Mosquito-Borne Diseases and Their Control Strategies: An Overview Focused on Green Synthesized Plant-Based Metallic Nanoparticles

Histological validation of in-vivo larvicidal efficacy of marine Streptomyces sp. RD06 secondary metabolites against filariasis causing Culex quinquefasciatus and statistical media optimization for larvicidal derivatives production

Mosquito-borne disease pandemics, such as the Zika virus and chikungunya, have escalated cognizance of how critical it is to implement proficient mosquito vector control measures. The prevention of Culicidae is becoming more difficult these days because of the expeditious imminence of synthetic pesticide resistance and the universal expansion of tremendously invasive mosquito vectors. The present study highlights the insecticidal and larvicidal efficacy of the prospective novel actinobacterium derived from the marine Streptomyces sp. RD06 secondary metabolites against Culex quinquefasciatus mosquito. The pupicidal activity of Streptomyces sp. RD06 showed LC50=199.22 ± 11.54 and LC90= 591.84 ± 55.41 against the pupa. The purified bioactive metabolites 1, 2-Benzenedicarboxylic acid, diheptyl ester from Streptomyces sp. RD06 exhibited an LC50 value of 154.13 ± 10.50 and an LC90 value of 642.84 ± 74.61 tested against Cx. quinquefasciatus larvae. The Streptomyces sp. RD06 secondary metabolites exhibited 100 % non-hatchability at 62.5 ppm, and 82 % of hatchability was observed at 250 ppm. In addition, media optimization showed that the highest biomass production was attained at a temperature of 41.44 °C, pH 9.23, nitrogen source 11.43 mg/ml, and carbon source 150 mg/ml. Compared to control larvae, the histology and confocal microscopy results showed destruction to the anal gill, lumen content, and epithelial layer residues in the treated larvae. Utilizing an eco-friendly method, these alternative inventive insecticidal derivatives from Streptomyces sp. RD06 eradicates Culex quinquefasciatus. This study highlights the promising potential of these Streptomyces sp. RD06 secondary metabolites to develop affordable and efficacious mosquito larvicides to replace synthetic insecticides in the future.

Evaluating the mosquito vector range for two orthobunyaviruses: Oya virus and Ebinur Lake virus

BACKGROUND

Mosquito-borne viruses cause various infectious diseases in humans and animals. Oya virus (OYAV) and Ebinur Lake virus (EBIV), belonging to the genus Orthobunyavirus within the family Peribunyaviridae, are recognized as neglected viruses with the potential to pose threats to animal or public health. The evaluation of vector competence is essential for predicting the arbovirus transmission risk.

METHODS

To investigate the range of mosquito vectors for OYAV (strain SZC50) and EBIV (strain Cu20-XJ), the susceptibility of four mosquito species (Culex pipiens pallens, Cx. quinquefasciatus, Aedes albopictus, and Ae. aegypti) was measured through artificial oral infection. Then, mosquito species with a high infection rate (IR) were chosen to further evaluate the dissemination rate (DR), transmission rate (TR), and transmission efficiency. The viral RNA in each mosquito sample was determined by RT-qPCR.

RESULTS

The results revealed that for OYAV, Cx. pipiens pallens had the highest IR (up to 40.0%) among the four species, but the DR and TR were 4.8% and 0.0%, respectively. For EBIV, Cx. pipiens pallens and Cx. quinquefasciatus had higher IR compared to Ae. albopictus (1.7%). However, the EBIV RNA and infectious virus were detected in Cx. pipiens pallens, with a TR of up to 15.4% and a transmission efficiency of 3.3%.

CONCLUSIONS

The findings indicate that Cx. pipiens pallens was susceptible to OYAV but had an extremely low risk of transmitting the virus. Culex pipiens pallens and Cx. quinquefasciatus were susceptible to EBIV, and Cx. pipiens pallens had a higher transmission risk to EBIV than Cx. quinquefasciatus.

Entomopathogenic fungi-based silver nanoparticles: a potential substitute of synthetic insecticides to counter behavioral and physiological immunity in Aedes aegypti mosquito (Diptera: Culicidae)

