AIMC Topic: HIV Infections
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AI-driven analysis by identifying risk factors of VL relapse in HIV co-infected patients.
Scientific reports
Jul 1, 2025
Visceral Leishmaniasis (VL), also known as Kala-Azar, poses a significant global public health challenge and is a neglected disease, with relapses and treatment failures leading to increased morbidity and mortality. This study introduces an explainab...
Machine learning based gut microbiota pattern and response to fiber as a diagnostic tool for chronic inflammatory diseases.
BMC microbiology
Jun 6, 2025
Gut microbiota has been implicated in the pathogenesis of multiple gastrointestinal (GI) and systemic metabolic and inflammatory disorders where disrupted gut microbiota composition and function (dysbiosis) has been found in multiple studies. Thus, h...
A Robust Cross-Platform Solution With the Sense2Quit System to Enhance Smoking Gesture Recognition: Model Development and Validation Study.
Journal of medical Internet research
May 20, 2025
BACKGROUND: Smoking is a leading cause of preventable death, and people with HIV have higher smoking rates and are more likely to experience smoking-related health issues. The Sense2Quit study introduces innovative advancements in smoking cessation t...
Predictive survival modelings for HIV-related cryptococcosis: comparing machine learning approaches.
Frontiers in cellular and infection microbiology
May 2, 2025
INTRODUCTION: HIV-associated cryptococcosis is marked by unpredictable disease trajectories and persistently high mortality rates worldwide. Although improved risk stratification and tailored clinical management are urgently needed to enhance patient...
The future of HIV diagnostics: an exemplar in infectious diseases.
The lancet. HIV
Apr 30, 2025
Over the past 40 years, diagnostics have become the backbone of HIV prevention, treatment, and retention in care, and are central to the achievement of UNAIDS 95-95-95 targets. Over the next decade, the global HIV response will face difficult challen...
A comparative study on TB incidence and HIVTB coinfection using machine learning models on WHO global TB dataset.
Scientific reports
Apr 21, 2025
Tuberculosis, a deadly and contagious disease caused by Mycobacterium tuberculosis, remains a significant global public health threat. HIV co-infection significantly increases the risk of active TB recurrence and prolongs medical treatment for tuberc...
Role of eccentricity based topological descriptors to predict anti-HIV drugs attributes with supervised machine learning algorithms.
Computers in biology and medicine
Mar 27, 2025
Chemical graphs are mathematical representations of molecular structures, where atoms are represented as vertices, while chemical bonds are depicted as edges of a graph. The chemical graphs are widely used in cheminformatics to analyze molecular prop...
An explainable web application based on machine learning for predicting fragility fracture in people living with HIV: data from Beijing Ditan Hospital, China.
Frontiers in cellular and infection microbiology
Mar 14, 2025
PURPOSE: This study aimed to develop and validate a novel web-based calculator using machine learning algorithms to predict fragility fracture risk in People living with HIV (PLWH), who face increased morbidity and mortality from such fractures.
Predicting the immunological nonresponse to antiretroviral therapy in people living with HIV: a machine learning-based multicenter large-scale study.
Frontiers in cellular and infection microbiology
Mar 11, 2025
BACKGROUND: Although highly active antiretroviral therapy (HAART) has greatly enhanced the prognosis for people living with HIV (PLWH), some individuals fail to achieve adequate immune reconstitution, known as immunological nonresponse (INR), which i...
Prediction of new HIV infection in men who have sex with men based on machine learning: secondary analysis of a prospective cohort study from Western China.
Annals of medicine
Mar 10, 2025
OBJECTIVE: This study aimed to construct a model based on machine learning to predict new HIV infections in HIV-negative men who have sex with men (MSM).
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