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Abstract 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 Field-derived metrics are critical for effective control of malaria, particularly in sub-Saharan Africa where the disease kills over half a million people yearly. One key metric is entomological inoculation rate, a direct measure of transmission intensities, computed as a product of human biting rates and prevalence of Plasmodium sporozoites in mosquitoes. Unfortunately, current methods for identifying infectious mosquitoes are laborious, time-consuming, and may require expensive reagents that are not always readily available. Here, we demonstrate the first field-application of mid-infrared spectroscopy and machine learning (MIRS-ML) to swiftly and accurately detect Plasmodium falciparum sporozoites in wild-caught Anopheles funestus, a major Afro-tropical malaria vector, without requiring any laboratory reagents. We collected 7178 female An. funestus from rural Tanzanian households using CDC-light traps, then desiccated and scanned their heads and thoraces using an FT-IR spectrometer. The sporozoite infections were confirmed using enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR), to establish references for training supervised algorithms. The XGBoost model was used to detect sporozoite-infectious specimen, accurately predicting ELISA and PCR outcomes with 92% and 93% accuracies respectively. These findings suggest that MIRS-ML can rapidly detect P. falciparum in field-collected mosquitoes, with potential for enhancing surveillance in malaria-endemic regions. The technique is both fast, scanning 60–100 mosquitoes per hour, and cost-efficient, requiring no biochemical reactions and therefore no reagents. Given its previously proven capability in monitoring key entomological indicators like mosquito age, human blood index, and identities of vector species, we conclude that MIRS-ML could constitute a low-cost multi-functional toolkit for monitoring malaria risk and evaluating interventions.

Abstract Background The degree to which Anopheles mosquitoes prefer biting humans over other vertebrate hosts, i.e. the human blood index (HBI), is a crucial parameter for assessing malaria transmission risk. However, existing techniques for identifying mosquito blood meals are demanding in terms of time and effort, involve costly reagents, and are prone to inaccuracies due to factors such as cross-reactivity with other antigens or partially digested blood meals in the mosquito gut. This study demonstrates the first field application of mid-infrared spectroscopy and machine learning (MIRS-ML), to rapidly assess the blood-feeding histories of malaria vectors, with direct comparison to PCR assays. Methods and results Female Anopheles funestus mosquitoes (N = 1854) were collected from rural Tanzania and desiccated then scanned with an attenuated total reflectance Fourier-transform Infrared (ATR-FTIR) spectrometer. Blood meals were confirmed by PCR, establishing the ‘ground truth’ for machine learning algorithms. Logistic regression and multi-layer perceptron classifiers were employed to identify blood meal sources, achieving accuracies of 88%–90%, respectively, as well as HBI estimates aligning well with the PCR-based standard HBI. Conclusions This research provides evidence of MIRS-ML effectiveness in classifying blood meals in wild Anopheles funestus, as a potential complementary surveillance tool in settings where conventional molecular techniques are impractical. The cost-effectiveness, simplicity, and scalability of MIRS-ML, along with its generalizability, outweigh minor gaps in HBI estimation. Since this approach has already been demonstrated for measuring other entomological and parasitological indicators of malaria, the validation in this study broadens its range of use cases, positioning it as an integrated system for estimating pathogen transmission risk and evaluating the impact of interventions.

