Artificial light at night (ALAN) is a common form of light pollution worldwide, and the intensity, timing, duration, and wavelength of light exposure can affect biological rhythms, which can lead to metabolic, reproductive, and immune dysfunctions and consequently, host-pathogen interactions. Insect vector-borne diseases are a global problem that needs to be addressed, and ALAN plays an important role in disease transmission by affecting the habits and physiological functions of vector organisms. In this work, we describe the mechanisms by which ALAN affects host physiology and biochemistry, host-parasite interactions, and vector-borne viruses and propose preventive measures for related infectious diseases to minimize the effects of artificial light on vector-borne diseases.

Smart technology coupled with digital sensors and deep learning networks have emerging scopes in various fields, including surveillance of mosquitoes. Several studies have been conducted to examine the efficacy of such technologies in the differential identification of mosquitoes with high accuracy. Some smart trap uses computer vision technology and deep learning networks to identify live Aedes aegypti and Culex quinquefasciatus in real time. Implementing such tools integrated with a reliable capture mechanism can be beneficial in identifying live mosquitoes without destroying their morphological features. Such smart traps can correctly differentiates between Cx. quinquefasciatus and Ae. aegypti mosquitoes, and may also help control mosquito-borne diseases and predict their possible outbreak. Smart devices embedded with YOLO V4 Deep Neural Network algorithm has been designed with a differential drive mechanism and a mosquito trapping module to attract mosquitoes in the environment. The use of acoustic and optical sensors in combination with machine learning techniques have escalated the automatic classification of mosquitoes based on their flight characteristics, including wing-beat frequency. Thus, such Artificial Intelligence-based tools have promising scopes for surveillance of mosquitoes to control vector-borne diseases. However working efficiency of such technologies requires further evaluation for implementation on a global scale.

Pulmonary hypertension (PH) is a consequence of pulmonary endarteritis during infection with Dirofilaria immitis in dogs. Echocardiography is the technique of choice but is not always accessible to all clinicians. This study aimed to evaluate the association of the radiological findings in dogs with heartworm disease and the presence or absence of echocardiographically characterised PH. The study included 62 heartworm-infected dogs that underwent thoracic radiographs and echocardiography. The studied dogs showed moderate to severe PH when the Right Pulmonary Artery Distensibility (RPAD) Index was <29.5%. The RPAD Index was used for comparison with thoracic radiographs. The Vertebral Heart Size (VHS), right cranial pulmonary artery passing through the fourth rib in the laterolateral projection (CrPA/R4) ratio, and right caudal pulmonary artery to the ninth rib in the dorsoventral projection (CdPA/R9) ratio showed significant differences between dogs with/without PH (p < 0.001). Sensitivity (sen) and specificity (sp) cut-off values were obtained: VHS ≥ 9.53 (sen 93.75%, sp 63.33%); CrPA/R4 ≥ 1.08 (sen 87.5%, sp 70%); and CdPA/R9 ≥ 1.10 (sen 96.88%, sp 76.66%). The CrPA/R4 and CdPA/R9 ratios showed potential as a preliminary screening tool for PH in heartworm-infected dogs, suggesting that they may reliably indicate the presence of PH and guide the decision for further diagnostic testing.

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BACKGROUND: Malaria is the world\'s most fatal and challenging parasitic disease, caused by the Plasmodium parasite, which is transmitted to humans by the bites of infected female mosquitoes. Bangladesh is the most vulnerable region to spread malaria because of its geographic position. In this paper, we have considered the dynamics of vector-host models and observed the stochastic behavior. This study elaborates on the seasonal variability and calculates the probability of disease outbreaks.
METHODS: We present a model for malaria disease transmission and develop its corresponding continuous-time Markov chain (CTMC) representation. The proposed vector-host models illustrate the malaria transmission model along with sensitivity analysis. The deterministic model with CTMC curves is depicted to show the randomness in real scenarios. Sequentially, we expand these studies to a time-varying stochastic vector-host model that incorporates seasonal variability. Phase plane analysis is conducted to explore the characteristics of the disease, examine interactions among various compartments, and evaluate the impact of key parameters. The branching process approximation is developed for the corresponding vector-host model to calculate the probability outbreak. Numerous numerical results are accomplished to observe the analytical investigation.
RESULTS: Seasonality and contact patterns affect the dynamics of disease outbreaks. The numerical illustration provides that the probability of a disease outbreak depends on the infected host or vector. Additionally, periodic transmission rates have a great influence on the probability outbreak. The basic reproduction number (R0) is derived, which is the main justification for studying the dynamical behavior of epidemic models.
CONCLUSIONS: Seasonal variability significantly impacts malaria transmission, and the probability of disease outbreaks is influenced by time and the initial number of infected individuals. Moreover, the branching process approximation is applicable when the population size is large enough and the basic reproduction number is less than 1. In the future, such analysis can help decision-makers understand the impact of various parameters and their stochastic behavior in the vector-host model to prevent such types of disease outbreaks.

