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

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

Correlation of Cytokines with Parasitic Infections, Undernutrition and Micronutrient Deficiency among Schoolchildren in Rural Tanzania: A Cross-Sectional Study

Malnutrition and parasitic infections are often interconnected in a vicious cycle. Malnutrition can lead to changes in immune response, which may affect cytokine concentrations and potentially increase susceptibility to infections. In turn, parasitic infections can exacerbate malnutrition by impairing nutrient absorption. This cross-sectional study aimed to explore this interplay. Schoolchildren aged 6–12 years living in rural Tanzania (n = 120) provided blood, stool and urine samples to determine the relationship between cytokine concentrations (interleukin 4 (IL-4), interferon gamma (IFNγ) and interleukin 17A (IL-17A)), parasitic infections, undernutrition and micronutrient deficiency adjusting for sex, age, inflammatory markers, socioeconomic status and school categories. All schoolchildren had a normal blood cell count. The concentration of IL-4 was significantly higher in schoolchildren diagnosed with stunting, Schistosoma mansoni infection, a high C-reactive protein concentration, nausea, poor housing and increasing age. The concentration of IFNγ was associated with Plasmodium falciparum and Entamoeba histolytica/Entamoeba dispar/Entamoeba moshkovskii infections, vitamin A deficiency, attending the most remote schools and low socioeconomic status. Our study confirms a potential relationship between cytokine concentrations and parasitic infections, malnutrition and low socioeconomic status. A better understanding of long-term effects of parasitic infections and malnutrition on the immune function could help in designing tailored and effective interventions