Sepsis

Improving mortality prediction in critically ill cancer patients with a multidimensional machine learning model

Background: Prognostic assessment in critically ill patients with cancer remains challenging, as conventional ICU severity scores often perform suboptimally in this population. Machine learning (ML) approaches may improve outcome prediction by integrating acute physiology, organ dysfunction, and oncologic variables. We aimed to develop and validate ML-based models to predict ICU mortality and 30-d...

Rare Event Early Detection: A Dataset of Sepsis Onset for Critically Ill Trauma Patients

Sepsis is a major public health concern due to its high morbidity, mortality, and cost. Its clinical outcome can be substantially improved through early detection and timely intervention. By leveraging publicly available datasets, machine learning (ML) has driven advances in both research and clinical practice. However, existing public datasets consider ICU patients (Intensive Care Unit) as a unif...

NPCNet: Navigator-Driven Pseudo Text for Deep Clustering of Early Sepsis Phenotyping

Sepsis is a heterogeneous syndrome. Identifying clinically distinct phenotypes may enable more precise treatment strategies. In recent years, many res...

BIG-TB: A benchmark for evaluating prediction and interpretability of sequence-based machine learning using *Mycobacterium tuberculosis* genomes

Foundation models aim to learn useful representations of biological sequences. However, the applicability of these representations for a wide range of...

Predicting Anemia Among Under-Five Children in Nepal Using Machine Learning and Deep Learning

Childhood anemia remains a major public health challenge in Nepal and is associated with impaired growth, cognition, and increased morbidity. Using Wo...

Individual Differences in Dopaminergic Modulation of Exploration-Exploitation Behaviour

Dopamine (DA) has been implicated in exploration-exploitation behaviour, i.e., exploring novel, potentiallybetter options vs. exploiting known, previo...

Diversity of antibiotic resistance genes increases in urbanized lakes: a multi-tool screening

Antimicrobial resistance (AMR) is a growing global public health threat projected to cause up to 10 million deaths annually by 2050 if no immediate ac...

Multi-omic deep learning identifies exercise-responsive ageing pathways in humans

Genome-wide association studies of physical activity traits have mapped numerous loci, yet the molecular mechanisms through which exercise influences ...

Temporal Sepsis Modeling: a Fully Interpretable Relational Way

Sepsis remains one of the most complex and heterogeneous syndromes in intensive care, characterized by diverse physiological trajectories and variable...

A Learning-based Framework for Spatial Impulse Response Compensation in 3D Photoacoustic Computed Tomography

Photoacoustic computed tomography (PACT) is a promising imaging modality that combines the advantages of optical contrast with ultrasound detection. U...

Deep Learning-Based Spatial Immunoprofiling of Multiplex Immunofluorescence Images Distinguishes Tuberculosis Disease States in Diversity Outbred Mice

Tuberculosis (TB), caused by Mycobacterium tuberculosis (M.tb), remains a major global health challenge, with approximately 10.8 million new cases and...

Electrophysiological Correlates of Reinforcement Learning in the Human Ventral Tegmental Area

The ventral tegmental area is the primary source of dopaminergic input to the human prefrontal cortex and plays a central role in reinforcement learni...

ReflexSplit: Single Image Reflection Separation via Layer Fusion-Separation

Single Image Reflection Separation (SIRS) disentangles mixed images into transmission and reflection layers. Existing methods suffer from transmission...

Clinical and Cross-Domain Validation of an LLM-Guided, Literature-Based Gene Prioritization Framework

Background: We previously published a literature based pipeline for sepsis gene prioritization (PS3 and candidate genes) using an LLM enabled retrieva...

AI-Powered Acoustic Surveillance for Early Detection of Calf Respiratory Disease

Effective management of Bovine Respiratory Disease Complex (BRDC) requires timely, non-invasive diagnostic tools to protect calf health and welfare. A...

AMPBAN: A Deep Learning Framework Integrating Protein Sequence and Structural Features for Antimicrobial Peptide Prediction

The escalating crisis of antimicrobial resistance poses a devastating and immediate threat to human life. Antimicrobial peptides (AMPs) are a promisin...

The Vesicular Glutamate Transporter Modulates Sex and Region-Specific Differences in Dopaminergic Neuron α-Synuclein Toxicity by Modifying Cytosolic Dopamine Levels

Parkinson's disease disproportionately affects males; however, the cause of this sex difference is unknown. We found that expressing mutant -synuclein...

An anatomical hotspot for striatal dopamine-acetylcholine interactions during reward and movement

Dopamine (DA) and acetylcholine (ACh) are key neuromodulators that regulate striatal circuits underlying movement and reinforcement learning. Evidence...

Attention-Based Offline Reinforcement Learning and Clustering for Interpretable Sepsis Treatment

Sepsis remains one of the leading causes of mortality in intensive care units, where timely and accurate treatment decisions can significantly impact ...

Neural signatures of model-based and model-free reinforcement learning across prefrontal cortex and striatum

Animals integrate knowledge about how the state of the environment evolves to choose actions that maximise reward. Such goal-directed behaviour - or m...