Evidence map›Paper›PMID 20334682›Full record

ArticleMalaria journal2010

Algorithms to predict cerebral malaria in murine models using the SHIRPA protocol.

Yuri C Martins, Guilherme L Werneck, Leonardo J Carvalho, Beatriz P T Silva, Bruno G Andrade, Tadeu M Souza, Diogo O Souza, Cláudio T Daniel-Ribeiro

Abstract read
In one paragraph

Article in Malaria journal, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Malaria Related Neurocognitive Deficits and Behavioral Alterations.Frontiers in cellular and infection microbiology · 2022
    Review
  8. Article
  9. Article
  10. Targeting Angiotensin II Type-1 Receptor (ATFrontiers in cellular and infection microbiology · 2017
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. S-nitrosoglutathione prevents experimental cerebral malaria.Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology · 2012
    Article
  18. Article
  19. Article
  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

8 authors.

Yuri C MartinsLaboratório de Pesquisas em Malária, Instituto Oswaldo Cruz, FIOCRUZ, Brasil, 4365 - Manguinhos, Cep: 21045-900 - Rio de Janeiro - RJ, Brasil. yuri@ioc.fiocruz.br
Guilherme L Werneck
Leonardo J Carvalho
Beatriz P T Silva
Bruno G Andrade
Tadeu M Souza
Diogo O Souza
Cláudio T Daniel-Ribeiro

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPlasmodium berghei ANKA infection in C57Bl/6 mice induces cerebral malaria (CM), which reproduces, to a large extent, the pathological features of human CM. However, experimental CM incidence is variable (50-100%) and the period of incidence may present a range as wide as 6-12 days post-infection. The poor predictability of which and when infected mice will develop CM can make it difficult to determine the causal relationship of early pathological changes and outcome. With the purpose of contributing to solving these problems, algorithms for CM prediction were built.

methodsSeventy-eight P. berghei-infected mice were daily evaluated using the primary SHIRPA protocol. Mice were classified as CM+ or CM- according to development of neurological signs on days 6-12 post-infection. Logistic regression was used to build predictive models for CM based on the results of SHIRPA tests and parasitaemia.

resultsThe overall CM incidence was 54% occurring on days 6-10. Some algorithms had a very good performance in predicting CM, with the area under the receiver operator characteristic ((au)ROC) curve > or = 80% and positive predictive values (PV+) > or = 95, and correctly predicted time of death due to CM between 24 and 72 hours before development of the neurological syndrome ((au)ROC = 77-93%; PV+ = 100% using high cut off values). Inclusion of parasitaemia data slightly improved algorithm performance.

conclusionThese algorithms work with data from a simple, inexpensive, reproducible and fast protocol. Most importantly, they can predict CM development very early, estimate time of death, and might be a valuable tool for research using CM murine models.

Indexed as

AlgorithmsNeurologic ExaminationAnimalsBehavior, AnimalDisease Models, AnimalFemaleForecastingLogistic ModelsMalaria, CerebralMiceMice, Inbred C57BLNervous System DiseasesNeuropsychological TestsPlasmodium berghei

Identifiers

PMID20334682
PMCPMC2850361

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LicenceCC BY
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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.