Identifying and prioritizing potential human-infecting viruses from their genome sequences.

Determining which animal viruses may be capable of infecting humans is currently intractable at the time of their discovery, precluding prioritization of high-risk viruses for early investigation and outbreak preparedness. Given the increasing use of genomics in virus discovery and the otherwise sparse knowledge of the biology of newly discovered viruses, we developed machine learning models that identify candidate zoonoses solely using signatures of host range encoded in viral genomes. Within a dataset of 861 viral species with known zoonotic status, our approach outperformed models based on the phylogenetic relatedness of viruses to known human-infecting viruses (area under the receiver operating characteristic curve [AUC] = 0.773), distinguishing high-risk viruses within families that contain a minority of human-infecting species and identifying putatively undetected or so far unrealized zoonoses. Analyses of the underpinnings of model predictions suggested the existence of generalizable features of viral genomes that are independent of virus taxonomic relationships and that may preadapt viruses to infect humans. Our model reduced a second set of 645 animal-associated viruses that were excluded from training to 272 high and 41 very high-risk candidate zoonoses and showed significantly elevated predicted zoonotic risk in viruses from nonhuman primates, but not other mammalian or avian host groups. A second application showed that our models could have identified Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) as a relatively high-risk coronavirus strain and that this prediction required no prior knowledge of zoonotic Severe Acute Respiratory Syndrome (SARS)-related coronaviruses. Genome-based zoonotic risk assessment provides a rapid, low-cost approach to enable evidence-driven virus surveillance and increases the feasibility of downstream biological and ecological characterization of viruses.

Rapid assessment of the blood-feeding histories of wild-caught malaria mosquitoes using mid-infrared spectroscopy and machine learning.

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.

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.

Using transfer learning and dimensionality reduction techniques to improve generalisability of machine-learning predictions of mosquito ages from mid-infrared spectra.

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.

Key considerations, target product profiles, and research gaps in the application of infrared spectroscopy and artificial intelligence for malaria surveillance and diagnosis.

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.

The parasitic worm product ES-62 promotes health- and life-span in a high calorie diet-accelerated mouse model of ageing.

Improvements in hygiene and health management have driven significant increases in human lifespan over the last 50 years. Frustratingly however, this extension of lifespan has not been matched by equivalent improvements in late-life health, not least due to the global pandemic in type-2 diabetes, obesity and cardiovascular disease, all ageing-associated conditions exacerbated and accelerated by widespread adoption of the high calorie Western diet (HCD). Recently, evidence has begun to emerge that parasitic worm infection might protect against such ageing-associated co-morbidities, as a serendipitous side-effect of their evolution of pro-survival, anti-inflammatory mechanisms. As a novel therapeutic strategy, we have therefore investigated the potential of ES-62, an anti-inflammatory secreted product of the filarial nematode Acanthocheilonema viteae, to improve healthspan (the period of life before diseases of ageing appear) by targeting the chronic inflammation that drives metabolic dysregulation underpinning ageing-induced ill-health. We administered ES-62 subcutaneously (at a dose of 1 µg/week) to C57BL/6J mice undergoing HCD-accelerated ageing throughout their lifespan, while subjecting the animals to analysis of 120 immunometabolic responses at various time-points. ES-62 improved a number of inflammatory parameters, but markedly, a range of pathophysiological, metabolic and microbiome parameters of ageing were also successfully targeted. Notably, ES-62-mediated promotion of healthspan in male and female HCD-mice was associated with different mechanisms and reflecting this, machine learning modelling identified sex-specific signatures predictive of ES-62 action against HCD-accelerated ageing. Remarkably, ES-62 substantially increased the median survival of male HCD-mice. This was not the case with female animals and unexpectedly, this difference between the two sexes could not be explained in terms of suppression of the chronic inflammation driving ageing, as ES-62 tended to be more effective in reducing this in female mice. Rather, the difference appeared to be associated with ES-62's additional ability to preferentially promote a healthier gut-metabolic tissue axis in male animals.

Supplemented nutrition decreases helminth burden and increases drug efficacy in a natural host-helminth system.

Gastrointestinal (GI) helminths are common parasites of humans, wildlife, and livestock, causing chronic infections. In humans and wildlife, poor nutrition or limited resources can compromise an individual's immune response, predisposing them to higher helminth burdens. This relationship has been tested in laboratory models by investigating infection outcomes following reductions of specific nutrients. However, much less is known about how diet supplementation can impact susceptibility to infection, acquisition of immunity, and drug efficacy in natural host-helminth systems. We experimentally supplemented the diet of wood mice (Apodemus sylvaticus) with high-quality nutrition and measured resistance to the common GI nematode Heligmosomoides polygyrus. To test whether diet can enhance immunity to reinfection, we also administered anthelmintic treatment in both natural and captive populations. Supplemented wood mice were more resistant to H. polygyrus infection, cleared worms more efficiently after treatment, avoided a post-treatment infection rebound, produced stronger general and parasite-specific antibody responses, and maintained better body condition. In addition, when applied in conjunction with anthelmintic treatment, supplemented nutrition significantly reduced H. polygyrus transmission potential. These results show the rapid and extensive benefits of a well-balanced diet and have important implications for both disease control and wildlife health under changing environmental conditions.

Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis.

BACKGROUND: The propensity of different Anopheles mosquitoes to bite humans instead of other vertebrates influences their capacity to transmit pathogens to humans. Unfortunately, determining proportions of mosquitoes that have fed on humans, i.e. Human Blood Index (HBI), currently requires expensive and time-consuming laboratory procedures involving enzyme-linked immunosorbent assays (ELISA) or polymerase chain reactions (PCR). Here, mid-infrared (MIR) spectroscopy and supervised machine learning are used to accurately distinguish between vertebrate blood meals in guts of malaria mosquitoes, without any molecular techniques. METHODS: Laboratory-reared Anopheles arabiensis females were fed on humans, chickens, goats or bovines, then held for 6 to 8 h, after which they were killed and preserved in silica. The sample size was 2000 mosquitoes (500 per host species). Five individuals of each host species were enrolled to ensure genotype variability, and 100 mosquitoes fed on each. Dried mosquito abdomens were individually scanned using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra (4000 cm-1 to 400 cm-1). The spectral data were cleaned to compensate atmospheric water and CO2 interference bands using Bruker-OPUS software, then transferred to Python™ for supervised machine-learning to predict host species. Seven classification algorithms were trained using 90% of the spectra through several combinations of 75-25% data splits. The best performing model was used to predict identities of the remaining 10% validation spectra, which had not been used for model training or testing. RESULTS: The logistic regression (LR) model achieved the highest accuracy, correctly predicting true vertebrate blood meal sources with overall accuracy of 98.4%. The model correctly identified 96% goat blood meals, 97% of bovine blood meals, 100% of chicken blood meals and 100% of human blood meals. Three percent of bovine blood meals were misclassified as goat, and 2% of goat blood meals misclassified as human. CONCLUSION: Mid-infrared spectroscopy coupled with supervised machine learning can accurately identify multiple vertebrate blood meals in malaria vectors, thus potentially enabling rapid assessment of mosquito blood-feeding histories and vectorial capacities. The technique is cost-effective, fast, simple, and requires no reagents other than desiccants. However, scaling it up will require field validation of the findings and boosting relevant technical capacity in affected countries.

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.

Parasitic nematodes simultaneously suppress and benefit from coccidian coinfection in their natural mouse host.

Within-host interactions among coinfecting parasites are common and have important consequences for host health and disease dynamics. However, these within-host interactions have traditionally been studied in laboratory mouse models, which often exclude important variation and use unnatural host-parasite combinations. Conversely, the few wild studies of within-host interactions often lack knowledge of parasite exposure and infection history. Here we exposed laboratory-reared wood mice (Apodemus sylvaticus) that were derived from wild-caught animals to two naturally-occurring parasites (nematode: Heligmosomoides polygyrus, coccidia: Eimeria hungaryensis) to investigate the impact of coinfection on parasite infection dynamics, and to determine if the host immune response mediates this interaction. Coinfection led to delayed worm expulsion and prolonged egg shedding in H. polygyrus infections and lower peak E. hungaryensis oocyst burdens. By comparing antibody levels between wild and colony-housed mice, we also found that wild mice had elevated H. polygyrus-IgG1 titres even if currently uninfected with H. polygyrus. Using this unique wild-laboratory system, we demonstrate, for the first time, clear evidence for a reciprocal interaction between these intestinal parasites, and that there is a great discrepancy between antibody levels measured in the wild vs those measured under controlled laboratory conditions in relation to parasite infection and coinfection.

The secreted triose phosphate isomerase of Brugia malayi is required to sustain microfilaria production in vivo.

