Latest AI and machine learning research in sepsis for healthcare professionals.
Long COVID (LC) affects millions of individuals worldwide, particularly those with preexisting comorbidities. However, whether these comorbidities should be defined before SARS-CoV-2 infection or before LC diagnosis remains unresolved, and this methodological choice may substantially bias estimates of comorbidity-associated LC risk. In addition, most previous studies were conducted during earlier ...
Background Quantifying the effect of Shigella diarrhea with and without antibiotic treatment on linear growth faltering is critical to understanding the potential impact of Shigella vaccines. Methods Using individual-level data from five multisite studies, we estimated the effect of Shigella diarrhea on length/height-for-age z-score (HAZ) 60-90 days after the episode compared to diarrhea episodes ...
Background: Antimicrobial resistance poses a major threat to global public health. Large language models (LLMs) offer new possibilities for optimizing...
Abdominal aortic aneurysm (AAA) patients in the ICU represent a heterogeneous, high-risk population with mortality risk evolving across distinct clini...
Classically, midbrain dopaminergic neuron activity is triggered by unexpected rewards, then, upon learning, by reward-predictive conditioned stimuli. ...
High-throughput proteomics has enabled detailed characterization of molecular states across health and disease. However, biological systems are inhere...
Dopamine activity in the tail of the striatum (TS) presents a novel challenge for reinforcement-learning theories of dopamine. Some studies suggest th...
Background. Clostridioides difficile infection (CDI) imposes a burden that extends well beyond the gastrointestinal tract, yet existing outcome measur...
Diarrheal disease remains a significant cause of morbidity and mortality in children under five years of age in low and middle-income countries. Ident...
Introduction: Standardizing fluid and vasopressor resuscitation in sep- tic shock is challenging due to patient heterogeneity. We trained a causal mod...
High-throughput transcriptomics has transformed disease biology, but its outputs often remain fragmented into gene and pathway lists that are difficul...
Motivation: Antimicrobial resistance (AMR) has been identified as a top global public health threat. Accurate AMR phenotype prediction from whole-geno...
Recent Vision-Language Models capture increasingly complex aspects of human cognition. Here we ask whether this alignment extends to reward valuation,...
Introduction: Plasma metagenomic next-generation sequencing (mNGS) may detect pathogens in solid organ transplant (SOT) recipients, but optimal patien...
Machine learning (ML) has accelerated molecular discovery, yet training models to generalize to out-of-distribution (OOD) chemical spaces remains fund...
Serotonin and dopamine make dissociable contributions to reinforcement learning (RL) sub-components, yet we lack neural biomarkers capable of detectin...
Background: Dopamine (DA) is a neurotransmitter critically involved in food-related reinforcement learning. While mesolimbic DA reward-associated sign...
Infection can substantially reduce host fitness and influence population dynamics, yet it is often difficult to detect and quantify in wild animal pop...
Striatal cholinergic interneurons (CINs) exhibit a transient pause in tonic firing in response to salient stimuli, a hallmark of reinforcement learnin...
Background: Standardized evaluation of agentic artificial intelligence (AI) for medication management is lacking. Given the potential lethality of med...