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While climate variability and global change alter the transmission of vector-borne diseases, they also interact with pathogens adaptation, host availability, changes in ecosystems and land use, demography, human behavior, and adaptive capacity. [...]these impacts on the epidemiology and incidence of most vector-borne diseases—changing the vector and reservoir distribution—can allow unexpected contact between vector reservoirs and pathogens. [...]it is only the detection of pathogenic micro-organisms (viruses, bacteria, parasites, etc.) that allows for the warning of a potential epidemy. [...]the real-time monitoring of hematophagous insects (such as mosquitoes) in the field and the identification of pathogens they carry is a challenge for foreseeing vaccination campaigns and restraining the potential spreading of diseases. [...]information is needed surrounding the possible influences of climate change and/or the COVID-19 pandemic on vectors and hosts distributions and dynamics, disease incidence and epidemiology, and the underlying factors which trigger vector-borne transmission.

This review focuses on the most reliable and up-to-date methods for diagnosing trypanosomoses, a group of diseases of wild and domestic mammals, caused by trypanosomes, parasitic zooflagellate protozoans mainly transmitted by insects. In Africa, the Americas and Asia, these diseases, which in some cases affect humans, result in significant illness in animals and cause major economic losses in livestock. A number of pathogens are described in this review, including several Salivarian trypanosomes, such as Trypanosoma brucei sspp. (among which are the agents of sleeping sickness, the human African trypanosomiasis [HAT]), Trypanosoma congolense and Trypanosoma vivax (causing “Nagana” or animal African trypanosomosis [AAT]), Trypanosoma evansi (“Surra”) and Trypanosoma equiperdum (“Dourine”), and Trypanosoma cruzi , a Stercorarian trypanosome, etiological agent of the American trypanosomiasis (Chagas disease). Diagnostic methods for detecting zoonotic trypanosomes causing Chagas disease and HAT in animals, as well as a diagnostic method for detecting animal trypanosomes in humans (the so-called “atypical human infections by animal trypanosomes” [a-HT]), including T. evansi and Trypanosoma lewisi (a rat parasite), are also reviewed. Our goal is to present an integrated view of the various diagnostic methods and techniques, including those for: (i) parasite detection; (ii) DNA detection; and (iii) antibody detection. The discussion covers various other factors that need to be considered, such as the sensitivity and specificity of the various diagnostic methods, critical cross-reactions that may be expected among Trypanosomatidae, additional complementary information, such as clinical observations and epizootiological context, scale of study and logistic and cost constraints. The suitability of examining multiple specimens and samples using several techniques is discussed, as well as risks to technicians, in the context of specific geographical regions and settings. This overview also addresses the challenge of diagnosing mixed infections with different Trypanosoma species and/or kinetoplastid parasites. Improving and strengthening procedures for diagnosing animal trypanosomoses throughout the world will result in a better control of infections and will significantly impact on “One Health,” by advancing and preserving animal, human and environmental health. Graphical Abstract

We present a new and innovative identification method based on deep learning of the wing interferential patterns carried by mosquitoes of the Anopheles genus to classify and assign 20 Anopheles species, including 13 malaria vectors. We provide additional evidence that this approach can identify Anopheles spp. with an accuracy of up to 100% for ten out of 20 species. Although, this accuracy was moderate (> 65%) or weak (50%) for three and seven species. The accuracy of the process to discriminate cryptic or sibling species is also assessed on three species belonging to the Gambiae complex. Strikingly, An. gambiae, An. arabiensis and An. coluzzii, morphologically indistinguishable species belonging to the Gambiae complex, were distinguished with 100%, 100%, and 88% accuracy respectively. Therefore, this tool would help entomological surveys of malaria vectors and vector control implementation. In the future, we anticipate our method can be applied to other arthropod vector-borne diseases.

Phlebotomus (Paraphlebotomus) sergenti is a widespread proven vector of Leishmania pathogens causing anthroponotic cutaneous leishmaniasis (ACL), due to L. tropica, in the old world. The activity of P. (Par.) sergenti is seasonal and sensitive to general variations in climate. Phenological data sets can thus provide a baseline for continuing investigations on P. (Par.) sergenti population dynamics that may impact future leishmaniasis transmission and control scenarios. A systematic review of the seasonality of P. (Par.) sergenti was undertaken globally. Six hundred eight scientific papers were identified, and data were extracted from 35 ones, with informative data on sand fly seasonal dynamics on trapping performed from 1992 to December 2021 on 63 sites from 12 countries. Morocco, Saudi Arabia, Iraq, Iran, Pakistan, Palestine, Turkey, Spain, Portugal, Italy, Cyprus, and Georgia. The data extracted from the literature survey were further normalized. Our analysis recorded that the highest P.(Par.) sergenti activity occurs during the hot and dry seasons, primarily in July and August, whatever the location studied. We noticed a relationship between the latitude of sites and sand fly presence (from early April to June) and the type of density trend, varying from a single peak to multiple peaks. On a geographical scale, P. (Par.) sergenti concentrates between 32-37° in latitude in a large interval following the longitude and the highest number of sites with high P. (Par.) sergenti activity is located at the latitude 32°. We also quoted a similar seasonal dynamic and geographic distribution with Phlebotomus (Phlebotomus) papatasi, a proven vector of L. major that causes cutaneous infection. No apparent risk for ACL occurred from December to March, at least in the years and geographic areas considered in this survey. Altogether, knowing that high P. (Par.) sergenti activity would be linked with an increased risk of leishmaniasis transmission, and our study provides information that can be used for control programs on ACL transmission. Despite variations, we found a relatively homogeneous pattern of P. (Par.) sergenti potential behavior in sites whose data are published. A higher risk for L. tropica transmission was identified in the June-October period. Still, such risk was not equally distributed throughout the area since density waves of adults occurred earlier and were more frequent in some territories, like Saudi Arabia.

