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A physics-informed neural network approach for estimating population-level pharmacokinetic parameters from aggregated concentration data.

The pharmacokinetic literature is rich in aggregated concentration data that contain valuable information, yet tools to extract this information remain limited. This work introduces distributional physics-informed neural networks (D-PINNs), a novel algorithm designed to enable statistical modelling within the PINN framework, allowing recovery of pharmacokinetic parameter distributions at the popul...

IgPose: A Generative Data-Augmented Pipeline for Robust Immunoglobulin-Antigen Binding Prediction.

MOTIVATION: Predicting immunoglobulin-antigen (Ig-Ag) binding remains a significant challenge due to the paucity of experimentally-resolved complexes and the limited accuracy of de novo Ig structure prediction. RESULTS: We introduce IgPose, a generalizable framework for Ig-Ag pose identification and scoring, built on a generative data-augmentation pipeline. To mitigate data scarcity, we constructe...

GFASNet: Gait feature attention-driven deep sequential network for dementia-related gait pattern analysis.

Deep learning models leveraging human activity data, such as gait, have shown promise for dementia prediction. However, their limited interpretability...

Towards improved decision making of unruptured intracranial aneurysms using automated segmentation from MRA-TOF with iterative pseudo labeling.

OBJECTIVE: To enable accurate 3D morphological assessment and support clinical decision making, DIVA-seg: a Deep learning-based method for Intracrania...

Predicting disease-specific histone modifications and functional effects of non-coding variants by leveraging DNA language models.

BACKGROUND: Epigenetic modifications play a vital role in the pathogenesis of human diseases, particularly neurodegenerative disorders such as Alzheim...

Predicting neoadjuvant immunotherapy efficacy with machine learning models in non-small cell lung cancer: A systematic review and meta analysis.

BACKGROUND: The response of resectable non-small cell lung cancer (NSCLC) to neoadjuvant immunotherapy is heterogeneous. Machine learning can integrat...

Computational modeling and optimization of scFv-based receptors to support CAR-T design targeting CD19 for enhanced binding robustness and reduced off-target propensity.

Chimeric Antigen Receptor T-cell (CAR-T) therapy has revolutionized the treatment of B-cell malignancies, with CD19 being a primary target due to its ...

Interpretable prognostic modeling of glioblastoma using cross-cohort transcriptome integration and machine learning approaches.

Glioblastoma (GBM) is an aggressive brain tumor with highly variable patient outcomes due to pronounced molecular heterogeneity. Prognosis remains dis...

Best Practices for Developing Linear Models With Multiple Explanatory Variables.

Linear models, including t-test, ANOVA, regression, ANCOVA, and generalized linear models, are foundational tools in statistical analysis. For large d...

Survival After Left Ventricular Assist Device: A Society of Thoracic Surgeons National Intermacs Database Risk Model Assessment.

BACKGROUND: Several Left Ventricular Assist Device (LVAD) risk models exist however, currently there is only one LVAD available, therefore we assessed...

Minor salivary gland biopsy in Sjogren's disease: a diagnostic and prognostic tool toward precision medicine.

Minor salivary gland biopsy occupies a distinctive position in the evaluation of Sjögren disease (SjD), offering diagnostic and prognostic insights th...

Beyond handcrafted radiomics in oncologic imaging: Innovations in deep, explainable, multi-site and multi-omics radiomics approaches.

Radiomics seeks to convert medical images into quantitative biomarkers capable of capturing tumor phenotype, microenvironment, and underlying biology....

Explainable ML for ACS culprit plaques: a multidimensional CCTA model highlighting hemodynamic increment.

To develop an interpretable, multi-parameter machine learning (ML) model that integrates plaque morphology, composition, perivascular inflammation, an...

MS-HIENet: multi-scale hybrid implicit-explicit registration network.

Medical image registration serves as a cornerstone for precision diagnosis and treatment, particularly for dynamic organs like the lungs, where high d...

Effect of Brain-Computer Interface-Controlled Ankle Robot Training on Post-Stroke Motor Rehabilitation and Resting QEEG Neuroplasticity: A Randomized Controlled Trial.

BACKGROUND: Persistent post-stroke ankle impairment hinders functional recovery. Brain-computer interface (BCI)-controlled ankle robot show rehabilita...

Development of retrieval-augmented generation-based large language model for drug-induced liver injury using Livertox data.

BACKGROUND: Idiosyncratic DILI is a complex clinical challenge requiring timely and accurate decision support. LiverTox, curated by the National Insti...

Automatic field-of-view planning for magnetic resonance shoulder imaging using Deep Learning.

INTRODUCTION: Accurate prescription of oblique coronal and oblique sagittal field of views (FOV) is essential for diagnostic shoulder MRI. Manual plan...

Start4All protocol for a Bayesian cost-effectiveness model of tuberculosis screening and diagnosis in seven high burden low-income and middle-income countries.

INTRODUCTION: High costs of screening and diagnostic tests remain a major barrier to timely tuberculosis (TB) identification in resource-limited setti...

Mediated Electrochemical Probing and Machine Learning for Cysteine and Reduced Monoclonal Antibody Quantification.

Biopharmaceutical manufacturing requires robust analytics and process controls throughout production to insure high yield of quality products. New met...

AP-Lab: An AI-Driven Autonomous Pilot-Scale Platform Bridging Materials Discovery and Industrial Manufacturing.

Artificial intelligence (AI) has accelerated materials discovery, yet its translation to industrial manufacturing remains limited due to two critical ...