Abstract Background Accurately determining the age and survival probabilities of adult mosquitoes is crucial for understanding parasite transmission, evaluating the effectiveness of control interventions and assessing disease risk in communities. This study was aimed at demonstrating the rapid identification of epidemiologically relevant age categories of Anopheles funestus, a major Afro-tropical malaria vector, through the innovative combination of infrared spectroscopy and machine learning, instead of the cumbersome practice of dissecting mosquito ovaries to estimate age based on parity status. Methods Anopheles funestus larvae were collected in rural south-eastern Tanzania and reared in an insectary. Emerging adult females were sorted by age (1–16 days old) and preserved using silica gel. Polymerase chain reaction (PCR) confirmation was conducted using DNA extracted from mosquito legs to verify the presence of An. funestus and to eliminate undesired mosquitoes. Mid-infrared spectra were obtained by scanning the heads and thoraces of the mosquitoes using an attenuated total reflection–Fourier transform infrared (ATR–FT-IR) spectrometer. The spectra (N = 2084) were divided into two epidemiologically relevant age groups: 1–9 days (young, non-infectious) and 10–16 days (old, potentially infectious). The dimensionality of the spectra was reduced using principal component analysis, and then a set of machine learning and multi-layer perceptron (MLP) models were trained using the spectra to predict the mosquito age categories. Results The best-performing model, XGBoost, achieved overall accuracy of 87%, with classification accuracy of 89% for young and 84% for old An. funestus. When the most important spectral features influencing the model performance were selected to train a new model, the overall accuracy increased slightly to 89%. The MLP model, utilizing the significant spectral features, achieved higher classification accuracy of 95% and 94% for the young and old An. funestus, respectively. After dimensionality reduction, the MLP achieved 93% accuracy for both age categories. Conclusions This study shows how machine learning can quickly classify epidemiologically relevant age groups of An. funestus based on their mid-infrared spectra. Having been previously applied to An. gambiae, An. arabiensis and An. coluzzii, this demonstration on An. funestus underscores the potential of this low-cost, reagent-free technique for widespread use on all the major Afro-tropical malaria vectors. Future research should demonstrate how such machine-derived age classifications in field-collected mosquitoes correlate with malaria in human populations. Graphical Abstract

Abstract Studies on the applications of infrared (IR) spectroscopy and machine learning (ML) in public health have increased greatly in recent years. These technologies show enormous potential for measuring key parameters of malaria, a disease that still causes about 250 million cases and 620,000 deaths, annually. Multiple studies have demonstrated that the combination of IR spectroscopy and machine learning (ML) can yield accurate predictions of epidemiologically relevant parameters of malaria in both laboratory and field surveys. Proven applications now include determining the age, species, and blood-feeding histories of mosquito vectors as well as detecting malaria parasite infections in both humans and mosquitoes. As the World Health Organization encourages malaria-endemic countries to improve their surveillance-response strategies, it is crucial to consider whether IR and ML techniques are likely to meet the relevant feasibility and cost-effectiveness requirements—and how best they can be deployed. This paper reviews current applications of IR spectroscopy and ML approaches for investigating malaria indicators in both field surveys and laboratory settings, and identifies key research gaps relevant to these applications. Additionally, the article suggests initial target product profiles (TPPs) that should be considered when developing or testing these technologies for use in low-income settings.

This paper discussed the analytic methods to solve Second order Volterra integral equations form using different methods. The domain decomposition method is the first technique. Depending on the hypothesis, the solution is by sequence. The second method is the successive approximation technique, which is used Picard iteration method. The third method used Laplace transformation. The modified decomposition technique is used as the fourth method. Depending on the Taylor series, the fifth method is called the series method. The last method solves the VIE using the functional correction technique called the variational iteration method. We introduce some examples to illustrate these methods.

Abstract Background Old mosquitoes are more likely to transmit malaria than young ones. Therefore, accurate prediction of mosquito population age can drastically improve the evaluation of mosquito-targeted interventions. However, standard methods for age-grading mosquitoes are laborious and costly. We have shown that Mid-infrared spectroscopy (MIRS) can be used to detect age-specific patterns in mosquito cuticles and thus can be used to train age-grading machine learning models. However, these models tend to transfer poorly across populations. Here, we investigate whether applying dimensionality reduction and transfer learning to MIRS data can improve the transferability of MIRS-based predictions for mosquito ages. Methods We reared adults of the malaria vector Anopheles arabiensis in two insectaries. The heads and thoraces of female mosquitoes were scanned using an attenuated total reflection-Fourier transform infrared spectrometer, which were grouped into two different age classes. The dimensionality of the spectra data was reduced using unsupervised principal component analysis or t-distributed stochastic neighbour embedding, and then used to train deep learning and standard machine learning classifiers. Transfer learning was also evaluated to improve transferability of the models when predicting mosquito age classes from new populations. Results Model accuracies for predicting the age of mosquitoes from the same population as the training samples reached 99% for deep learning and 92% for standard machine learning. However, these models did not generalise to a different population, achieving only 46% and 48% accuracy for deep learning and standard machine learning, respectively. Dimensionality reduction did not improve model generalizability but reduced computational time. Transfer learning by updating pre-trained models with 2% of mosquitoes from the alternate population improved performance to ~ 98% accuracy for predicting mosquito age classes in the alternative population. Conclusion Combining dimensionality reduction and transfer learning can reduce computational costs and improve the transferability of both deep learning and standard machine learning models for predicting the age of mosquitoes. Future studies should investigate the optimal quantities and diversity of training data necessary for transfer learning and the implications for broader generalisability to unseen datasets.

