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.

Discovery of knock-down resistance in the major African malaria vector Anopheles funestus.

A major mechanism of insecticide resistance in insect pests is knock-down resistance (kdr) caused by mutations in the voltage-gated sodium channel (Vgsc) gene. Despite being common in most malaria Anopheles vector species, kdr mutations have never been observed in Anopheles funestus, the principal malaria vector in Eastern and Southern Africa. While monitoring 10 populations of An. funestus in Tanzania, we unexpectedly found resistance to DDT, a banned insecticide, in one location. Through whole-genome sequencing of 333 An. funestus samples from these populations, we found 8 novel amino acid substitutions in the Vgsc gene, including the kdr variant, L976F (L1014F in An. gambiae), in tight linkage disequilibrium with another (P1842S). The mutants were found only at high frequency in one region, with a significant decline between 2017 and 2023. Notably, kdr L976F was strongly associated with survivorship to the exposure to DDT insecticide, while no clear association was noted with a pyrethroid insecticide (deltamethrin). Further study is necessary to identify the origin and spread of kdr in An. funestus, and the potential threat to current insecticide-based vector control in Africa.

Genetic markers associated with the widespread insecticide resistance in malaria vector Anopheles funestus populations across Tanzania.

BACKGROUND: Anopheles funestus is a leading vector of malaria in most parts of East and Southern Africa, yet its ecology and responses to vector control remain poorly understood compared with other vectors such as Anopheles gambiae and Anopheles arabiensis. This study presents the first large-scale survey of the genetic and phenotypic expression of insecticide resistance in An. funestus populations in Tanzania. METHODS: We performed insecticide susceptibility bioassays on An. funestus mosquitoes in nine regions with moderate-to-high malaria prevalence in Tanzania, followed by genotyping for resistance-associated mutations (CYP6P9a, CYP6P9b, L119F-GSTe2) and structural variants (SV4.3 kb, SV6.5 kb). Generalized linear models were used to assess relationships between genetic markers and phenotypic resistance. An interactive R Shiny tool was created to visualize the data and support evidence-based interventions. RESULTS: Pyrethroid resistance was universal but reversible by piperonyl-butoxide (PBO). However, carbamate resistance was observed in only five of the nine districts, and dichloro-diphenyl-trichloroethane (DDT) resistance was found only in the Kilombero valley, south-eastern Tanzania. Conversely, there was universal susceptibility to the organophosphate pirimiphos-methyl in all sites. Genetic markers of resistance had distinct geographical patterns, with CYP6P9a-R and CYP6P9b-R alleles, and the SV6.5 kb structural variant absent or undetectable in the north-west but prevalent in all other sites, while SV4.3 kb was prevalent in the north-western and western regions but absent elsewhere. Emergent L119F-GSTe2, associated with deltamethrin resistance, was detected in heterozygous form in districts bordering Mozambique, Malawi and the Democratic Republic of Congo. The resistance landscape was most complex in western Tanzania, in Tanganyika district, where all five genetic markers were detected. There was a notable south-to-north spread of resistance genes, especially CYP6P9a-R, though this appears to be interrupted, possibly by the Rift Valley. CONCLUSIONS: This study underscores the need to expand resistance monitoring to include An. funestus alongside other vector species, and to screen for both the genetic and phenotypic signatures of resistance. The findings can be visualized online via an interactive user interface and could inform data-driven decision-making for resistance management and vector control. Since this was the first large-scale survey of resistance in Tanzania's An. funestus, we recommend regular updates with greater geographical and temporal coverage.

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.

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.

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.

Distribution and insecticide resistance profile of the major malaria vector Anopheles funestus group across the African continent.

