Sylhet

Methanol poisoning is a serious health problem, especially in low- and middle-income countries, where outbreaks can cause severe harm to communities. Diagnosing methanol poisoning is difficult because its symptoms resemble other conditions, and traditional tests require costly laboratory equipment. This research aims to evaluate a new bedside test that uses a single drop of blood to detect formate, a substance only present in methanol poisoning, to improve diagnosis speed and accuracy. The research includes two parts first, an observational study comparing the new point-of-care formate test to standard laboratory tests to check its accuracy. If the new test shows good sensitivity, a second study will follow, which is a feasibility trial where hospitals are randomly assigned to different diagnostic approaches. This trial will investigate whether using the bedside test can lead to faster diagnosis and treatment, reduce unnecessary treatments, and evaluate clinical and cost outcomes. Participants suspected of methanol poisoning or unexplained metabolic acidosis at large hospitals in Bangladesh and India will be involved. During the studies, timing from patient arrival to diagnosis and treatment will be measured, along with clinical outcomes such as death rates and treatment needs. The research will also assess how well hospitals can be recruited for this type of trial and aims to raise awareness about methanol poisoning and improve care practices over the study period ending in 2028.

Researchers are evaluating the use of an artificial intelligence AI-enabled digital stethoscope to improve antibiotic use in children aged 2 to 59 months with lower respiratory infections in rural Bangladesh. The study aims to determine if clinical guidelines enhanced by this digital stethoscope can safely reduce unnecessary antibiotic use while maintaining treatment effectiveness. The trial also assesses whether this approach is a cost-effective alternative to standard care, addressing the global problem of rising antibiotic resistance. Participants will be randomly assigned to one of two groups. One group will receive care guided by clinical guidelines enhanced by the AI-enabled digital stethoscope, which classifies lung sounds to decide if amoxicillin syrup or placebo should be given twice daily for five days. The other group will receive standard care with amoxicillin as per guidelines. Digital lung sound recordings are taken from four chest positions using a wireless stethoscope, and analysis is done by an automated algorithm. Masking is maintained by applying the stethoscope to all children and hiding the results. Children will be followed for two weeks with at least six study visits, including home visits by community health workers on days 2, 3, 5, and 14, and a clinic visit on day 7 to assess treatment success or failure. Caregivers can contact the study team anytime via a hotline. Safety is closely monitored, with referrals to hospital for treatment failure signs or low oxygen levels. The trial will track treatment failure, relapse, and safety outcomes, aiming to provide evidence on whether AI-enhanced diagnosis can safely reduce antibiotic use in children with pneumonia symptoms.