Evidence map›Paper›PMID 25385554›Full record

ArticleBMC systems biology2014

Autoimmunity and tumor immunology: two facets of a probabilistic immune system.

Jaime Iranzo, Pablo Villoslada

Abstract read
In one paragraph

Article in BMC systems biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. The immune signatures of multiple sclerosis: Lessons from twin studies.Proceedings of the National Academy of Sciences of the United States of America · 2020
    Article
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  5. 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.

Jaime IranzoCentro de Astrobiología, INTA - CSIC, Madrid, Spain. iranzosj@cab.inta-csic.es.
Pablo VillosladaInstitute of Biomedical Research August Pi Sunyer (IDIBAPS), Hospital Clinic of Barcelona, Casanova 145, Cellex Center 3A, 08036, Barcelona, Spain. pvilloslada@clinic.ub.es.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe immune system of vertebrates has evolved the ability to mount highly elaborate responses to a broad range of pathogen-driven threats. Accordingly, it is quite a challenge to understand how a primitive adaptive immune system that probably lacked much of its present complexity could provide its bearers with significant evolutionary advantage, and therefore, continue to be selected for.

resultsWe have developed a very simple model of the immune system that captures the probabilistic communication between its innate and adaptive components. Probabilistic communication arises specifically from the fact that antigen presenting cells collect and present a range of antigens from which the adaptive immune system must (probabilistically) identify its target. Our results show that although some degree of self-reactivity in the immune repertoire is unavoidable, the system is generally able to correctly target pathogens rather than self-antigens. Particular circumstances that impair correct targeting and that may lead to infection-induced autoimmunity can be predicted within this framework. Notably, the probabilistic immune system exhibits the remarkable ability to detect sudden increases in the abundance of rare self-antigens, which represents a first step towards developing anti-tumoral responses.

conclusionA simple probabilistic model of the communication between the innate and adaptive immune system provides a robust immune response, including targeting tumors, but at the price of being at risk of developing autoimmunity.

Indexed as

Biological EvolutionModels, ImmunologicalAdaptive ImmunityAnimalsAutoimmunityImmunity, InnateNeoplasmsProbabilitySpecies SpecificityVertebrates

Identifiers

PMID25385554
PMCPMC4236429

What OpenQuestion holds

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Registered trials

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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.