Life Sciences

In this cross-sectional comparative study, 49 second-year dental students completed a 45-item multiple-choice final examination in prosthodontic technology under standardized conditions, the same items were submitted to three AI systems (ChatGPT, Gemini, and DeepSeek), and analysis using ANOVA, Cronbach Alpha, and Tukey HSD showed that DeepSeek achieved perfect scores on high-difficulty questions while ChatGPT showed adaptive improvement across attempts, both significantly outperforming the student cohort, whereas Gemini demonstrated lower and consistent accuracy, with significant differences between models (P=0.036, eta-squared=0.891), leading the authors to argue that unsupervised online summative examinations require critical reevaluation of assessment integrity.

This review systematically examines the molecular mechanisms of perineural invasion (PNI) in pancreatic ductal adenocarcinoma, proposes a unified four-stage model spanning mutual chemotaxis between tumors and nerves, adhesion and invasion at the tumor-nerve interface, extracellular matrix remodeling, and neural plasticity alterations, defines the perineural invasion microenvironment as a neuro-immune privileged sanctuary, and summarizes therapeutic strategies targeting the neuro-immune-tumor axis, ongoing clinical trials, and the applications of multi-omics and artificial intelligence in PNI diagnosis, mechanistic discovery, and therapeutic optimization.

This work constructed PlantRegMoD, an AI-powered integrated multi-omics database dedicated to plant regeneration, hosting 20.54 TB of standardized multi-omics data from 147 projects across 32 plant species and 2,593 samples, establishing a unified hierarchical classification system covering five major categories and nine regeneration models, curating 236 regeneration genes and their 28,190 homologs across 58 representative plant species, and containing 196,423 single cells and over 8.81 million epigenetic peaks, equipped with nine online omics tools and a RAG-based intelligent Q&A system.

The loaded text contains only the paper title, "RaFT-DM: A Residue-Aware Fusion Transformer With Domain-Wise Memory for Accurate Multi-Label Protein Function Prediction," together with IEEE Xplore navigation, account, copyright, and front-end script template content, with no abstract, methods, experiments, or results, so it can only be confirmed that the work proposes a method named RaFT-DM that combines a residue-aware fusion transformer with domain-wise memory for multi-label protein function prediction, while its specific approach and findings cannot be summarized.

This work introduces an intelligent sequential proximity ligation assay (iseqPLA) read out by spinning disk confocal microscopy and 3D reconstruction to profile endogenous NF-κB protein-protein interactions inside single cells, treating clusters of co-localized puncta as a measure of supercomplex spatial organization, and tracks supercomplex dissociation, p65 nuclear translocation, and negative-feedback engagement across cytokine time courses in NIH-3T3 mouse fibroblasts, cystic fibrosis (CF) patient-derived macrophage co-cultures with IMR-90 human fibroblasts, and an independent set of healthy- and CF-donor monocyte-fibroblast co-cultures, reporting three findings: 3D volumetric quantification reduces the variance in nuclear-to-cytoplasmic ratio measurements relative to 2D projections, th

The study evaluates nine LLM variants across three families (GPT-4.1, GPT-5, Gemini 2.5) on three MoleculeNet datasets (Delaney solubility, Lipophilicity, QM7 atomization energy) using a systematic blinding procedure that iteratively reduces available information, complemented by 0-, 60-, and 1000-shot in-context sample sizes, in order to determine whether molecular property prediction reflects genuine in-context regression or verbatim retrieval of memorized target values, and it adds positive and negative controls for the memorization experiments, structural reference baselines for the multi-shot experiments, and bootstrap confidence intervals for all results; it finds no evidence of verbatim retrieval on these legacy benchmarks and shows that blinding exposes conflicts between pre-traine

This thesis explores how sensor signals combined with AI models can quantify equine locomotion and respiration, finding that a single limb sensor can accurately classify terrain type even at low sampling rates, that a combination of head, withers, and pelvis sensors can discriminate between sound and lame strides with performance varying by front or hind limb lameness, that vertical ground forces are better estimated from upper-body sensors than limb sensors, that dynamic respiratory rate can be computed from downsampled audio signals, and that explainable AI confirms models rely on kinematic landmarks similar to those used by veterinarians, while also presenting a data collection platform for harness racing horses and publishing an audio dataset.

Using 29,530 fundus images from a health screening cohort, this study trained a multi-task model to predict retinal age and evaluated the retinal age gap (RAG) in two sub-cohorts, finding that higher RAG was significantly associated with smoking (ex-smokers beta = +0.46 years; current smokers beta = +0.50 years) and clinical diabetes (+2.52 years), and that RAG was significantly higher in eyes with age-related macular degeneration (+0.60 years) and cataract (+1.86 years) than in normal controls.

The authors present MicroClaw, an AI agent that advises on experiment- and system-specific parameters and approaches and collaboratively plans and executes diverse, complex, and reusable imaging workflows across microscope platforms, without requiring expert knowledge.