There has been significant progress in malaria control in the last 2 decades, with a decline in mortality and morbidity. However, these gains are jeopardised by insecticide resistance, which negatively impacts the core interventions, such as insecticide-treated nets (ITN) and indoor residual spraying (IRS). While most malaria control and research efforts are still focused on Anopheles gambiae complex mosquitoes, Anopheles funestus remains an important vector in many countries and, in some cases, contributes to most of the local transmission. As countries move towards malaria elimination, it is important to ensure that all dominant vector species, including An. funestus, an important vector in some countries, are targeted. The objective of this review is to compile and discuss information related to A. funestus populations' resistance to insecticides and the mechanisms involved across Africa, emphasising the sibling species and their resistance profiles in relation to malaria elimination goals. Data on insecticide resistance in An. funestus malaria vectors in Africa were extracted from published studies. Online bibliographic databases, including Google Scholar and PubMed, were used to search for relevant studies. Articles published between 2000 and May 2023 reporting resistance of An. funestus to insecticides and associated mechanisms were included. Those reporting only bionomics were excluded. Spatial variation in species distribution and resistance to insecticides was recorded from 174 articles that met the selection criteria. It was found that An. funestus was increasingly resistant to the four classes of insecticides recommended by the World Health Organisation for malaria vector control; however, this varied by country. Insecticide resistance appears to reduce the effectiveness of vector control methods, particularly IRS and ITN. Biochemical resistance due to detoxification enzymes (P450s and glutathione-S-transferases [GSTs]) in An. funestus was widely recorded. However, An. funestus in Africa remains susceptible to other insecticide classes, such as organophosphates and neonicotinoids. This review highlights the increasing insecticide resistance of An. funestus mosquitoes, which are important malaria vectors in Africa, posing a significant challenge to malaria control efforts. While An. funestus has shown resistance to the recommended insecticide classes, notably pyrethroids and, in some cases, organochlorides and carbamates, it remains susceptible to other classes of insecticides such as organophosphates and neonicotinoids, providing potential alternative options for vector control strategies. The study underscores the need for targeted interventions that consider the population structure and geographical distribution of An. funestus, including its sibling species and their insecticide resistance profiles, to effectively achieve malaria elimination goals.

Des progrès importants ont été réalisés dans le contrôle du paludisme au cours des deux dernières décennies, qui se traduisent par une baisse de la mortalité et de la morbidité. Cependant, ces gains sont compromis par la résistance aux insecticides, ce qui a un impact négatif sur les interventions de base, telles que les moustiquaires imprégnées d'insecticides et la pulvérisation intradomicilliare (PID). Alors que la plupart des efforts de contrôle et de recherche sur le paludisme sont toujours axés sur les moustiques du complexes Anopheles gambiae, Anopheles funestus reste un vecteur important dans de nombreux pays et, dans certains cas, contribue à la majeure partie de la transmission locale. Au moment où certains pays se dirigent vers l'élimination du paludisme, il serait important de prendre en considération toutes les espèces vectrices dominantes, y compris An. funestus. L'objectif de cette revue est de compiler et de discuter des informations liées à la résistance des populations d'An. funestus aux insecticides et les mécanismes impliqués à travers l'Afrique, en mettant l'accent sur les sous espèces et leurs profils de résistance en relation avec les objectifs d'élimination du paludisme. Les données sur la résistance aux insecticides chez An. funestus vecteurs du paludisme en Afrique ont été extraites d'études publiées dans des bases de données bibliographiques comme Google Scholar et PubMed. Les articles publiés entre 2000 et mai 2023, rapportant la résistance de An. funestus aux insecticides et les mécanismes associés ont été inclus. Ceux portant uniquement sur la bionomie ont été exclus. Au total 174 articles portant sur la variation spatiale de la résistance des espèces du groupe An. funestus aux insecticides répondaient aux critères de sélection. De ces analyses, il ressort qu'An. funestus était de plus en plus résistant aux quatre classes d'insecticides recommandées par l'Organisation Mondiale de la Santé (OMS) pour le contrôle des vecteurs du paludisme ce qui semble réduire l'efficacité des méthodes de contrôle des vecteurs, en particulier les moustiquaires imprégnées d'insecticide et la pulvérisation intradomiciliaire. avec des variations en fonction des pays. Les mécanismes de résistance aux insecticides de type biochimique liée aux enzymes de détoxification (P450S et GST) ont été largement rapportés chez An. funestus. De nombreux gènes P450 associés à la résistance métabolique ont été mis en évidence chez An. funestus collecté sur le terrain. Cependant, An. funestus en Afrique reste sensible à d'autres classes d'insecticides, telles que les organophosphorés et les néonicotinoïdes. La résistance aux insecticides. Cette revue met en évidence la résistance croissante aux insecticides chez les moustiques du groupe Funestus, un vecteur important du paludisme en Afrique, posant ainsi un défi important aux efforts de contrôle du paludisme. Tandis que An. funestus a montré une résistance aux classes d'insecticide recommandées, notamment les pyréthroïdes et, dans certains cas, les organochlorés et les carbamates, il reste sensible à d'autres classes d'insecticides tels que les organophosphorés et les néonicotinoïdes, offrant des options alternatives potentielles de contrôle des vecteurs. L'étude souligne la nécessité d'interventions ciblées qui considèrent la structure de la population et la distribution géographique d'An. funestus, y compris ses sous espèces et leurs profils de résistance aux insecticides, pour atteindre efficacement les objectifs d'élimination du paludisme.

