Transplantation

Clinicopathological features for the prediction of immunosuppressive treatment responses in sarcoidosis-related kidney involvement: a single-center retrospective study.

BACKGROUND/AIM: Sarcoidosis is a multisystem disorder that affects many organs, including the kidneys. This single-center retrospective study investigated the clinical, pathological, and laboratory findings of patients with kidney sarcoidosis who were treated with immunosuppressives.

Machine learning based analysis of single-cell data reveals evidence of subject-specific single-cell gene expression profiles in acute myeloid leukaemia patients and healthy controls.

Acute Myeloid Leukaemia (AML) is characterized by uncontrolled growth of immature myeloid cells, disrupting normal blood production. Treatment typically involves chemotherapy, targeted therapy, and stem cell transplantation but many patients develop chemoresistance, leading to poor outcomes due to the disease's high heterogeneity. In this study, we used publicly available single-cell RNA sequencin...

DeepAIP: Deep learning for anti-inflammatory peptide prediction using pre-trained protein language model features based on contextual self-attention network.

Non-steroidal anti-inflammatory drugs (NSAIDs), glucocorticoids, and other immunosuppressants are commonly used medications for treating inflammation....

Machine learning for post-liver transplant survival: Bridging the gap for long-term outcomes through temporal variation features.

BACKGROUND: The long-term survival of liver transplant (LT) recipients is essential for optimizing organ allocation and estimating mortality outcomes....

Real-time segmentation of biliary structure in pure laparoscopic donor hepatectomy.

Pure laparoscopic donor hepatectomy (PLDH) has become a standard practice for living donor liver transplantation in expert centers. Accurate understan...

Identification of immune patterns in idiopathic pulmonary fibrosis patients driven by PLA2G7-positive macrophages using an integrated machine learning survival framework.

Patients with advanced idiopathic pulmonary fibrosis (IPF), a complex and incurable lung disease with an elusive pathology, are nearly exclusive candi...

COVID-19 vaccinations and their side effects: a scoping systematic review.

The COVID-19 virus has impacted people worldwide, causing significant changes in their lifestyles. Since the emergence of the epidemic, attempts have...

An integrated machine learning model enhances delayed graft function prediction in pediatric renal transplantation from deceased donors.

BACKGROUND: Kidney transplantation is the optimal renal replacement therapy for children with end-stage renal disease; however, delayed graft function...

Children Are Not Small Adults: Addressing Limited Generalizability of an Adult Deep Learning CT Organ Segmentation Model to the Pediatric Population.

Deep learning (DL) tools developed on adult data sets may not generalize well to pediatric patients, posing potential safety risks. We evaluated the p...

Employ machine learning to identify NAD+ metabolism-related diagnostic markers for ischemic stroke and develop a diagnostic model.

Ischemic stroke (IS) is a severe condition regulated by complex molecular alterations. This study aimed to identify potential nicotinamide adenine din...

Splam: a deep-learning-based splice site predictor that improves spliced alignments.

The process of splicing messenger RNA to remove introns plays a central role in creating genes and gene variants. We describe Splam, a novel method fo...

Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members' vision - part 1.

Artificial intelligence (AI) is revolutionizing the field of biomedical research and treatment, leveraging machine learning (ML) and advanced algorith...

A Predictive Model of Pressure Injury in Children Undergoing Living Donor Liver Transplantation Based on Machine Learning Algorithm.

AIMS: The aim of our study was to formulate and validate a prediction model using machine learning algorithms to forecast the risk of pressure injurie...

A multiscale distributed neural computing model database (NCMD) for neuromorphic architecture.

Distributed neuromorphic architecture is a promising technique for on-chip processing of multiple tasks. Deploying the constructed model in a distribu...

Rewireable Building Blocks for Enzyme-Powered DNA Computing Networks.

Neural networks enable the processing of large, complex data sets with applications in disease diagnosis, cell profiling, and drug discovery. Beyond e...

Machine learning analysis of contrast-enhanced ultrasound (CEUS) for the diagnosis of acute graft dysfunction in kidney transplant recipients.

AIM: The aim of the study was to develop machine learning algorithms (MLA) for diagnosing acute graft dysfunction (AGD) in kidney transplant recipient...

Probing Nanotopography-Mediated Macrophage Polarization via Integrated Machine Learning and Combinatorial Biophysical Cue Mapping.

Inflammatory responses, leading to fibrosis and potential host rejection, significantly hinder the long-term success and widespread adoption of biomed...

Deep learning for the harmonization of structural MRI scans: a survey.

Medical imaging datasets for research are frequently collected from multiple imaging centers using different scanners, protocols, and settings. These ...

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...

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 retro...