Improving large language models for miRNA information extraction via prompt engineering.

Objective: Large language models (LLMs) demonstrate significant potential in biomedical knowledge discovery, yet their performance in extracting fine-grained biological information, such as miRNA, remains insufficiently explored. Accurate extraction of miRNA-related information is essential for understanding disease mechanisms and identifying biomarkers. This study aims to comprehensively evaluate the capabilities of LLMs in miRNA information extraction through diverse prompt learning strategies.

Graph retrieval augmented large language models for facial phenotype associated rare genetic disease.

Many rare genetic diseases have recognizable facial phenotypes that serve as diagnostic clues. While Large Language Models (LLMs) have shown potential in healthcare, their application to rare genetic diseases still faces challenges like hallucination and limited domain knowledge. To address these challenges, Retrieval-Augmented Generation (RAG) is an effective method, while Knowledge Graphs (KGs) provide more accurate and reliable information.

Immunomodulatory effects of anti-diabetic therapies: Cytokine and chemokine modulation by metformin, sodium-glucose cotransporter 2 inhibitors, and glucagon-like peptide-1 receptor agonists (2013-2025).

Diabetes mellitus (DM) is both a metabolic and chronic inflammatory disease, wherein immune dysregulation contributes to multisystem complications. Beyond glycemic control, anti-diabetic agents-Metformin (Met), sodium-glucose cotransporter 2 inhibitors (SGLT-2i), and glucagon-like peptide-1 receptor agonists (GLP-1 RAs)-exert immunomodulatory effects through cytokine and chemokine modulation. This review summarizes mechanistic and experimental findings (2013-2025), showing that Met suppresses pro-inflammatory cytokines (e.

Microbiome-mediated colonization resistance to carbapenem-resistant Klebsiella pneumoniae in ICU patients.

Carbapenem-resistant Klebsiella pneumoniae (CRKP) causes serious intensive care unit (ICU)-acquired infections, yet the mechanisms of microbiota-mediated colonization resistance remain unclear. We analyzed the gut microbiome and metabolic profiles of healthy individuals and ICU patients, distinguishing those with and without CRKP colonization. ICU patients showed distinct microbial communities compared to healthy controls, and CRKP-positive patients exhibited unique microbial and metabolic signatures.

Advancing Alzheimer's Diagnosis with AI-Enhanced MRI: A Review of Challenges and Implications.

Neurological disorders are marked by neurodegeneration, leading to impaired cognition, psychosis, and mood alterations. These symptoms are typically associated with functional changes in both emotional and cognitive processes, which are often correlated with anatomical variations in the brain. Hence, brain structural magnetic resonance imaging (MRI) data have become a critical focus in research, particularly for predictive modeling.

Chrysoeriol Chemosensitized Renal Cell Carcinoma (RCC) Cell Lines to Trigger TRAIL Induced Apoptosis In Vitro by Up-Regulating Pro-Apoptotic and Down-Regulating Anti-Apoptotic Genetic Factors.

Renal carcinoma is a lethal cancer, researched by several studies to get insights into the molecular causes of disease, in order to come up with advanced therapeutic treatments. Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), is an anticancer cytokine posing therapeutic effects to treat cancer. However, certain cancer types including renal cell carcinoma developed resistance towards TRAIL, hence limiting its usefulness in cancer treatment.

Age-related and postmenopausal breast cancer progression and treatment management: The significance of pro-inflammatory cytokines and CXC chemokines.

Older age is one of the leading risk indicators for advanced breast cancer. It is critical to extensively investigate how aging affects breast cancer, considering the increasing rate of population aging. Human body aging and death are caused by cellular senescence and alterations in the aging microenvironment .

A Knowledge-Enhanced Platform (MetaSepsisKnowHub) for Retrieval Augmented Generation-Based Sepsis Heterogeneity and Personalized Management: Development Study.

Background: Sepsis is a severe syndrome of organ dysfunction caused by infection; it has high heterogeneity and high in-hospital mortality, representing a grim clinical challenge for precision medicine in critical care.