Excessive use of synthetic insecticides has resulted in environmental contamination and adverse effects on humans and other non-target organisms. Entomopathogenic fungi offer eco-friendly alternatives; however, their application for pest control requires significant advancement owing to limitations like slow killing time and effectiveness only when applied in higher amounts, whereas exposure to UV radiation, high temperature, and humidity can also reduce their viability and shelf-life. The nanoparticles synthesized using fungal extracellular extracts provide a new approach to use fungal pathogens. Our study focused on the synthesis of Metarhizium anisopliae-based silver nanoparticles (AgNPs) and evaluation of their efficiency on various physiological and behavioral parameters of the mosquito Aedes aegypti. The synthesis, size (27.6 d.nm, PDI = 0.209), zeta potential (- 24.3 mV), and shape of the AgNPs were determined through dynamic light scattering, scanning and transmission electron microscopic, and UV-visual spectroscopic analyses (432 nm). Our results showed significantly reduced survival (100% decrease in case of 3.2 and 1.8 μL/cm2 volumes, and 60% decrease in case of 0.8 μL/cm2 volume), phenoloxidase activity (t = 39.91; p = 0.0001), and gut microbiota, with increased oxidative stress and cell apoptosis in AgNPs-challenged mosquitoes. Furthermore, the AgNPs-exposed mosquitoes presented a concentration-specific decrease in flight locomotor activity (F = 17.312; p < 0.0001), whereas no significant changes in antifungal activity, self-grooming frequencies, or time spent were found. These findings enhance our understanding of mosquito responses to AgNPs exposure, and offer a more efficient mosquito control strategy using entomopathogenic fungi.

Prediction of malaria positivity using patients' demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment

BACKGROUND

Current malaria diagnosis methods that rely on microscopy and Histidine Rich Protein-2 (HRP2)-based rapid diagnostic tests (RDT) have drawbacks that necessitate the development of improved and complementary malaria diagnostic methods to overcome some or all these limitations. Consequently, the addition of automated detection and classification of malaria using laboratory methods can provide patients with more accurate and faster diagnosis. Therefore, this study used a machine-learning model to predict Plasmodium falciparum (Pf) antigen positivity (presence of malaria) based on sociodemographic behaviour, environment, and clinical features.

METHOD

Data from 200 Nigerian patients were used to develop predictive models using nested cross-validation and sequential backward feature selection (SBFS), with 80% of the dataset randomly selected for training and optimisation and the remaining 20% for testing the models. Outcomes were classified as Pf-positive or Pf-negative, corresponding to the presence or absence of malaria, respectively.

RESULTS

Among the three machine learning models examined, the penalised logistic regression model had the best area under the receiver operating characteristic curve for the training set (AUC = 84%; 95% confidence interval [CI]: 75-93%) and test set (AUC = 83%; 95% CI: 63-100%). Increased odds of malaria were associated with higher body weight (adjusted odds ratio (AOR) = 4.50, 95% CI: 2.27 to 8.01, p < 0.0001). Even though the association between the odds of having malaria and body temperature was not significant, patients with high body temperature had higher odds of testing positive for the Pf antigen than those who did not have high body temperature (AOR = 1.40, 95% CI: 0.99 to 1.91, p = 0.068). In addition, patients who had bushes in their surroundings (AOR = 2.60, 95% CI: 1.30 to 4.66, p = 0.006) or experienced fever (AOR = 2.10, 95% CI: 0.88 to 4.24, p = 0.099), headache (AOR = 2.07; 95% CI: 0.95 to 3.95, p = 0.068), muscle pain (AOR = 1.49; 95% CI: 0.66 to 3.39, p = 0.333), and vomiting (AOR = 2.32; 95% CI: 0.85 to 6.82, p = 0.097) were more likely to experience malaria. In contrast, decreased odds of malaria were associated with age (AOR = 0.62, 95% CI: 0.41 to 0.90, p = 0.012) and BMI (AOR = 0.47, 95% CI: 0.26 to 0.80, p = 0.006).

CONCLUSION

Newly developed routinely collected baseline sociodemographic, environmental, and clinical features to predict Pf antigen positivity may be a valuable tool for clinical decision-making.

Effective Preventative Measures are Essential to Lower Disease Burden From Dengue and COVID-19 Co-infection in Bangladesh

Bangladesh is widely recognized as one of the dengue prone nations, and empirical evidence has consistently demonstrated an upward trend in the severity of the disease over time. With the persistent occurrence of dengue in Bangladesh and the ongoing presence of COVID-19, which has not been fully eradicated and may persist for an uncertain period of time, there is a high probability of co-infection between these 2 illnesses. Given the circumstances, the concurrent occurrence of the COVID-19 and dengue epidemics, along with the potential co-infection, may pose an overwhelming burden on healthcare systems that are already grappling with challenges in meeting the existing demand. Due to a lack of awareness, an inadequate health infrastructure, and ineffective disease prevention initiatives, the country is now more susceptible to the threat posed by a co-infection that has been found to be associated with more severe outcomes, marked by significant morbidity and mortality. The objective of this opinion piece is to explore the gravity of co-infection in Bangladesh, as well as the potential challenges to overcome and the preventative measures that need to be implemented to address the severity. This opinion piece proposes a set of modern preventative strategies that, when integrated with conventional methods, have the potential to mitigate disease severity, avert the occurrence of co-infection between COVID-19 and dengue, and halt the co-epidemics of COVID-19 and dengue.