Human lymphatic filariasis is a major tropical disease transmitted through mosquito vectors which take up microfilarial larvae from the blood of infected subjects. Microfilariae are produced by long-lived adult parasites, which also release a suite of excretory-secretory products that have recently been subject to in-depth proteomic analysis. Surprisingly, the most abundant secreted protein of adult Brugia malayi is triose phosphate isomerase (TPI), a glycolytic enzyme usually associated with the cytosol. We now show that while TPI is a prominent target of the antibody response to infection, there is little antibody-mediated inhibition of catalytic activity by polyclonal sera. We generated a panel of twenty-three anti-TPI monoclonal antibodies and found only two were able to block TPI enzymatic activity. Immunisation of jirds with B. malayi TPI, or mice with the homologous protein from the rodent filaria Litomosoides sigmodontis, failed to induce neutralising antibodies or protective immunity. In contrast, passive transfer of neutralising monoclonal antibody to mice prior to implantation with adult B. malayi resulted in 60-70% reductions in microfilarial levels in vivo and both oocyte and microfilarial production by individual adult females. The loss of fecundity was accompanied by reduced IFNγ expression by CD4⁺ T cells and a higher proportion of macrophages at the site of infection. Thus, enzymatically active TPI plays an important role in the transmission cycle of B. malayi filarial parasites and is identified as a potential target for immunological and pharmacological intervention against filarial infections.

Comparative analysis of the secretome from a model filarial nematode (Litomosoides sigmodontis) reveals maximal diversity in gravid female parasites.

Filarial nematodes (superfamily Filarioidea) are responsible for an annual global health burden of ∼6.3 million disability-adjusted life-years, which represents the greatest single component of morbidity attributable to helminths affecting humans. No vaccine exists for the major filarial diseases, lymphatic filariasis and onchocerciasis; in part because research on protective immunity against filariae has been constrained by the inability of the human-parasitic species to complete their lifecycles in laboratory mice. However, the rodent filaria Litomosoides sigmodontis has become a popular experimental model, as BALB/c mice are fully permissive for its development and reproduction. Here, we provide a comprehensive analysis of excretory-secretory products from L. sigmodontis across five lifecycle stages and identifications of host proteins associated with first-stage larvae (microfilariae) in the blood. Applying intensity-based quantification, we determined the abundance of 302 unique excretory-secretory proteins, of which 64.6% were present in quantifiable amounts only from gravid adult female nematodes. This lifecycle stage, together with immature microfilariae, released four proteins that have not previously been evaluated as vaccine candidates: a predicted 28.5 kDa filaria-specific protein, a zonadhesin and SCO-spondin-like protein, a vitellogenin, and a protein containing six metridin-like ShK toxin domains. Female nematodes also released two proteins derived from the obligate Wolbachia symbiont. Notably, excretory-secretory products from all parasite stages contained several uncharacterized members of the transthyretin-like protein family. Furthermore, biotin labeling revealed that redox proteins and enzymes involved in purinergic signaling were enriched on the adult nematode cuticle. Comparison of the L. sigmodontis adult secretome with that of the human-infective filarial nematode Brugia malayi (reported previously in three independent published studies) identified differences that suggest a considerable underlying diversity of potential immunomodulators. The molecules identified in L. sigmodontis excretory-secretory products show promise not only for vaccination against filarial infections, but for the amelioration of allergy and autoimmune diseases.

Immunity in society: diverse solutions to common problems.

Understanding how organisms fight infection has been a central focus of scientific research and medicine for the past couple of centuries, and a perennial object of trial and error by humans trying to mitigate the burden of disease. Vaccination success relies upon the exposure of susceptible individuals to pathogen constituents that do not cause (excessive) pathology and that elicit specific immune memory. Mass vaccination allows us to study how immunity operates at the group level; denser populations are more prone to transmitting disease between individuals, but once a critical proportion of the population becomes immune, "herd immunity" emerges. In social species, the combination of behavioural control of infection--e.g., segregation of sick individuals, disposal of the dead, quality assessment of food and water--and aggregation of immune individuals can protect non-immune members from disease. While immune specificity and memory are well understood to underpin immunisation in vertebrates, it has been somewhat surprising to find similar phenomena in invertebrates, which lack the vertebrate molecular mechanisms deemed necessary for immunisation. Indeed, reports showing alternative forms of immune memory are accumulating in invertebrates. In this issue of PLoS Biology, Konrad et al. present an example of fungus-specific immune responses in social ants that lead to the active immunisation of nestmates by infected individuals. These findings join others in showing how organisms evolved diverse mechanisms that fulfil common functions, namely the discrimination between pathogens, the transfer of immunity between related individuals, and the group-level benefits of immunisation.

Assessing the feasibility of applying machine learning to diagnosing non-effusive feline infectious peritonitis.