Abstract Background Malaria transmission can be highly heterogeneous between and within localities, and is influenced by factors such as survival and biting frequencies of Anopheles mosquitoes. This study investigated the relationships between the biological age, distance from aquatic habitats and pyrethroid resistance status of Anopheles funestus mosquitoes, which currently dominate malaria transmission in south-east Tanzania. The study also examined how such relationships may influence malaria transmission and control. Methods Female An. funestus were collected in houses located 50–100 m, 150–200 m or over 200 m from the nearest known aquatic habitats. The mosquitoes were exposed to 1×, 5× and 10× the diagnostic doses of deltamethrin or permethrin, or to the synergist, piperonyl butoxide (PBO) followed by the pyrethroids, then monitored for 24 h-mortality. Ovaries of exposed and non-exposed mosquitoes were dissected to assess parity as a proxy for biological age. Adults emerging from larval collections in the same villages were tested against the same insecticides at 3–5, 8–11 or 17–20 days old. Findings Mosquitoes collected nearest to the aquatic habitats (50-100 m) had the lowest mortalities compared to other distances, with a maximum of 51% mortality at 10× permethrin. For the age-synchronized mosquitoes collected as larvae, the insecticide-induced mortality assessed at both the diagnostic and multiplicative doses (1×, 5× and 10×) increased with mosquito age. The highest mortalities at 1× doses were observed among the oldest mosquitoes (17–20 days). At 10× doses, mortalities were 99% (permethrin) and 76% (deltamethrin) among 8–11 day-olds compared to 80% (permethrin) and 58% (deltamethrin) among 3–5 day-olds. Pre-exposure to PBO increased the potency of both pyrethroids. The proportion of parous females was highest among mosquitoes collected farthest from the habitats. Conclusion In this specific setting, older An. funestus and those collected farthest from the aquatic habitats (near the centre of the village) were more susceptible to pyrethroids than the younger ones and those caught nearest to the habitats. These findings suggest that pyrethroid-based interventions may remain at least moderately effective despite widespread pyrethroid-resistance, by killing the older, less-resistant and potentially-infective mosquitoes. Further studies should investigate how and whether these observations could be exploited to optimize malaria control in different settings.

Abstract Background It is often assumed that the population dynamics of the malaria vector Anopheles funestus, its role in malaria transmission and the way it responds to interventions are similar to the more elaborately characterized Anopheles gambiae. However, An. funestus has several unique ecological features that could generate distinct transmission dynamics and responsiveness to interventions. The objectives of this work were to develop a model which will: (1) reconstruct the population dynamics, survival, and fecundity of wild An. funestus populations in southern Tanzania, (2) quantify impacts of density dependence on the dynamics, and (3) assess seasonal fluctuations in An. funestus demography. Through quantifying the population dynamics of An. funestus, this model will enable analysis of how their stability and response to interventions may differ from that of An. gambiae sensu lato. Methods A Bayesian State Space Model (SSM) based on mosquito life history was fit to time series data on the abundance of female An. funestus sensu stricto collected over 2 years in southern Tanzania. Prior values of fitness and demography were incorporated from empirical data on larval development, adult survival and fecundity from laboratory-reared first generation progeny of wild caught An. funestus. The model was structured to allow larval and adult fitness traits to vary seasonally in response to environmental covariates (i.e. temperature and rainfall), and for density dependency in larvae. The effects of density dependence and seasonality were measured through counterfactual examination of model fit with or without these covariates. Results The model accurately reconstructed the seasonal population dynamics of An. funestus and generated biologically-plausible values of their survival larval, development and fecundity in the wild. This model suggests that An. funestus survival and fecundity annual pattern was highly variable across the year, but did not show consistent seasonal trends either rainfall or temperature. While the model fit was somewhat improved by inclusion of density dependence, this was a relatively minor effect and suggests that this process is not as important for An. funestus as it is for An. gambiae populations. Conclusion The model's ability to accurately reconstruct the dynamics and demography of An. funestus could potentially be useful in simulating the response of these populations to vector control techniques deployed separately or in combination. The observed and simulated dynamics also suggests that An. funestus could be playing a role in year-round malaria transmission, with any apparent seasonality attributed to other vector species.

