Infectious Disease

SBC-SHAP: Increasing the Accessibility and Interpretability of Machine Learning Algorithms for Sepsis Prediction.

BACKGROUND: Sepsis is a life-threatening condition that is one of the major causes of death worldwide. Early detection of sepsis is required for fast initialization of an appropriate therapy. Complete blood count data containing information about white blood cells, platelets, hemoglobin, red blood cells, and mean corpuscular volume could serve as early indicators. However, previous approaches are ...

Antimicrobial Peptides Design Using Deep Learning and Rational Modifications: Activity in Bacteria, Candida albicans, and Cancer Cells.

Resistance to antimicrobial agents has become a global threat, estimated to cause 10-million deaths annually by 2050. Antimicrobial peptides are emerging as an alternative and offer advantages over traditional antibiotics. Antimicrobial peptides generated by artificial intelligence (AI) strategies are potential alternatives that reduce costs and development time. This work optimized a set of pepti...

Advancing fungal phylogenetics: integrating modern sequencing, dark taxa discovery, and machine learning.

The study of fungal genetics has undergone transformative advancements in recent decades, profoundly reshaping our understanding of fungal diversity, ...

Combating Antimicrobial Resistance: Role of Key Stakeholders with Focus on the Pharmaceutical Sector.

Antimicrobial resistance is a pressing global health threat fueled by a complex interplay of biological, social, and economic factors. Despite widespr...

RadientFusion-XR: A Hybrid LBP-HOG Model for COVID-19 Detection Using Machine Learning.

The rapid and accurate detection of COVID-19 (coronavirus disease 2019) from normal and pneumonia chest x-ray images is essential for timely diagnosis...

Multi-walled carbon nanotubes green-functionalized with iron nanoparticles for continuous removal of pharmaceutical pollutants through fixed-bed adsorption: Integrated experimental and machine learning approaches.

Pharmaceutical residues, including losartan and diclofenac, are insufficiently removed by conventional wastewater treatment plants, leading to persist...

Machine Learning Algorithms Enhance the Accuracy of Radiographic Diagnosis of Dental Caries: A Comparative Study.

OBJECTIVES: This study evaluated the influence of cognitive aids, including machine learning (ML) algorithms and checklists, on the diagnostic accurac...

AI Predictive Model of Mortality and Intensive Care Unit Admission in the COVID-19 Pandemic: Retrospective Population Cohort Study of 12,000 Patients.

BACKGROUND: One of the main challenges with COVID-19 has been that although there are known factors associated with a worse prognosis, clinicians have...

Inflammatory, fibrotic and endothelial biomarker profiles in COVID-19 patients during and following hospitalization.

Survivors of severe COVID-19 often suffer from long-term respiratory issues, but the molecular drivers of this damage remain unclear. This study explo...

Predicting antiretroviral therapy adherence status of adult HIV-positive patients using machine-learning Northwest, Ethiopia, 2025.

BACKGROUND: Adherence with Anti-Retroviral Therapy (ART) reduces viral load, as well as HIV-related morbidity and mortality. Despite the expanded avai...

Improving meningitis surveillance and diagnosis with machine learning: Insights from São Paulo.

INTRODUCTION: Meningitis, an inflammatory condition of the membranes surrounding the brain and spinal cord, can be caused by various agents. Bacterial...

Advancing Tuberculosis Screening: The Clinical Potential of Artificial Intelligence-Generated Radiology Reports for Chest Radiographs.

Identifying Health Care Services Offered in the HIV Care Continuum via a Machine Learning-Based Topic Modeling Approach: Exploratory Literature Review.

BACKGROUND: It remains unclear whether the existing health care services reflect the HIV care continuum, which underscores the need for integrated car...

Association Between COVID-19 During Pregnancy and Preterm Birth by Trimester of Infection: Retrospective Cohort Study Using Large-Scale Social Media Data.

BACKGROUND: Preterm birth, defined as birth at <37 weeks of gestation, is the leading cause of neonatal death globally and the second leading cause of...

AADNet: An End-to-End Deep Learning Model for Auditory Attention Decoding.

Auditory attention decoding (AAD) is the process of identifying the attended speech in a multi-talker environment using brain signals, typically recor...

Ethical Implications of Artificial Intelligence in Vaccine Equity: Protocol for Exploring Vaccine Distribution Planning and Scheduling in Pandemics in Low- and Middle-Income Countries.

BACKGROUND: The COVID-19 pandemic highlighted significant disparities in vaccine distribution, particularly in low- and middle-income countries (LMICs...

Inhibiting heme piracy by pathogenic Escherichia coli using de novo-designed proteins.

Iron is an essential nutrient for most bacteria and is often growth-limiting during infection, due to the host sequestering free iron as part of the i...

Prediction and characterisation of the human B cell response to a heterologous two-dose Ebola vaccine.

Ebola virus disease (EVD) outbreaks are increasing, posing significant threats to affected communities. Effective outbreak management depends on prote...

Enhancing medical students' diagnostic accuracy of infectious keratitis with AI-generated images.

BACKGROUND: Developing students' ability to accurately diagnose various types of keratitis is challenging. This study aims to compare the effectivenes...

Development of a deep learning-based MRI diagnostic model for human Brucella spondylitis.

INTRODUCTION: Brucella spondylitis (BS) and tuberculous spondylitis (TS) are prevalent spinal infections with distinct treatment protocols. Rapid and ...