Sepsis

Context makes the difference: Temporally Resolved Dopaminergic Teaching Signals Shape Associative Memory in Drosophila Larvae

Animals can adapt their behavioral responses to environmental cues by learning from experience. This ability relies on the formation and recall of memories that are shaped by beneficial or detrimental consequences and regulated by the dopaminergic system, which is highly conserved across insect species. In the Drosophila melanogaster larva, eight of total [~]120 dopaminergic neurons (DANs) innerva...

Data-Driven Geospatial Modeling and Forecasting of Malaria Burden to Support Control and Elimination in Africa

Malaria elimination is shaped by complex interactions among climatic, environmental, socioeconomic, demographic, health-system, and intervention-related factors. However most studies examine only subsets of these drivers, limiting understanding of their combined influence on epidemiological risks. In this study, we integrated 25 years of data from 44 African countries on malaria burden and control...

Discovery of Selective Small-Molecule Ligands of SV2C by AI-Enhanced Virtual Screening and Experimental Validation

Synaptic vesicle glycoprotein 2C (SV2C) is a vesicular protein enriched in dopaminergic neurons of the basal ganglia that modulates dopamine storage a...

VTA dopamine neuron activity produces spatially organized stimulus and action value representations through conditioned reinforcement

What is the neural architecture by which dopamine (DA) determines choice? Reinforcement learning (RL) has suggested an algorithmic chain: prediction e...

Learning from human and chemical languages to predict biological function

Understanding how molecular structure encodes biological function remains a grand challenge in drug discovery. Here, we present PubCheF-1, a deep lear...

The Role of Natural Language Understanding in Multimodal Video-Based Dengue Diagnosis

Detecting infection-related behavioral changes in mosquitoes from video data is challenging because mosquitoes are small, move rapidly and irregularly...

Unmasking Toxic Mimicry in Medical Offline Reinforcement Learning for ICU Sepsis Management via Counterfactual Clinical Audits

Offline reinforcement learning (RL) offers considerable promise for optimizing ICU treatment decisions, yet standard evaluation metrics Mean Squared E...

Protective effects of testosterone replacement therapy on brain tumor outcomes: the Mayo Clinic Experience

BackgroundBiological sex and endocrine signaling influence cancer biology, immune response, and therapeutic outcomes. Recent evidence suggests that te...

Multimodal artificial intelligence using entire electronic health record and complete pathogen genome data for patient outcome prediction from life-threatening infection: the SuperbugAI Platform

Artificial intelligence (AI) has the potential to transform healthcare, with advanced multimodal approaches showing great promise in leveraging divers...

AI-guided discovery of antimicrobial peptides for urinary tract infections leveraging a new catalogue of the human urinary microbiome

Urinary tract infections (UTIs) are common infections that pose a critical burden on healthcare and society. Despite growing recognition that the huma...

Interpretable machine learning prediction of in-hospital mortality in ICU patients with cancer and sepsis using first-day data: Development using MIMIC-IV and external validation in eICU-CRD

Background: Critically ill patients with cancer and sepsis have high in-hospital mortality, but externally validated prediction models are limited. Ob...

Striatal acetylcholine enables latent-state creation during reversal learning

Like dopamine, acetylcholine is modulated in the striatum by reward-predicting cues and outcomes, yet its computational role remains unclear. Here, we...

CalTwin: Towards Calibrated, Shift-Robust Medical World Models via Fisher-Information Regularisation

Medical world models aim to learn a latent state of patient or organ physiology and a transition function that forecasts how that state evolves under ...

Interpretable gene networks from single-cell foundation models reveal conserved neurogenic dysfunction in Parkinson's disease

Interpreting large-scale singlecell transcriptomic data remains a major challenge for understanding disease mechanisms. Recent single-cell foundation ...

Constraints for spatially and temporally precise learning in a neural circuit model of reinforcement learning

Reinforcement learning is a key means by which animals learn appropriate actions in a given context. A large body of work suggests that such learning ...

Biologically Plausible Dopamine-Modulated STDP Model of Pavlovian Learning in Spiking Neural Networks

Spike-timing-dependent plasticity (STDP) and dopamine (DA) are fundamental to reward-based learning and memory formation. A widely used DA-modulated S...

The Mechanism Matters: When Knowledge Graphs Help Reinforcement Learning

Knowledge graphs (KGs) are widely used to inject prior knowledge into reinforcement learning (RL), yet the literature is dominated by single-domain, p...

Aligning Reinforcement Learning with Clinical Practice for Safe Decision Support in Pediatric Sepsis

Offline reinforcement learning (RL) has emerged as a promising framework for clinical decision support in sepsis, yet most existing studies focus excl...

Biological Amnesia in ICU Time-Series Prediction: A Drift-Adaptive Two-Stream Architecture with Temporal Retrieval

Background: Clinical decision support systems degrade silently as treatment protocols evolve, yet standard adaptation methods treat models as monolith...

Evaluation of four large language models on complex, infectious disease case scenarios

Objectives: Large language models (LLMs) are increasingly used in medicine, but evaluation is often on multiple choice questions and management of com...