BACKGROUND: Aedes and Anopheles mosquitoes are responsible for tremendous global health burdens from their transmission of pathogens causing malaria, lymphatic filariasis, dengue, and yellow fever. Innovative vector control strategies will help to reduce the prevalence of these diseases. Mass rearing of mosquitoes for research and support of these strategies presently depends on meals of vertebrate blood, which is subject to acquisition, handling, and storage issues. Various blood-free replacements have been formulated for these mosquitoes, but none of these replacements are in wide use, and little is known about their potential impact on competence of the mosquitoes for Plasmodium infection.
METHODS: Colonies of Aedes aegypti and Anopheles stephensi were continuously maintained on a blood-free replacement (SkitoSnack; SS) or bovine blood (BB) and monitored for engorgement and hatch rates. Infections of Ae. aegypti and An. stephensi were assessed with Plasmodium gallinaceum and P. falciparum, respectively.
RESULTS: Replicate colonies of mosquitoes were maintained on BB or SS for 10 generations of Ae. aegypti and more than 63 generations of An. stephensi. The odds of engorgement by SS- relative to BB-maintained mosquitoes were higher for both Ae. aegypti (OR = 2.6, 95% CI 1.3-5.2) and An. stephensi (OR 2.7, 95% CI 1.4-5.5), while lower odds of hatching were found for eggs from the SS-maintained mosquitoes of both species (Ae. aegypti OR = 0.40, 95% CI 0.26-0.62; An. stephensi OR = 0.59, 95% CI 0.36-0.96). Oocyst counts were similar for P. gallinaceum infections of Ae. aegypti mosquitoes maintained on SS or BB (mean ratio = [mean on SS]/[mean on BB] = 1.11, 95% CI 0.85-1.49). Similar oocyst counts were also observed from the P. falciparum infections of SS- or BB-maintained An. stephensi (mean ratio = 0.76, 95% CI 0.44-1.37). The average counts of sporozoites/mosquito showed no evidence of reductions in the SS-maintained relative to BB-maintained mosquitoes of both species.
CONCLUSIONS: Aedes aegypti and An. stephensi can be reliably maintained on SS over multiple generations and are as competent for Plasmodium infection as mosquitoes maintained on BB. Use of SS alleviates the need to acquire and preserve blood for mosquito husbandry and may support new initiatives in fundamental and applied research, including novel manipulations of midgut microbiota and factors important to the mosquito life cycle and pathogen susceptibility.

The Schmallenberg virus (SBV), an emerging Orthobunyavirus of mainly ruminant hosts, caused a substantial epidemic in European ruminant populations between 2011 and 2013. The pathogen is transmitted by arthropod vectors (Culicoides spp.) and can cause reproductive disorders and severe malformations of the offspring or stillbirth. The present study aimed to assess SBV seroprevalence among German sheep and goats a few years after the first virus detection in the country (November 2011). In addition, an extensive risk factor analysis including host-specific and husbandry-related factors was implemented. Seroprevalence was determined by examining serum samples from 2759 sheep and 446 goats out of a total of 70 flocks across five German federal states. The samples were withdrawn in the period between 2017 and 2018. Using a commercial competitive ELISA, antibodies against SBV were detected in all 70 investigated flocks. A percentage of 60.1 % (1657/2759) of the sheep and 40.4 % (180/446) of the goat sera contained SBV antibodies. Generalized linear mixed modeling revealed significant effects of host species (sheep > goats), age (old > young) and sex (female > male) on SBV seroprevalence. For both species, also the farming purpose, and for goats, ectoparasite treatment and the presence of cattle on the farm played a role in terms of risk for SBV exposure. The observations from this study still emphasize a wide distribution of the pathogen in Germany. Nevertheless, the observed seroprevalence might not be sufficient to achieve effective herd immunity. Pinpointing risk factors identified susceptible populations for targeted vaccination programs to reduce potential animal losses caused by SBV.

