Infectious Disease

A systematic review of machine learning techniques for real-time speech and noise classification in hearing aids.

Introduction: Hearing loss significantly impairs speech comprehension in noisy environments, creating major communication challenges for individuals with hearing impairment. Modern hearing aids increasingly rely on intelligent systems capable of real-time speech and noise classification to enhance speech intelligibility while suppressing background noise. Researchers have explored signal processin...

Automated quantification of interstitial lung abnormalities and emphysema on computed tomography: a predictive marker for postoperative pulmonary complications after esophagectomy.

BACKGROUND: Postoperative pulmonary complications (PPCs), including pneumonia, acute lung injury, and acute respiratory distress syndrome, are common morbidities associated with mortality following esophagectomy. This study aimed to assess the association of chest computed tomography (CT) texture features with PPCs following esophagectomy. METHODS: Between 2016 and 2022, data from 765 patients who...

Transforming Pediatric Care Through AI: Bridging the Digital Divide in Health Informatics.

Health informatics and artificial intelligence (AI) technologies are increasingly influencing pediatric health care delivery across diverse health sys...

Comparative analysis of traditional and deep learning time series architectures for influenza A infectious disease forecasting.

Influenza A remains a major cause of respiratory mortality worldwide, motivating accurate forecasting to support timely preparedness and resource allo...

Performance of large language models (GPT-5.2, Gemini 3 Pro, Claude Sonnet 4.6 and Grok 4.1) on the Fellowship of The Royal College of Surgeons Urology Part A examination.

OBJECTIVES: To assess and compare the performance of four contemporary frontier large language models (LLMs)-GPT-5.2 (OpenAI), Gemini 3 Pro (Google De...

Identifying host-specific patterns in viral protein sequences to predict host spillover risk in animal and plant kingdoms.

Emerging pandemics are often driven by viral spillover events. However, predicting them remains difficult due to the complex interplay between viral g...

Usefulness of lung sound data collection using Skeeper SM-300® device: A pilot study.

BACKGROUND: Digital stethoscope has been introduced to clinical practice and are developing in many aspects. Overwhelming pandemic requires non-contac...

Machine learning-driven decision support for antibiotic optimization in typhoid fever based on patient profiles.

INTRODUCTION: Typhoid fever remains a major Global public health concern, with treatment outcomes strongly influenced by antimicrobial resistance (AMR...

Decoding bacterial transcriptional regulatory networks through integrated multi-omics datasets for artificial intelligence-driven design of programmable biological systems.

Advances in next-generation sequencing (NGS) have provided refined insight into bacterial transcriptional regulatory networks (TRNs) on a genome-wide ...

The VP1 Protein of Foot-and-Mouth Disease Virus: Bridging Pathogenicity and Control Strategies.

The Foot-and-mouth disease virus (FMDV) is a highly contagious pathogen affecting cloven-hoofed animals. Its major structural protein, VP1, serves as ...

ARDSMLpred: A machine learning model for predicting SCAP-associated ARDS based on MIMIC-IV database.

BACKGROUND: Early identification of acute respiratory distress syndrome (ARDS) in severe community-acquired pneumonia (SCAP) are crucial for reducing ...

Multi-omics precision diagnosis of brucellosis: Advances in biomarker discovery and clinical application.

Brucellosis, a neglected zoonosis caused by intracellular Brucella bacteria, remains a formidable global public health challenge, especially in develo...

Predicting biliary complications after liver transplantation: a machine learning and nomogram-based exploratory study.

BACKGROUND: Accurate risk stratification of biliary complications (BCs) after liver transplantation (LT) remains challenging. This study aimed to deve...

WormSpot: a machine learning-powered viability scoring platform in C. elegans for Candida pathogenicity studies.

UNLABELLED: Invasive Candida infections pose a critical health challenge, exacerbated by emerging antifungal resistance. Caenorhabditis elegans (C. el...

Host-Linked Virome Assembly and Turnover Predict Bacterial Community Structure in Wastewater Treatment Systems.

Viruses play crucial roles in bacterial ecology and evolution through virus-host interactions; however, their distribution, assembly mechanisms, and t...

Dissecting the pathobiology of suspected sepsis through a comparative analysis of endothelial inflammatory and clinical prediction models.

Sepsis remains a formidable challenge in critical care, and is characterized by profound circulatory and cellular abnormalities driven by both systemi...

Deep neural networks in medical microbiology for bacterial colonies classification.

While automation has transformed many areas inside clinical laboratories, microbiology still relies heavily on manual tasks, particularly the culture ...

Application of machine-learning algorithms to identify the key determinants of risk for HIV, hepatitis C and hepatitis B in primary care settings.

BACKGROUND: Testing for Blood-Borne-Viruses (BBVs) such as the human immunodeficiency virus (HIV), hepatitis C virus (HCV) and hepatitis B virus (HBV)...

Engineering immune cells for next-generation therapies.

Immune cell engineering has emerged as a transformative frontier in medicine, reshaping therapeutic strategies for cancer, autoimmunity and infectious...

Integrated Prediction of Ceftriaxone Resistance in Major Salmonella Serotypes Using MALDI-TOF MS Data with Balanced Datasets and Machine Learning.

OBJECTIVES: Salmonella causes gastroenteritis and invasive infections worldwide, and rising ceftriaxone resistance complicates empirical therapy. Conv...