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Catalytic asymmetric formal nucleophilic C2-substitution of indoles via C2-umpolung toward 2,3’-bisindole atropisomers
Integrated experimental and machine learning investigation of green-synthesized ZnO nanoparticle-dispersed neem biodiesel: effects on diesel engine performance, combustion, and emissions
Heterogeneity and multi-scale dynamics in the molecular bearing of the bacterial flagellum
Abstract The bacterial flagellum is a protein-based rotary machine that drives bacterial motility. It comprises the bacterial flagellar motor (BFM), consisting of a stator which is anchored to the cell wall and a rotor in the cytoplasmic membrane, linked via the flagellar rod to the extracellular hook and filament. We observe passive rotational diffusion of six individual Escherichia coli flagella lacking torque-generating units via polarization microscopy of single gold nanorods attached to the hook, sampled at 250 kHz. Transitions across energy barriers of the 26-fold symmetric LP-ring/rod flagellar bearing exhibit highly non-Poissonian kinetics spanning four orders of magnitude in time scale. At sub-millisecond timescales we observe anomalous ultra-slow diffusion typically associated with disordered systems, despite the ordered crystalline atomic structure of the bearing revealed by cryo-Electron Microscopy. Over longer periods, we observe dynamic shifts in the preferred angular positions, indicating that the bearing’s energy landscape evolves over time.
Clinical and genomic characterization of Influenza A co-infection with SARS-CoV-2 and Influenza B: a respiratory surveillance study in Assam, India
Abstract Influenza and SARS-CoV-2 are the primary contributors to seasonal respiratory infections and frequently co-circulate, creating significant health challenges. The present respiratory surveillance study was conducted in Dibrugarh, Assam, India from January 2025 to August 2025 to investigate the genomic characteristics of circulating viruses and identify potential co-infections. Overall, 4,948 respiratory samples were screened using multiplex real-time PCR, followed by subtyping of Influenza A and Influenza B. Next-generation sequencing (NGS) was performed in selected positives of SARS-CoV-2 and Influenza A. Genomic analysis included mutational profiling, phylogenetic analysis and N-glycosylation site prediction using bioinformatics tools. Two co-infection cases were detected: one involving Influenza A (H3N2) with SARS-CoV-2 (Omicron XFG lineage) and another involving Influenza A (H3N2) with Influenza B (Victoria lineage). Both patients experienced mild illness without hospitalisation. NGS revealed that the Influenza A (H3N2) viruses belonged to clade 3C.2a1b.2a.2a.3a.1 while SARS-CoV-2 sequence was classified under the Omicron XFG lineage. Mutational analysis of the HA gene showed several amino acid differences compared to the reference vaccine strain A/Darwin/6/2021. N-glycosylation analysis predicted conserved sites at positions 79, 181, 262, and 301 in all strains along with an additional predicted site at position 110 in both co-infection cases. Although the co-infection cases presented with mild clinical manifestations, the observed genomic variations indicate a potential role of co-infecting viruses in shaping viral evolution. Given the limited genomic data available from Northeast India, the study underscores the need for sustained large scale follow up and genomic surveillance to monitor emerging mutations and target future vaccine strategies.
Self-filtering monolithic organic/PbS quantum dot photodetector for visible and short-wave infrared selective vision in low-light
Effect of olive paste pH during malaxation on virgin olive oil properties
Multimodal foundation models exploit text to make medical image predictions
Finerenone attenuates myocardial injury and enhances vascular repair via regulation of autophagy, apoptosis, and angiogenesis
PAR3-mediated coordination of hepatocyte proliferation, maturation, and architecture in liver development and regeneration
High-intensity interval training differentially modulates acute BDNF and cognitive responses in young adult males: a randomized crossover trial
Abstract Exercise is widely recognized for its beneficial effects on brain health, yet the extent to which exercise intensity modulates acute neurochemical and cognitive responses remains unclear. Particularly, the role of exercise intensity in shaping brain-derived neurotrophic factor (BDNF), lactate responses, and executive function requires further investigation. This study compared the acute effects of low-intensity continuous training (LICT), moderate-intensity continuous training (MICT), high-intensity interval training (HIIT), and a resting control condition (CTRL) on BDNF levels, blood lactate concentration, and cognitive responses in healthy young adult males. Twelve healthy young adult males completed LICT, MICT, HIIT, and the control condition using a randomized crossover design with a 7-day washout period. Serum BDNF, blood lactate concentration, and executive function assessed by the Stroop Test were measured before and immediately after each experimental condition. HIIT induced significantly greater post-exercise increases in BDNF and lactate compared with all other conditions, while MICT elicited moderate elevations relative to LICT and rest. Lactate responses increased progressively with exercise intensity. Improvements in executive function were observed exclusively following HIIT, reflected by significantly faster Stroop Test completion times. HIIT produced concurrent elevations in lactate and serum BDNF together with improved executive function performance. HIIT may represent an effective acute stimulus for cognitive benefits, with potential relevance for exercise approaches aimed at supporting brain health via neurotrophic signaling. Trial registration: The study was retrospectively registered on ClinicalTrials.gov (identifier: NCT07137611; https://clinicaltrials.gov/study/NCT07137611 ) on 22 August 2025.
