Infectious Disease

[Environmental medicine and AI, migration, and emerging and reemerging diseases in Mexico].

Mexico is facing an increasingly challenging health scenario, marked by the resurgence of emerging and re-emerging diseases. This rise is linked to factors such as climate change, unregulated urban development, and massive, uncontrolled migration flows. In 2024, over 110,000 cases of dengue were reported, including in non-endemic areas. This highlights the urgent need to strengthen epidemiological...

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 to develop and compare machine learning models to predict resistance to nine antibiotic classes in hospitalized patients, addressing the high-cost labeling challenge of medical data. We conducted a retrospective study using electronic medical records ...

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...

Phenogenomics reveals the ecology and evolution of Trichoderma fungi for sustainable agriculture.

Trichoderma fungi support sustainable agriculture by suppressing plant diseases and improving crop performance. However, emerging pathogenicity of Tri...

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...

Predicting pyrazinamide resistance in Mycobacterium tuberculosis using a graph convolutional network.

Tuberculosis Drug Discovery in the Age of Artificial Intelligence.

There is an urgent need to develop additional treatments for tuberculosis (TB) to complement the small panel of approved drugs and to devise shorter t...

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...

Potent efficacy of an NA-targeting antibody against a broad spectrum of H5N1 influenza viruses.

For nearly 30 years, Goose/Guangdong-derived highly pathogenic avian influenza H5N1 viruses have posed significant risks to economic stability, food s...

Advancing data science research education in Africa through datathon-driven innovations.

The rapid expansion of biomedical datasets generated by international research programs across Africa highlights an urgent need for advanced data scie...

Multimodal AI-based 28-day mortality prediction of pneumonia patients at ED discharge: a multicenter study.

AI in Childbirth and Emerging Alternatives to Traditional Fertility Treatments.

Artificial Intelligence (AI) is revolutionizing reproductive medicine by enhancing fertility treatments, childbirth monitoring, and postnatal care. AI...

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...

Interpretable machine learning model for predicting the risk of dysphagia in patients after cardiac surgery.

BACKGROUND: Dysphagia is recognized as one of the most common severe complications following cardiac surgery, with the potential to result in adverse ...

Mortality risk during the COVID-19 pandemic is shaped by human development.

BACKGROUND: During the global COVID-19 pandemic (2020-2021), excess mortality varied substantially across countries. Notably, upper-middle-income coun...

Sustainable Strategies in Tuberculosis Management: Bridging Ethnobotanical Pharmacology, Advanced Drug Delivery, and AI-Driven Innovation.

Tuberculosis (TB) persists as a leading infectious disease, with progress in global control complicated by emerging drug resistance, limited access to...

Master of Metals2: a graph neural network based architecture for the prediction of zinc binding sites in protein structures.

Zinc ions play essential structural and catalytic roles in a wide range of proteins. Accurate prediction of their binding sites is crucial for structu...