Infectious Disease

Rapid detection of carbapenem-resistant Escherichia coli and carbapenem-resistant Klebsiella pneumoniae in positive blood cultures via MALDI-TOF MS and tree-based machine learning models.

BACKGROUND: Bloodstream infection (BSI) is a systemic infection that predisposes individuals to sepsis and multiple organ dysfunction syndrome. Early identification of infectious agents and determination of drug-resistant phenotypes can help patients with BSI receive timely, effective, and targeted treatment and improve their survival. This study was based on matrix-assisted laser desorption/ioniz...

A commercial AI tool untrained for COVID-19 demonstrates slight improvement in the interpretation of COVID-19 pneumonia x-rays, especially among inexperienced readers.

INTRODUCTION: Our objective is to evaluate how useful an artificial intelligence (AI) tool is to chest radiograph readers with various levels of expertise for the diagnosis of COVID-19 pneumonia when the tool has been trained on a non-COVID-19 pneumonia pathology.

Machine learning model to predict the adherence of tuberculosis patients experiencing increased levels of liver enzymes in Indonesia.

Indonesia is still the second-highest tuberculosis burden country in the world. The antituberculosis adverse drug reaction and adherence may influence...

Current update on surgical management for spinal tuberculosis: a scientific mapping of worldwide publications.

INTRODUCTION: Spinal tuberculosis (TB), or Pott's disease, remains a significant global health issue, particularly in regions with high TB prevalence....

The feasibility of using machine learning to predict COVID-19 cases.

BACKGROUND: Coronavirus Disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, emerged as a global health crisis in 2019, resulting in widespread mo...

Nanobody screening and machine learning guided identification of cross-variant anti-SARS-CoV-2 neutralizing heavy-chain only antibodies.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to persist, demonstrating the risks posed by emerging infectious diseases to na...

Machine Learning Potential for Identifying and Forecasting Complex Environmental Drivers of Infections in the United States.

BACKGROUND: Environmental change in coastal areas can drive marine bacteria and resulting infections, such as those caused by , with both foodborne an...

The role of CTGF and MFG-E8 in the prognosis assessment of SCAP: a study combining machine learning and nomogram analysis.

BACKGROUND: Severe Community-Acquired Pneumonia (SCAP) is a serious global health issue with high incidence and mortality rates. In recent years, the ...

Leveraging AI-driven nudge theory to enhance hand hygiene compliance: paving the path for future infection control.

Hand hygiene is critical for preventing infections, yet maintaining compliance remains challenging across healthcare, schools, and communities. Despit...

Predicting Postoperative Infection After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy with Splenectomy.

BACKGROUND: Hematologic changes after splenectomy and hyperthermic intraperitoneal chemotherapy (HIPEC) can complicate postoperative assessment of inf...

Deep learning-based image classification reveals heterogeneous execution of cell death fates during viral infection.

Cell fate decisions, such as proliferation, differentiation, and death, are driven by complex molecular interactions and signaling cascades. While sig...

Interpretable COVID-19 chest X-ray detection based on handcrafted feature analysis and sequential neural network.

Deep learning methods have significantly improved medical image analysis, particularly in detecting COVID-19 chest X-rays. Nonetheless, these methodol...

Pre-trained Maldi Transformers improve MALDI-TOF MS-based prediction.

For the last decade, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been the reference method for spe...

Machine learning prediction models for mortality risk in sepsis-associated acute kidney injury: evaluating early versus late CRRT initiation.

BACKGROUND: Sepsis-associated acute kidney injury (S-AKI) has a significant impact on patient survival, with continuous renal replacement therapy (CRR...

Global insights into MRSA bacteremia: a bibliometric analysis and future outlook.

BACKGROUND: Methicillin-resistant (MRSA) bloodstream infections (BSIs) pose a significant challenge to global public health, characterized by high mo...

Robotic versus manual disinfection of global priority pathogens at COVID-19-dedicated hospitals.

BACKGROUND: Twelve bacterial families identified as global priority pathogens (GPPs) pose the greatest threat to human health due to declining antibio...

Bacteria Flagella-Mimicking Polymer Multilayer Magnetic Microrobots.

Mass production of biomedical microrobots demands expensive and complex preparation techniques and versatile biocompatible materials. Learning from na...

Machine learning based prediction models for the prognosis of COVID-19 patients with DKA.

Patients with Diabetic ketoacidosis (DKA) have increased critical illness and mortality during coronavirus diseases 2019 (COVID-19). The aim of our st...

Paying attention to the SARS-CoV-2 dialect : a deep neural network approach to predicting novel protein mutations.

Predicting novel mutations has long-lasting impacts on life science research. Traditionally, this problem is addressed through wet-lab experiments, wh...

Developing a rapid screening tool for high-risk ICU patients of sepsis: integrating electronic medical records with machine learning methods for mortality prediction in hospitalized patients-model establishment, internal and external validation, and visualization.

OBJECTIVES: To develop a machine learning-based prediction model using clinical data from the first 24 h of ICU admission to enable rapid screening an...