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Mechanistic Language Modeling and Oxygenated 3D Screening Reveal Berberine and Enzalutamide Synergy in Resistant Prostate Cancer.

Resistance to androgen receptor inhibitors remains a primary challenge in prostate cancer treatment, yet identifying synergistic co-therapies is hindered by immense combinatorial search spaces and the limited interpretability of predictive computation models. Here, we developed an integrated discovery-validation axis coupling knowledge-augmented large language models with oxygen-supplemented 3D spheroid assays. By leveraging inherent model stochasticity, our framework measures the degree of consensus across independent predictions to establish a formal metric for predictive accuracy. This prin…

[Advances in multi-source medical data-driven identification and spatiotemporal cluster analysis of acute respiratory infections].

Acute respiratory infections (ARIs) are characterized by high morbidity, strong transmissibility, and non-specific clinical manifestations, … dologies have progressively evolved from keyword matching to deep semantic understanding powered by large language models; regarding multimodal medical data fusion, the review covers data-level, feature-level, and decision-level fusion strategies; and in spatiotemporal cluster analysis, both traditional statistical methods and artificial intelligence-based models are d …

Agentic campaign control for high-throughput de novo binder design.

Progress in artificial intelligence has produced a rapidly growing ecosystem of methods for de novo protein design. With access to many specialized and often complementary tools, how does one use them effectively, especially with a finite compute budget? Here, we introduce Target-adaptive Rescue-Explore-eXploit (T-REX) , an agentic protein binder design framework that orchestrates multiple state-of-the-art protein generative models and structure evaluators. Over the course of a design campaign, T-REX leverages large language model (LLM) agents to reason over accumulating outcomes and decide wh

Where Machine Learning Fails in Predicting Emerging Contaminant Adsorption: A Decision-Oriented Framework for Model Credibility and Transferability.

Adsorption behavior of emerging contaminants (ECs) plays a central role in their environmental fate and the efficiency of artificial removal … ugmentation, physics-informed machine learning (PIML), and automated literature data extraction via large language models. This work provides guidance for prospective ML model developers to build more robust and reliable models, laying the foundation for transformative advances in EC adsorption prediction research and applications.

Assessing Readability and DISCERN Quality of Osteoporosis Education Materials Generated by ChatGPT and Deepseek for Diverse Health Literacy Levels: A Cross-Sectional Study.

BACKGROUND AND AIMS: Large language models (LLMs) are increasingly used for patient education, but their output quality across health literacy levels is unknown. This cross-sectional study compared ChatGPT-4o, ChatGPT-5, DeepSeek-V3.1, and DeepSeek-R2 in generating osteoporosis education materials tailored to low, moderate, and high health literacy. METHODS: Six clinical domains were posed to each LLM, with prompts adapted for three literacy tiers per model (18 outputs per model). Outputs were aggregated into 12 composite texts (4 models × 3 tiers). Three blinded clinicians assessed informati…

AI Methods Tailored to Influenza, RSV, HIV, and SARS-CoV-2: A Focused Review.

Artificial intelligence (AI) techniques-ranging from hybrid mechanistic-machine learning (ML) ensembles to gradient-boosted decision trees, … persistent challenges remain-data heterogeneity, limited model interpretability, hallucinations in large language models (LLMs), and infrastructure gaps in low-resource settings. We recommend standardized open-access data pipelines and integration of explainable-AI methodologies to ensure safe, equitable deployment of AI-driven interventions in future viral-outbreak r …

LabQAR: A Manually Curated Dataset for Question Answering on Laboratory Test Reference Ranges and Interpretation.

Laboratory tests are crucial for diagnosing and managing health conditions, providing essential reference ranges for result interpretation. … ecimen types, and other factors impacting interpretation. We also assess the performance of several large language models (LLMs), including LLaMA 3.1, GatorTronGPT, GPT-3.5, GPT-4, and GPT-4o, in predicting reference ranges and classifying results as normal, low, or high. The findings indicate that GPT-4o outperforms other models, showcasing the potential of LLMs in cl …

Революция онкологии: роль искусственного интеллекта (AI) в качестве антителного дизайна и средств оптимизации.

