Sepsis

Supervised machine learning models for predicting sepsis-associated acute kidney injury in children: a real-world evaluation.

BACKGROUND: Sepsis-associated acute kidney injury (S-AKI) substantially increases mortality. The recent Phoenix criteria have redefined pediatric sepsis, yet AKI risk factors under this framework remain unclear. This study aimed to develop a machine learning (ML) model to identify key predictors of AKI in pediatric patients with sepsis as defined by the Phoenix criteria. METHODS: This retrospectiv...

A Fluorescence Imaging- and Deep Learning-Based Approach for Detecting Hepatitis B Virus Integration into Host Genomes.

Hepatitis B virus (HBV) infection can lead to hepatocellular carcinoma, and HBV integration into the host genome is regularly observed in the liver of chronic HBV carriers and is speculated to trigger carcinogenesis. To detect HBV integration, PCR-based methods are sensitive but may not be effective depending on the HBV integration site. High-throughput sequencing reveals not only HBV integration ...

Machine Learning-Driven Capillary Microfluidic Design Automation for Programmable Gradient Generation and Antimicrobial Testing.

Rapid, portable, and automated determination of minimum inhibitory concentration (MIC) is critical for antimicrobial susceptibility testing in decentr...

Prediction Model for Delirium in Patients with Sepsis-Associated Liver Injury: An Interpretable Machine Learning Approach.

BackgroundPatients with sepsis-associated liver injury (SALI) are at marked risk of delirium, a severe complication strongly linked with poor neurolog...

Dual-Scale StaphAIR: Predictive Modeling for the Diagnosis of S. aureus Infection via Simultaneous Detection and Quantification of Cytokines and Antibodies.

Staphylococcus aureus causes life-threatening bacterial infections. Current diagnostics for bone infection consist of invasive sample collection and l...

Highly Efficient Broad-Spectrum Antibacterial Carbon Dots through Hierarchical Machine Learning Framework.

The global threat of antibiotic resistance necessitates intelligent design strategies for next-generation antibacterial nanomaterials. Herein, high-ef...

Single-cell and spatial analyses identify a glycolysis-high state driven by STOML2 in epithelial ovarian cancer.

OBJECTIVE: Metabolic heterogeneity contributes to therapeutic resistance and poor prognosis in epithelial ovarian cancer (EOC), yet the regulatory dri...

Machine learning in diagnostic stewardship: A technical approach in improving diagnostic accuracy, optimizing antibiotic use in false positive CSF cultures.

Diagnostic stewardship emphasises ordering the right tests, at the right time for the patient and also promotes the judicious use of rapid and accurat...

Efficacy and safety evaluation of artificial intelligence-identified antimicrobial peptides targeting avian pathogenic Escherichia coli in broiler chickens.

BACKGROUND: The overuse of antibiotics in both veterinary and human medicine has resulted in the emergence of antibiotic-resistant bacteria, prompting...

Integrative bioinformatics and machine learning reveal an association of LTF and MMP8 with systemic inflammation and lung injury.

BACKGROUND: Acute lung injury and its severe form, acute respiratory distress syndrome (ARDS), represent life-threatening conditions with high mortali...

Interfacial impedance engineering in ionic hydrogels for integrated enzymatic and electrophysiological sensing.

Interfacial charge-transfer resistance remains a critical bottleneck in wearable enzymatic biosensors, particularly in soft, deformable systems, where...

Efficacy of short-course bevacizumab for brain tumor radiation necrosis with a volumetric analysis.

PURPOSE: Radiation necrosis (RN) is a challenging complication of cranial irradiation, often requiring corticosteroids for management. This study eval...

The dynamics of hepatitis B transmission with treatment and vaccination: A case study in the Ili Region of Xinjiang, China, by a physics-informed neural networks approach.

Hepatitis B virus (HBV) infection remains a leading cause of cirrhosis and hepatocellular carcinoma worldwide, and long-term control relies on sustain...

Diagnostic value of cytokine detection in children with Mycoplasma pneumoniae pneumonia complicated by bacterial or viral co-infections.

BACKGROUND: To evaluate the diagnostic value of cytokine levels in paediatric patients with Mycoplasma pneumoniae pneumonia (MPP) complicated by bacte...

On the usage of artificial intelligence for identifying main attributes and predicting neonatal sepsis.

During the neonatal period, newborns are more susceptible to developing conditions and diseases due to their fragility of the transition and adaptatio...

Predictive triage for testing may improve control of a COVID-19 epidemic while reducing testing requirements.

BACKGROUND: Extensive population testing played a crucial role in mitigating the COVID-19 pandemic. However, scaling up testing capacity requires a co...

Development of AI-based dopamine transporter (DAT) image generation technique using early phase [18F]-FP-CIT PET imaging.

OBJECTIVES: To develop and validate a deep learning-based model capable of generating dopamine transporter (DAT) images from early-phase [18F]-FP-CIT ...

Machine learning combined with WGCNA reveals potential calcium uptake-associated biomarkers in Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is the second most common neurodegenerative disease, characterised by progressive loss of dopaminergic neurons in...

Blood biomarker trajectories in ICU-directed prediction models - A scoping review.

BACKGROUND: Despite advanced analytical methods and increasing data availability, most intensive care unit (ICU) prediction models rely on static meas...

Predicting mortality risk in pediatric severe pneumonia using a CNN-BiLSTM model with dynamic clinical indicators.

BACKGROUND: Severe pneumonia is a leading cause of mortality in children under 5 years old, and there is currently a lack of reliable biomarkers for e...