Evidence map›Paper›PMID 28575319›Full record

ArticleILAR journal2017

Translational Research in the Nonhuman Primate Model of Tuberculosis.

Taylor W Foreman, Smriti Mehra, Andrew A Lackner, Deepak Kaushal

Open access · bronzeAbstract read
In one paragraph

Article in ILAR journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
3.2field-weighted citation impact, top 8% 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

33 citing papers in PubMed, 47 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. Review
  7. Challenges in developing new tuberculosis vaccines.Memorias do Instituto Oswaldo Cruz · 2025
    Review
  8. Review
  9. Review
  10. Review
  11. Article
  12. Article
  13. Animal models for COVID-19 and tuberculosis.Frontiers in immunology · 2023
    Review
  14. Article
  15. Review
  16. Exhaled aerosol increases with COVID-19 infection, age, and obesity.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Observational
  17. Advancing Adjuvants forFrontiers in immunology · 2021
    Review
  18. Article
  19. Review
  20. 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

4 authors at 1 institution in 1 country.

Taylor W ForemanTulane National Primate Research Center, Covington, Louisiana.
Smriti MehraLouisiana State University School, Veterinary Medicine, Baton Rouge, Louisiana.
Andrew A LacknerTulane National Primate Research Center, Covington, Louisiana.
Deepak KaushalTulane National Primate Research Center, Covington, Louisiana.
Tulane University · US

Funding

Tulane NPRC SPF Sheltered Outdoor Enclosure ExpansionP51OD011104 · OD · TULANE UNIVERSITY OF LOUISIANA · PI L Lee HAMM · 2012 to 2026
$142.4M
BAA-NIH-NIAID-NCRR-DMID-03-036UC6AI058609 · NIAID · TULANE UNIVERSITY OF LOUISIANA · PI LACKNER, ANDREW A · 2003 to 2006
$21.1M
Protein CoreP30GM110760 · NIGMS · LOUISIANA STATE UNIV A&M COL BATON ROUGE · PI KOUSOULAS, KONSTANTIN G · 2014 to 2018
$5.6M
Perturbation of antigen-specific T cell responses in latent TB/SIV co-infectionR01AI111943 · NIAID · TULANE UNIVERSITY OF LOUISIANA · PI KAUSHAL, DEEPAK, RENGARAJAN, JYOTHI · 2015 to 2019
$3.9M
Genetic Requirements for the survival of Tubercle Bacilli in Nonhuman PrimatesR01AI089323 · NIAID · TULANE UNIVERSITY OF LOUISIANA · PI KAUSHAL, DEEPAK · 2010 to 2014
$3.2M
Macaque model of TB/AIDS co-infection via natural routes of exposureR21RR026006 · NCRR · TULANE UNIVERSITY OF LOUISIANA · PI KAUSHAL, DEEPAK · 2009 to 2010
$452k
Correlates of protection from TB and TB/AIDS comorbidityR21AI127222 · NIAID · LOUISIANA STATE UNIV A&M COL BATON ROUGE · PI KOUSOULAS, KONSTANTIN G · 2016 to 2017
$425k
Characterization of Protective Immunity to MTB in a Setting of HIV CoinfectionR21AI127160 · NIAID · LOUISIANA STATE UNIV A&M COL BATON ROUGE · PI KOUSOULAS, KONSTANTIN G · 2016 to 2017
$299k
NCRR NIH HHS R21 RR026006NIAID NIH HHS R01 AI089323NIAID NIH HHS R01 AI111943NIAID NIH HHS R21 AI127160NIAID NIH HHS R21 AI127222NIAID NIH HHS UC6 AI058609NIGMS NIH HHS P30 GM110760NIH HHS P51 OD011104
6 · The paper itself

Abstract

Infection with Mycobacterium tuberculosis predominantly establishes subclinical latent infection over the lifetime of an individual, with a fraction of infected individuals rapidly progressing to active disease. The immune control in latent infection can be perturbed by comorbidities such as diabetes mellitus, obesity, smoking, and coinfection with helminthes or HIV. Modeling the varying aspects of natural infection remains incomplete when using zebrafish and mice. However, the nonhuman primate model of tuberculosis offers a unique and accurate model to investigate host responses to infection, test novel therapeutics, and thoroughly assess preclinical vaccine candidates. Rhesus macaques and cynomolgus macaques manifest the full gamut of clinical and pathological findings in human Mycobacterium tuberculosis infection, including the ability to co-infect macaques with Simian Immunodeficiency Virus to model HIV co-infection. Here we discuss advanced techniques to assay various clinical outcomes of the natural progression of infection as well as therapeutics in development and novel preclinical vaccines. Finally, we survey the translational aspects of nonhuman primate research and argue the urgent need to thoroughly examine preclinical therapeutics and vaccines using this model prior to clinical implementation.

Indexed as

AnimalsCoinfectionHIV InfectionsMacaca mulattaMiceMycobacterium tuberculosisSimian Immunodeficiency VirusTranslational Research, BiomedicalTuberculosisZebrafishhost directed therapeuticnonhuman primatetuberculosisvaccine

Identifiers

PMID28575319
PMCPMC6279141
OpenAlexW2619564211

What OpenQuestion holds

Textmetadata
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.