Objective: We aimed to extract reported sepsis biomarkers to provide users with comprehensive biomedical information and integrate retrieval augmented generation (RAG) and prompt engineering to enhance the accuracy, stability, and interpretability of clinical decisions recommended by large language models (LLMs).

Methods: To address the challenge, we established and updated the first knowledge-enhanced platform, MetaSepsisKnowHub, comprising 427 sepsis biomarkers and 423 studies, aiming to systematically collect and annotate sepsis biomarkers to guide personalized clinical decision-making in the diagnosis and treatment of human sepsis.

Enhancing human gut health: Global innovations in dysbiosis management.

The microbiota, comprising all the microorganisms within the body, plays a critical role in maintaining good health. Dysbiosis represents a condition resulting from an imbalance or alteration of the microbiota. This study comprehensively investigates the patent literature on dysbiosis over the past 20 years.

A comprehensive review on dietary heterocyclic amines as potential carcinogens: From formation to cancer incidence.

Heterocyclic amines (HCAs) are carcinogenic compounds, formed through complex chemical reactions between amino acids, sugars and creatine in muscle meats, while thermal processing. This review primarily examined food safety concerns related to HCAs in cooked meats, by focusing on their formation, potential health risks and carcinogenic effects. Meanwhile, it explored influencing factors and variations in HCAs formation by meat types and cooking methods.

NDDRF 2.0: An update and expansion of risk factor knowledge base for personalized prevention of neurodegenerative diseases.

Introduction: Neurodegenerative diseases (NDDs) are chronic diseases caused by brain neuron degeneration, requiring systematic integration of risk factors to address their heterogeneity. Established in 2021, Knowledgebase of Risk Factors for Neurodegenerative Diseases (NDDRF) was the first knowledge base to consolidate NDD risk factors. NDDRF 2.

: Advancing Proteomics Data Interpretation through GPT-4o Reannotated Topic Ontology and Data-Driven Analysis.

The interpretation of proteomics data often relies on functional enrichment analysis, such as Gene Ontology (GO) enrichment, to uncover the biological functions of proteins, as well as the examination of protein expression patterns across data sets like the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. However, conventional approaches to functional enrichment frequently produce extensive and redundant term lists, complicating interpretation and synthesis. Moreover, the absence of specialized tools tailored to proteomics researchers limits the efficient exploration of protein expression within specific biological contexts.

Outcome measurement instruments in neurodegenerative diseases: Comprehensive analysis and platform construction.

Objectives: The selection of appropriate outcome measurement instruments (OMIs) in neurodegenerative disease (NDD) researches remains complex and often inconsistent. This study aims to consolidate knowledge on OMIs applied in NDD over the last two decades and to develop outcome measurement instruments in neurodegenerative diseases (NDDOMI), a web-based knowledge platform for OMIs selection.

Methods: We collected clinical trials from the past two decades across six prevalent NDDs from ClinicalTrials.

An explainable dataset linking facial phenotypes and genes to rare genetic diseases.

Distinctive facial phenotypes serve as crucial diagnostic markers for many rare genetic diseases. Although AI-driven image recognition achieves high diagnostic accuracy, it often fails to explain its predictions. In this study, we present the Facial phenotype-Gene-Disease Dataset (FGDD), an explainable dataset collected from 509 research publications.

The comprehensive clinical benefits of digital phenotyping: from broad adoption to full impact.

Digital phenotyping collects health data digitally, supporting early disease diagnosis and health management. This paper systematically reviews the diversity of research methods in digital phenotyping and its clinical benefits, while also focusing on its importance within the P4 medicine paradigm and its core role in advancing its application in biobanks. Furthermore, the paper envisions the continued clinical benefits of digital phenotyping, driven by technological innovation, global collaboration, and policy support.

In Silico ADMET Studies, Molecular Docking and Molecular Dynamics Simulation of Thiadiazole Derivatives for the Identification of Putative HsaA Monooxygenase Inhibitors.

