive cylindrical medium representing wood tissues. A physics-based electromagnetic model of the stem is incorporated into the design process, enabling accurate optimization under realistic dielectric loading conditions. The antenna consists of a compact quasi-planar dipole with blended arms integrated with a medium-adaptive dual-ring balun that ensures balanced current excitation and stable impedance matching under strong near-field loading. Both simulation and experimental measurements demonstrate wideband impedance matching to a 50 Ω source over the 2.0–3.0 GHz frequency range. Surface current distribution and specific absorption rate (SAR) analyses confirm efficient electromagnetic coupling into the stem tissues with minimal radiation leakage. To evaluate the sensing capability of the proposed design, a conceptual two-element antenna system is introduced as a feasibility study for cavity detection. The detection performance is assessed through a sensitivity-driven framework based on variations in both self- and mutual-scattering parameters. A comprehensive sensitivity analysis is conducted to quantify the response of the system to changes in cavity diameter, radial position, and angular location. The results demonstrate that while the reflection coefficient is primarily sensitive to near-surface inhomogeneities, the mutual coupling between antenna elements provides strong and reliable sensitivity to internal cavity characteristics. Based on the sensitivity analysis, an optimal operating frequency band centered at 2.76 GHz and an appropriate antenna clearance are identified to maximize detection performance. The proposed antenna and sensing methodology are further validated through experimental measurements, confirming the consistency with numerical results. Simulation results demonstrate cavity-detection sensitivity, while experimental measurements validate the antenna impedance matching and mutual-coupling characteristics. The compact geometry of the antenna enables scalable multi-element configurations, establishing a practical framework for non-destructive, microwave-based monitoring of internal tree degradation in agricultural and forestry applications.
Uncertainty-guided mamba network for efficient medical image segmentation with evidential deep learning
Exploring microbial-derived chondroitin sulfate as a suppressor of microglial inflammation and pyroptosis
Deep learning single-cell analysis for cytologic evaluation of oral potentially malignant disorders
Abstract Oral potentially malignant disorders (OPMDs) such as leukoplakia and erythroplakia may harbor dysplasia or progress to oral squamous cell carcinoma (OSCC), yet visual inspection alone is unreliable for risk assessment. Cytology offers a minimally invasive adjunct, but conventional approaches depend on manual feature extraction and subjective review. Herein we report a deep learning (DL) object detection model that directly classifies four cell phenotypes: differentiated squamous epithelial (DSE) cells, small round (SR) cells, leukocytes, and lone nuclei. The DL model produced cytology-derived parameters that correlated strongly with histopathologic diagnoses across 692 subjects with OPMDs, OSCC, and healthy controls, including declining DSE cell proportion and increasing SR cells and leukocytes with disease severity ( p < 0.0001). The oral cancer numerical index (OCNI) achieved AUROC values up to 0.99 for malignant versus healthy lesions, with excellent reliability (intra-class correlation coefficient ≥ 0.96). This reproducible, minimally invasive test provides a robust platform for early detection and surveillance of OPMDs.
Influence of milk type, fat reduction and ripening time on the proteolysis, textural properties and volatile compounds in acid- and heat-coagulated Circassian cheese
The multiple mediating effects of emotional distress and inhibitory control between physical activity and adolescent internet addiction
Spatial-aware energy learning for out-of-distribution segmentation in railway intrusion detection
Mosquito-mediated dissemination of pyriproxyfen from pyriproxyfen-based painted surfaces causes metamorphosis disruption in mosquito aquatic stages
Fractal Sierpinski triangle block division for retina-based glaucoma detection using an optimized hybrid deep learning model
Abstract Glaucoma is a primary cause of permanent vision loss, and it often gets worse without anybody noticing. This makes it very important to find it early to stop vision loss. Manually evaluating retinal fundus images frequently necessitates considerable effort and is prone to observer-dependent discrepancies. To address these constraints, a new automated method for detecting glaucoma is presented. It combines fractal-inspired Sierpinski triangle spatial decomposition with multi-scale triangular segmentation to reliably identify clinically important areas of the retina. Handcrafted descriptors that include statistical, frequency-domain, wavelet, morphological, and texture-uniformity data are taken from small areas, giving a detailed and unique picture of the structures in the retina. A hybrid deep learning system that combines bidirectional LSTM, bidirectional GRU, and CNN with Bi-LSTM models that have attention layers captures spatial-temporal connections while highlighting essential visual cues for diagnosis. The Harris Hawks Optimizer, Grey Wolf Optimizer, Red Fox Optimizer, and Social Feature Optimizer are all examples of meta-heuristic algorithms that improve the feature subset. The HHO-based version gives the best results. A thorough examination using benchmark datasets shows that the system works very well, with 98.48% accuracy on Drishti-GS, 98.99% on Origa, 97.44% on RimOne-V2, 97.80% on HVD Binary-Class, 99.02% on HVD-Advance, 98.05% on HVD-Early, and 97.11% on HVD Multi-Class trials. The proposed system shows high reliability and stability in glaucoma diagnosis, delivering consistent performance across multiple datasets. It provides a scalable, non-invasive, and efficient solution for automatic detection, supporting doctors in early treatment and improving patients chances of recovery.