Infectious Disease

The role of digital tools and artificial intelligence in supporting antimicrobial stewardship: Study protocol for a systematic review.

INTRODUCTION: Since the post-antibiotic era, there has been significant difficulty in treating infectious diseases due to the increase in antimicrobial resistance, the scarcity of new antimicrobials, and the complexity of the healthcare system. The World Health Organization (WHO) recognized it as one of the main public health problems. To mitigate this issue, Antimicrobial Stewardship Programs (AS...

Stage-specific FeS-mediated regulation of Paracoccus denitrificans during carbon consumption by machine learning-assisted comparative proteomics.

Inorganic electron donors improve low C/N wastewater denitrification performance and management. This study used an integrated strategy combining proteomics, machine learning, and electron transfer system characterization to elucidate FeS's regulatory mechanisms on microbial denitrification during carbon source depletion. Monitoring results revealed significant differences in denitrification perfo...

Evaluating Retrieval-Augmented Generation-Large Language Models for Infective Endocarditis Prophylaxis: Clinical Accuracy and Efficiency.

INTRODUCTION AND AIMS: The use of large language models (LLMs) in healthcare is expanding. Retrieval-augmented generation (RAG) addresses key LLM limi...

Automating wound assessment: convolutional neural network-based mobile application for SINBAD classification system.

INTRODUCTION: Diabetic foot ulcer (DFU) assessment using the SINBAD system is essential for clinical decision-making but often limited by access to sp...

Integrated assessment of total airway count and pneumonia volume on chest computed tomography as a prognostic biomarker for coronavirus disease.

OBJECTIVES: The clinical relevance of computed tomography (CT)-based airway tree structure is unclear. Herein, we used artificial intelligence to segm...

Early prediction of immunological non-responders in people living with HIV using machine learning: Model development and validation in multicenter cohorts in China.

BACKGROUND: Despite antiretroviral therapy (ART), 10-40 % of people living with HIV (PLWH) fail to normalize CD4+ T cells, known as immune non-respond...

The Future of Antibiotics and Artificial Intelligence: Some Thoughts from Discovery to Bedside.

Antibiotic discovery and antibiotic prescribing represent two domains that both stand to benefit from artificial intelligence (AI)-driven progress in ...

Ensemble-labeling of infectious disease time series to evaluate early warning systems.

Early warning systems (EWSs) for detecting disease outbreaks can help make informed public health decisions and organize necessary responses. During t...

Acute kidney injury prediction and prognostication using machine learning.

Acute Kidney Injury (AKI) is a major health concern with high costs and poor outcomes, partly due to late diagnosis. This paper reviews the applicatio...

The spread of non-native species.

The global redistribution of species through human agency is one of the defining ecological signatures of the Anthropocene, with biological invasions ...

Ensemble machine learning for early mortality risk stratification in septic orthopedic trauma: an international cohort study.

BACKGROUND: Sepsis represents a life-threatening complication in severe orthopedic trauma, significantly increasing short-term mortality risk. Despite...

Causal models and network instability in neurological and psychiatric disorders: Computational and clinical perspectives.

Self-diagnosis-the capacity of a system to detect and correct its own failures-is a defining property of adaptive systems. In the brain, recursive sel...

DSSA-PPI: enhancing binding affinity change prediction upon protein mutations using disentangled structure-sequence aware attention.

Accurate assessment of the effects of mutations on protein-protein interactions (PPIs) is crucial for understanding disease pathogenesis and the devel...

HERC5, IFI6, IFIT3, and OASL as potential diagnostic biomarkers for systemic lupus erythematosus: an integrated bioinformatics, machine learning and clinical validation.

OBJECTIVES: Systemic lupus erythematosus (SLE) is a life-threatening autoimmune disorder causing multi-organ damage. Current diagnostic methods are hi...

Widely targeted metabolomics and machine learning identify succinate as a key metabolite in sepsis-associated encephalopathy.

Sepsis-associated encephalopathy (SAE) is a common and serious complication of sepsis that leads to acute brain dysfunction and long-term cognitive im...

Breaking resistance with machine and deep learning: A computational intelligence hunt for AmvR (TetR) inhibitors in Acinetobacterbaumannii.

Multidrug-resistant Acinetobacter baumannii infections have driven the development of innovative therapeutic approaches to address global challenges. ...

Unveiling sulfonamide adsorption on biochar using explainable machine learning.

Sulfonamide antibiotics are emerging aquatic contaminants due to environmental persistence and antimicrobial resistance risks. Biochar is a promising ...

Machine Learning in Microbiome Research and Engineering.

Microbiomes, complex communities of microorganisms and their genetic material, hold immense potential for addressing global challenges in diverse sect...

Boosting Array-based Diagnosis of Urinary Tract Infections via a Heterobifunctional Bridge-Driven Strategy.

Array-based sensing platforms have witnessed significant progress in recent years, demonstrating considerable potential for high-throughput and multip...

Integrating machine learning and functional genomics to study cross-species gene regulatory evolution.

Understanding the genetic basis of phenotypic differences across species has been a longstanding goal of evolutionary biology since Darwin. While a re...