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An intelligent evaluation framework for cultural communication strategies based on the T Spherical Fuzzy CURLI MCDM method
Single-cell analyses of liver and blood reveal distinct immune cell signatures of HBV-related HCC patients
Subspace communication in the hippocampal–retrosplenial axis
Efficacy of heat-treated postbiotic Lacticaseibacillus rhamnosus in patients with functional bowel disorders: a randomized, double-blind, placebo-controlled clinical trial
Abstract Functional bowel disorders (FBDs) are characterized by chronic abdominal discomfort, altered bowel habits, and bloating, impairing quality of life. Current treatments, including dietary interventions and laxatives, have limited effectiveness and raise safety concerns. Although probiotic-based interventions have gained attention, their mechanisms remain poorly understood. This randomized, double-blind, placebo-controlled pilot trial tested the heat-treated postbiotic Lacticaseibacillus rhamnosus IDCC 3201 (RHT) over an 8-week period in patients with FBDs (RHT, n = 19; placebo, n = 15). Outcomes were measured using the IBS Symptom Severity Scale (IBS-SSS) and IBS Quality of Life (IBS-QOL) questionnaire. Results suggested that the RHT group showed reductions in IBS-SSS scores, alongside improvements in IBS-QOL and bowel activity across physical and psychosocial domains. Gut microbiota profiling revealed decreased Klebsiella pneumoniae and increased beneficial taxa, including Fusicatenibacter saccharivorans and Bacteroides caccae . Metabolomics analysis revealed progressive alterations in the RHT group, with clear distinction from baseline by week 8. Amino acid metabolism-related metabolites increased, whereas inflammation-associated eicosanoids decreased. These findings suggest that RHT alleviates FBD symptoms and improves quality of life by modulating gut microbiota and fecal metabolome, supporting its potential as a postbiotic-based therapeutic strategy.
GW250114 reveals signatures of post-merger black-hole horizon
Interfacial reconfiguration for low-shear sliding and wear suppression in WS2-mediated boundary lubrication
Astrocytes connect specific brain regions through plastic networks
Exploring the pathways between personality traits, alexithymia, and resilience among medical students and interns: a cross-sectional study in Egypt
Abstract Resilience is essential for medical students, yet how personality traits are associated with resilience through alexithymia remains underexplored. This cross-sectional study, conducted from December 2023 to May 2024 using printed surveys and convenience sampling, included 598 participants (412 medical students and 186 interns). The survey included the Connor-Davidson Resilience Scale (CD-RISC-10), the Toronto Alexithymia Scale (TAS-20), and the NEO Five-Factor Inventory (NEO-FFI). Pearson’s correlation ( r ), multivariable linear regression, and path analysis were conducted to examine factors associated with resilience. Resilience was negatively correlated with neuroticism and alexithymia ( r = -0.41 and -0.32, p < 0.001) and positively correlated with extraversion and conscientiousness ( r = 0.29 and 0.36, p < 0.001). Regression identified neuroticism, externally-oriented thinking, agreeableness, and current psychiatric disorder as negative predictors (β = -0.32, -0.14, -0.10, and -0.45 respectively, p ≤ 0.012), while extraversion and conscientiousness were positive predictors (β = 0.14 and 0.24 respectively, p < 0.001). On path analysis, externally-oriented thinking provided a significant indirect statistical link in the associations of conscientiousness (β = 0.02, p = 0.019) and openness (β = 0.03, p = 0.010) with resilience. Addressing neuroticism and alexithymia subcomponents may enhance resilience and support student well-being.
AI tool spots antibiotics that fight drug-resistant gonorrhoea
Association of mean arterial pressure and resting heart rate with mortality in older adults
Analysis of dose reconstruction techniques in pre-treatment QA of large-field RapidArc technique with a 2D detector array
Role and mechanism of AOPPs-induced NOX4-mediated ferroptosis in intervertebral disc degeneration
BlockFedZTA: a trust-aware federated learning framework for secure multi-organizational intrusion detection
Abstract The design of a privacy-preserved intrusion detection system for supply chain networks is challenging because of strict data privacy requirements, heterogeneous data distributions, and unreliable participating nodes. This study proposes BlockFedZTA, a framework that integrates federated learning, XGBoost, trust-aware aggregation, and a lightweight commitment-based integrity verification mechanism. In the proposed approach, each participant trains a local model and shares only a salted SHA-256 commitment without exposing model parameters. The aggregation mechanism assigns weights according to validation performance, reducing the influence of low-quality or potentially malicious updates. Experiments were conducted using a unified dataset containing 100,000 instances and 130 features representing five classes (Normal, DoS, Probe, R2L, and U2R), distributed among three organizations under non-IID conditions. The framework was evaluated under no-drift, moderate-drift, and severe-drift scenarios. Five-fold cross-validation produced average accuracies of 0.966, 0.964, and 0.963, respectively. Statistical analysis confirmed that the trust-aware aggregation strategy significantly outperformed FedAvg under drift conditions ( p < 0.01). Additional comparison with FedAvg, Krum, Multi-Krum, Median Aggregation, Trimmed Mean, FLTrust, and FoolsGold revealed higher performance with an accuracy of 0.96470 in both mild and severe situations of the data drift problem. Moreover, our model was highly resistant to label poisonings, ensuring an accuracy of more than 0.962 even with a high level of 60%. Scaling analysis with up to 50 clients again confirmed high performance and superiority over FedAvg with moderate communication overhead. For example, communication costs went up from 1640.74 KB to 20451.89 KB per round; meanwhile, the number of audit log bytes needed rose only from 3.40 KB to 174.02 KB. Repeated runs of the algorithm ensured a stable average accuracy of 0.9647 with a standard deviation of 0.0002. Thus, BlockFedZTA ensures a robust federated IDS approach in a supply chain environment.