Clinical Research Directory

Artificial Intelligence-driven Tuberculosis Landscape Analysis & Stratification Research

The goal of this observational study is to establish and validate a comprehensive AI-driven clinical decision support system (AI-CDSS) in whole-chain management for pulmonary tuberculosis (TB) patients. The main question it aims to answer is: How is the predictive performance of this system in terms of multiple key links during TB diagnosis and treatment? Can real-world benefits be derived from this system? This AI framework supports clinicians in making smarter decisions, ultimately improving cure rates and ensuring that every patient receives the most effective, personalized care possible.

Pharmacokinetics of Drugs Used to Treat Drug Sensitive Tuberculosis in Breastfeeding Mother-infant Pairs

Pregnant or lactating women requiring treatment for drug sensitive-TB will be identified and invited for sampling. If they are pregnant when identified, they will be invited for sampling after delivery. Plasma and breastmilk samples will be obtained pre-dose and at 2, 4, 6, and 8 hours post-dose. If logistics permit (for example living close to the research unit), the participant will be invited for a further sample at 24 hours post-dose. A heelprick sample will also be obtained from their breastfed infants at maternal trough (prior to maternal dose) and at a random timepoint (once per infant) over the 8-hour pharmacokinetic sampling visit in order to characterize concentrations of these drugs over an 8-hour dosing interval. Total concentrations of plasma and breastmilk Isoniazid, Rifampicin, Pyrazinamide and Ethambutol will be determined. If a participant has her first pharmacokinetic profile in the intensive phase of TB treatment (whilst on all four of isoniazid, rifampicin, pyrazinamide and ethambutol), she will be invited for a subsequent sampling day with the same time points when she is on the continuation phase of therapy (rifampicin and isoniazid).