Infectious Disease

Staphylococcus aureus resistance to non-β-lactam antibiotics: global genomic epidemiology and machine learning feasibility assessment.

Staphylococcus aureus is increasingly resistant to β-lactam antibiotics, making non-β-lactam cell-wall-targeting drugs crucial alternatives. Growing resistance to these agents highlights the need to identify genomic factors influencing susceptibility. Machine learning can integrate genomic and phenotypic data to predict minimum inhibitory concentrations (MICs) and uncover resistance mechanisms acr...

Risk prediction modelling of 30-day all-cause mortality following percutaneous coronary intervention in an Australian population: leveraging machine learning.

BACKGROUND: Preprocedural risk prediction of 30-day all-cause mortality after percutaneous coronary intervention (PCI) aids in clinical decision-making and benchmarking hospital performance. This study aimed to identify preprocedural factors to predict the risk of 30-day all-cause mortality post-PCI using machine learning (ML) approaches. METHODS: The study analysed 93 055 consecutive PCI procedur...

[The evolution of antimicrobial therapy for sepsis].

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host immune response to infection, representing a critical global public healt...

A Research Classification for Long COVID: Symptom Frequency and Severity Improve Accuracy of Machine Learning Models.

Long COVID definitions based solely on symptom occurrence may reduce diagnostic specificity.Machine-learning models incorporating symptom frequency an...

Identifying and timing patient outcomes in clinician notes using large language models.

BACKGROUND: Key challenges in leveraging unstructured clinician notes for predictive models include identifying and timing patient outcomes. To addres...

The essential role of methylation in maize (Zea mays) defense against the seed-colonizing fungus Aspergillus flavus.

UNLABELLED: The interaction between maize (Zea mays) and Aspergillus species poses a huge threat to global food safety due to the production of aflato...

CAMPER: mechanistic artificial intelligence for designing peptides that target MRSA persisters.

Developing short, stable, and potent antimicrobial peptides is a promising strategy to combat antibiotic resistance and persistence. We present CAMPER...

SHAP based feature selection for machine learning prediction of mycoplasma pneumoniae pneumonia with atelectasis in children.

Resveratrol ameliorates sepsis-induced immune dysfunction by targeting GADD45A to maintain hematopoietic stem cell homeostasis: A predictive study integrating ensemble machine learning, single-cell transcriptomics, and molecular dynamics simulation.

OBJECTIVE: To investigate the potential pharmacological mechanisms of resveratrol (RES) in ameliorating sepsis-associated immune dysfunction via the G...

Revolutionizing hepatic fibrosis staging: A machine learning approach combining clinical, biochemical, and microbiome insights.

Non-alcoholic Steatohepatitis (NASH) is a common disease that not only affects adults but has also been seen to affect all ages. This includes young a...

Bridging immunization gaps in Sub-Saharan Africa: a narrative review of microplanning, geospatial, and machine learning approaches to reach zero-dose children and under-immunised children.

Immunization inequities persist across Sub-Saharan Africa, with significant numbers of zero-dose and under-immunised children contributing to preventa...

Sociodemographic Risk Factors for Incomplete Routine Childhood Immunizations Among Children Born During the COVID-19 Pandemic, Louisiana, March 1, 2020-July 1, 2021.

OBJECTIVE: Routine childhood vaccination rates declined nationwide among children born during 2020-2021 compared with prior birth cohorts. We identifi...

Dysbiosis of fecal virome in pediatric Crohn's disease and its dynamic changes during infliximab therapy.

UNLABELLED: The gut virome is an emerging but underexplored component of the human microbiota, especially in pediatric Crohn's disease (CD). This stud...

De Novo Multi-Mechanism Antimicrobial Peptide Design via Multimodal Deep Learning.

Artificial intelligence (AI)-driven discovery of antimicrobial peptides (AMPs) is yet to fully utilize their three-dimensional (3D) structural charact...

LRF-UNet: Low-Rank Factorized Convolution Deep-Learning Networks for Visceral Adipose and Muscle Tissue Segmentation in Abdominal Computed Tomography Image.

Quantitative analysis of skeletal muscle (SM) and visceral adipose tissue (VAT) cross-sectional volumes at the third lumbar vertebral level (L3) on ab...

Artificial intelligence based approaches for prediction of antimicrobial resistance in ruminant host pathogens.

Usability of a deep learning platform for detecting radiographic bone loss and furcation involvement.

BACKGROUND: Assessing radiographic bone condition is important for periodontal diagnosis. The accuracy of radiographic interpretation depends highly o...

The adaptive large language models for vaccine prediction: A novel approach to vaccine demand prediction with engineered deviation prompts.

Accurate vaccine demand forecasting is crucial for minimizing wastage and ensuring efficient immunization programs. In this study, we introduce an Ada...

Leveraging machine learning for accurate forecasting of pulmonary tuberculosis epidemics in a coastal city in China.

BACKGROUND: While pulmonary tuberculosis (PTB) remains a leading notifiable cause of death in China, city-level monthly forecasts with sufficient reso...

The role of medical imaging in translational research into early pulmonary tuberculosis.

Early detection of tuberculosis (TB) is central to global efforts for TB care and prevention. Conventional symptom-based screening and sputum microbio...