Tuberculosis

Rifky Waluyajati Rachman of the West Java Provincial Health Laboratory in Indonesia will employ targeted next-generation sequencing (NGS) to support genomic surveillance of drug-resistant tuberculosis (TB) in Indonesia. Indonesia has the second highest number of TB cases globally and a growing burden of largely undetected multidrug-resistant TB, yet no drug resistance surveillance in place. They will perform targeted NGS on over 5,000 positive sputum samples to more accurately estimate drug-resistant TB prevalence.

Essa Mohamedali and Kalebu Gwalugano of the Tanzania AI Community in Tanzania will use ChatGPT-4 to develop a chatbot and support tool to help healthcare workers adhere to the Integrated Management of Child Illness (IMCI) guidelines and access updates and alternative treatment options by linking them to the latest research via their mobile phones. Access to formal training on the IMCI guidelines is limited for healthcare workers, particularly in the private sector, and its duration makes it prohibitively expensive for companies.

Mohlopheni Marakalala of the Africa Health Research Institute in South Africa and Eric Rubin of the Harvard TH Chan School of Public Health in the U.S. will use a genetic screening tool, Tn-seq, to identify the specific bacterial genes protecting Mycobacterium tuberculosis (MTB) from immune destruction that could be used to develop new therapeutic approaches to fight tuberculosis, which causes over 1.5 million deaths annually. BCG is the only approved tuberculosis vaccine, but its effect is limited, particularly in adults.

Stephan Sieber of the Technical University of Munich in Germany will work together with Véronique Dartois of Hackensack Meridian Health in the U.S. to test whether his new antibiotic, which uniquely activates, as well as inactivates, molecular pathways to destroy certain pathogenic bacteria, can be adapted to kill the related Mycobacterium tuberculosis (Mtb), which causes tuberculosis. Current antibiotic treatments are lengthy, and it remains difficult to completely destroy all the bacteria in the body.

Fatema Khatun of the International Centre for Diarrhoeal Disease Research, Bangladesh in Bangladesh will develop a digital intervention to enable sharing of existing digital health data between community health workers and provide them with feedback indicators along with tailored messaging to parents to improve timeliness and coverage of vaccination against tuberculosis in rural Bangladesh. Tuberculosis is the number one cause of death by infectious disease worldwide, and 95% of deaths occur in developing countries.

Mohlopheni Marakalala of the Africa Health Research Institute in South Africa will study the role of specific proteins associated with immune cell death in tuberculosis patients to better understand how the disease progresses and help develop new diagnostics and therapies. Tuberculosis (TB) is a bacterial disease that causes 1.5 million deaths per year, mostly in poor countries. Understanding how the human immune system responds to TB infection could help develop more effective, host-targeted treatments.

Yingda Xie of Rutgers, The State University of NJ and JoAnne Flynn of the University of Pittsburgh, both in the U.S., will develop a non-invasive approach for testing candidate anti-tuberculosis compounds in animal models and patients using positron emission tomography-x-ray computed tomography (PET/CT). Tuberculosis (TB) is a leading cause of death in developing countries, and rates are sustained by the causative bacterium, Mycobacterium tuberculosis, developing resistance to current drugs.

Taslimarif Saiyed from Centre for Cellular and Molecular Platforms (C-CAMP) in India will develop microfluidics-based pH sensors for antimicrobial resistance (AMR) detection. AMR is big healthcare challenge worldwide and particularly in India. Susceptibility assays are vital to study the emergence of new AMR strains in a community or geography, especially during epidemics. The proposed system - Rapid Personalized Antibiotic Susceptibility Assay (r-PASA) - is a DNA-based assay with pH sensors on a microfluidics platform.

The researcher will use machine learning techniques and a linked database to analyze mortality from drug-resistant tuberculosis. The goal is to better understand how the flow of patients through the health services network have influenced, or not, the occurrence of resistance.

Clif Barry of The National Institute of Allergy and Infectious Diseases in the U.S., working with Qian Gao of Shanghai Medical College Fudan University in China, will support a clinical trial to shorten the treatment time for tuberculosis (TB) from six months to four months by helping to identify predictive biomarkers in individuals that only require the shorter treatment. Shortening treatment when possible will substantially reduce costs and the emergence of drug resistance, which is a major barrier to eradicating this deadly disease.