Evidence map›Paper›PMID 28989808›Full record

ArticleComputation (Basel, Switzerland)2016

A Multi-Compartment Hybrid Computational Model Predicts Key Roles for Dendritic Cells in Tuberculosis Infection.

Simeone Marino, Denise E Kirschner

Open access · goldAbstract read
In one paragraph

Article in Computation (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
3.3field-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

18 citing papers in PubMed, 44 citations in OpenAlex.

  1. Characterizing the immune response toFrontiers in immunology · 2024
    Review
  2. Article
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Cellular and molecular bioengineering · 2021
    Article
  10. Article
  11. Article
  12. Frontiers in cellular and infection microbiology · 2020
    Review
  13. Article
  14. Agent-based models of inflammation in translational systems biology: A decade later.Wiley interdisciplinary reviews. Systems biology and medicine · 2019
    Review
  15. Article
  16. Review
  17. Article
  18. 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

2 authors at 1 institution in 1 country.

Simeone MarinoDepartment of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
Denise E KirschnerDepartment of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
University of Michigan–Ann Arbor · US

Funding

Predicting immune responses that correlate with protection against tuberculosisR01HL110811 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI FLYNN, JOANNE L., KIRSCHNER, DENISE E · 2012 to 2016
$5.9M
Predicting protective T-cell responses in Tuberculosis using a systems biology approachR01AI123093 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI FLYNN, JOANNE L., KIRSCHNER, DENISE E · 2016 to 2020
$3.6M
A Multi-scale systems pharmacology approach to TB therapyU01HL131072 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DARTOIS, VERONIQUE, FLYNN, JOANNE L. · 2016 to 2019
$2.9M
A multi-scale model to predict outcomes of immunomodulation and drug therapy duriR01EB012579 · NIBIB · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI FLYNN, JOANNE L., KIRSCHNER, DENISE E · 2011 to 2014
$2.3M
NHLBI NIH HHS R01 HL110811NHLBI NIH HHS U01 HL131072NIAID NIH HHS R01 AI123093NIBIB NIH HHS R01 EB012579
6 · The paper itself

Abstract

Tuberculosis (TB) is a world-wide health problem with approximately 2 billion people infected with

resultsThe model was calibrated using experimental data from the lungs and blood of NHPs. The addition of DCs allowed us to investigate in greater detail mechanisms of recruitment, trafficking and antigen presentation and their role in tuberculosis infection.

conclusionThe main conclusion of this study is that early events after Mtb infection are critical to establishing a timely and effective response. Manipulating CD8+ and CD4+ T cell proliferation rates, as well as DC migration early on during infection can determine the difference between bacterial clearance vs. uncontrolled bacterial growth and dissemination.

Indexed as

agent-based modeldendritic cellsmulti-compartmental modeltuberculosisuncertainty and sensitivity analysis

Identifiers

PMID28989808
PMCPMC5627612
OpenAlexW2538555370

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.