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Rapid Research in Diagnostics Development for TB Network
Sponsor: University of California, San Francisco
Summary
To reduce the burden of TB worldwide through more accurate, faster, simpler, and less expensive diagnosis of TB Every year, more than 3 million people with TB remain undiagnosed and 1 million die. Better diagnostics are essential to reducing the enormous burden of TB worldwide. The Rapid Research in Diagnostics Development for TB Network (R2D2 TB Network) brings together experts in TB care, technology assessment, diagnostics development, laboratory medicine, epidemiology, health economics and mathematical modeling with highly experienced clinical study sites in 10 countries.
Official title: Rapid Research in Diagnostics Development for TB Network (R2D2 TB Network) Study
Key Details
Gender
All
Age Range
12 Years - Any
Study Type
INTERVENTIONAL
Enrollment
26436
Start Date
2021-04-14
Completion Date
2031-05-31
Last Updated
2025-11-14
Healthy Volunteers
No
Conditions
Interventions
Novel mycobacterial culture techniques
We will evaluate tests intended to make culture more sensitive, faster, and have less contamination.
Novel sputum smear microscopy techniques
We will evaluate new staining techniques or visualization methods to increase the sensitivity of smear microscopy.
Sputum-based molecular assays
We will evaluate semi-automated or automated molecular assays intended for use at near point of care or point of care.
Tongue swab-based molecular assays
We will evaluate semi-automated or automated molecular assays intended for use at near point of care or point of care.
Urine LAM assays
We will evaluate urine LAM assays incorporating techniques such as analyte concentration, higher sensitivity or specificity antibodies, or enhanced visualization to improve LAM detection.
Blood-based host immune response assays
We will evaluate assays measuring host immune response parameters intended for use at near point of care or point of care.
Breath-based assays
We will evaluate assays assessing volatile organic compounds or exhaled breath condensate for near point of care of point of care detection of TB.
Artificial intelligence-based digital health tools
We will evaluate AI-based algorithms evaluating images (chest x-ray, ultrasound) or sounds (cough sounds, lung sounds) including an Infrasound-to-ultrasound e-stethoscope (Level 42 AI, USA).
Phage-based assays
We will evaluate assays using phages to lyse mycobacterial cells for detection of DNA or antigens.
Cartridge-based molecular assays for detecting drug resistance
We will evaluate semi-automated or automated molecular assays intended for use at near point of care or point of care.
Sequencing-based assays for detecting drug resistance
We will evaluate targeted and whole genome sequencing assays.
Locations (16)
National Center for Tuberculosis and Lung Diseases
Tbilisi, Georgia
Chitoor (Christian Medical College satellite campus)
Vellore, India
Christian Medical College CMC Pulmonary Outpatient Department
Vellore, India
Primary care clinics (Shalom/LCC, CHAD)
Vellore, India
Zankli Research Center
Abuja, Nigeria
De La Salle Medical and Health Sciences Institute
Dasmariñas, Philippines
Brooklyn Chest Hospital
Cape Town, South Africa
Khayelitsha District Health Center
Cape Town, South Africa
Kraaifontein Community Health Clinic
Cape Town, South Africa
Scottsdene primary care clinic
Cape Town, South Africa
Wallacedene primary care clinic
Cape Town, South Africa
Kisenyi Health Center
Kampala, Uganda
Mulago Outpatient Department
Kampala, Uganda
Hanoi Lung Hospital, Outpatient departments
Hanoi, Vietnam
National Lung Hospital, Outpatient departments
Hanoi, Vietnam
Centre for Infectious Disease Research in Zambia
Lusaka, Zambia