Liver Transplantation

Therapeutic horizons in metabolic dysfunction-associated steatohepatitis.

Metabolic dysfunction-associated steatohepatitis (MASH), the progressive inflammatory form of MASLD, is now a leading cause of chronic liver disease worldwide. Driven by obesity and type 2 diabetes, MASH significantly increases the risk of cirrhosis, hepatocellular carcinoma, and liver failure. While public health interventions remain essential, therapeutic strategies targeting metabolic dysfuncti...

Regulation and induction of fungal secondary metabolites: a comprehensive review.

Fungal secondary metabolites (SMs) represent a vast reservoir of bioactive compounds with immense therapeutic, agricultural, and industrial potential. These small molecules, including antibiotics, immunosuppressants, and anticancer agents, are synthesized through dedicated biosynthetic gene clusters (BGCs) regulated by various epigenetic, transcriptional, and environmental mechanisms. However, the...

Predicting long-term kidney graft failure using novel multi-omic blood-based biomarkers and artificial intelligence tools.

Kidney transplantation (KT) remains the preferred treatment for end-stage renal disease. With advancements in immunosuppressive regimens and KT survei...

Modeling the prediction of spontaneous rupture and bleeding in hepatocellular carcinoma via machine learning algorithms.

This study aimed to identify the risk factors associated with spontaneous rupture and bleeding in hepatocellular carcinoma, establish a prediction mod...

Construction and validation of HBV-ACLF bacterial infection diagnosis model based on machine learning.

OBJECTIVE: To develop and validate a novel diagnostic model for detecting bacterial infections in patients with hepatitis B virus-related acute-on-chr...

MCAUnet: a deep learning framework for automated quantification of body composition in liver cirrhosis patients.

Traditional methods for measuring body composition in CT scans rely on labor-intensive manual delineation, which is time-consuming and imprecise. This...

Development of an ensemble prediction model for acute graft-versus-host disease in allogeneic transplantation based on machine learning.

BACKGROUND: Acute graft-versus-host disease (aGVHD) is a major post-transplantation complication and one of the most significant causes of non-relapse...

Boosting Checkpoint Blockade Immunotherapy with T Cell Membrane Redox Homeostasis Regulation and Deep Learning Enhanced NIR-II Imaging.

Immune checkpoint blockade therapy has revolutionized clinical oncotherapy by intercepting immunosuppressive pathways in tumor tissues. Nevertheless, ...

Should We Use Behavioural Predictions in Organ Allocation?

Medical predictions, for example, concerning a patient's likelihood of survival, can be used to efficiently allocate scarce resources. Predictions of ...

HEalth Record Optimization for Identifying Candidates for HIV PRe-Exposure Prophylaxis: A Community-Informed Approach to Model Development.

Electronic health record (EHR)-based models to identify individuals who may benefit from pre-exposure prophylaxis (PrEP) outperform traditional risk s...

Early prediction of adverse outcomes in liver cirrhosis using a CT-based multimodal deep learning model.

PURPOSE: Early-stage cirrhosis frequently presents without symptoms, making timely identification of high-risk patients challenging. We aimed to devel...

Comparative investigation of bagging enhanced machine learning for early detection of HCV infections using class imbalance technique with feature selection.

Around 1.5 million new cases of Hepatitis C Virus (HCV) are diagnosed globally each year (World Health Organization, 2023). Consequently, there is a p...

Demarcating the Development of Cirrhosis and Its Complications via Plasma Proteomic Features in an Asymptomatic Population.

The early detection of liver cirrhosis and its complications is a conundrum in clinical practice. We aim to address this conundrum using proteomic fea...

An In-depth overview of artificial intelligence (AI) tool utilization across diverse phases of organ transplantation.

Artificial Intelligence (AI) offers a revolutionary approach to improve decision-making in medicine through the use of advanced computational tools. I...

Improving Hepatitis B outcome prediction with ensemble machine learning: A study on predictive models and interpretability.

OBJECTIVE: Hepatitis B virus (HBV) is a significant global health threat, responsible for severe liver diseases such as liver failure, cirrhosis, and ...

The role of tumor microenvironment and immune cell crosstalk in triple-negative breast cancer (TNBC): Emerging therapeutic opportunities.

Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by its lack of estrogen, progesterone, and HER2 receptors, leading to limi...

Signature gene expression model for quantitative evaluation of MASH-like liver injury in mice.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a spectrum of chronic pathologic conditions strongly associated with metabolic syn...

Machine learning using serial changes in proteinuria during initial steroid therapy to predict treatment response and immunosuppressant use in pediatric idiopathic nephrotic syndrome.

BACKGROUND: Epidemiological studies on idiopathic nephrotic syndrome (INS) in children have identified no definitive factors predicting steroid-resist...

28-day all-cause mortality in patients with alcoholic cirrhosis: a machine learning prediction model based on the MIMIC-IV.

To develop and validate a machine learning prediction model for 28-day all-cause mortality in patients with alcoholic cirrhosis using data from the MI...

Development of an immune scoring system based on exosome-related gene expression for prognosis and treatment response prediction in breast cancer.

Breast cancer (BC) is a heterogeneous disease with diverse subtypes that influence prognosis and treatment outcomes. While advances have been made in ...