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Statistical prediction of immunity to placental malaria based on multi-assay antibody data for malarial antigens

Overview of attention for article published in Malaria Journal, September 2017
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  • Above-average Attention Score compared to outputs of the same age (62nd percentile)
  • Average Attention Score compared to outputs of the same age and source

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7 tweeters

Citations

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2 Dimensions

Readers on

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26 Mendeley
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Title
Statistical prediction of immunity to placental malaria based on multi-assay antibody data for malarial antigens
Published in
Malaria Journal, September 2017
DOI 10.1186/s12936-017-2041-3
Pubmed ID
Authors

Chathura Siriwardhana, Rui Fang, Ali Salanti, Rose G. F. Leke, Naveen Bobbili, Diane Wallace Taylor, John J. Chen

Abstract

Plasmodium falciparum infections are especially severe in pregnant women because infected erythrocytes (IE) express VAR2CSA, a ligand that binds to placental trophoblasts, causing IE to accumulate in the placenta. Resulting inflammation and pathology increases a woman's risk of anemia, miscarriage, premature deliveries, and having low birthweight (LBW) babies. Antibodies (Ab) to VAR2CSA reduce placental parasitaemia and improve pregnancy outcomes. Currently, no single assay is able to predict if a woman has adequate immunity to prevent placental malaria (PM). This study measured Ab levels to 28 malarial antigens and used the data to develop statistical models for predicting if a woman has sufficient immunity to prevent PM. Archival plasma samples from 1377 women were screened in a bead-based multiplex assay for Ab to 17 VAR2CSA-associated antigens (full length VAR2CSA (FV2), DBL 1-6 of the FCR3, 3D7 and 7G8 lines, ID1-ID2a (FCR3 and 3D7) and 11 antigens that have been reported to be associated with immunity to P. falciparum (AMA-1, CSP, EBA-175, LSA1, MSP1, MSP2, MSP3, MSP11, Pf41, Pf70 and RESA)). Ab levels along with clinical variables (age, gravidity) were used in the following seven statistical approaches: logistic regression full model, logistic regression reduced model, recursive partitioning, random forests, linear discriminant analysis, quadratic discriminant analysis, and support vector machine. The best and simplest model proved to be the logistic regression reduced model. AMA-1, MSP2, EBA-175, Pf41, and MSP11 were found to be the top five most important predictors for the PM status based on overall prediction performance. Not surprising, significant differences were observed between PM positive (PM+) and PM negative (PM-) groups for Ab levels to the majority of malaria antigens. Individually though, these malarial antigens did not achieve reasonably high performances in terms of predicting the PM status. Utilizing multiple antigens in predictive models considerably improved discrimination power compared to individual assays. Among seven different classifiers considered, the reduced logistic regression model produces the best overall predictive performance.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 31%
Researcher 4 15%
Student > Bachelor 3 12%
Student > Ph. D. Student 3 12%
Student > Doctoral Student 2 8%
Other 3 12%
Unknown 3 12%
Readers by discipline Count As %
Medicine and Dentistry 5 19%
Immunology and Microbiology 3 12%
Biochemistry, Genetics and Molecular Biology 3 12%
Agricultural and Biological Sciences 2 8%
Social Sciences 2 8%
Other 6 23%
Unknown 5 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 November 2017.
All research outputs
#6,154,428
of 12,083,996 outputs
Outputs from Malaria Journal
#1,716
of 3,527 outputs
Outputs of similar age
#100,281
of 270,946 outputs
Outputs of similar age from Malaria Journal
#66
of 120 outputs
Altmetric has tracked 12,083,996 research outputs across all sources so far. This one is in the 48th percentile – i.e., 48% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,527 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has gotten more attention than average, scoring higher than 50% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 270,946 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.
We're also able to compare this research output to 120 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.