Sepsis

Correction: Interpretable machine learning-based real-time sepsis diagnosis.

Integrating host mRNA signatures and clinical trajectories with machine learning for risk stratification and survival prediction in sepsis: an ICU-based prospective cohort study.

BACKGROUND: Sepsis remains a leading cause of death in critically ill patients. Host mRNA biomarkers may provide complementary biological information for prognostic assessment in sepsis. This study aimed to integrate host mRNA biomarkers and clinical parameters to develop and compare predictive models for short-term mortality using both conventional and machine learning (ML) approaches. METHODS: I...

Risk Factor Analysis and Nomogram for Predicting 28-day in-hospital Mortality in ICU Patients with Liver Necrosis.

INTRODUCTION: The mortality risk factors in intensive care unit (ICU) patients with liver necrosis (LN) remain unclear. This study aimed to develop a ...

Programmable Electrohydrodynamic Printing of Pt-CNTs Nanointerfaces via In Situ Thermal Regulation for High-Fidelity Biosensing.

The accurate simultaneous detection of uric acid (UA) and dopamine (DA) remains a challenge in clinical diagnostics due to the insufficient sensitivit...

A highly interpretable machine learning model for predicting lung cancer bone metastasis: uncovering the synergistic effect of routine biochemical markers.

BACKGROUND: Bone metastasis (BM) significantly impairs lung cancer prognosis and patient quality of life. Conventional imaging modalities often face l...

Allosteric targeting of the ACE2 dimer interface by a medium-sized compound inhibits SARS-CoV-2 entry.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), enters host cells via intera...

Characterising 'Watch and Wait' prescribing patterns in paediatric otitis media using large language models and pharmacy dispense data.

OBJECTIVES: Acute otitis media (AOM) is a leading cause of antibiotic prescribing in children although many cases resolve without treatment. 'Watch an...

EGID: A Comprehensive Multi-label Endoscopic Image Dataset for Gastritis Classification.

Chronic atrophic gastritis is a well-recognized precancerous condition, emphasizing the need for accurate endoscopic classification. However, existing...

Computational screening of antimicrobial peptide analogs targeting AdeB efflux transporter protein in Acinetobacter baumannii associated with multidrug resistance.

Overexpression of the AdeABC efflux pump is crucial in conferring multidrug resistance (MDR) in Acinetobacter baumannii (A. baumannii). Its primary su...

PCT-Anchored Machine Learning for Pre-Culture Identification of Gram-Negative Sepsis in Children: A Four-Site Study.

OBJECTIVE: Procalcitonin (PCT) has moderate accuracy for bacteremia detection but is infrequently used in pediatric emergency departments, partly due ...

Circulating acidic α-glucosidase as a potential biomarker for lactate-related immunometabolism in ischemic stroke.

BACKGROUND: The role of lactate metabolism in ischemic stroke (IS) remains incompletely understood. This study aimed to characterize lactate-related t...

A machine learning model for predicting pneumothorax risk after computed tomography-guided percutaneous transthoracic needle biopsy: A two-hospital retrospective study.

Pneumothorax is a common complication after computed tomography-guided percutaneous transthoracic needle biopsy (PTNB), but individualized prediction ...

Association between long-term PM2.5 exposure and risk of urinary tract infection in children: a nationwide longitudinal cohort study.

BACKGROUND: Urinary tract infection (UTI) is among the most common bacterial infections in children and can lead to both acute and chronic complicatio...

Online Sepsis Prediction Using Vital Signs and Multiscale Temporal-Aware Contrastive Learning: Model Development and Validation Study.

BACKGROUND: Real-time prediction of sepsis is a critical yet highly challenging task. Existing studies face 2 major limitations. First, they often rel...

Sweat-based electrochemical wearable biosensors for sports performance and fatigue monitoring: from laboratory prototypes to field deployment.

Wearable biosensing technologies are advancing sports performance monitoring by enabling the continuous and real-time measurement of physiological and...

Artificial Intelligence-Based Risk Prediction Models for Complications After Tongue Cancer Surgery.

IMPORTANCE: Glossectomy and reconstruction for tongue tumors carries substantial risk of postoperative morbidity, yet current tools offer limited indi...

Mortality Risk Prediction from Dense PICU Data from Patients with Suspected Infection: Data-Derived Physiological Trajectories Outperform Expert Assessments When Temporal Resolution Is High.

OBJECTIVE: Early identification of mortality risk in critically ill children with suspected infection remains challenging. Whether severity scores (SI...

Risk stratification for in-hospital mortality in sepsis-associated acute kidney injury patients receiving continuous renal replacement therapy: an interpretable, externally validated machine learning study.

Patients with sepsis-associated acute kidney injury (SA-AKI) requiring continuous renal replacement therapy (CRRT) have a high risk of in-hospital mor...

Rapid identification of carbapenem-resistant Acinetobacter baumannii based on MALDI-TOF mass spectrometry and machine learning.

BACKGROUND: Carbapenem-resistant Acinetobacter baumannii (CRAB) is a major pathogen in hospital-acquired infections, and rapid detection is critical f...

Automated diagnosis of Helicobacter pylori infection from routine endoscopic images using deep learning: a development and validation study.

BACKGROUND: Real-time endoscopic diagnosis of Helicobacter pylori infection remains challenging and often requires biopsy-based testing, delaying trea...