Infectious Disease

Illuminating the black box: Machine learning enhances preoperative prediction in intrahepatic cholangiocarcinoma.

The study by Huang , published in the , advances intrahepatic cholangiocarcinoma (ICC) management by developing a machine-learning model to predict textbook outcomes (TO) based on preoperative factors. By analyzing data from 376 patients across four Chinese medical centers, the researchers identified key variables influencing TO, including Child-Pugh classification, Eastern Cooperative Oncology Gr...

Identification of Hub Genes and Key Pathways Associated with Sepsis Progression Using Weighted Gene Co-Expression Network Analysis and Machine Learning.

Sepsis is a life-threatening condition driven by dysregulated immune responses, resulting in organ dysfunction and high mortality rates. Identifying key genes and pathways involved in sepsis progression is crucial for improving diagnostic and therapeutic strategies. This study analyzed transcriptomic data from 49 samples (37 septic patients across days 0, 1, and 8, and 12 healthy controls) using w...

New Strategies and Artificial Intelligence Methods for the Mitigation of Toxigenic Fungi and Mycotoxins in Foods.

The proliferation of toxigenic fungi in food and the subsequent production of mycotoxins constitute a significant concern in the fields of public heal...

TimeTracker: Event-based Continuous Point Tracking for Video Frame Interpolation with Non-linear Motion

Video frame interpolation (VFI) that leverages the bio-inspired event cameras as guidance has recently shown better performance and memory efficienc...

argNorm: normalization of antibiotic resistance gene annotations to the Antibiotic Resistance Ontology (ARO).

SUMMARY: Currently available and frequently used tools for annotating antibiotic resistance genes (ARGs) in genomes and metagenomes provide results us...

Comparison of different AI systems for diagnosing sepsis, septic shock, and cardiogenic shock: a retrospective study.

Sepsis, septic shock, and cardiogenic shock are life-threatening conditions associated with high mortality rates, but differentiating them is complex ...

Evaluating the Impact of AI-Powered Audiovisual Personalization on Learner Emotion, Focus, and Learning Outcomes

Independent learners often struggle with sustaining focus and emotional regulation in unstructured or distracting settings. Although some rely on am...

Early Prediction of Sepsis: Feature-Aligned Transfer Learning

Sepsis is a life threatening medical condition that occurs when the body has an extreme response to infection, leading to widespread inflammation, o...

Beyond the Monitor: Mixed Reality Visualization and AI for Enhanced Digital Pathology Workflow

Pathologists rely on gigapixel whole-slide images (WSIs) to diagnose diseases like cancer, yet current digital pathology tools hinder diagnosis. The...

A Sloppy approach to QSP: XAI enabling fit-for-purpose models

Quantitative Systems Pharmacology (QSP) promises to accelerate drug development, enable personalized medicine, and improve the predictability of cli...

Enhancing LLMs' Clinical Reasoning with Real-World Data from a Nationwide Sepsis Registry

Although large language models (LLMs) have demonstrated impressive reasoning capabilities across general domains, their effectiveness in real-world ...

Diagnostic Uncertainty in Pneumonia Detection using CNN MobileNetV2 and CNN from Scratch

Pneumonia Diagnosis, though it is crucial for an effective treatment, it can be hampered by uncertainty. This uncertainty starts to arise due to som...

Deep Representation Learning for Electronic Design Automation

Representation learning has become an effective technique utilized by electronic design automation (EDA) algorithms, which leverage the natural repr...

Optimization of fermentation conditions for enhanced hydrogen production by newly isolated Clostridium butyricum ZYZCB strain.

AIMS: The foundation of biohydrogen production lies in the utilization of efficient hydrogen-producing strains. This study aims to isolate highly effi...

GP or ChatGPT? Ability of large language models (LLMs) to support general practitioners when prescribing antibiotics.

INTRODUCTION: Large language models (LLMs) are becoming ubiquitous and widely implemented. LLMs could also be used for diagnosis and treatment. Nation...

Current Diagnosing Strategies for Mycobacterium tuberculosis and its Drug Resistance: A Review.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), remains a major global health threat, compounded by the rise of extensively drug-resist...

AdCare-VLM: Leveraging Large Vision Language Model (LVLM) to Monitor Long-Term Medication Adherence and Care

Chronic diseases, including diabetes, hypertension, asthma, HIV-AIDS, epilepsy, and tuberculosis, necessitate rigorous adherence to medication to av...

Probability Score for the Diagnosis of Periprosthetic Joint Infection: Development and Validation of a Practical Multi-analyte Machine Learning Model.

Background and objective The diagnosis of periprosthetic joint infection (PJI) relies on established criteria-based systems requiring interpretation a...

Dengue Early Warning System and Outbreak Prediction Tool in Bangladesh Using Interpretable Tree-Based Machine Learning Model.

BACKGROUND AND AIMS: A life-threatening vector-borne disease, dengue fever (DF), poses significant global public health and economic threats, includin...

Generative AI mitigates representation bias and improves model fairness through synthetic health data.

Representation bias in health data can lead to unfair decisions and compromise the generalisability of research findings. As a consequence, underrepre...