Background Chagas disease, caused by the parasite Trypanosoma cruzi , remains a major public health concern. While Rhodnius ecuadoriensis , a key vector, is traditionally sylvatic, environmental disturbances have driven its adaptation to human-influenced habitats. This study explores a novel factor: how anthropogenic waste affects vector ecology by altering nest compositions. Unlike prior research, this study examines whether human-derived materials in nests influence triatomine colonization. Given rising pollution, particularly post-COVID-19, understanding its role in disease transmission is essential for developing innovative vector control strategies. Methods Nest records were collected and analyzed in 2018, 2022, and 2023, across eight communities in Loja province, Ecuador. Nests were categorized as peridomestic if found < 30 m from a house and sylvatic if > 30 m away. The number of insects found in these nests was quantified using entomological indices. Pearson correlation analysis was applied to assess relationships between nest location and the presence of anthropogenic materials. Results A total of 389 nests were examined, yielding 1,089 individuals of Rhodnius ecuadoriensis (including both nymphs and adults). The infestation index in peridomestic areas dramatically decreased from 33.3% in 2018 to 0% in 2022, highlighting an intriguing temporal shift that warrants further investigation. In contrast, sylvatic areas showed fluctuating infestation rates (27.5% in 2018, 16.5% in 2022, and 22.2% in 2023). The study uniquely identified a significant association between triatomine infestation and mammal nests, with 50.1% of infested nests located within mammal habitats. Notably, 35.2% of these nests contained anthropogenic materials, particularly near human-altered landscapes such as roads and paths. The weak to moderate negative correlation between the presence of anthropogenic materials and proximity to roads or rivers ( r = -0.361, p  = 0.039) highlights an innovative exploration of the influence of human environmental changes on vector ecology. Conclusions This study offers a novel perspective on the dual impact of increasing pollution levels on wildlife. It highlights how anthropogenic waste not only reduces vector populations but also increases mortality rates through entanglement in waste materials. These findings underscore the urgent need for environmental education programs focused on waste management within local communities. Furthermore, the study paves the way for further research to assess the rate of T. cruzi infection in relation to environmental and anthropogenic factors, offering a critical foundation for understanding and potentially mitigating Chagas disease transmission.

In this paper, we test the possibility of using Wing Interference Patterns (WIPs) and deep learning (DL) for the identification of Culex mosquitoes species to evaluate the extent to which a generic method could be developed for surveying Dipteran insects of major importance to human health. Previous applications of WIPs and DL have successfully demonstrated their utility in identifying Anopheles , Aedes , sandflies, and tsetse flies, providing the rationale for extending this approach to Culex . Accurate identification of these mosquitoes is crucial for vector-borne disease control, yet traditional methods remain labor-intensive and are often hindered by cryptic species or damaged samples. To address these challenges, we applied WIPs, generated by thin-film interference on wing membranes, in combination with convolutional neural networks (CNNs) for species classification. Our results achieved over genus-level accuracy and up to species-level accuracy. Nonetheless, challenges with underrepresented species emphasize the need for larger datasets and complementary techniques such as molecular barcoding. This study highlights the potential of WIPs and DL to enhance mosquito identification and contribute to scalable tools for broader surveys of health-relevant Dipteran insects.

Sandflies (Diptera; Psychodidae) are medical and veterinary vectors that transmit diverse parasitic, viral, and bacterial pathogens. Their identification has always been challenging, particularly at the specific and sub-specific levels, because it relies on examining minute and mostly internal structures. Here, to circumvent such limitations, we have evaluated the accuracy and reliability of Wing Interferential Patterns (WIPs) generated on the surface of sandfly wings in conjunction with deep learning (DL) procedures to assign specimens at various taxonomic levels. Our dataset proves that the method can accurately identify sandflies over other dipteran insects at the family, genus, subgenus, and species level with an accuracy higher than 77.0%, regardless of the taxonomic level challenged. This approach does not require inspection of internal organs to address identification, does not rely on identification keys, and can be implemented under field or near-field conditions, showing promise for sandfly pro-active and passive entomological surveys in an era of scarcity in medical entomologists.

