Screening of malaria infections in human blood samples with varying parasite densities and anaemic conditions using AI-Powered mid-infrared spectroscopy.

BACKGROUND: Effective testing for malaria, including the detection of infections at very low densities, is vital for the successful elimination of the disease. Unfortunately, existing methods are either inexpensive but poorly sensitive or sensitive but costly. Recent studies have shown that mid-infrared spectroscopy coupled with machine learning (MIRs-ML) has potential for rapidly detecting malaria infections but requires further evaluation on diverse samples representative of natural infections in endemic areas. The aim of this study was, therefore, to demonstrate a simple AI-powered, reagent-free, and user-friendly approach that uses mid-infrared spectra from dried blood spots to accurately detect malaria infections across varying parasite densities and anaemic conditions. METHODS: Plasmodium falciparum strains NF54 and FCR3 were cultured and mixed with blood from 70 malaria-free individuals to create various malaria parasitaemia and anaemic conditions. Blood dilutions produced three haematocrit ratios (50%, 25%, 12.5%) and five parasitaemia levels (6%, 0.1%, 0.002%, 0.00003%, 0%). Dried blood spots were prepared on Whatman™ filter papers and scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) for machine-learning analysis. Three classifiers were trained on an 80%/20% split of 4655 spectra: (I) high contrast (6% parasitaemia vs. negative), (II) low contrast (0.00003% vs. negative) and (III) all concentrations (all positive levels vs. negative). The classifiers were validated with unseen datasets to detect malaria at various parasitaemia levels and anaemic conditions. Additionally, these classifiers were tested on samples from a population survey in malaria-endemic villages of southeastern Tanzania. RESULTS: The AI classifiers attained over 90% accuracy in detecting malaria infections as low as one parasite per microlitre of blood, a sensitivity unattainable by conventional RDTs and microscopy. These laboratory-developed classifiers seamlessly transitioned to field applicability, achieving over 80% accuracy in predicting natural P. falciparum infections in blood samples collected during the field survey. Crucially, the performance remained unaffected by various levels of anaemia, a common complication in malaria patients. CONCLUSION: These findings suggest that the AI-driven mid-infrared spectroscopy approach holds promise as a simplified, sensitive and cost-effective method for malaria screening, consistently performing well despite variations in parasite densities and anaemic conditions. The technique simply involves scanning dried blood spots with a desktop mid-infrared scanner and analysing the spectra using pre-trained AI classifiers, making it readily adaptable to field conditions in low-resource settings. In this study, the approach was successfully adapted to field use, effectively predicting natural malaria infections in blood samples from a population-level survey in Tanzania. With additional field trials and validation, this technique could significantly enhance malaria surveillance and contribute to accelerating malaria elimination efforts.

Sub-lethal exposure to chlorfenapyr reduces the probability of developing Plasmodium falciparum parasites in surviving Anopheles mosquitoes.

BACKGROUND: Pyrethroid resistance in the key malaria vectors threatens the success of pyrethroid-treated nets. To overcome pyrethroid resistance, Interceptor® G2 (IG2), a 'first-in-class' dual insecticidal net that combines alpha-cypermethrin with chlorfenapyr, was developed. Chlorfenapyr is a pro-insecticide, requiring bio-activation by oxidative metabolism within the insect's mitochondria, constituting a mode of action preventing cross-resistance to pyrethroids. Recent epidemiological trials conducted in Benin and Tanzania confirm IG2's public health value in areas with pyrethroid-resistant Anopheles mosquitoes. As chlorfenapyr might also interfere with the metabolic mechanism of the Plasmodium parasite, we hypothesised that chlorfenapyr may provide additional transmission-reducing effects even if a mosquito survives a sub-lethal dose. METHODS: We tested the effect of chlorfenapyr netting to reduce Plasmodium falciparum transmission using a modified WHO tunnel test with a dose yielding sub-lethal effects. Pyrethroid-resistant Anopheles gambiae s.s. with L1014F and L1014S knockdown resistance alleles and expression levels of pyrethroid metabolisers CYP6P3, CYP6M2, CYP4G16 and CYP6P1 confirmed by quantitative reverse transcription polymerase chain reaction (RT-qPCR) prior to conducting experiments were exposed to untreated netting and netting treated with 200 mg/m3 chlorfenapyr for 8 h overnight and then fed on gametocytemic blood meals from naturally infected individuals. Prevalence and intensity of oocysts and sporozoites were determined on day 8 and day 16 after feeding. RESULTS: Both prevalence and intensity of P. falciparum infection in the surviving mosquitoes were substantially reduced in the chlorfenapyr-exposed mosquitoes compared to untreated nets. The odds ratios in the prevalence of oocysts and sporozoites were 0.33 (95% confidence interval; 95% CI 0.23-0.46) and 0.43 (95% CI 0.25-0.73), respectively, while only the incidence rate ratio for oocysts was 0.30 (95% CI 0.22-0.41). CONCLUSION: We demonstrated that sub-lethal exposure of pyrethroid-resistant mosquitoes to chlorfenapyr substantially reduces the proportion of infected mosquitoes and the intensity of the P. falciparum infection. This will likely also contribute to the reduction of malaria in communities beyond the direct killing of mosquitoes.