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Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis.

ABSTRACT: BACKGROUND:Epidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study investigated whether mid-infrared (MIR) spectroscopy coupled with supervised machine learning could constitute an alternative method for rapid malaria screening, directly from dried human blood spots. METHODS:Filter papers containing dried blood spots (DBS) were obtained from a cross-sectional malaria survey in 12 wards in southeastern Tanzania in 2018/19. The DBS were scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra in the range 4000 cm-1 to 500 cm-1. The spectra were cleaned to compensate for atmospheric water vapour and CO2 interference bands and used to train different classification algorithms to distinguish between malaria-positive and malaria-negative DBS papers based on PCR test results as reference. The analysis considered 296 individuals, including 123 PCR-confirmed malaria positives and 173 negatives. Model training was done using 80% of the dataset, after which the best-fitting model was optimized by bootstrapping of 80/20 train/test-stratified splits. The trained models were evaluated by predicting Plasmodium falciparum positivity in the 20% validation set of DBS. RESULTS:Logistic regression was the best-performing model. Considering PCR as reference, the models attained overall accuracies of 92% for predicting P. falciparum infections (specificity?=?91.7%; sensitivity?=?92.8%) and 85% for predicting mixed infections of P. falciparum and Plasmodium ovale (specificity?=?85%, sensitivity?=?85%) in the field-collected specimen. CONCLUSION:These results demonstrate that mid-infrared spectroscopy coupled with supervised machine learning (MIR-ML) could be used to screen for malaria parasites in human DBS. The approach could have potential for rapid and high-throughput screening of Plasmodium in both non-clinical settings (e.g., field surveys) and clinical settings (diagnosis to aid case management). However, before the approach can be used, we need additional field validation in other study sites with different parasite populations, and in-depth evaluation of the biological basis of the MIR signals. Improving the classification algorithms, and model training on larger datasets could also improve specificity and sensitivity. The MIR-ML spectroscopy system is physically robust, low-cost, and requires minimum maintenance.

SUBMITTER: Mwanga EP

PROVIDER: S-EPMC6781347 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis.

Mwanga Emmanuel P EP Minja Elihaika G EG Mrimi Emmanuel E Jiménez Mario González MG Swai Johnson K JK Abbasi Said S Ngowo Halfan S HS Siria Doreen J DJ Mapua Salum S Stica Caleb C Maia Marta F MF Olotu Ally A Sikulu-Lord Maggy T MT Baldini Francesco F Ferguson Heather M HM Wynne Klaas K Selvaraj Prashanth P Babayan Simon A SA Okumu Fredros O FO

Malaria journal 20191007 1

<h4>Background</h4>Epidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study investigated whether mid-infrared (MIR) spectroscopy coupled with supervised machine learning could constitute an alternative method for rapid malaria screening, directly from dried human blood spots.<h4>Methods</h4>Filter papers containing dried blood spots (DBS) were obtained from a cross-sectional ...[more]

PMID: 31590669

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Project description:BackgroundScreening malaria-specific antibody responses on protein microarrays can help identify immune factors that mediate protection against malaria infection, disease, and transmission, as well as markers of past exposure to both malaria parasites and mosquito vectors. Most malaria protein microarray work has used serum as the sample matrix, requiring prompt laboratory processing and a continuous cold chain, thus limiting applications in remote locations. Dried blood spots (DBS) pose minimal biohazard, do not require immediate laboratory processing, and are stable at room temperature for transport, making them potentially superior alternatives to serum. The goals of this study were to assess the viability of DBS as a source for antibody profiling and to use DBS to identify serological signatures of low-density Plasmodium falciparum infections in malaria-endemic regions of Myanmar.MethodsMatched DBS and serum samples from a cross-sectional study in Ingapu Township, Myanmar were probed on protein microarrays populated with P. falciparum antigen fragments. Signal and trends in both sample matrices were compared. A case-control study was then performed using banked DBS samples from malaria-endemic regions of Myanmar, and a regularized logistic regression model was used to identify antibody signatures of ultrasensitive PCR-positive P. falciparum infections.ResultsApproximately 30% of serum IgG activity was recovered from DBS. Despite this loss of antibody activity, antigen and population trends were well-matched between the two sample matrices. Responses to 18 protein fragments were associated with the odds of asymptomatic P. falciparum infection, albeit with modest diagnostic characteristics (sensitivity 58%, specificity 85%, negative predictive value 88%, and positive predictive value 52%).ConclusionsMalaria-specific antibody responses can be reliably detected, quantified, and analysed from DBS, opening the door to serological studies in populations where serum collection, transport, and storage would otherwise be impossible. While test characteristics of antibody signatures were insufficient for individual diagnosis, serological testing may be useful for identifying exposure to asymptomatic, low-density malaria infections, particularly if sero-surveillance strategies target individuals with low previous exposure as sentinels for population exposure.

Dataset Information

Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis.

Publications

Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis.

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