Sepsis

Comment on: "Predicting SIRS after PCNL using machine learning: the joint impact of sarcopenia and staghorn stones".

The use of artificial intelligence to predict postoperative outcomes following percutaneous nephrolithotomy: A systematic review of prognostic modeling studies.

Artificial intelligence (AI) is increasingly applied in clinical practice to enhance prediction of postoperative outcomes. This systematic review evaluated the performance and clinical relevance of AI-based prognostic models for patients undergoing percutaneous nephrolithotomy (PCNL). A comprehensive search of PubMed, Embase, Scopus, Web of Science, and Google Scholar was conducted on 7 August 202...

Boron-Doped Diamond Electrodes for Voltammetric Determination of Major Antibiotic Classes: Electrochemical Mechanisms, Intelligent Analytical Systems, and Critical Perspectives.

This review sort of critically assesses how boron-doped diamond electrodes (BDDEs) are becoming more important for voltammetric determination of major...

Voriconazole Exacerbates Subtherapeutic Linezolid Exposure in Critically Ill Patients with Staphylococcus aureus infection: Drug-Drug Interaction Evaluation and Dose Optimization Based on Population Modeling and Ensemble Learning.

OBJECTIVE: This study systematically evaluated the pharmacokinetic interactions between linezolid and voriconazole in critically ill patients with Sta...

Rewiring immunometabolism in hepato-biliary-pancreatic cancers: Unraveling TME-mediated metabolic suppression and advancing next-generation immunotherapeutic approaches.

Hepato-biliary-pancreatic cancers, notorious for their pronounced heterogeneity and poor prognosis, continue to be a dominant factor in cancer-related...

Retraction Note: A diagnostic model for sepsis using an integrated machine learning framework approach and its therapeutic drug discovery.

The role of machine learning in early detection, accurate diagnosis, and timely treatment of diseases.

The contemporary healthcare landscape is experiencing a significant transformation driven by the rapid growth of digital health data and advancements ...

Machine learning models identify prognostic factors in systemic lupus erythematosus patients with epstein-barr virus infection.

OBJECTIVE: To identify poor prognostic factors in Epstein-Barr virus (EBV)-positive systemic lupus erythematosus (SLE) using interpretable machine-lea...

Drug repurposing opportunities across 92 CNS-related conditions using deep learning and whole-genome sequencing.

CNS-related conditions span tumors, vascular, neurodevelopmental, and psychiatric disorders, yet therapeutic development for CNS disorders continues t...

Screening of potential targets of (2S)-2'-methoxykurarinone against sepsis and in vivo verification in zebrafish model.

OBJECTIVE: To screen the potential core targets of (2S)-2'-Methoxykurarinone, a dimethyldihydroflavonoid derived from Sophora flavescens, against seps...

An interpretable machine learning approach to predict left ventricular aneurysm formation following primary PCI in STEMI: A retrospective study with independent external validation.

BACKGROUND: Left ventricular aneurysm (LVA) remains a clinically important structural complication after primary percutaneous coronary intervention (p...

Artificial Intelligence-Assisted Triage in Emergency Departments: A Scoping Review of Clinical Applications, Outcomes, and Implementation Challenges.

OBJECTIVE: Emergency department (ED) triage determines patient prioritization, early risk recognition, and allocation of limited resources. Artificial...

Current Treatment Options and Emerging Technologies for Pelvic Bone Tumors: From Limb Salvage to Precision Oncology.

In our practice, the treatment of pelvic bone tumors should begin with a multidisciplinary sarcoma-board evaluation that integrates biopsy-proven hist...

Explainable deep learning for early sepsis detection from ICU time-series data using XAI techniques.

Sepsis is one of the most deadly illnesses with a high risk of mortality. Consequently, identifying it at the beginning of illness symptoms is crucial...

The deep learning radiomics nomogram for risk stratification in multiple myeloma using automatic whole-body [18F]FDG PET/CT segmentation approach.

PURPOSE: To develop a deep learning (DL) approach for automatic segmentation and accurate risk stratification in multiple myeloma (MM) using whole-bod...

From Raw Hospital Records to an AI-Ready Surveillance Dataset: A FAIR-Compliant Data Pipeline for Healthcare-Associated Infection Research in a Chinese District Hospital.

The proliferation of hospital surveillance data in China has not been matched by publicly available, machine-readable datasets suitable for artificial...

Image-based identification and DEA-based optimization modeling of antibiotic packaging using unsupervised learning techniques.

BACKGROUND: Ensuring medication safety requires accurate identification of antibiotic packaging, especially within pharmacy automation and dispensing ...

Longitudinal Changes in Acid-Base Parameters From Dialysis Initiation to Death in Maintenance Hemodialysis Patients.

INTRODUCTION: Blood gas analysis is routinely performed in hemodialysis patients to monitor acid-base status and serum potassium. Despite frequent tes...

Global genomic surveillance of β-lactam resistance in Escherichia coli across human, animal, and environmental reservoirs.

BACKGROUND: Escherichia coli poses a global health threat from increasing β-lactam resistance. This study uses genomic and One-Health data to map resi...

Structure-function coupling abnormalities in adolescent obsessive-compulsive disorder and their molecular correlates.

BACKGROUND: Obsessive-compulsive disorder (OCD) frequently emerges during adolescence, a critical period of brain network maturation. Although altered...