Антитела играют решающую роль в защите человеческого тела от болезней, включая такие опасные для жизни заболевания, как рак. Они оказывают посреднические услуги в связи с иммунной реакцией на иностранные антигены и, в некоторых случаях, на самоантигены. С течением времени антителные технологии эволюционировали от моноклонных антител (mAbs) до химерика антигенных рецепторов Т (клеток CAR-T), оказывая значительное влияние на биотехнологию, диагностику и лечение. Хотя эти достижения способствовали активизации терапевтического вмешательства, интеграция искусственного интеллекта (АИ) революционизир

Совпадение в аннотации оригинала
Antibodies play a crucial role in defending the human body against diseases, including life-threatening conditions like cancer. They mediate … utionizing antibody design and optimization. This review explores recent AI advancements, including large language models (LLMs), diffusion models, and generative AI-based applications, which have transformed antibody discovery by accelerating de novo generation, enhancing immune response precision, and optimizing therapeutic efficacy. Through advanced data analysis, A …

Optimizing in-context exemplars for a generalist vision-language model to support region of interest selection in ovarian tumor grossing.

Pathological gross examination guides tissue sampling in heterogeneous ovarian tumors, where mixed components can complicate selection of representative regions. We evaluated whether a generalist large vision-language model can support gross image analysis through pathology-informed in-context exemplars. Using Qwen-2.5-Vision-Language-72B, we developed single-component exemplar optimization and histology-guided exemplar optimization to promote component-aware reasoning and region-level risk flagging. In a single-institution retrospective cohort, the optimized eight-shot strategy achieved 89.8%

Unsupervised Classification of English Words Based on Phonological Information: Discovery of Germanic and Latinate Clusters.

Cross-linguistically, native words and loanwords follow different phonological rules. In English, for example, words of Germanic and Latinate origin exhibit different stress patterns, and a certain syntactic structure-double-object datives-is predominantly associated with Germanic verbs rather than Latinate verbs. From the perspective of language acquisition, however, such etymology-based generalizations raise learnability concerns, since the historical origins of words are presumably inaccessible information for general language learners. In this study, we present computational evidence indic

Multimodal Visual Question Answering System for Survival Prognosis in PSMA-Targeted Radiopharmaceutical Therapy.

Large language models show great promise for clinical decision support but generally lack medical imaging integration. Therefore, vision-language models have been proposed, but there is little evidence on their performance, particularly compared with conventional machine learning. Prostate-specific membrane antigen (PSMA)-targeted radiopharmaceutical therapy is a well-established treatment for metastatic castration-resistant prostate cancer (mCRPC), but it is still not known which patients will be good long-term responders. Therefore, this study proposes a physician-inspired visual question an…

Context-aware causal reasoning for explanatory visual question answering.

Explanatory Visual Question Answering (EVQA) is a multimodal reasoning task that answers visually relevant natural language questions and generates user-friendly multimodal explanations. Although current EVQA methods can generate grammatically sound and vocabulary appropriate explanations, they still face the challenge of explanatory illusion (i.e., wrong explanations arrive at correct answers). To address this issue, we propose a novel Context-Aware Causal Reasoning (CACR) algorithm. Specifically, we first leverage a Multimodal Large Language Model and design prompting strategies to generate

Dyslexia associated gene KIAA0319 regulates cell cycle during human neuroepithelial cell development.

Dyslexia, also known as reading disability, is defined as difficulty processing written language in individuals with normal intellectual capacity and educational opportunity. The prevalence of dyslexia is between 5 and 17%, and the heritability ranges from 44 to 75%. Genetic linkage analysis and association studies have identified several genes and regulatory elements linked to dyslexia and reading ability. However, their functions and molecular mechanisms are not well understood. Prominent among these is KIAA0319, encoded in the DYX2 locus of human chromosome 6p22. The association of KIAA0319

Iowa Brain-Behavior Modeling Toolkit: An Open-Source MATLAB Tool for Inferential and Predictive Modeling of Imaging-Behavior and Lesion-Deficit Relationships.

