Transplantation

A self-orchestrating physics-discovering neural architecture for adaptive and fault-resilient robotic motion control via RPA and digital twins.

To autonomously control a robot's movement in complex and unpredictable environments, adaptive models must be developed that can continuously learn about and refine their future physical dynamics. This paper introduces a Self-Organising Physics Discovered Neural Architecture (SOPDNA) that unites Physics-Informed Neural Networks (PINNs) with Robotics Process Automation (RPA) and Digital Twin DT tec...

A deep learning framework for histopathological analysis of pixel-level extracellular matrix variation in standard H&E-stained images.

Artificial intelligence-driven image analysis has enabled significant advances in digital pathology. However, most approaches have focused on cell or organ structures. This manuscript presents a reproducible deep learning methodology for pixel-level analysis of amorphic patterns in haematoxylin and eosin-stained whole-slide histological images. This study analysed the pixel patterns in the extrace...

A case of artificial intelligence-enhanced diagnostics leading to heart transplantation.

PCT-Anchored Machine Learning for Pre-Culture Identification of Gram-Negative Sepsis in Children: A Four-Site Study.

OBJECTIVE: Procalcitonin (PCT) has moderate accuracy for bacteremia detection but is infrequently used in pediatric emergency departments, partly due ...

Machine Learning Models for Prediction of Response to Therapeutic Plasma Exchange in Pediatric Acute Liver Failure.

BACKGROUND: Therapeutic plasma exchange (TPE) artificially lowers prognostic markers such as INR and bilirubin, complicating timely decisions regardin...

Contrastive Machine Learning to Quantify Hypertensive Multiorgan Damage and Identify New Disease Phenotypes: A Multinational Multimodal Study.

BACKGROUND: Hypertension induces structural and functional damage in multiple organs. Evidence of subclinical damage increases risk of vascular events...

Performance evaluation of AkidaNet converted to spiking domain for the classification of weeds in cotton fields.

The infestation of weeds in cotton fields poses a serious threat to the production of lint. Weeds are eliminated either by spraying weed kill sprays, ...

Machine Learning-Assisted Discovery of Thermally Activated Delayed Fluorescence Emitters.

This study presents a machine learning (ML)-assisted framework for the discovery and screening of novel TADF emitters. A dataset of 366 known compound...

Stem cell-derived extracellular vesicles for rare diseases: A clinical‑bottleneck‑to‑mechanism framework for translation.

Rare diseases impose a disproportionate clinical burden, and yet therapeutic progress is hindered by small cohorts, biological heterogeneity, and limi...

Engineered Neutrophils in Translational Medicine: Gene Editing, Nanotechnology, and AI-Driven Clinical Breakthroughs.

Engineered neutrophils, modified via advanced biotechnological tools, are emerging as pivotal agents in translational medicine. By integrating gene ed...

Integrative multi-omics analysis and machine learning identify M2 macrophage-induced ferroptosis resistance in glioblastoma.

Glioblastoma (GBM) is characterized by profound intratumoral heterogeneity and an immunosuppressive microenvironment that drive therapeutic resistance...

A HRH1-YAP1 feedback loop drives pancreatic cancer progression and predicts therapeutic response.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with a dismal prognosis, characterized by an immunosuppressive tumor microenviro...

Barriers and enablers for publishing results of randomized trials: a cross-sectional survey of principal investigators in sub-Saharan Africa.

OBJECTIVES: To investigate the views and experiences of principal investigators (PIs) based in sub-Saharan Africa (SSA) regarding publication bias of ...

Advanced systems for intraoperative cartilage evaluation and treatment demonstrate early feasibility and a shift towards integrating artificial intelligence: A scoping review.

PURPOSE: To review the current literature evaluating AI and advanced technologies for intraoperative cartilage management. METHODS: A comprehensive se...

Dissecting T-cell exhaustion heterogeneity and immune ecosystem dynamics in colorectal cancer through multi-omics machine learning.

BACKGROUND: Immunotherapy has shown limited efficacy in a substantial subset of CRC patients, yet the mechanisms underlying therapeutic resistance rem...

Body composition radiomics integrated with machine learning to predict prognosis in advanced NSCLC treated with first-line immunochemotherapy and concurrent radiotherapy.

OBJECTIVE: Adipose tissue is a highly heterogeneous and plastic endocrine and immune organ whose characteristics have been linked to prognosis in NSCL...

Development of a Knowledge Map for Managing Symptoms of Graft-Versus-Host Disease in Patients Undergoing Hematopoietic Stem Cell Transplantation.

BACKGROUND: Graft-versus-host disease (GVHD) remains a critical complication affecting patients undergoing hematopoietic stem cell transplantation (HS...

A four-gene signature for diagnosis of acute kidney injury following kidney transplantation.

Acute kidney injury (AKI) is a common and severe complication following renal transplantation, largely driven by ischemia-reperfusion injury (IRI). Ho...

Computational prediction of TCR cross-reactivity: principles, challenges and translational opportunities.

T-cell receptor (TCR) cross-reactivity, whereby a single TCR recognizes multiple peptide-MHC (pMHC) ligands, is essential for immune surveillance but ...

Donor-Independent Metabolomics Enables Bloodstain Age Determination at Crime Scenes.

Determining when bloodstains were deposited remains an unsolved challenge in forensic science, limiting investigators' ability to reconstruct events a...