Stem Cell Research

Implementation of pathogen genomics in clinical microbiology laboratories.

SUMMARYPathogen genomics, including whole-genome sequencing (WGS) and clinical metagenomics, is a transformative technology increasingly being implemented in clinical microbiology, including in hospital laboratories. Pathogen genomics can improve the control of healthcare-associated infections, provide rapid infection diagnosis, and could enable replacement of laborious microbiology tests. To date...

Automated Eosinophil Quantification Using Deep Learning to Predict Therapy Escalation in Pediatric Ulcerative Colitis.

BACKGROUND: Emerging evidence implicates eosinophils as important modulators of disease activity and therapeutic response in ulcerative colitis. Automated image analysis provides a scalable and reproducible approach to their evaluation, overcoming the limitations of manual quantification. METHODS: We developed a U-Net-based deep learning (DL) model to automate eosinophil detection, using whole-sli...

Development of a community home-based dual-supplier cooperation care model for people with dementia based on an intelligent decision-assistance system: protocol of a 1-year randomised controlled trial in China.

INTRODUCTION: Dementia imposes significant care and financial burdens on families and countries globally. While high-quality home-based care is crucia...

Current and emerging therapies for knee osteoarthritis: From conventional approaches to machine learning and stem cell innovations.

Osteoarthritis is characterized by cartilage degradation and joint distortion. The most prevalent version of osteoarthritis, knee osteoarthritis (KOA)...

A machine learning-driven framework integrating cell death and senescence signatures for multi-target drug design and immunotherapy optimization in ovarian cancer.

Ovarian cancer (OC) remains therapeutic challenge due to its complex molecular heterogeneity and therapy-induced adaptive resistance. While non-apopto...

Biological effects of ultrasonic cavitation in cancer therapy: from physics to mechanobiology.

The therapeutic efficacy of systemic treatments in cancer therapy is invariably limited by the biophysical barriers, including vascular endothelial ba...

Brain PET in the era of anti-amyloid-β antibody therapy for Alzheimer disease.

Here the current and emerging roles of brain positron emission tomography (PET) in Alzheimer's disease (AD) in the era of anti-amyloid-β antibody ther...

Identification of Mitochondrial Signature Biomarkers and Molecular Mechanisms in Atherosclerotic Tissues and Blood: Combined Single-Cell and Bulk RNA Sequencing Analysis.

Atherosclerosis (AS) is a leading cause of cardiovascular disease and stroke. Although mitochondria's role in AS is recognized, effective molecular ta...

CrunchLLM: Multitask LLMs for Structured Business Reasoning and Outcome Prediction.

Predicting the success of startup companies, defined as achieving an exit through acquisition or IPO, is a critical problem in entrepreneurship and in...

Integrating multi-omics data for enhanced prognosis prediction in gastric cancer post-neoadjuvant therapy.

BACKGROUND: Neoadjuvant therapy is crucial for locally advanced gastric cancer (LAGC), yet response varies significantly. Traditional models based on ...

MechanoAge, a machine learning platform to identify individuals susceptible to breast cancer based on mechanical properties of single cells.

BACKGROUND: Emerging evidence links cellular ageing and biophysical alterations with cancer susceptibility. Existing breast cancer risk models inadequ...

AI-driven navigated hematoma aspiration vs. conservative treatment for spontaneous intracerebral hemorrhage: study protocol for a randomized controlled trial.

BACKGROUND: Spontaneous intracerebral hemorrhage (ICH) remains one of the most devastating types of stroke, with high mortality and long-term disabili...

Development trends and translational opportunities in self-assembled herbal bioactive materials research.

BACKGROUND: Self-assembled herbal bioactive materials (SAHBMs) represent a rapidly advancing frontier integrating phytochemistry, supramolecular chemi...

Building a Science-Driven Business: How National Institutes of Health Funding Enabled an Evidence-Based Approach to Maternal Mental Health Innovation.

The digital mental health (DMH) industry has grown drastically over the last decade; yet, many DMH products have failed to demonstrate meaningful clin...

A Simulation-Free Radiation Therapy Workflow Using Synthetic Computed Tomography Generated from Diagnostic Magnetic Resonance Imaging for Personalized Hippocampal-Sparing Whole-Brain Treatment.

PURPOSE: The conventional computed tomography (CT)-based consultation to simulation process for hippocampal-sparing whole-brain radiation therapy (HS-...

Detection of young-onset type 2 diabetes using deep learning across primary and secondary care: a nationwide, retrospective cohort study.

BACKGROUND: Once considered a disease observed in older adults, type 2 diabetes is now increasingly seen in youth and adolescence. Young-onset (<40 ye...

Effectiveness of a Fully Automated Mobile Therapeutic Versus a General Chatbot in Reducing Depression and Anxiety and Improving Well-Being: Feasibility Randomized Controlled Trial.

BACKGROUND: Given the increasing prevalence of depression and anxiety disorders and enduring barriers to care, there is a critical need for alternativ...

Divergent macrophage-regulated T cell states determine response to Bacillus Calmette-Guérin vaccine in high-risk bladder cancer.

BACKGROUNDPrimary therapy for high-risk bladder cancer (BCa) is repeated instillations of the tuberculosis vaccine Bacillus Calmette-Guérin (BCG). Alt...

Single-cell co-mapping reveals relationship between chromatin state and gene expression in early zebrafish development.

Establishing a cell type-specific chromatin landscape is crucial for the maintenance of cell identity during embryonic development. However, our knowl...

Deep learning-based early prediction of carotid plaque response to lipid-lowering therapy using longitudinal multimodal ultrasound imaging.

OBJECTIVE: This study aimed to develop and validate a deep learning prediction model using longitudinal multimodal ultrasound imaging for early identi...