Universal precautions

Toward AI foundation models for epidemics: Promise, challenges, and paths forward.

Foundation models-large AI systems pretrained on broad, heterogeneous data-are transforming scientific discovery. These models (e.g., GPT, GenCast, AlphaFold) excel at learning generalizable representations and adapting to new tasks with limited data. Yet, epidemic modeling has not experienced a comparable transformation. Traditional models remain pathogen-specific and often struggle to generate r...

Cattle lameness detection using depth image and deep learning.

Lameness in cattle is a significant welfare and economic concern. To address this, we developed an end-to-end deep learning framework for 24/7 lameness monitoring using top-down depth images of cattle. The framework integrates three key stages: instance segmentation for detection, a custom multi-object tracking algorithm for identity preservation, and a spatio-temporal model for classification. We...

Visit-level prediction of missed HIV appointments using machine learning and transformer-based models in Uganda.

Machine learning diagnostic framework for liver fibrosis in chronic hepatitis B based on routine laboratory tests.

Early detection of liver fibrosis in chronic hepatitis B (CHB) patients is crucial for improving their prognosis. This study aims to develop a machine...

Machine learning-based modeling for monitoring and predicting the detection rate and severity of pathogenic microorganisms in seafood.

Pathogenic microbial contamination in seafood presents persistent risks to food safety and public health. Conventional monitoring methods frequently l...

Rapid Proteome-Wide Discovery of Protein-Protein Interactions With ppIRIS.

Protein-protein interactions (PPIs) are central to cellular processes and host-pathogen dynamics across all domains of life, yet comprehensive interac...

NRSeg: Noise-Resilient Learning for BEV Semantic Segmentation via Driving World Models.

Birds' Eye View (BEV) semantic segmentation is an indispensable perception task in end-to-end autonomous driving systems. Unsupervised and semi-superv...

Exploring the mechanism of polymorphonuclear neutrophils against sepsis based on immune model.

BACKGROUND: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection and remains a major global health chall...

Automatic deep learning-based segmentation of cornea and lens in 2D OCT images of rabbit eyes.

Anterior-segment optical coherence tomography (AS-OCT) supports cataract surgery planning by revealing corneal and crystalline lens geometry, but clin...

Optimization and validation of air impingement cleaning parameters for removing nonfat dry milk powder from stainless steel surfaces by machine learning.

Air impingement cleaning can remove residues from food contact surfaces without introducing moisture, thereby controlling allergen cross-contact and p...

Fingerprinting Fluorescent In Situ Hybridization Enables Multiplexed Identification of Pathogenic Bacteria.

Fluorescence in situ hybridization (FISH) is a highly specific technique for pathogenic bacteria detection that requires no culturing and provides sim...

Latent Supervision: A Method for Improved Performance and Calibration of Machine Learning Classification Models in Ophthalmology.

PURPOSE: Standard supervised learning assumes deterministic labels (e.g., positive or negative, present or absent), neglecting the diagnostic uncertai...

A machine learning-based model to predict multi-time-point prognosis for acute-on-chronic hepatitis B liver failure.

Our prognostic model and mobile application enable multi-time-point prognostic evaluation for patients with acute-on-chronic hepatitis B liver failure...

Advances in Pathogen Detection by Nanosensors: Biorecognition Strategies, Signal Amplification, and Platform Engineering.

The escalating global threat of infectious diseases, compounded by antimicrobial resistance (AMR), calls for improved diagnostic strategies. Conventio...

Biosensing technologies for foodborne pathogen detection and healthcare: principles, emerging materials, and intelligent platforms.

Investigating the Effect of Hospital Infection Control Informatization on Optimizing Microbiological Specimen Submission Before Antibiotic Therapy: Failure Mode and Effects Analysis.

BACKGROUND: Antimicrobial resistance (AMR) poses a critical global health threat, with inappropriate antibiotic use being a major driver. Timely micro...

[The evolution of antimicrobial therapy for sepsis].

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host immune response to infection, representing a critical global public healt...

DNA-Logic Trimodal Biofuel Cell Platform for AI-Assisted Dual Pathogen Detection.

A DNA-logic-gated, trimodal, self-powered biosensing platform is developed for the rapid, simultaneous, and on-site detection of two major sugarcane p...

The role of medical imaging in translational research into early pulmonary tuberculosis.

Early detection of tuberculosis (TB) is central to global efforts for TB care and prevention. Conventional symptom-based screening and sputum microbio...

AMR-GNN: a multi-representation graph neural network framework to enable genomic antimicrobial resistance prediction.

Whole-genome sequencing (WGS) data are an invaluable resource for understanding antimicrobial resistance (AMR) mechanisms. However, WGS data are high-...