Purpose This study aimed to investigate the genetic diversity of Wolbachia in field-caught bed bug species in Paris areas. Methods The bed bug specimens were captured from various infested localities in Paris and surrounding cities. They belonged to diverse life stages, including egg, nymph, and adult. They were then identified using morphological and molecular approaches. Furthermore, Wolbachia was detected, and its genetic diversity was investigated by conventional PCR of 16S-rRNA and Wolbachia surface protein (wsp) genes. Results A total of 256 bed bug specimens belonging to various life stages [adult (183 specimens), nymph (48), and egg (25)] were captured from seven private apartments, five social apartments, three houses, two immigrant residences, and one retirement home situated in 10 districts of Paris and 8 surrounding cities. They were identified as Cimex lectularius (237 specimens) and C. hemipterus (19) using morphological and molecular approaches. The presence and diversity of Wolbachia were ascertained by targeting 16S-rRNA and wsp genes. Based on molecular analysis, 182 and 148 out of 256 processed specimens were positive by amplifying 16S-rRNA and wsp fragments, respectively. The inferred phylogenetic analysis with 16S-rRNA and wsp sequences displayed monophyletic Wolbachia strains clustering each one in three populations. The median-joining network, including the Wolbachia 16S-rRNA and wsp sequences of C. lectularius and C. hemipterous specimens, indicated a significant genetic differentiation among these populations in Paris areas which was consent with Neighbor-Joining analyses. A phylogenetic analysis of our heterogenic Wolbachia sequences with those reported from other arthropod species confirmed their belonging to supergroup F. Moreover, no difference between Wolbachia sequences from eggs, nymphs, and adults belonging to the same clade and between Wolbachia sequences of C. lectularius and C. hemipterus were observed after sequence alignment. Furthermore, no significant correlation was found between multiple geographical locations (or accomodation type) where bed bugs were collected and the genetic diversity of Wolbachia. Conclusions We highlight a significant heterogeneity within Wolbachia symbionts detected in C. lectularius and C. hemipterus. No correlation between Wolbachia species and bed bug species (C. lectularius versus C. hemipterus), physiological stages (egg, nymph, and adult), and sampling location was recorded in this study.

A simple method for accurately identifying Glossina spp in the field is a challenge to sustain the future elimination of Human African Trypanosomiasis (HAT) as a public health scourge, as well as for the sustainable management of African Animal Trypanosomiasis (AAT). Current methods for Glossina species identification heavily rely on a few well-trained experts. Methodologies that rely on molecular methodologies like DNA barcoding or mass spectrometry protein profiling (MALDI TOFF) haven’t been thoroughly investigated for Glossina sp. Nevertheless, because they are destructive, costly, time-consuming, and expensive in infrastructure and materials, they might not be well adapted for the survey of arthropod vectors involved in the transmission of pathogens responsible for Neglected Tropical Diseases, like HAT. This study demonstrates a new type of methodology to classify Glossina species. In conjunction with a deep learning architecture, a database of Wing Interference Patterns (WIPs) representative of the Glossina species involved in the transmission of HAT and AAT was used. This database has 1766 pictures representing 23  Glossina species. This cost-effective methodology, which requires mounting wings on slides and using a commercially available microscope, demonstrates that WIPs are an excellent medium to automatically recognize Glossina species with very high accuracy.

Hematophagous insects belonging to the Aedes genus are proven vectors of viral and filarial pathogens of medical interest. Aedes albopictus is an increasingly important vector because of its rapid worldwide expansion. In the context of global climate change and the emergence of zoonotic infectious diseases, identification tools with field application are required to strengthen efforts in the entomological survey of arthropods with medical interest. Large scales and proactive entomological surveys of Aedes mosquitoes need skilled technicians and/or costly technical equipment, further puzzled by the vast amount of named species. In this study, we developed an automatic classification system of Aedes species by taking advantage of the species-specific marker displayed by Wing Interferential Patterns. A database holding 494 photomicrographs of 24 Aedes spp. from which those documented with more than ten pictures have undergone a deep learning methodology to train a convolutional neural network and test its accuracy to classify samples at the genus, subgenus, and species taxonomic levels. We recorded an accuracy of 95% at the genus level and > 85% for two ( Ochlerotatus and Stegomyia ) out of three subgenera tested. Lastly, eight were accurately classified among the 10 Aedes sp. that have undergone a training process with an overall accuracy of > 70%. Altogether, these results demonstrate the potential of this methodology for Aedes species identification and will represent a tool for the future implementation of large-scale entomological surveys.