Evidence map›Paper›PMID 35252594›Full record

ArticleJournal of clinical tuberculosis and other mycobacterial diseases2022

Detecting rifampin and isoniazid resistance in

Rebecca E Colman, Christopher Hagan, Peter Chiles, Marva Seifert, Donald G Catanzaro, Alexander V Kukhtin, Ryan Norville, Laura Hauns, Arial Bueno, Rebecca C Holmberg and 2 more

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical tuberculosis and other mycobacterial diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 41% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 3 institutions in 1 country.

Rebecca E ColmanDepartment of Medicine, University of California, San Diego, La Jolla, CA, USA.
Christopher HaganDepartment of Medicine, University of California, San Diego, La Jolla, CA, USA.
Peter ChilesDepartment of Medicine, University of California, San Diego, La Jolla, CA, USA.
Marva SeifertDepartment of Medicine, University of California, San Diego, La Jolla, CA, USA.
Donald G CatanzaroDepartment of Biological Sciences, University of Arkansas, Fayetteville, AR, USA.
Alexander V KukhtinAkonni Biosystems, Inc., Frederick, MD, USA.
Ryan NorvilleAkonni Biosystems, Inc., Frederick, MD, USA.
Laura HaunsAkonni Biosystems, Inc., Frederick, MD, USA.
Arial BuenoAkonni Biosystems, Inc., Frederick, MD, USA.
Rebecca C HolmbergAkonni Biosystems, Inc., Frederick, MD, USA.
Christopher G CooneyAkonni Biosystems, Inc., Frederick, MD, USA.
Timothy C RodwellDepartment of Medicine, University of California, San Diego, La Jolla, CA, USA.
Akonni Biosystems (United States) · USUniversity of California, San Diego · USUniversity of Arkansas at Fayetteville · US

Funding

UC San Diego Clinical and Translational Research InstituteUL1TR001442 · NCATS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI FIRESTEIN, GARY S, HOGARTH, MICHAEL · 2015 to 2024
$88.3M
VirologyP30AI036214 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI David Mitchell Smith · 1994 to 2026
$78.4M
Sequencing CoreU19AI109755 · NIAID · HARVARD MEDICAL SCHOOL · PI CHANDLER, DARRELL P, CULHANE, AEDIN C · 2014 to 2018
$29.1M
An Integrated Tabletop Platform for Rapid Detection of XDR-TB in Clinical SamplesR01AI111435 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI RODWELL, TIMOTHY CHARLES · 2014 to 2018
$5.4M
TB Diagnostics at the Point of CareR44AI138903 · NIAID · AKONNI BIOSYSTEMS, INC. · PI COONEY, CHRISTOPHER GERARD · 2018 to 2020
$3.0M
Lab-on-a-Film Multiplexed Test for Respiratory Mycobacterial InfectionsR44AI162346 · NIAID · AKONNI BIOSYSTEMS, INC. · PI COONEY, CHRISTOPHER GERARD · 2022 to 2024
$3.0M
TB Diagnostics at the Point of CareR44EB011274 · NIBIB · AKONNI BIOSYSTEMS, INC. · PI COONEY, CHRISTOPHER GERARD · 2013 to 2015
$1.2M
Lab-on-a-Film Multiplexed Test for Respiratory Mycobacterial InfectionsR43AI162346 · NIAID · AKONNI BIOSYSTEMS, INC. · PI COONEY, CHRISTOPHER GERARD · 2021 to 2021
$300k
NCATS NIH HHS UL1 TR001442NIAID NIH HHS HHSN272201700001CNIAID NIH HHS HHSN272201700001GNIAID NIH HHS P30 AI036214NIAID NIH HHS R01 AI111435NIAID NIH HHS R43 AI162346NIAID NIH HHS R44 AI138903NIAID NIH HHS R44 AI162346NIAID NIH HHS U19 AI109755NIBIB NIH HHS R44 EB011274
6 · The paper itself

Abstract

While there has been progress in detection of drug resistant tuberculosis globally, WHO estimates only about half of the patients with bacteriologically confirmed tuberculosis were tested for rifampicin resistance over the past two years. To close this drug resistance diagnostic gap, an expansion of testing for rifampicin and isoniazid resistance is critically needed. The Akonni Biosystem Integrated System combines DNA extraction and a Lab-on-a-Film assembly (LFA) to perform rapid probe and PCR-based detection of resistance associated mutations to first-line anti-tuberculosis drugs. Using raw sputum samples from 25 tuberculosis patients at risk for drug resistance, we conducted a proof-of-concept study of the Integrated System with an MDR-TB assay. Performance of the Integrated System was compared to liquid Mycobacteria Growth Indicator Tube (MGIT) culture reference phenotypes using 2012 WHO endorsed critical concentrations for rifampicin and isoniazid. The overall percent agreement for rifampicin and isoniazid was 91.7% and 100% respectively, with agreement for rifampicin increasing to 95.7% after low-level resistance mutations in

Indexed as

Benchtop automated systemMultidrug-resistant tuberculosis diagnostic

Identifiers

PMID35252594
PMCPMC8891689
OpenAlexW4213190280

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.