Geospatial modelling of dry season habitats of the malaria vector, Anopheles funestus, in south-eastern Tanzania.

BACKGROUND: Anopheles funestus is a major malaria vector in Eastern and Southern Africa and is currently the dominant malaria-transmitting vector in many parts of Tanzania. Previous research has identified its preference for specific aquatic habitats, especially those that persist in dry months. This observation suggests the potential for targeted control through precise habitat mapping and characterization. In this study, we investigated the influence of habitat characteristics, land cover and human population densities on An. funestus distribution during dry seasons. Based on the results, we developed a habitat suitability model for this vector species in south-eastern Tanzania. METHODS: Eighteen villages in south-eastern Tanzania were surveyed during the dry season from September-December 2021. Water bodies were systematically inspected for mosquito larvae and characterized by their physico-chemical characteristics and surrounding environmental features. A generalized linear model was used to assess the presence of An. funestus larvae as a function of the physico-chemical characteristics, land use and human population densities. The results obtained from this model were used to generate spatially explicit predictions of habitat suitability in the study districts. RESULTS: Of the 1466 aquatic habitats surveyed, 440 were positive for An. funestus, with river streams having the highest positivity (74%; n = 322) followed by ground pools (15%; n = 67). The final model had an 83% accuracy in predicting positive An. funestus habitats, with the most important characteristics being permanent waters, clear waters with or without vegetation or movement and shading over the habitats. There was also a positive association of An. funestus presence with forested areas and a negative association with built-up areas. Human population densities had no influence on An. funestus distribution. CONCLUSIONS: The results of this study underscore the crucial role of both the specific habitat characteristics and key environmental factors, notably land cover, in the distribution of An. funestus. In this study area, An. funestus predominantly inhabits river streams and ground pools, with a preference for clear, perennial waters with shading. The strong positive association with more pristine environments with tree covers and the negative association with built-up areas underscore the importance of ecological transitions in vector distribution and malaria transmission risk. Such spatially explicit predictions could enable more precise interventions, particularly larval source management, to accelerate malaria control.

Pharyngo-tonsillar involvement of Mpox in a cohort of men who have sex with men (MSM): A serious risk of missing diagnosis.

During the 2022-outbreak, peculiar clinical presentations of Mpox have been described, some of which can make the diagnosis of the disease extremely challenging. Here we report a case series of fourteen patients with Mpox pharynogotonsillar involvement (PTI) seen at National Institute for Infectious Diseases, "Lazzaro Spallanzani", in Rome, Italy from May to September 2022. All included patients were men who have sex with men (median age 38 years) reporting unprotected sex within three weeks from symptoms onset. Seven out of fourteen patients needed hospitalization due to uncontrolled pain, reduced airspace and difficulty swallowing, of whom five were effectively treated with tecovirimat or cidofovir. The remaining two patients were treated with symptomatic drugs. The typical Mpox muco-cutaneous manifestations were not observed simultaneously with PTI in three patients, two of whom developed the lesions after several days, while one never manifested them. Polymerase Chain Reaction (PCR) for Mpox virus was positive in oropharyngeal swab, saliva and serum. Although PTI occurs in only a small percentage of Mpox cases, its diagnosis is of utmost importance. In fact, this localization, if not identified, could lead to serious complications in the absence of early antiviral treatment and to missed diagnosis with an increased risk of disease transmission.

Molecular beacon decorated silver nanowires for quantitative miRNA detection by a SERS approach.

