Evidence map›Paper›PMID 36189351›Full record

ReviewFrontiers in cellular and infection microbiology2022

Failing upwards: Genetics-based strategies to improve antibiotic discovery and efficacy in

Francesca G Tomasi, Eric J Rubin

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in cellular and infection microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 30% 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

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Review
  3. Target Identification in Anti-Tuberculosis Drug Discovery.International journal of molecular sciences · 2023
    Review
  4. Review
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

2 authors at 1 institution in 1 country.

Francesca G TomasiDepartment of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, United States.
Eric J RubinDepartment of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, United States.
Harvard University · US

Funding

Vulnerabilities in Mycobacterial Cell-Wall BiogenesisP01AI095208 · NIAID · UNIVERSITY OF CALIFORNIA-IRVINE · PI SACCHETTINI, JAMES C · 2012 to 2022
$19.8M
NIAID NIH HHS P01 AI095208
6 · The paper itself

Abstract

Therapeutic advances in the 20th century significantly reduced tuberculosis (TB) mortality. Nonetheless, TB still poses a massive global health challenge with significant annual morbidity and mortality that has been amplified during the COVID-19 pandemic. Unlike most common bacterial infectious diseases, successful TB treatment requires months-long regimens, which complicates the ability to treat all cases quickly and effectively. Improving TB chemotherapy by reducing treatment duration and optimizing combinations of drugs is an important step to reducing relapse. In this review, we outline the limitations of current multidrug regimens against TB and have reviewed the genetic tools available to improve the identification of drug targets. The rational design of regimens that sterilize diverse phenotypic subpopulations will maximize bacterial killing while minimizing both treatment duration and infection relapse. Importantly, the TB field currently has all the necessary genetic and analytical tools to screen for and prioritize drug targets

Indexed as

COVID-19Mycobacterium tuberculosisTuberculosis, Lymph NodeAntitubercular AgentsHumansPandemicsRecurrenceAntitubercular Agentsantibiotic resistancebacterial geneticschemical genetic profilingCRISPRidrug discoveryTnSeqtuberculosis

Identifiers

PMID36189351
PMCPMC9519881
OpenAlexW4295926418

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.