Viral Genetic Diversity and Latent Reservoir Size in HIV Patients Undergoing Antiretroviral Therapy

While highly active antiretroviral therapy (ART) suppresses HIV replication and promotes immune recovery, 15–30% of patients fail to restore CD4+ T-cell counts despite effective viral suppression. These individuals, known as immunological non-responders (INRs), may experience impaired CD4+ T-cell production or increased destruction due to HIV pathogenesis, chronic immune activation, and host genetic factors. HIV persists in latent reservoirs where replication-competent virus remain hidden and can reactivate. Most studies focus on latent reservoirs in resting CD4+ T cells, but little is known about reservoir size and viral genetics in macrophages and other cell types. This research aims to investigate viral genetic diversity and latent reservoir size in macrophages and resting CD4+ T cells among immunological responders and non-responders following ART.

Microfluidic CRISPR-Based Diagnostic Platforms for Emerging Viral Diseases in Resource-Limited Settings

Emerging and re-emerging infectious diseases (EIDs/REIDs) are challenging to diagnose due to overlapping clinical symptoms among pathogens. Molecular methods, particularly syndromic panel assays, are widely used for their sensitivity and speed. However, these panels typically target only common pathogens, limiting their utility for novel or re-emerging viruses. The lack of standardized diagnostic methods further complicates rapid outbreak response. Next-generation sequencing (NGS) remains a powerful tool for identifying unknown pathogens but is time-consuming, costly, and requires specialized expertise. Although point-of-care testing (POCT) is available in some decentralized settings, current platforms are limited in scope and accessibility. This research aims to develop an affordable, portable POCT platform capable of both specific and pan-family viral detection for use in remote and resource-limited settings. The project is conducted in collaboration with the Department of Biomedical Engineering, Faculty of Engineering, Mahidol University; VISTEC; UAMS; and the Ministry of Public Health, Thailand.

Viral Genetic Evolution, Molecular Surveillance, and AI-Driven Tracking Systems

The rapid evolution of viral genomes poses significant challenges to disease control, particularly for emerging and re-emerging infectious diseases. Mutations can lead to increased transmissibility, immune escape, and resistance to treatment, emphasizing the need for continuous genomic monitoring. Next-generation sequencing (NGS) provides a powerful tool for capturing viral genetic changes in real time. When integrated with artificial intelligence (AI), these data can be used to detect variants of concern, predict transmission dynamics, and inform targeted interventions. AI-driven analytics enhance the speed and accuracy of genomic surveillance, enabling early outbreak detection and improving epidemic forecasting. This research supports the development of precision public health strategies by combining molecular surveillance with predictive modeling to strengthen global preparedness and response to viral threats.