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Stem Cell Research

Latest AI and machine learning research in stem cell research for healthcare professionals.

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How does CBT work? Causal discovery modelling locates the mechanisms of change in cognitive behavioural treatment for eating disorders

Background: Cognitive behavioural therapy (CBT) is the most frequently used and recommended therapy program for mental health conditions, including for eating disorders. Despite decades of use, little is known about how change is produced in CBT across mental illnesses, and there has been no empirical validation of the causal structure of change for CBT treatment of eating disorders. Progress in o...

A Multimodal Multiomics Machine Learning (MMM) approach for biomarker discovery and acceleration of clinical trial readiness for childhood-onset neurological disorders

Background Childhood neurodegenerative disorders are usually rare, genetic, and life-limiting. Whilst targeted approaches present huge potential, significant hurdles include disease rarity, geographical dispersion of patients, funding, clinical trial design, and execution. Crucially, the paucity of robust biomarkers and objective measures of disease progression hampers evaluation of efficacy, drug...

Neocortical astrocyte diversity stems from distinct developmental origins

Key regulators of neural network activity in multiple advanced cognitive processes and essential components of the blood-brain barrier, astrocytes con...

Deep learning representations of human Immune Health for precision immunology

The human immune system is composed of [~]30-50 distinct cell types, each of which can exist in different states of activation or differentiation. Ind...

Machine learning combining FIT with up to 1,025 clinical variables: limited referral reduction but potential for faster diagnosis

Background The faecal immunochemical test (FIT) is central to triaging symptomatic patients with suspected colorectal cancer (CRC) in UK primary care,...

Spatio-temporal 3D Mapping of Mouse Cerebellar Vascularization during Embryonic Development

Despite major advances in the study of cerebellar neurogenesis, cerebellar angiogenesis during embryogenesis remains poorly described. Recent advances...

Selectivity Drives Efficiency: Dataset Pruning for Visual Place Recognition

Recent visual place recognition (VPR) studies have increasingly relied on large-scale datasets to train more robust and discriminative models. Althoug...

Jul 16 2026 2607.14897v1
Utilising Large Language Models for the Automated Mapping of Medical Research to Translational Stages

Classification of research articles according to translational research stages enables funding bodies, academic and medical institutes, and policymake...

Explainable machine learning for the prediction of motor fluctuations and Levodopa-induced dyskinesias in Parkinson's disease

Background: Motor complications, such as motor fluctuations and Levodopa-induced dyskinesias (LID), significantly impair quality of life in persons wi...

Data-driven trajectories of atrophy explain clinical heterogeneity across Lewy body diseases

Background: Lewy body diseases (LBD) collectively share alpha-synuclein Lewy pathology, yet present wide clinical heterogeneity, with overlapping moto...

Multimodal profiling for prediction of primary resistance to anti-PD-(L)1 therapy in advanced non-small-cell lung cancer: the prospective PIONeeR biomarkers study

Background Pretreatment prediction of primary resistance to anti-PD-(L)1 therapy in advanced non-small-cell lung cancer (NSCLC) remains an unmet clini...

Managing AI-Enabled Uncertainty in Clinical AI Deployment: Mixed-Methods Study of Governance, Workflow, and Organizational Learning in an ICU Decision Support Pilot

BackgroundHealth care organizations are increasingly required to make strategic decisions about artificial intelligence (AI) systems before their clin...

Safe Redosable Low-Immunogenic In Vivo CAR-T Therapy for B Cell Malignancies and Solid Tumors

In vivo CAR-T cell therapy eliminates manufacturing complexities associated with ex vivo autologous approaches, but safety concerns have limited adopt...

Artificial Intelligence in Medical Imaging With Emphasis on Generative and Foundation-Based Methods: A Bibliometric Analysis of Global and United Kingdom Research, 2017-2025

Background: Artificial intelligence (AI), including generative and foundation-based methods, has rapidly expanded within medical imaging research. How...

Causally measuring aging and rejuvenation through transcriptomic damage

Aging is caused, fully in large part, by the progressive accumulation of damage, yet quantifying age-related damage across tissues and conditions rema...

Students' Perceptions of an AI-Enhanced Ethics Learning Platform: A Pilot Study on Interprofessional Healthcare Education

Introduction: Generative artificial intelligence (AI) can produce realistic clinical scenarios on demand and deliver immediate, individualized feedbac...

A next-generation electronic frailty index leveraging deep learning on unstructured health records extends risk prediction across the full frailty spectrum

Background: Existing electronic frailty indices (eFI) are typically based on structured data and designed for older adults. We developed an eFI that i...

Sequence-to-function modeling uncovers the context-specific grammar of Drosophila chromatin insulation

Chromatin is organized into self-interacting topologically associating domains partitioned by boundary elements that insulate adjacent domains and res...

Inferring Cell Fate Trajectories in Time-Resolved Metabolic RNA Labeling data

Single-cell RNA sequencing provides high-resolution snapshots of cellular states but lacks direct information about transcriptional dynamics. Metaboli...

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