UNASSIGNED: Dengue fever is rapidly becoming Malaysia\'s most pressing health concern, as the reported cases have nearly doubled over the past decade. Without efficacious antiviral medications, vector control remains the primary strategy for battling dengue, while the recently introduced tetravalent immunization is being evaluated. The most significant and dangerous risk increasing recently is vector-borne illnesses. These illnesses induce significant human sickness and are transmitted by blood-feeding arthropods such as fleas, parasites, and mosquitos. A thorough grasp of various factors is necessary to improve prediction accuracy and typically generate inaccurate and unstable predictions, as well as machine learning (ML) models, weather-driven mechanisms, and numerical time series.
UNASSIGNED: In this research, we propose a novel method for forecasting vector-borne disease risk using Radial Basis Function Networks (RBFNs) and the Darts Game Optimizer (DGO) algorithm.
UNASSIGNED: The proposed approach entails training the RBFNs with historical disease data and enhancing their parameters with the DGO algorithm. To prepare the RBFNs, we used a massive dataset of vector-borne disease incidences, climate variables, and geographical data. The DGO algorithm proficiently searches the RBFN parameter space, fine-tuning the model\'s architecture to increase forecast accuracy.
UNASSIGNED: RBFN-DGO provides a potential method for predicting vector-borne disease risk. This study advances predictive demonstrating in public health by shedding light on effectively controlling vector-borne diseases to protect human populations. We conducted extensive testing to evaluate the performance of the proposed method to standard optimization methods and alternative forecasting methods.
UNASSIGNED: According to the findings, the RBFN-DGO model beats others in terms of accuracy and robustness in predicting the likelihood of vector-borne illness occurrences.

Measuring vector-human contact in a natural setting can inform precise targeting of interventions to interrupt transmission of vector-borne diseases. One approach is to directly match human DNA in vector bloodmeals to the individuals who were bitten using genotype panels of discriminative short tandem repeats (STRs). Existing methods for matching STR profiles in bloodmeals to the people bitten preclude the ability to match most incomplete profiles and multi-source bloodmeals to bitten individuals.We developed bistro, an R package that implements 3 preexisting STR matching methods as well as the package\'s namesake, bistro, a new algorithm described here. bistro employs forensic analysis methods to calculate likelihood ratios and match human STR profiles in bloodmeals to people using a dynamic threshold. We evaluated the algorithm\'s accuracy and compared it to existing matching approaches using a publicly-available panel of 188 single-source and 100 multi-source samples containing DNA from 50 known human sources. Then we applied it to match 777 newly field-collected mosquito bloodmeals to a database of 645 people.The R package implements four STR matching algorithms in user-friendly functions with clear documentation. bistro correctly matched 99% (187/188) of profiles in single-source samples, and 62% (224/359) of profiles from multi-source samples, resulting in a sensitivity of 0.75 (vs < 0.51 for other algorithms). The specificity of bistro was 0.9998 (vs. 1 for other algorithms). Furthermore, bistro identified 79% (720/906) of all possible matches for field-derived mosquitoes, yielding 1.4x more matches than existing algorithms.bistro identifies more correct bloodmeal-human matches than existing approaches, enabling more accurate and robust analyses of vector-human contact in natural settings. The bistro R package and corresponding documentation allow for straightforward uptake of this algorithm by others.

Background: Tick-borne encephalitis (TBE) is caused by the tick-borne encephalitis virus (TBEV). TBEV infection can cause symptoms of central nervous system (CNS) inflammation and result in severe consequences including death. TBE is an increasing health threat in the Czech Republic and elsewhere in Europe. In 2020, 23% of 3734 TBE cases reported to the European Centre for Disease Prevention and Control were from the Czech Republic. TBE vaccination is universally recommended in the Czech Republic, but a full analysis of TBE vaccine effectiveness (VE) in the Czech Republic has not been published. Methods: TBE is a notifiable disease in the Czech Republic with mandatory reporting of cases (i.e., laboratory-confirmed TBEV infected patient with symptoms of CNS inflammation) and vaccination history to public health authorities. TBE VE was estimated using the screening method utilizing public health surveillance data from 2018 to 2022 and online household surveys of the general population on TBE vaccine uptake conducted in 2019-2022. Results: In 2018-2022, 3648 TBE cases were reported in the Czech Republic; 98.1% (3105/3166) of TBE cases with known vaccination history were unvaccinated. Among 42,671 persons surveyed from the general population who had known TBE vaccination history, 66.5% were unvaccinated. VE against TBE was 97.6% (95% confidence interval 95.7-98.7). When stratified by age group, VE was 97.1% (88.4-99.3) in 1-15 years of age, 97.9% (95.3-99.0) in 16-59 years of age, and 96.9% (90.5-99.0) in ≥60 years of age. TBE vaccination averted an estimated 1020 TBE cases in the Czech Republic from 2018 to 2022. Conclusions: This first published study with a full analysis of TBE VE in the Czech Republic showed that vaccination was highly effective for the prevention of TBE including in children, an age group with increasing TBE disease burden. Vaccination averted hundreds of TBE cases and hospitalizations despite the relatively low compliance with TBE vaccine recommendations. To prevent additional TBE cases in the Czech Republic, enhanced efforts to increase TBE vaccine uptake are needed.