Abstract Background Wild populations of Anopheles mosquitoes are generally thought to mate outdoors in swarms, although once colonized, they also mate readily inside laboratory cages. This study investigated whether the malaria vectors Anopheles funestus and Anopheles arabiensis can also naturally mate inside human dwellings. Method Mosquitoes were sampled from three volunteer-occupied experimental huts in a rural Tanzanian village at 6:00 p.m. each evening, after which the huts were completely sealed and sampling was repeated at 11:00 p.m and 6 a.m. the next morning to compare the proportions of inseminated females. Similarly timed collections were done inside local unsealed village houses. Lastly, wild-caught larvae and pupae were introduced inside or outside experimental huts constructed inside two semi-field screened chambers. The huts were then sealed and fitted with exit traps, allowing mosquito egress but not entry. Mating was assessed in subsequent days by sampling and dissecting emergent adults caught indoors, outdoors and in exit traps. Results Proportions of inseminated females inside the experimental huts in the village increased from approximately 60% at 6 p.m. to approximately 90% the following morning despite no new mosquitoes entering the huts after 6 p.m. Insemination in the local homes increased from approximately 78% to approximately 93% over the same time points. In the semi-field observations of wild-caught captive mosquitoes, the proportions of inseminated An. funestus were 20.9% (95% confidence interval [CI]: ± 2.8) outdoors, 25.2% (95% CI: ± 3.4) indoors and 16.8% (± 8.3) in exit traps, while the proportions of inseminated An. arabiensis were 42.3% (95% CI: ± 5.5) outdoors, 47.4% (95% CI: ± 4.7) indoors and 37.1% (CI: ± 6.8) in exit traps. Conclusion Wild populations of An. funestus and An. arabiensis in these study villages can mate both inside and outside human dwellings. Most of the mating clearly happens before the mosquitoes enter houses, but additional mating happens indoors. The ecological significance of such indoor mating remains to be determined. The observed insemination inside the experimental huts fitted with exit traps and in the unsealed village houses suggests that the indoor mating happens voluntarily even under unrestricted egress. These findings may inspire improved vector control, such as by targeting males indoors, and potentially inform alternative methods for colonizing strongly eurygamic Anopheles species (e.g. An. funestus) inside laboratories or semi-field chambers. Graphical Abstract

Abstract Background Climate change creates opportune conditions that favour the spread of pests and diseases outside their known active range. Modelling climate change scenarios is oftentimes useful tool to assess the climate analogues to unveil the potential risk of spreading suitability conditions for pests and diseases and hence allows development of appropriate responses to address the impending challenge. In the current study, we modelled the impact of climate change on the distribution of Varroa destructor, a parasitic mite that attacks all life forms of honey bees and remains a significant threat to their survival and productivity of bee products in Tanzania and elsewhere. Methods The data about the presence of V. destructor were collected in eight regions of Tanzania selected in consideration of several factors including potentials for beekeeping activities, elevation (highlands vs. lowlands) and differences in climatic conditions. A total of 19 bioclimatic datasets covering the entire country were used for developing climate scenarios of mid-century 2055 and late-century 2085 for both rcp4.5 and rcp8.5. We thereafter modelled the current and future risk distribution of V. destructor using MaxEnt. Results The results indicated a model performance of AUC = 0.85, with mean diurnal range in temperature (Bio2, 43.9%), mean temperature (Bio1, 20.6%) and mean annual rainfall (Bio12, 11.7%) as the important variables. Future risk projections indicated mixed responses of the potential risk of spreads of V. destructor, exhibiting both decrease and increases in the mid-century 2055 and late-century 2085 on different sites. Overall, there is a general decline of highly suitable areas of V. destructor in mid- and late-century across all scenarios (rcp4.5 and rcp8.5). The moderately suitable areas indicated a mixed response in mid-century with decline (under rcp4.5) and increase (under rcp8.5) and consistent increase in late century. The marginally suitable areas show a decline in mid-century and increase in late-century. Our results suggest that the climate change will continue to significantly affect the distribution and risks spread of V. destructor in Tanzania. The suitability range of V. destructor will shift where highly suitable areas will be diminishing to the advantage of the honey bees’ populations, but increase of moderately suitable sites indicates an expansion to new areas. The late century projections show the increased risks due to surge in the moderate and marginal suitability which means expansion in the areas where V. destructor will operate. Conclusion The current and predicted areas of habitat suitability for V. destructor’s host provides information useful for beekeeping stakeholders in Tanzania to consider the impending risks and allow adequate interventions to address challenges facing honey bees and the beekeeping industry. We recommend further studies on understanding the severity of V. destructor in health and stability of the honey bees in Tanzania. This will provide a better picture on how the country will need to monitor and reduce the risks associated with the increase of V. destructor activities as triggered by climate change. The loss of honey bees’ colonies and its subsequent impact in bees’ products production and pollination effect have both ecological and economic implications that need to have prioritization by the stakeholders in the country to address the challenge of spreading V. destructor.

