Sepsis

AI-enabled wearable microfluidics for next-generation infection monitoring and therapeutics.

Wearable biosensors have revolutionized healthcare by enabling continuous, minimally invasive monitoring of health parameters. While traditional wearables primarily measure physiological signals, recent advancements now allow biochemical sensing of microbial biomarkers across diverse human biofluids, including sweat, saliva, wound exudate, interstitial fluid, tears, breath, and urine. These biomar...

In-Pocket 3D Graphs Enhance Ligand-Target Compatibility in Generative Small-Molecule Creation: A Dopamine D2 Receptor Model System.

Proteins in complex with small-molecule ligands represent the core of structure-based drug discovery. However, three-dimensional representations are absent from most deep-learning-based generative models. Here, we present a graph-based generative modeling technology that encodes explicit 3D protein-ligand contacts within a relational graph architecture and evaluate its behavior using the dopamine ...

Speech-to-Speech Voice-Cloning Care (SVCC) for improving ICU-acquired anxiety for critically ill patients in a tertiary hospital in Beijing, China: protocol of a randomised, controlled trial.

INTRODUCTION: Intensive care unit (ICU) visiting restrictions in hospitals, implemented due to infection control and other factors, limited contact be...

Explainable machine learning with routine biomarkers identifies culture-defined bacteremic urosepsis.

Urosepsis is a severe complication of urinary tract infection (UTI) and may lead to organ dysfunction and death. Early identification remains challeng...

An AI-based approach accelerates the discovery of protein-protein interaction modulators targeting NCS-1.

Drug repurposing is an efficient strategy to accelerate the identification of therapeutic compounds by finding new uses for existing drugs. Here, we l...

In-Hospital Mortality Prediction of Patients Requiring Extracorporeal Membrane Oxygenation Using Composite Lactate Metrics and Weight of Evidence Modeling.

BACKGROUND: Mortality prognostication in adult patients requiring extracorporeal membrane oxygenation (ECMO) is not accurate or established. We hypoth...

Mesocorticostriatal reinforcement learning of state representation and value with implications for the mechanisms of schizophrenia.

Mesocorticostriatal dopamine projections are crucial for value learning, motivational control, and cognitive functions. However, while dopamine's role...

Multi task learning based early prediction model for antibiotic resistance using multi institutional cohort data.

Antibiotic resistance poses a significant global health challenge, with its rapid emergence driven by inappropriate antibiotic use. This study aimed t...

Development and multicenter validation of an explainable machine learning diagnostic criteria for pediatric abdominal sepsis.

Accurate identification of early pediatric abdominal sepsis (PAS) is essential to improving outcomes, yet most existing pediatric sepsis criteria and ...

Evaluating deep learning sepsis prediction models in ICUs under distribution shift: a multi-centre retrospective cohort study.

Sepsis prediction models trained on ICU data often fail to generalize under external validation because of distribution shift. Prior studies have focu...

Proteomics-based machine learning model for predicting secondary infection in HBV-related liver failure.

Patients with Hepatitis B Virus-related liver failure are highly vulnerable to secondary infections (SI), yet early predictive tools remain limited. I...

Automated phenotyping of rodent behavior in the Cylinder Exploration Test using machine learning.

Rodent models are essential in neuroscience research for investigating brain function, CNS disease mechanisms, and therapeutic interventions. Beyond m...

MycoPermeNet-v2: Improved Prediction of Mycomembrane Permeation Using Fusion Noisy Student Self-Distillation.

Machine learning (ML) techniques offer a promising path for accelerating antibiotic discovery by computationally predicting and optimizing desirable p...

Rapid Diagnosis of Bacteremia in Febrile Pediatric Oncology Patients via Host-Response Infrared Spectroscopy of Blood and Machine Learning.

Bacteremia is a life-threatening complication and a leading cause of sepsis and septic shock in patients. Conventional diagnostic methods, such as blo...

Diagnosing melioidosis and tracking treatment outcomes using breath.

Melioidosis is a life-threatening infectious disease caused by Burkholderia pseudomallei (Bp). Rapid diagnosis and appropriate antimicrobial treatment...

Nano-antimicrobial peptides (Nano-AMPs) to combat resistant gram-negative bacteria.

The emergence of antimicrobial resistance (AMR) poses a critical threat to public health worldwide, making conventional antibiotics ineffective agains...

Identifying Drugs Associated With Parkinson's Disease Risk Using Machine Learning.

Machine learning (ML)-based methods have been proposed as a potential approach for identifying candidate drugs to be repurposed as disease-modifying t...

Predicting early discontinuation of adalimumab in patients with rheumatoid arthritis using machine learning: A specialty pharmacy-based approach.

BACKGROUND: Patients with rheumatoid arthritis (RA) prescribed adalimumab often discontinue treatment within 6 months because of a perceived lack of b...

Evaluating Guideline-Adherent Antibiotic Use for Skin Infections Using Natural Language Processing: A Pilot Study.

OBJECTIVE: To develop and evaluate an internally validated natural language processing (NLP) model to determine guideline adherence of antibiotic deci...

Gut check: Exploring tools, techniques, and future directions in microbiome research.

Poultry microbial communities are now recognized as key contributors to host nutrition, immune function, disease resilience, and overall health and pe...