Sepsis

Integrating a host transcriptomic biomarker with a large language model for diagnosis of lower respiratory tract infection.

BACKGROUND: Lower respiratory tract infections (LRTIs) are a leading cause of mortality worldwide and can be difficult to diagnose in critically ill patients, as non-infectious causes of respiratory failure can present with similar clinical features.

Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial.

The COmmunicating Narrative Concerns Entered by RNs (CONCERN) early warning system (EWS) uses real-time nursing surveillance documentation patterns in its machine learning algorithm to identify deterioration risk. We conducted a 1-year, multisite, pragmatic trial with cluster-randomization of 74 clinical units (37 intervention; 37 usual care) across 2 health systems. Eligible adult hospital encoun...

Niuhuang jiedu prescription alleviates realgar-induced dopaminergic and GABAergic neurotoxicity in Caenorhabditis elegans.

ETHNOPHARMACOLOGICAL RELEVANCE: Niuhuang Jiedu (NHJD) is a Chinese medicine prescription containing realgar (AsS), which is neurotoxic, and seven othe...

Development, validation, and clinical evaluation of a machine-learning based model for diagnosing early infection after cardiovascular surgery (DEICS): a multi-center cohort study.

BACKGROUND: This study addresses the critical need for timely and accurate diagnosis of early postoperative infection (EPI) following cardiac surgery....

Harness machine learning for multiple prognoses prediction in sepsis patients: evidence from the MIMIC-IV database.

BACKGROUND: Sepsis, a severe systemic response to infection, frequently results in adverse outcomes, underscoring the urgency for prompt and accurate ...

Constructing an early warning model for elderly sepsis patients based on machine learning.

Sepsis is a serious threat to human life. Early prediction of high-risk populations for sepsis is necessary especially in elderly patients. Artificial...

Large Language Model-Driven Knowledge Graph Construction in Sepsis Care Using Multicenter Clinical Databases: Development and Usability Study.

BACKGROUND: Sepsis is a complex, life-threatening condition characterized by significant heterogeneity and vast amounts of unstructured data, posing s...

Nanopsychiatry: Advancing psychiatric diagnosis and monitoring through nanotechnology-based detection.

Nanopsychiatry, operating at the nanoscale, leverages engineered nanomaterials and nanodevices to revolutionize psychiatric diagnostics and therapeuti...

Machine learning-based prognostic model for bloodstream infections in hematological malignancies using Th1/Th2 cytokines.

OBJECTIVE: Bloodstream infection (BSI) is a significant cause of mortality in patients with hematologic malignancies(HMs), particularly amid rising an...

Weighted-VAE: A deep learning approach for multimodal data generation applied to experimental T. cruzi infection.

Chagas disease (CD), caused by the protozoan parasite Trypanosoma cruzi (T. cruzi), represents a major public health concern in most of the American c...

Mitigation of the toxic effects of nitrite: Role and mechanism of isoleucine in mitigating mitochondrial DNA leakage-induced inflammation in grass carp (Ctenopharyngodon idella) under nitrite exposure.

The physiological and growth processes of fish are closely associated with their surrounding environment. This study investigated the role and underly...

Infection and Inflammation in Nuclear Medicine Imaging: The Role of Artificial Intelligence.

Infectious and inflammatory diseases represent a global challenge. Delayed diagnosis and treatment lead to death, disabilities and impairment of the q...

Explainable SHAP-XGBoost models for pressure injuries among patients requiring with mechanical ventilation in intensive care unit.

pressure injuries are significant concern for ICU patients on mechanical ventilation. Early prediction is crucial for enhancing patient outcomes and r...

Machine learning-based prediction of vesicoureteral reflux outcomes in infants under antibiotic prophylaxis.

We aimed to investigate the independent outcome predictors of continuous antibiotic prophylaxis (CAP) in vesicoureteral reflux, train a model to predi...

Real-Time Monitoring of Personal Protective Equipment Adherence Using On-Device Artificial Intelligence Models.

Personal protective equipment (PPE) is crucial for infection prevention and is effective only when worn correctly and consistently. Health organizatio...

Assessment for antibiotic resistance in : A practical and interpretable machine learning model based on genome-wide genetic variation.

() antibiotic resistance poses a global health threat. Accurate identification of antibiotic resistant strains is essential for the control of infect...

Forewarning the seasonal dynamics of corn leafhopper and mollicutes through neural networks.

The corn leafhopper (CL), Dalbulus maidis (DeLong & Wolcott) (Hemiptera: Cicadellidae), has become the most important corn pest in Brazil and other co...

Exploring the role of breastfeeding, antibiotics, and indoor environments in preschool children atopic dermatitis through machine learning and hygiene hypothesis.

The increasing global incidence of atopic dermatitis (AD) in children, especially in Western industrialized nations, has attracted considerable attent...

Biofilm-mediated infections; novel therapeutic approaches and harnessing artificial intelligence for early detection and treatment of biofilm-associated infections.

A biofilm is a group of bacteria that have self-produced a matrix and are grouped together in a dense population. By resisting the host's immune syste...

Predicting coronavirus disease 2019 severity using explainable artificial intelligence techniques.

Predictive models for determining coronavirus disease 2019 (COVID-19) severity have been established; however, the complexity of the interactions amon...