“Candidatus Phytoplasma aurantifolia” is associated with witches’ broom disease of lime in Oman and the UAE. A previous study showed that an infection by phytoplasma may not necessarily result in the physical appearance of witches’ broom symptoms in some locations in Oman and the UAE. This study investigated whether phytoplasma strains belonging to “Ca. P. aurantifolia” (based on the 16S rRNA gene analysis) in locations where disease symptoms are expressed are different from phytoplasma in locations where disease symptoms are not expressed. About 21 phytoplasma strains (15 from areas and trees with disease symptoms and six from areas and trees without disease symptoms) were included in the analysis. The study utilized sequences of the imp and SAP11 genes to characterize the 21 strains. Phylogenetic analysis of both genes showed that the 21 strains are similar to each other and to reference strains in GenBank. The study shows that there is a low level of diversity among all phytoplasma strains. In addition, it shows that phytoplasma in places where witches’ broom symptoms are not expressed are similar to phytoplasma in places where disease symptoms are expressed. This may suggest that disease expression is not linked to the presence of different phytoplasma strains, but may be due to other factors such as weather conditions.

This paper presents investigation of possibility for electric arc furnace dust (EAFD) and electronic waste (e-waste) valorization trough stabilization process, in order to achieve concurrent management of these two serious ecological problems. EAFD is an ineviTab. waste material coming from the electric arc furnace steel production process, classified as a hazardous waste. Furthermore, it is well known that residual materials generated in the ewaste recycling process, like LCD (Liquid crystal displays) waste glass, are not suiTab. for landfill or incineration. In this study, these two materials were used for investigation of possibility for their valorization in ceramic industry. Thus, an innovative synergy of waste streams from metallurgical and e-waste recycling industry is presented. Investigation included a complex characterization of raw materials and their mixtures, using chemical methods, optical microscopy, scanning electron microscopy, as well as methods for determining the physical and mechanical properties. Based on these results, it was found that material suiTab. for use in ceramics industry as a partial substituent of quartzite and fluxing components can be produced. Besides solving the environmental problem related to EAFD and LCD disposal, by replacement of raw materials certain economic effects can be achieved. [Projekat Ministarstva nauke Republike Srbije, br. 34033]

Hussain Issa1, Mohamad A Aljama1, Ahmed H Al-Salem21Department of Internal Medicine, King Fahad Specialist Hospital, 2Department of Pediatric Surgery, Maternity and Children Hospital, Dammam, Saudi ArabiaAbstract: Strongyloides stercoralis is an intestinal nematode that is able to infect the host tissue and persist asymptomatic for many years through autoinfection. It causes life-threatening hyperinfection in immunocompromised hosts. This report describes a rare case of strongyloidiasis in a 40-year-old male following renal transplant, which was diagnosed by colonoscopic biopsy. The literature on the subject is also reviewed.Keywords: Strongyloides stercoralis, hyperinfection, immunosuppression