ReviewInfection2024
Blazing the trail for innovative tuberculosis diagnostics.
Review in Infection, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 22 citations in OpenAlex.
- Expanding Xpert MTB/RIF Ultra and lateral flow urine lipoarabinomannan testing for diagnosis of tuberculosis among adults living with HIV admitted to hospitals in Tanzania and Mozambique (EXULTANT): a randomised controlled trial.The Lancet. Infectious diseases · 2026Trial
- Occurrence of drug resistance, associated risk factors, and expression of cytokine IL-33 in human TB patients.Annals of medicine and surgery (2012) · 2026Article
- "That is why I trust": a qualitative study on acceptability and feasibility of novel tongue swab diagnostics to assess people presenting with tuberculosis symptoms in Viet Nam and Zambia.BMC health services research · 2026Article
- Operational performance of GeneXpert for tuberculosis diagnosis in West Java province, Indonesia: a public health evaluation.BMC health services research · 2026Article
- Diagnostic Yield of Tongue Swab- Compared to Sputum-Based Molecular Testing for Tuberculosis in Four High-Burden Countries.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026Article
- Diagnostic Accuracy of Tuberculosis Screening Tests in a Prospective Multinational Cohort: Chest Radiography With Computer-Aided Detection, Xpert Tuberculosis Host Response, and C-Reactive Protein.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026Article
- From gaps to progress: five-year global advances in the diagnosis of pediatric tuberculosis.Frontiers in pediatrics · 2026Review
- Article
- Sensitive pathogen DNA detection by a multi-guide RNA Cas12a assay favoring trans- versus cis-cleavage.Nature communications · 2025Article
- AveloMask, a novel breath aerosol collection kit for airborneJournal of clinical microbiology · 2025Article
- Screening for latent tuberculosis infection in patients with chronic kidney disease: a review of evidence and current practice in the UK.Clinical kidney journal · 2025Review
- Oral Swab Testing With Xpert MTB/RIF Ultra for the Diagnosis of Tuberculosis in Children Aged <5 Years in Uganda: An Exploratory Interim Analysis of Diagnostic Accuracy in the NOD-pedFEND Cohort.Open forum infectious diseases · 2025Article
- A Comprehensive Review of the Diagnostics for Pediatric Tuberculosis Based on Assay Time, Ease of Operation, and Performance.Microorganisms · 2025Review
- Cost-effective chemiresistive biosensor with MWCNT-ZnO nanofibers for early detection of tuberculosis (TB) lipoarabinomannan (LAM) antigen.Mikrochimica acta · 2024Article
- Innovative COVID-19 Point-of-Care Diagnostics Suitable for Tuberculosis Diagnosis: A Scoping Review.Journal of clinical medicine · 2024Article
- Diagnostic accuracy of a sequence-specificMicrobiology spectrum · 2024Article
- Advances in TB diagnostics: A critical element for the elimination toolkit.The Indian journal of medical research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 6 institutions in 3 countries.
Funding
Abstract
The COVID-19 pandemic brought diagnostics into the spotlight in an unprecedented way not only for case management but also for population health, surveillance, and monitoring. The industry saw notable levels of investment and accelerated research which sparked a wave of innovation. Simple non-invasive sampling methods such as nasal swabs have become widely used in settings ranging from tertiary hospitals to the community. Self-testing has also been adopted as standard practice using not only conventional lateral flow tests but novel and affordable point-of-care molecular diagnostics. The use of new technologies, including artificial intelligence-based diagnostics, have rapidly expanded in the clinical setting. The capacity for next-generation sequencing and acceptance of digital health has significantly increased. However, 4 years after the pandemic started, the market for SARS-CoV-2 tests is saturated, and developers may benefit from leveraging their innovations for other diseases; tuberculosis (TB) is a worthwhile portfolio expansion for diagnostics developers given the extremely high disease burden, supportive environment from not-for-profit initiatives and governments, and the urgent need to overcome the long-standing dearth of innovation in the TB diagnostics field. In exchange, the current challenges in TB detection may be resolved by adopting enhanced swab-based molecular methods, instrument-based, higher sensitivity antigen detection technologies, and/or artificial intelligence-based digital health technologies developed for COVID-19. The aim of this article is to review how such innovative approaches for COVID-19 diagnosis can be applied to TB to have a comparable impact.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.