Heart Transplantation

Machine learning models for predicting the rejection of organic pollutants by forward osmosis and reverse osmosis membranes and unveiling the rejection mechanisms.

While forward osmosis (FO) and reverse osmosis (RO) processes have been proven effective in rejecting organic pollutants, the rejection rate is highly dependent on compound and membrane characteristics, as well as operating conditions. This study aims to establish machine learning (ML) models for predicting the rejection of organic pollutants by FO and RO and providing insights into the underlying...

Machine-learning model to predict the tacrolimus concentration and suggest optimal dose in liver transplantation recipients: a multicenter retrospective cohort study.

Titrating tacrolimus concentration in liver transplantation recipients remains a challenge in the early post-transplant period. This multicenter retrospective cohort study aimed to develop and validate a machine-learning algorithm to predict tacrolimus concentration. Data from 443 patients undergoing liver transplantation between 2017 and 2020 at an academic hospital in South Korea were collected ...

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 milieu, which is associated with resistance to immune...

Improving the diagnostic value of lineup rejections.

Erroneous eyewitness identification evidence is likely the leading cause of wrongful convictions. To minimize this error, scientists recommend collect...

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 immunosuppressive effects. The evaluation of this effect h...

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 related to the development of acute rejection after ki...

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 checkpoint inhibitors have demonstrated promising t...

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

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 and rejection. However, in the realm of cardiac su...

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 neonates will improve the reproducibility and spee...

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 (PRRS) imposes substantial economic losses on the swin...

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

BACKGROUND: Diagnosis of kidney transplant rejection currently relies on manual histopathological assessment, which is subjective and susceptible to i...

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 development of non-invasive, precise diagnostic technolo...

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. However, most existing OOD detection methods hav...

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 transplantation is the only possible therapy, for instance ...

Cherry on Top or Real Need? A Review of Explainable Machine Learning in Kidney Transplantation.

Research on solid organ transplantation has taken advantage of the substantial acquisition of medical data and the use of artificial intelligence (AI)...

Biomimetic piezoelectric nanomaterial-modified oral microrobots for targeted catalytic and immunotherapy of colorectal cancer.

Lactic acid (LA) accumulation in the tumor microenvironment poses notable challenges to effective tumor immunotherapy. Here, an intelligent tumor trea...

Effects of tacrolimus on proteinuria in Chinese and Indian patients with idiopathic membranous nephropathy: the results of machine learning study.

PURPOSE: The present study aims to explore the effects of tacrolimus on proteinuria in patients with idiopathic membranous nephropathy (IMN) and recom...

Assessing portfolio diversification via two-sample graph kernel inference. A case study on the influence of ESG screening.

In this work we seek to enhance the frameworks practitioners in asset management and wealth management may adopt to asses how different screening rule...

Applications of artificial intelligence (AI) in drinking water treatment processes: Possibilities.

In water treatment processes (WTPs), artificial intelligence (AI) based techniques, particularly machine learning (ML) models have been increasingly a...