In:
PLOS ONE, Public Library of Science (PLoS), Vol. 17, No. 9 ( 2022-9-29), p. e0275096-
Abstract:
Plasmodium blood-stage infections can be identified by assaying for protein products expressed by the parasites. While the binary result of an antigen test is sufficient for a clinical result, greater nuance can be gathered for malaria infection status based on quantitative and sensitive detection of Plasmodium antigens and machine learning analytical approaches. Methods Three independent malaria studies performed in Angola and Haiti enrolled persons at health facilities and collected a blood sample. Presence and parasite density of P . falciparum infection was determined by microscopy for a study in Angola in 2015 (n = 193), by qRT-PCR for a 2016 study in Angola (n = 208), and by qPCR for a 2012–2013 Haiti study (n = 425). All samples also had bead-based detection and quantification of three Plasmodium antigens: pAldolase, pLDH, and HRP2. Decision trees and principal component analysis (PCA) were conducted in attempt to categorize P . falciparum parasitemia density status based on continuous antigen concentrations. Results Conditional inference trees were trained using the known P . falciparum infection status and corresponding antigen concentrations, and PCR infection status was predicted with accuracies ranging from 73–96%, while level of parasite density was predicted with accuracies ranging from 59–72%. Multiple decision nodes were created for both pAldolase and HRP2 antigens. For all datasets, dichotomous infectious status was more accurately predicted when compared to categorization of different levels of parasite densities. PCA was able to account for a high level of variance ( 〉 80%), and distinct clustering was found in both dichotomous and categorical infection status. Conclusions This pilot study offers a proof-of-principle of the utility of machine learning approaches to assess P . falciparum infection status based on continuous concentrations of multiple Plasmodium antigens.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0275096
DOI:
10.1371/journal.pone.0275096.g001
DOI:
10.1371/journal.pone.0275096.g002
DOI:
10.1371/journal.pone.0275096.g003
DOI:
10.1371/journal.pone.0275096.g004
DOI:
10.1371/journal.pone.0275096.t001
DOI:
10.1371/journal.pone.0275096.s001
DOI:
10.1371/journal.pone.0275096.s002
DOI:
10.1371/journal.pone.0275096.s003
DOI:
10.1371/journal.pone.0275096.s004
DOI:
10.1371/journal.pone.0275096.s005
DOI:
10.1371/journal.pone.0275096.s006
DOI:
10.1371/journal.pone.0275096.s007
DOI:
10.1371/journal.pone.0275096.s008
DOI:
10.1371/journal.pone.0275096.r001
DOI:
10.1371/journal.pone.0275096.r002
DOI:
10.1371/journal.pone.0275096.r003
DOI:
10.1371/journal.pone.0275096.r004
DOI:
10.1371/journal.pone.0275096.r005
DOI:
10.1371/journal.pone.0275096.r006
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2267670-3