Kidney Transplantation

Machine learning-based identification of biomarkers and drugs in immunologically cold and hot pancreatic adenocarcinomas.

BACKGROUND: Pancreatic adenocarcinomas (PAADs) often exhibit a "cold" or immunosuppressive tumor mil...

The application of blood flow sound contrastive learning to predict arteriovenous graft stenosis of patients with hemodialysis.

End-stage kidney disease (ESKD) presents a significant public health challenge, with hemodialysis (H...

Improving the diagnostic value of lineup rejections.

Erroneous eyewitness identification evidence is likely the leading cause of wrongful convictions. To...

Coagulation Risk Predicting in Anticoagulant-Free Continuous Renal Replacement Therapy.

INTRODUCTION: Continuous renal replacement therapy (CRRT) is a prolonged continuous extracorporeal b...

Using machine learning to classify the immunosuppressive activity of per- and polyfluoroalkyl substances.

Per- and polyfluoroalkyl substances (PFASs), one of the persistent organic pollutants, have immunosu...

APOD: A biomarker associated with oxidative stress in acute rejection of kidney transplants based on multiple machine learning algorithms and animal experimental validation.

BACKGROUND: Oxidative stress is an unavoidable process in kidney transplantation and is closely rela...

Integrated machine learning screened glutamine metabolism-associated biomarker SLC1A5 to predict immunotherapy response in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) stands as one of the most prevalent malignancies. While PD-1 immune c...

Optimizing post-transplantation outcomes: the role of multi-omics, artificial intelligence, and animal models in addressing immunosuppression-associated hepatotoxicity.

Predicting graft and patient outcomes following kidney transplantation using interpretable machine learning models.

The decision to accept a deceased donor organ offer for transplant, or wait for something potentiall...

The Advent of Artificial Intelligence into Cardiac Surgery: A Systematic Review of Our Understanding.

When faced with questions about artificial intelligence (AI), many surgeons respond with scepticism ...

Machine learning-based prediction of vancomycin concentration after abdominal administration in patients with peritoneal dialysis-related peritonitis.

INTRODUCTION: Peritonitis is a serious complication of peritoneal dialysis (PD), in which insufficie...

A machine learning artefact detection method for single-channel infant event-related potential studies.

. Automated detection of artefact in stimulus-evoked electroencephalographic (EEG) data recorded in ...

Multi-Omics Analysis by Machine Learning Identified Lysophosphatidic Acid as a Biomarker and Therapeutic Target for Porcine Reproductive and Respiratory Syndrome.

As a significant infectious disease in livestock, porcine reproductive and respiratory syndrome (PRR...

Artificial intelligence and machine learning in peritoneal dialysis: a systematic review of clinical outcomes and predictive modeling.

BACKGROUND: The integration of artificial intelligence (AI) and machine learning (ML) in peritoneal ...

Deep learning-enabled classification of kidney allograft rejection on whole slide histopathologic images.

BACKGROUND: Diagnosis of kidney transplant rejection currently relies on manual histopathological as...

Data-driven prediction of continuous renal replacement therapy survival.

Continuous renal replacement therapy (CRRT) is a form of dialysis prescribed to severely ill patient...

Diagnosis and classification of kidney transplant rejection using machine learning-assisted surface-enhanced Raman spectroscopy using a single drop of serum.

The quest to reduce kidney transplant rejection has emphasized the urgent requirement for the develo...

When an extra rejection class meets out-of-distribution detection in long-tailed image classification.

Detecting Out-of-Distribution (OOD) inputs is essential for reliable deep learning in the open world...

Predictive approach for liberation from acute dialysis in ICU patients using interpretable machine learning.

Renal recovery following dialysis-requiring acute kidney injury (AKI-D) is a vital clinical outcome ...

Prediction of 30-Day Mortality Following Revision Total Hip and Knee Arthroplasty: Machine Learning Algorithms Outperform CARDE-B, 5-Item, and 6-Item Modified Frailty Index Risk Scores.

BACKGROUND: Although risk calculators are used to prognosticate postoperative outcomes following rev...

Risk assessment of organ transplant operation: A fuzzy hybrid MCDM approach based on fuzzy FMEA.

Nowadays, most fatal diseases are attributed to the malfunction of bodily. Sometimes organ transplan...