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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. [ABSTRACT FROM AUTHOR]

Background: "Regeneration time" (RT) denotes the time required to obtain a stable mortality rate for mosquitoes exposed to insecticide-treated nets (ITNs) after three consecutive washes of a net in a day. The RT informs the wash interval used to artificially age ITNs to simulate their lifetime performance under user conditions (20 washes). RT was estimated following World Health Organization (WHO) longitudinal method (LM) procedures. Longitudinal evaluation may introduce heterogeneity due to mosquito batch variability, complicating RT determination. To overcome this, nets at each stage of regeneration (i.e., 1, 2, 3, 5 and 7 days post wash) were prepared in advance and refrigerated; then, a complete regeneration series was tested with a single mosquito batch on 1 testing day, completing four series over 4 days. This study compared the complete series method (CSM) against the LM. Methods: The overall heterogeneity in the methods for estimating RT of one incorporated alpha-cypermethrin and piperonyl butoxide (PBO) and one incorporated permethrin with PBO ITNs was determined using laboratory-reared resistant Anopheles arabiensis under standard laboratory conditions. LM methods and CSM were compared in two experiments with refrigerated nets acclimated for (i) 2 h (test 1) and (ii) 3 h (test 2). Four regeneration replicates per day were tested per ITN product with 50 mosquitoes exposed per replicate (equivalent sample size to LM). The heterogeneity from these methods was compared descriptively. Results: The intra-method variability for unwashed pieces was minimal, with variance of 1.26 for CSM and 1.18 for LM. For unwashed nets, LM had substantially greater variance and ratio of LM:CSM was 2.66 in test 1 and 2.49 in test 2. The magnitude of mortality measured in bioassays depended on sample acclimation after refrigeration. Conclusions: The CSM is a convenient method for determining the regeneration times. ITNs are prepared in advance, reducing pressure to prepare all samples to start on a single day. A complete regeneration series of samples is removed from the refrigerator, defrosted and evaluated on a single day with one mosquito batch reducing the influence of mosquito batch heterogeneity on results. Replicates can be conducted over several days but do not have to be conducted on consecutive days, allowing easy facility scheduling. [ABSTRACT FROM AUTHOR]

Background: Increasing metabolic resistance in malaria vector mosquitoes resulted in the development of insecticide-treated nets (ITNs) with active ingredients (AI) that target them. Bioassays that accurately measure the mortality induced by these AIs on ITNs are needed. Mosquito metabolic enzyme expression follows a circadian rhythm. Thus, this study assessed (i) influence of the time of day of mosquito exposure and (ii) timing of assessment of mortality post exposure (24 and 72 h) to ITNs against vectors that are susceptible to pyrethroids and those with metabolic and knockdown resistance mechanisms. Methods: Two cone bioassay experiments were conducted following World Health Organization (WHO) guidelines. Firstly, on ITNs incorporated with 2 g AI/kg of deltamethrin (DM) alone, or combined with 8 g AI/kg piperonyl butoxide (PBO) synergist, during the day (9:00–14:00 h) and repeated in the evening (18:00–20:00 h). This was followed by a confirmatory experiment during the afternoon (12:00–14:00 h) and repeated in the night (22:00–24:00 h) using mosquitoes unexposed or pre-exposed to PBO for 1 h before exposure to DM ITNs. Each net piece was tested with a minimum of eight cones per time (N = 24). The outcome was mortality after 24 h (M24) or 72 h (M72) of holding. Results: The cone bioassays performed using metabolic resistant mosquitoes during the evening showed significantly lower M24 than those performed in the day for DM: odds ratio (OR) 0.14 [95% confidence interval (CI) 0.06–0.30, p < 0.0001] and DM PBO [OR 0.29 (95% CI 0.18—0.49, p < 0.0001). M72 was higher than M24 for metabolic resistant mosquitoes exposed to DM [OR 1.44 (95% CI 1.09–1.88), p = 0.009] and DM PBO [OR 1.82 (95% CI 1.42–2.34), p < 0.0001]. An influence of hour of experiment and time of assessment was not observed for mosquitoes that had knockdown resistance or that were pyrethroid-susceptible. Conclusions: Time of day of experiment and hour of assessment of delayed mortality after exposure of mosquitoes are important considerations in evaluating insecticides that interact with mosquito metabolism to counter metabolic resistant mosquitoes. This is important when evaluating field-aged ITNs that may have lower concentrations of AI. [ABSTRACT FROM AUTHOR]