Introduction: The rise of drug-resistant strains of Mycobacterium tuberculosis (Mtb) represents a substantial public health challenge. Current TB treatments involve the combination of several antibiotics and other agents. However, the development of drug resistance, reduced bioavailability, and elevated toxicity have rendered most of the drugs less effective.

MIO: An ontology for annotating and integrating medical knowledge in myocardial infarction to enhance clinical decision making.

As biotechnology and computer science continue to advance, there's a growing amount of biomedical data worldwide. However, standardizing and consolidating these data remains challenging, making analysis and comprehension more difficult. To enhance research on complex diseases like myocardial infarction (MI), an ontology is necessary to ensure consistent data labeling and knowledge representation.

Advanced treatment strategies for high-altitude pulmonary hypertension employing natural medicines: A review.

High-altitude pulmonary hypertension (HAPH) occurs when blood pressure in the pulmonary arteries rises due to exposure to high altitudes above 2,500 m. At these elevations, reduced atmospheric pressure leads to lower oxygen levels, triggering a series of physiological responses, including pulmonary artery constriction, which elevates blood pressure. This review explored the complex pathophysiological mechanisms of HAPH and reviewed current pharmaceutical interventions for its management.

Large Language Model-Driven Knowledge Graph Construction in Sepsis Care Using Multicenter Clinical Databases: Development and Usability Study.

Background: Sepsis is a complex, life-threatening condition characterized by significant heterogeneity and vast amounts of unstructured data, posing substantial challenges for traditional knowledge graph construction methods. The integration of large language models (LLMs) with real-world data offers a promising avenue to address these challenges and enhance the understanding and management of sepsis.

Objective: This study aims to develop a comprehensive sepsis knowledge graph by leveraging the capabilities of LLMs, specifically GPT-4.

Predictive model for daily risk alerts in sepsis patients in the ICU: visualization and clinical analysis of risk indicators.

This study introduces a novel Transformer-based time-series framework designed to revolutionize risk stratification in Intensive Care Units (ICUs) by predicting patient outcomes with high temporal precision. Leveraging sequential data from the eICU database, our two-stage architecture dynamically captures evolving health trajectories throughout a patient's ICU stay, enabling real-time identification of high-risk individuals and actionable insights for personalized interventions. The model demonstrated exceptional predictive power, achieving a progressive AUC increase from 0.

Evolutionarily new genes in humans with disease phenotypes reveal functional enrichment patterns shaped by adaptive innovation and sexual selection.

New genes (or young genes) are genetic novelties pivotal in mammalian evolution. However, their phenotypic impacts and evolutionary patterns over time remain elusive in humans owing to the technical and ethical complexities of functional studies. Integrating gene age dating with Mendelian disease phenotyping, we reveal a gradual rise in disease gene proportion as gene age increases.

Translational Informatics Driven Drug Repositioning for Neurodegenerative Disease.

Neurodegenerative diseases represent a prevalent category of age-associated diseases. As human lifespans extend and societies become increasingly aged, neurodegenerative diseases pose a growing threat to public health. The lack of effective therapeutic drugs for both common and rare neurodegenerative diseases amplifies the medical challenges they present.

Exploration of innovative drug repurposing strategies for combating human protozoan diseases: Advances, challenges, and opportunities.

Protozoan infections (e.g., malaria, trypanosomiasis, and toxoplasmosis) pose a considerable global burden on public health and socioeconomic problems, leading to high rates of morbidity and mortality.

Demystifying furan formation in foods: Implications for human health, detection, and control measures: A review.

Furan (C₄H₄O), an unintended hazardous compound, is formed in various thermally processed foods through multiple pathways, raising concerns due to its potential carcinogenicity in humans. The aim of this comprehensive review was to synthesize and evaluate the latest research on furan, from its formation by different precursors to its presence in diverse food matrices, as well as the emerging methods for its detection and mitigation. Emphasizing the toxicity of furan, it explored evidence from in vitro and in vivo studies, including reproductive toxicity, carcinogenic effects, and related biomarkers.