Sepsis

Exploring proteomic signatures in sepsis and non-infectious systemic inflammatory response syndrome

Background: The search for new biomarkers that allow an early diagnosis in sepsis has become a necessity in medicine. The objective of this study is to identify potential protein biomarkers of differential expression between sepsis and non-infectious systemic inflammatory response syndrome (NISIRS). Methods: Prospective observational study of a cohort of septic patients activated by the Sepsis...

Machine Learning-Based Prediction of ICU Mortality in Sepsis-Associated Acute Kidney Injury Patients Using MIMIC-IV Database with Validation from eICU Database

Background: Sepsis-Associated Acute Kidney Injury (SA-AKI) leads to high mortality in intensive care. This study develops machine learning models using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database to predict Intensive Care Unit (ICU) mortality in SA-AKI patients. External validation is conducted using the eICU Collaborative Research Database. Methods: For 9,474 identi...

A Novel Multi-Task Teacher-Student Architecture with Self-Supervised Pretraining for 48-Hour Vasoactive-Inotropic Trend Analysis in Sepsis Mortality Prediction

Sepsis is a major cause of ICU mortality, where early recognition and effective interventions are essential for improving patient outcomes. However,...

Optimizing Gene-Based Testing for Antibiotic Resistance Prediction

Antibiotic Resistance (AR) is a critical global health challenge that necessitates the development of cost-effective, efficient, and accurate diagno...

Freezing of Gait as a Complication of Pallidal Deep Brain Stimulation in DYT- KMT2B Patients with Evidence of Striatonigral Degeneration

Background: Mutations in KMT2B are a recognized cause of early-onset complex dystonia, with deep brain stimulation (DBS) of the internal globus pall...

Prediction of Clinical Complication Onset using Neural Point Processes

Predicting medical events in advance within critical care settings is paramount for patient outcomes and resource management. Utilizing predictive m...

CL-MFAP: A Contrastive Learning-Based Multimodal Foundation Model for Molecular Property Prediction and Antibiotic Screening

Due to the rise in antimicrobial resistance, identifying novel compounds with antibiotic potential is crucial for combatting this global health issu...

Machine learning algorithm approach to complete blood count can be used as early predictor of COVID-19 outcome.

Although the SARS-CoV-2 infection has established risk groups, identifying biomarkers for disease outcomes is still crucial to stratify patient risk a...

Enhancing Pneumonia Diagnosis and Severity Assessment through Deep Learning: A Comprehensive Approach Integrating CNN Classification and Infection Segmentation

Lung disease poses a substantial global health challenge, with pneumonia being a prevalent concern. This research focuses on leveraging deep learnin...

The Application of MATEC (Multi-AI Agent Team Care) Framework in Sepsis Care

Under-resourced or rural hospitals have limited access to medical specialists and healthcare professionals, which can negatively impact patient outc...

The role of the dopamine system in autism spectrum disorder revealed using machine learning: an ABIDE database-based study.

This study explores the diagnostic value of dopamine system imaging characteristics in children with autism spectrum disorder. Functional magnetic res...

Photodynamic, UV-curable and fibre-forming polyvinyl alcohol derivative with broad processability and staining-free antibacterial capability

Antimicrobial photodynamic therapy (APDT) is a promising antibiotic-free strategy for broad-spectrum infection control in chronic wounds, minimising...

Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics

Recent technological advances have introduced new high-throughput methods for studying host-virus interactions, but testing synergistic interactions...

Machine Learning Model Predictors of Intrapleural Tissue Plasminogen Activator and DNase Failure in Pleural Infection: A Multicenter Study.

Intrapleural enzyme therapy (IET) with tissue plasminogen activator (tPA) and DNase has been shown to reduce the need for surgical intervention for c...

Reviewing on AI-Designed Antibiotic Targeting Drug-Resistant Superbugs by Emphasizing Mechanisms of Action.

The emergence of drug-resistant bacteria, often referred to as "superbugs," poses a profound and escalating challenge to global health systems, surpas...

Identification of DNA damage repair-related genes in sepsis using bioinformatics and machine learning: An observational study.

Sepsis is a life-threatening disease with a high mortality rate, for which the pathogenetic mechanism still unclear. DNA damage repair (DDR) is essent...

Vagus nerve stimulation as a modulator of feedforward and feedback neural transmission

Vagus nerve stimulation (VNS) has emerged as a promising therapeutic intervention across various neurological and psychiatric conditions, including ...

DepoRanker: A Web Tool to predict Klebsiella Depolymerases using Machine Learning

Background: Phage therapy shows promise for treating antibiotic-resistant Klebsiella infections. Identifying phage depolymerases that target Klebsie...

Leveraging 13C NMR spectroscopic data derived from SMILES to predict the functionality of small biomolecules by machine learning: a case study on human Dopamine D1 receptor antagonists

This study contributes to ongoing research which aims to predict small biomolecule functionality using Carbon-13 Nuclear Magnetic Resonance ($^{13}$...

Reducing Size Bias in Sampling for Infectious Disease Spread on Networks

Epidemiological models can aid policymakers in reducing disease spread by predicting outcomes based on disease dynamics and contact network characte...