Advantages of biosensors based on surface enhanced Raman scattering (SERS) rely on improved sensitivity and specificity, and suited reproducibility in detecting a target molecule that is localized in close proximity to a SERS-active surface. Herein, a comprehensive study on the realization of a SERS biosensor designed for detecting miRNA-183, a miRNA biomarker that is specific for chronic obstructive pulmonary disease (COPD), is presented. The used strategy exploits a signal-off mechanism by means of a labelled molecular beacon (MB) as the oligonucleotide biorecognition element immobilized on a 2D SERS substrate, based on spot-on silver nanowires (AgNWs) and a multi-well low volume cell. The MB was properly designed by following a dedicated protocol to recognize the chosen miRNA. A limit of detection down to femtomolar concentration (3 × 10-16 M) was achieved and the specificity of the biosensor was proved. Furthermore, the possibility to regenerate the sensing system through a simple procedure is shown: with regeneration by using HCl 1 mM, two detection cycles were performed with a good recovery of the initial MB signal (83%) and a reproducible signal after hybridization.

Adhesion and virulence properties of native Metarhizium fungal strains from Burkina Faso for the control of malaria vectors.

BACKGROUND: Local strains of the entomopathogenic fungus Metarhizium pingshaense in Burkina Faso have demonstrated remarkable virulence against malaria vectors, positioning them as promising candidates for inclusion in the future arsenal of malaria control strategies. However, the underlying mechanisms responsible for this virulence remain unknown. To comprehend the fungal infection process, it is crucial to investigate the attachment mechanisms of fungal spores to the mosquito cuticle and explore the relationship between virulence and attachment kinetics. This study aims to assess the adhesion and virulence properties of native Metarhizium fungal strains from Burkina Faso for controlling malaria vectors. METHODS: Fungal strains were isolated from 201 insects and 1399 rhizosphere samples, and four strains of Metarhizium fungi were selected. Fungal suspensions were used to infect 3-day-old female Anopheles coluzzii mosquitoes at three different concentrations (106, 107, 108 conidia/ml). The survival of the mosquitoes was measured over 14 days, and fungal growth was quantified after 1 and 24 h to assess adhesion of the fungal strains onto the mosquito cuticle. RESULTS: All four fungi strains increased mosquito mortality compared to control (Chi-square test, χ2 = 286.55, df = 4, P < 0.001). Adhesion of the fungal strains was observed on the mosquito cuticle after 24 h at high concentrations (1 × 108 conidia/ml), with one strain, having the highest virulent, showing adhesion after just 1 h. CONCLUSION: The native strains of Metarhizium spp. fungi found in Burkina Faso have the potential to be effective biocontrol agents against malaria vectors, with some strains showing high levels of both virulence and adhesion to the mosquito cuticle.

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.

Evaluation of diffuse reflectance spectroscopy for predicting age, species, and cuticular resistance of Anopheles gambiae s.l under laboratory conditions.

Mid-infrared spectroscopy (MIRS) combined with machine learning analysis has shown potential for quick and efficient identification of mosquito species and age groups. However, current technology to collect spectra is destructive to the sample and does not allow targeting specific tissues of the mosquito, limiting the identification of other important biological traits such as insecticide resistance. Here, we assessed the use of a non-destructive approach of MIRS for vector surveillance, micro diffuse reflectance spectroscopy (µDRIFT) using mosquito legs to identify species, age and cuticular insecticide resistance within the Anopheles gambiae s.l. complex. These mosquitoes are the major vectors of malaria in Africa and the focus on surveillance in malaria control programs. Legs required significantly less scanning time and showed more spectral consistence compared to other mosquito tissues. Machine learning models were able to identify An. gambiae and An. coluzzii with an accuracy of 0.73, two ages groups (3 and 10 days old) with 0.77 accuracy and we obtained accuracy of 0.75 when identifying cuticular insecticide resistance. Our results highlight the potential of different mosquito tissues and µDRIFT as tools for biological trait identification on mosquitoes that transmit malaria. These results can guide new ways of identifying mosquito traits which can help the creation of innovative surveillance programs by adapting new technology into mosquito surveillance and control tools.

Advances in the genetic characterization of the malaria vector, Anopheles funestus, and implications for improved surveillance and control.

Anopheles mosquitoes present a major public health challenge in sub-Saharan Africa; notably, as vectors of malaria that kill over half a million people annually. In parts of the east and southern Africa region, one species in the Funestus group, Anopheles funestus, has established itself as an exceptionally dominant vector in some areas, it is responsible for more than 90% of all malaria transmission events. However, compared to other malaria vectors, the species is far less studied, partly due to difficulties in laboratory colonization and the unresolved aspects of its taxonomy and systematics. Control of An. funestus is also increasingly difficult because it has developed widespread resistance to public health insecticides. Fortunately, recent advances in molecular techniques are enabling greater insights into species identity, gene flow patterns, population structure, and the spread of resistance in mosquitoes. These advances and their potential applications are reviewed with a focus on four research themes relevant to the biology and control of An. funestus in Africa, namely: (i) the taxonomic characterization of different vector species within the Funestus group and their role in malaria transmission; (ii) insecticide resistance profile; (iii) population genetic diversity and gene flow, and (iv) applications of genetic technologies for surveillance and control. The research gaps and opportunities identified in this review will provide a basis for improving the surveillance and control of An. funestus and malaria transmission in Africa.

