Sepsis

Systematic review of prediction models and meta-analysis of risk factors for invasive fungal infection in children.

OBJECTIVES: To review the application of prediction models and risk factors identified by prediction models for invasive fungal infection (IFI) in children, and assess model performance, methodological rigour and applicability. DESIGN: This is a systematic review of diagnostic prediction models and a meta-analysis of the risk factors. This study was registered on PROSPERO and performed according t...

Machine learning prediction of positive urine cultures in an academic pediatric emergency department (ED).

BACKGROUND: Urinary tract infections (UTIs) are among the most common pediatric infections, but urine culture, the diagnostic gold standard, requires 1-3 days for results. This delay may lead to unnecessary antibiotic use or treatment delays. OBJECTIVE: To develop and validate a machine learning (ML) model that predicts urine culture positivity in pediatric emergency department (ED) encounters usi...

Integrating machine learning and artificial intelligence in the management of Acinetobacter infections: a narrative review.

Acinetobacter baumannii, particularly in its multidrug-resistant (MDR) and carbapenem-resistant (CRAB) forms, has become a major global health concern...

Focusing on Data to Improve Machine Learning-Guided Antibiotic Discovery.

Machine learning (ML) is poised to accelerate antibiotic discovery by rapidly identifying and generating compounds with desirable properties. Despite ...

Prediction-guided clustering for sepsis phenotyping: a retrospective cohort analysis.

BACKGROUND: Sepsis is a major cause of morbidity and mortality worldwide, with its heterogeneous and dynamically evolving clinical presentation compli...

Development and validation of an artificial intelligence-based model for predicting teicoplanin plasma concentrations in intensive care unit patients with pulmonary infections: a retrospective study.

INTRODUCTION: Teicoplanin is widely used to treat pulmonary infections, particularly in critically ill patients with gram-positive bacterial infection...

Telehealth Approaches for Pediatric Otitis Media and Clinical Outcomes: Scoping Review.

BACKGROUND: Otitis media (OM) is a common pediatric infection worldwide. Conventionally, accurate diagnosis depends on in-person pneumatic otoscopy, w...

Development and Validation of Machine Learning Model to Predict Refractory Septic Shock.

This study developed and validated a machine learning model to predict refractory septic shock in patients with sepsis admitted to a tertiary care cen...

Machine learning-assisted Zr-MOFFe@SECHA fluorescence and colorimetric dual-mode intelligent platform for sensitive LPS detection.

BACKGROUND: Lipopolysaccharide (LPS) is a crucial diagnosis biomarker for sepsis. Accurate and sensitive determination of LPS in real samples (human s...

Characterising responses in group-housed pigs to Salmonella typhimurium infection through integrated computer vision-based behavioural monitoring and statistical analyses.

BACKGROUND: Health monitoring is crucial for early disease detection and prompt intervention to mitigate the disease. Computer vision is one of the no...

Noninvasive Detection of Occult yet Significant Liver Pathology in Alanine Aminotransferase-Normal Chronic Hepatitis B: A Multicenter Study.

BACKGROUND: Individuals with chronic hepatitis B (CHB) may harbor occult yet significant liver pathology despite normal alanine aminotransferase (ALT)...

Novel Deep-Learning Unsupervised Domain Adaptation Method for Mitigating Batch, Strain, and Instrument Variations to Enhance Raman Spectroscopy-Based Bacterial Pathogen Identification.

The escalating threat of antibiotic resistance demands a rapid and accurate pathogen identification. While Raman spectroscopy combined with deep learn...

Single-Cell Nanomotion and Machine Learning for Parallel Bacterial Identification and Antibiotic Screening.

Rapid and accurate identification of bacterial infections and their resistance to antibiotics is critical to effective clinical decision-making and co...

Using Generative Artificial Intelligence for Healthcare-Associated Infection Surveillance.

Generative artificial intelligence (GenAI) is becoming widespread in society but has had limited impact on medicine. GenAI can rapidly digest large do...

Accuracy of Diagnosis and Anticipation of Future Treatment in Pediatric Peripelvic Infections: The Role of Multisurgeon Review and Implications for Future Machine Learning Algorithms.

INTRODUCTION: MRI is commonly used to evaluate pelvic musculoskeletal infections. Limited "quick" MRI protocols enable timely imaging without intraven...

Evaluation of a commercial AI-assisted cell counting software for dopaminergic neurons across species.

Quantification of dopaminergic neurons in the substantia nigra pars compacta (SNc) of animal models is important for understanding the pathogenesis of...

Integrated molecular and clinical profiling of primary mitochondrial oxidative phosphorylation disorders in an Indian cohort: Insights from genetics, neuroimaging, and machine learning.

Primary mitochondrial disorders are clinically and genetically heterogeneous and remain underdiagnosed in resource-limited settings. We performed a re...

Machine Learning in the ICU: Predicting Mortality in Patients with Carbapenem-Resistant Gram-Negative Bacilli Bloodstream Infections.

Background and ObjectiveMultidrug and carbapenem resistant gram-negative bacilli bloodstream infections cause high mortality in intensive care units (...

Unveiling the ecology and spatial dynamics of Trypanosoma cruzi, its DTUs and Triatoma vitticeps in the Atlantic Forest of south-eastern Espírito Santo State, Brazil.

The transmission dynamics of Trypanosoma cruzi in natural environments exhibit considerable variation at the micro-locality scale. However, the specif...

A pan-European assessment of multi-sector drivers of human hantavirus risk: climate, biodiversity, and socio-economic factors as key determinants.

The landscape of emerging zoonoses is being rapidly reshaped by concurrent climate change, environmental transformation, and biodiversity loss. These ...