High-throughput Raman-activated cell sorting of microalgal genome-wide edited library revealed a regulatory pathway for carotenoid synthesis
Assessment of gabapentin efficacy in patients with KCNQ2-developmental epileptic encephalopathy
Compositionality of social gaze in the prefrontal-amygdala circuits
Joint association of triglyceride-glucose index and atherogenic lipid markers with incident stroke risk
Abstract The triglyceride-glucose (TyG) index and atherogenic cholesterol markers, including non-HDL cholesterol and remnant cholesterol, are significant predictors of atherosclerotic cardiovascular disease (ASCVD). However, the joint effects and predictive value of TyG and atherogenic cholesterol markers for incident stroke remain insufficiently understood. We included participants from the China Health and Retirement Longitudinal Study (CHARLS) enrolled at baseline in 2011 and followed them through 2020. Participants were categorized into four groups according to the median values of the TyG index and each cholesterol marker, with those having both values below the median serving as the reference group. Cox proportional hazards models were used to evaluate the independent and joint associations of TyG and atherogenic cholesterol markers with incident stroke. Restricted cubic spline models were applied to assess dose–response relationships, and receiver operating characteristic (ROC) curve analyses were used to examine predictive performance. A total of 8,544 participants were included (mean [SD] age 59.0 [9.5] years; 52.1% female). 522 incident stroke events occurred during a maximum follow-up of 9.0 years. TyG, non-HDL cholesterol and remnant cholesterol were all independently associated with stroke risk. Compared with participants with both TyG and cholesterol markers below median, those with both markers above median had the greatest stroke risk in fully adjusted models (HR for high TyG and high non-HDL cholesterol: 1.76 [95% CI 1.41–2.20]; HR for high TyG and high remnant cholesterol: 1.45 [95% CI 1.18–1.77]). Adding TyG and each cholesterol marker to traditional risk factors modestly improved risk discrimination, with the model combining TyG and non-HDL cholesterol yielding the highest AUC. The TyG index and atherogenic cholesterol markers were independently and jointly associated with increased stroke risk among middle-aged and older adults. Adding these routinely available metabolic and lipid markers modestly improved discrimination for incident stroke.
Dual-phase eutectic ceramics with improved hardness and toughness via nano-coherent high-entropy oxides
Performance of GPT-based large language models in hepatocellular carcinoma stratification: liver function assessment, BCLC staging, and treatment recommendations
Abstract Large language models (LLMs) like GPT have been proposed to support complex clinical decision-making. This study evaluated the performance of GPT-based LLM in analyzing clinical, radiological, and laboratory data from patients with hepatocellular carcinoma (HCC) to assess liver function, assign BCLC stage, and recommend treatment. Data from 106 HCC patients (82% male, median age 65 [22–86]) were compiled into anonymized integrated reports. Four GPT-versions (4, o1, o3, 5.4) were prompted—using both short and long instructions—to calculate MELD, ALBI, and Child–Pugh scores, assign BCLC stage, and generate treatment recommendations based on current guidelines. Outputs were compared to expert consensus and tumor board decisions. Errors were categorized by type and source. Time and cost analyses compared GPT to clinical staff. All GPT versions achieved high accuracy (> 85%) in liver function assessment, with MELD calculation being the most error-prone. BCLC staging accuracy ranged from 46.2% (version 4) to 84.0% (o3), with misclassification of radiological reports as the main error source. Reasoning-optimized models (o1, o3) performed best for treatment recommendations, achieving an overall accuracy (correct suggestions and acceptable alternatives) of up to 90.6%. In 9–14% of cases, GPT suggestions were retrospectively more guideline-concordant than tumor board decisions. GPT processing was significantly faster and reduced costs by approximately 300- to 1300-fold compared to clinical staff. GPT-based LLMs show potential as decision-support tools for liver function assessment, BCLC staging, and treatment guidance in HCC. Particularly with reasoning-optimized models and detailed prompting, LLMs may serve as valuable adjuncts in multidisciplinary HCC workflows. However, a non-negligible error rate requires expert oversight and further model refinement.