The traditional analytical framework taken by neuroimaging studies in general, and lesion-behavior studies in particular, has been inferential in nature and has focused on identifying and interpreting statistically significant effects within the sample under study. While this framework is well-suited for hypothesis testing approaches, achieving the modern goal of precision medicine requires a different framework that is predictive in nature and that focuses on maximizing the predictive power of models and evaluating their ability to generalize beyond the data that were used to train them. Howe

How different judgment strategies affect accuracy in generative AI text discrimination: BERT-based clustering and hierarchical Bayesian logistic regression.

BACKGROUND: Large language models (LLMs)-generated texts become increasingly similar to human writing. The purpose of this study was to examine (1) how individuals distinguish between human-written and LLM-generated texts, focusing on their judgment strategies; (2) the relationship between judgment strategies and detection accuracy; and (3) the interaction effects of gender, age, and frequency of large language model (LLM) use. METHODS: Participants were asked to judge whether the given texts (e.g., public comments) were written by LLMs or humans and to explain their reasoning using open-ende…

Clinical decision support in hematological malignancies using a case-grounded AI agent.

Multidisciplinary tumor boards integrate longitudinal treatment histories, molecular profiling and rapidly evolving evidence to guide decisions in hematological malignancies, yet access to this level of subspecialty deliberation is increasingly uneven. Here we develop HemaGuide, a locally deployable, modular large language model agent that converts unstructured clinical documents into structured case representations, autonomously routes cases to specialized decision modes ('guideline', 'advanced' and 'molecular') and grounds recommendations in disease-specific guideline flowcharts and a clinic

Iowa Brain-Behavior Modeling Toolkit: An Open-Source MATLAB Tool for Inferential and Predictive Modeling of Imaging-Behavior and Lesion-Deficit Relationships.

The traditional analytical framework taken by neuroimaging studies in general, and lesion-behavior studies in particular, has been inferential in nature and has focused on identifying and interpreting statistically significant effects within the sample under study. While this framework is well-suited for hypothesis testing approaches, achieving the modern goal of precision medicine requires a different framework that is predictive in nature and that focuses on maximizing the predictive power of models and evaluating their ability to generalize beyond the data that were used to train them. Howe

Benchmarking Vision LLMs in fetal ultrasound interpretation: a five-point expert evaluation of standard vs. custom prompts.

PURPOSE: Fetal ultrasound imaging is critical for prenatal care, demanding accurate anatomical interpretation. This study evaluates the potential of Vision Large Language Models (LLMs) in interpreting fetal ultrasound images, exploring whether tailored prompts can enhance performance compared to standard prompts, and assessing their utility in clinical settings. METHODS: Nine fetal ultrasound images were analyzed using six advanced Vision LLMs via the Chatbot Arena platform. Standard prompts were compared against expert-crafted tailored questions. Three expert sonographers assessed the models…

Evaluating Medical Entity Recognition in Health Care: Entity Model Quantitative Study.

BACKGROUND: Named entity recognition (NER) models are essential for extracting structured information from unstructured medical texts by identifying entities such as diseases, treatments, and conditions, enhancing clinical decision-making and research. Innovations in machine learning, particularly those involving Bidirectional Encoder Representations From Transformers (BERT)-based deep learning and large language models, have significantly advanced NER capabilities. However, their performance varies across medical datasets due to the complexity and diversity of medical terminology. Previous st…

Syntactic complexity of written text as a structural linguistic marker of depressive symptoms.

BACKGROUND: Language analysis methods have been increasingly explored for the detection of depressive symptoms. However, current approaches have largely focused on analysing the thematic content of language, while the underlying structure and grammatical composition of language remains underexamined. The current study assessed features of syntactic complexity in written text as potential linguistic markers of depressive symptoms. METHODS: Adult participants (n = 196) experiencing depressive symptoms completed four writing tasks (Personal Biography; Neutral Writing; Narrative Imagery; Letter t