Infectious Disease

Predicting Quality of Life in People Living with HIV: A Machine Learning Model Integrating Multidimensional Determinants.

OBJECTIVE: With survival steadily improving among people living with HIV(PLWH), quality of life (QoL) has emerged as the ultimate benchmark of therapeutic success. We therefore aimed to develop and validate machine learning models that predict QoL trend in PLWH, identifying key determinants to inform personalized interventions and optimize long-term well-being.

MPNN-CWExplainer: An enhanced deep learning framework for HIV drug bioactivity prediction with class-weighted loss and explainability.

AIMS: Human Immunodeficiency Virus (HIV) remains a critical global health concern due to its impact on the immune system and its progression to Acquired Immunodeficiency Syndrome (AIDS) if untreated. While antiretroviral therapy has advanced significantly, challenges such as drug resistance, adverse effects, and viral mutation necessitate the development of novel therapeutic strategies. This study...

Content-based X-ray image retrieval using fusion of local neighboring patterns and deep features for lung disease detection.

This paper introduces a Content-Based Medical Image Retrieval (CBMIR) system for detecting and retrieving lung disease cases to assist doctors and rad...

Exploring Inflammatory Bowel Disease Discourse on Reddit Throughout the COVID-19 Pandemic Using OpenAI's GPT-3.5 Turbo Model: Classification Model Validation and Case Study.

BACKGROUND: Inflammatory bowel disease (IBD) is a chronic autoimmune disorder with an increasing prevalence in the general population. Internet-based ...

Deep learning-driven insights into the transmission dynamics of hepatitis B virus with treatment.

Viral infections have spread globally, profoundly affecting social and economic aspects of life and causing widespread suffering. Infection caused by ...

Machine learning for the prediction of urosepsis using electronic health record data.

Urosepsis, a medical condition resulting from the progression of urinary tract infection (UTI), is a leading cause of death in the US. Urosepsis occur...

Development and validation of a deep learning ultrasound radiomics model for predicting drug resistance in lymph node tuberculosis a multicenter study.

BACKGROUND: To develop and validate an ensemble machine learning ultrasound radiomics model for predicting drug resistance in lymph node tuberculosis ...

Development of machine learning prediction models for systemic inflammatory response following controlled exposure to a live attenuated influenza vaccine in healthy adults using multimodal wearable biosensors in Canada: a single-centre, prospective controlled trial.

BACKGROUND: Presymptomatic or asymptomatic immune system signals and subclinical physiological changes might provide a more objective measure of early...

Advances in Microbiome Research: Implications for Infectious Disease Management and Treatment.

INTRODUCTION: The human microbiome plays a pivotal role in health and disease, with microbial imbalances (dysbiosis) increasingly linked to heightened...

Unveiling etiology and mortality risks in community-acquired pneumonia: A machine learning approach.

Community-acquired pneumonia (CAP) is associated with high mortality, and accurate diagnosis and risk prediction are essential for improving patient o...

Unlocking microbial community succession and key influencing factors during bioelectrocatalytically-driven simultaneous removal of ammonia nitrogen and sulfate from wastewater.

Ammonia nitrogen (NH-N) and sulfate (SO) removal by Anaerobic ammonium oxidation (Anammox) and sulfate-reducing bacteria (SRB) was studied in dual-cha...

Predicting and Diagnosing Pneumonia in Patients Undergoing Elective Cardiac Surgery via Machine Learning Analysis of Exhaled Volatile Carbonyl Compounds.

OBJECTIVE: Pneumonia remains one of the most common post-operative complications after elective cardiac surgery. Early intervention could lead to impr...

Multimodal Generative Artificial Intelligence Model for Creating Radiology Reports for Chest Radiographs in Patients Undergoing Tuberculosis Screening.

Chest radiographs play a crucial role in tuberculosis screening in high-prevalence regions, although widespread radiographic screening requires exper...

Employing machine learning for identifying antifungal compounds against .

AIMS: To evaluate the efficacy of a machine learning approach in developing classification and regression models for antifungal activity against .

Integrating bioinformatics and machine learning to investigate the mechanisms by which three major respiratory infectious diseases exacerbate heart failure.

Heart failure (HF) is a severe cardiovascular disease often worsened by respiratory infections like influenza, COVID-19, and community-acquired pneumo...

Predictive modelling of air pollution affecting human tuberculosis risk on Mainland China.

In this study, we investigated the correlation between air pollution indicators and pulmonary tuberculosis (TB) incidence and mortality rates across p...

Dynamic mode decomposition for analysis and prediction of metabolic oscillations from time-lapse imaging of cellular autofluorescence.

Oscillations are a common phenomenon in cell biology. They are based on non-linear coupling of biochemical reactions and can show rich dynamic behavio...

Development of an electronic health record-based Human Immunodeficiency Virus (HIV) risk prediction model for women, incorporating social determinants of health.

BACKGROUND: Human Immunodeficiency Virus (HIV) pre-exposure prophylaxis (PrEP) prevents HIV transmission but has low uptake among women. Identifying w...

Integrating CT radiomics and clinical features using machine learning to predict post-COVID pulmonary fibrosis.

BACKGROUND: The lack of reliable biomarkers for the early detection and risk stratification of post-COVID-19 pulmonary fibrosis (PCPF) underscores the...

Associations between weight gain, integrase inhibitors antiretroviral agents, and gut microbiome in people living with HIV: a cross-sectional study.

Dolutegravir and bictegravir are second-generation HIV integrase strand transfer inhibitors (INSTIs) that were previously associated with abnormal wei...