Biosensing by Polymer-Coated Etched Long-Period Fiber Gratings Working near Mode Transition and Turn-around Point.

A methodology to enhance the sensitivity of long-period fiber gratings (LPFGs) based on the combination of three different enhancement approaches is presented; the methods here adopted are the working near mode transition (MT) of a cladding mode (CM), working near the turn-around point of a CM and the enhancement of the evanescent field of CMs by reducing the cladding diameter or by increasing the order number of CMs. In order to combine these enhancement methodologies, an electrostatic self-assembly (ESA) process was used to deposit a polymeric overlay, with a chosen thickness, onto the etched fiber. The add-layer sensitivity of the sensor was theoretically calculated, and the demonstration of the real applicability of the developed LPFG as a biosensor was performed by means of an IgG/anti-IgG immunoassay in human serum in a thermostated microfluidic system. The limits of detection (LODs) calculated by following different procedures (three times the standard deviation of the blank and the mean value of the residuals) were 6.9 × 10-8 µg/mL and 4.5 × 10-6 µg/mL, respectively. The calculated LODs demonstrate the effectiveness of the applied methodology for sensitivity enhancement.

Lymphofollicular lesions associated with monkeypox (Mpox) virus proctitis.

In the recent 2022 monkeypox (Mpox) global outbreak, cases have been mostly documented among men who have sex with men. Proctitis was reported in almost 14% of cases. In this study, four Mpox-confirmed cases requiring hospitalizations for severe proctitis were characterized by clinical, virological, microbiological, endoscopic, and histological aspects. The study showed the presence of lymphofollicular lesions associated with Mpox virus rectal infection for the first time.

What is the impact of post-COVID-19 syndrome on health-related quality of life and associated factors: a cross-sectional analysis.

BACKGROUND: After the acute phase, symptoms or sequelae related to post-COVID-19 syndrome may persist for months. In a population of patients, previously hospitalized and not, followed up to 12 months after the acute infection, we aim to assess whether and to what extent post-COVID-19 syndrome may have an impact on health-related quality of life (HRQoL) and to investigate influencing factors. METHODS: We present the cross-sectional analysis of a prospective study, including patients referred to the post-COVID-19 service. Questionnaires and scales administered at 3, 6, 12 months were: Short-Form 36-item questionnaire (SF-36); Visual Analogue Scale of the EQ5D (EQ-VAS); in a subgroup, Beck Anxiety Inventory (BAI), Beck Depression Inventory (BDI-II) and Pittsburgh Sleep Quality Index (PSQI). Linear regression models were fitted to identify factors associated with HRQoL. RESULTS: We considered the first assessment of each participant (n = 572). The mean scores in SF-36 and in EQ-VAS were significantly lower than the Italian normative values and remained stable over time, except the mental components score (MCS) of the SF-36 and EQ-VAS which resulted in lower ratings at the last observations. Female gender, presence of comorbidities, and corticosteroids treatment during acute COVID-19, were associated with lower scores in SF-36 and EQ-VAS; patients previously hospitalized (54%) reported higher MCS. Alterations in BAI, BDI-II, and PSQI (n = 265)were associated with lower ratings in SF-36 and EQ-VAS. CONCLUSIONS: This study provides evidence of a significantly bad perception of health status among persons with post-COVID-19 syndrome, associated with female gender and, indirectly, with disease severity. In case of anxious-depressive symptoms and sleep disorders, a worse HRQoL was also reported. A systematic monitoring of these aspects is recommended to properly manage the post-COVID-19 period.

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.

Dried out but alive: how mosquitoes survive 8 months.

How malaria mosquitoes persist during the dry season in the Sahel and rapidly rebound at the onset of rains is unclear. Recently, Faiman and colleagues demonstrated that aestivation, a summer dormancy mechanism, is a major persistence strategy of Anopheles mosquitoes, which could be targeted by vector control.