Sepsis

Toxoplasma gondii infection in wildlife in China (1985-2024): A systematic review and meta-analysis with machine learning.

The globally distributed zoonotic protozoan parasite Toxoplasma gondii presents escalating conservation and public health challenges through expanding wildlife reservoir infections. This systematic review with meta-analysis, integrated with machine learning approaches (Random Forest and SHAP analysis), aims to quantify aggregated prevalence, identify key epidemiological drivers, and map infection ...

Algorithmic dopamine economies: emerging psychiatric challenges in an artificial-intelligence-mediated attention ecology.

Acute deep neck infection MRI: deep learning segmentation and clinical relevance of retropharyngeal edema volume.

OBJECTIVE: Retropharyngeal edema (RPE) on MRI in patients with acute neck infection is associated with disease severity. We explored the potential rol...

Flexible Organic Clip as an Adapter to Enhance Host-Guest Nanopore Single-Molecule Sensing.

The host-guest nanopore analysis method provided an avenue for hydrophobic analytes detection. However, the fixed cavity of conventional rigid hosts r...

A novel framework integrating a coupled mixing rule with deep learning for toxicity prediction and environmental risk assessment of antibiotic mixtures.

Co-exposure of multiple antibiotics in the environment is widespread, and accurate prediction of antibiotic mixture toxicity is essential for ecologic...

Development and validation of a machine learning-based nomogram model integrating LASSO regression and multiple evaluation methods for predicting early sepsis risk in acute abdomen.

BACKGROUND: Sepsis secondary to acute abdomen is a critical driver of clinical deterioration and mortality, with outcomes closely linked to sepsis sev...

From machine learning to causal insight: a robust 12-gene signature to distinguish sepsis risk from systemic inflammatory response syndrome.

BACKGROUND: Sepsis, a life-threatening condition driven by a dysregulated host response, poses significant challenges for early diagnosis and early in...

Lactate metabolism-driven tumor heterogeneity and molecular signatures in intrahepatic cholangiocarcinoma.

BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is a highly aggressive liver malignancy with limited therapeutic options and poor prognosis. Recent ...

Emerging trends and converging evidence in tumor evolution: A comprehensive review.

BACKGROUND: Tumor evolution is a spatiotemporal dynamic process orchestrated by the interplay of genetic mutations, epigenetic reprogramming, and bidi...

Immunotherapy in Triple-Negative Breast Cancer: From Molecular Mechanisms to Precision Medicine-Overcoming Resistance and Optimizing Clinical Outcomes.

Triple-negative breast cancer (TNBC) is the most violent type of breast cancer, in which estrogen receptors (ER), progesterone receptors (PR), and hum...

Metabolic factor-based machine learning model for mortality prediction in acute hepatitis E: Development and validation from a dual-center cohort.

BACKGROUND: Hepatitis E virus (HEV) infection remains a major cause of liver failure with high short-term mortality, yet predictive models incorporati...

Antimicrobial resistance gene diversity, prevalence, and mobility within four landfills.

Antibiotics in landfills create selection pressures on the microorganisms present, selecting for antibiotic resistance genes (ARGs) and antibiotic res...

Hierarchical Distribution of Reward Representation in the Cortical and Hippocampal Regions.

Dopaminergic inputs to various brain regions, such as the striatum, orbitofrontal cortex, and amygdala, play a critical role in processing reward acqu...

Molecular interplay of ASNS and the PI3K-AKT-mTOR pathway in CMV and HIV co-infections: Therapeutic implications.

CMV/HIV coinfection markedly exacerbates disease progression, elevates treatment failure risk, and worsens patient outcomes, yet the underlying molecu...

Generation of machine-learning derived cancer vulnerability indicator to determine the spatial burden of cancer outcomes.

BACKGROUND: Due to the difficulty of obtaining population-based individual-level data, ecological studies are often used to explore factors related to...

A Unified Machine Learning Model for Relapse Prediction in Clinical Stage I Testicular Cancer.

BACKGROUND: Approximately one-fourth of patients with clinical stage I testicular cancer relapse. For decades, risk stratification has been based on d...

Post-treatment infection prediction in CLL using domain adaptation of lymphoma electronic health records.

BACKGROUND AND PURPOSE: Infections are the leading cause of morbidity and mortality in patients with chronic lymphocytic leukemia (CLL) and occur duri...

Design and development of a convolutional neural network based on human cognitive attention mechanism for automatic classification of leukemia.

Cancer occurs when healthy cells in the body grow abnormally and out of control. Leukemia is a type of cancer that affects White Blood Cells (WBCs) an...

Vulnerability and short clinical outcomes in patients on the deceased-donor kidney transplant waiting list.

BACKGROUND: With prolonged waiting times for deceased-donor kidney transplantation (DDKT) in Japan, objective data on frailty among wait-listed patien...

Interpretable machine learning-based real-time sepsis diagnosis.