Feline infectious peritonitis (FIP) is a severe feline coronavirus-associated syndrome in cats, which is invariably fatal without anti-viral treatment. In the majority of non-effusive FIP cases encountered in practice, confirmatory diagnostic testing is not undertaken and reliance is given to the interpretation of valuable, but essentially non-specific, clinical signs and laboratory markers. We hypothesised that it may be feasible to develop a machine learning (ML) approach which may be applied to the analysis of clinical data to aid in the diagnosis of disease. A dataset encompassing 1939 suspected FIP cases was scored for clinical suspicion of FIP on the basis of history, signalment, clinical signs and laboratory results, using published guidelines, comprising 683 FIP (35.2%), and 1256 non-FIP (64.8%) cases. This dataset was used to train, validate and evaluate two diagnostic machine learning ensemble models. These models, which analysed signalment and laboratory data alone, allowed the accurate discrimination of FIP and non-FIP cases in line with expert opinion. To evaluate whether these models may have value as a diagnostic tool, they were applied to a collection of 80 cases for which the FIP status had been confirmed (FIP: n = 58 (72.5%), non-FIP: n = 22 (27.5%)). Both ensemble models detected FIP with an accuracy of 97.5%, an area under the curve (AUC) of 0.969, sensitivity of 95.45% and specificity of 98.28%. This work demonstrates that, in principle, ML can be usefully applied to the diagnosis of non-effusive FIP. Further work is required before ML may be deployed in the laboratory as a diagnostic tool, such as training models on datasets of confirmed cases and accounting for inter-laboratory variation. Nevertheless, these results illustrate the potential benefit of applying ML to standardising and accelerating the interpretation of clinical pathology data, thereby improving the diagnostic utility of existing laboratory tests.

Rapid classification of epidemiologically relevant age categories of the malaria vector, Anopheles funestus.

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.

Reagent-free detection of Plasmodium falciparum malaria infections in field-collected mosquitoes using mid-infrared spectroscopy and machine learning.

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.

Filarial parasites develop faster and reproduce earlier in response to host immune effectors that determine filarial life expectancy.

Humans and other mammals mount vigorous immune assaults against helminth parasites, yet there are intriguing reports that the immune response can enhance rather than impair parasite development. It has been hypothesized that helminths, like many free-living organisms, should optimize their development and reproduction in response to cues predicting future life expectancy. However, immune-dependent development by helminth parasites has so far eluded such evolutionary explanation. By manipulating various arms of the immune response of experimental hosts, we show that filarial nematodes, the parasites responsible for debilitating diseases in humans like river blindness and elephantiasis, accelerate their development in response to the IL-5 driven eosinophilia they encounter when infecting a host. Consequently they produce microfilariae, their transmission stages, earlier and in greater numbers. Eosinophilia is a primary host determinant of filarial life expectancy, operating both at larval and at late adult stages in anatomically and temporally separate locations, and is implicated in vaccine-mediated protection. Filarial nematodes are therefore able to adjust their reproductive schedules in response to an environmental predictor of their probability of survival, as proposed by evolutionary theory, thereby mitigating the effects of the immune attack to which helminths are most susceptible. Enhancing protective immunity against filarial nematodes, for example through vaccination, may be less effective at reducing transmission than would be expected and may, at worst, lead to increased transmission and, hence, pathology.

Future prospects and challenges of vaccines against filariasis.

Filarial infections remain a major public health and socio-economic problem across the tropics, despite considerable effort to reduce disease burden or regionally eliminate the infection with mass drug administration programmes. The sustainability of these programmes is now open to question owing to a range of issues, not least of which is emerging evidence for drug resistance. Vaccination, if developed appropriately, remains the most cost-effective means of long-term disease control. The rationale for the feasibility of vaccination against filarial parasites including onchocerciasis (river blindness, Onchocerca volvulus) and lymphatic filariasis (Wuchereria bancrofti or Brugia malayi) is founded on evidence from both humans and animal models for the development of protective immunity. Nonetheless, enormous challenges need to be faced in terms of overcoming parasite-induced suppression without inducing pathology as well as the need to both recognize and tackle evolutionary and ecological obstacles to successful vaccine development. Nonetheless, new technological advances in addition to systems biology approaches offer hope that optimal immune responses can be induced that will prevent infection, disease and/or transmission.

Vaccine-induced time- and age-dependent mucosal immunity to gastrointestinal parasite infection.

Individuals vary broadly in their response to vaccination and subsequent challenge infection, with poor vaccine responders causing persistence of both infection and transmission in populations. Yet despite having substantial economic and societal impact, the immune mechanisms that underlie such variability, especially in infected tissues, remain poorly understood. Here, to characterise how antihelminthic immunity at the mucosal site of infection developed in vaccinated lambs, we inserted gastric cannulae into the abomasa of three-month- and six-month-old lambs and longitudinally analysed their local immune response during subsequent challenge infection. The vaccine induced broad changes in pre-challenge abomasal immune profiles and reduced parasite burden and egg output post-challenge, regardless of age. However, age affected how vaccinated lambs responded to infection across multiple immune pathways: adaptive immune pathways were typically age-dependent. Identification of age-dependent and age-independent protective immune pathways may help refine the formulation of vaccines, and indicate specificities of pathogen-specific immunity more generally.