New biomaterial to replace humans in mosquito bite trials
A biomaterial that potentially does away with the requirement for human volunteers or animal subjects in mosquito bite research has been created through a partnership between Rice University and Tulane University.
"Several groups are dedicated to finding ways to stop mosquitoes from biting, but bringing new repellents to market is challenging," said Prof Omid Veiseh, Rice University. "This study attempts to increase testing throughput and decrease dependence on human volunteers and animal subjects," he added.
Mimicking human skin
To analyze the data, the scientists created a platform that incorporated 3D bio-printed hydrogels to resemble human skin with video monitoring and computer vision algorithms.
The data was used to create a machine-learning model to distinguish between mosquitoes that had eaten from the hydrogels and mosquitoes that had not. The mosquitoes were observed as they consumed blood perfused within the hydrogels. This made it simpler to swiftly and efficiently examine data on numerous feeding mosquitoes with an average precision of 92.5 percent, as per Phys.
The scientists experimented on sets of plain hydrogels covered in DEET and coated in a plant-based repellant to see how the mosquitoes reacted to each type of hydrogel.
Blood heated to 98.60 ℉ (37 degrees Celsius) was infused throughout each hydrogel. While 13.8 percent of the mosquitoes in the control cage consumed blood, none of the insects given repellent-coated hydrogels did. Even though this is a relatively low fraction, the scientists hypothesized that it might be because of the hydrogel's limited surface area, which might be fixed by scaling up.
"All of the experiments used lab strains of mosquitoes, and the majority involved one particular species: Aedes aegypti, the vector of the yellow fever virus, dengue virus, Zika virus, and others," said Prof Dawn Wesson, Tulane University, co-corresponding author.
"It may take time to optimize our experimental platform and machine learning model to study other species. Also, since the behavior of laboratory strains sometimes differs from that of mosquitoes found in the wild, it would be important to validate our results on wild mosquito populations."
"Overall, our results suggest that our experimental platform could be scaled up and adapted to screen different compounds for their effects on mosquitoes," said Veiseh, looking forward to future research.
Mosquitoes carry a number of deadly pathogens that are transmitted while feeding on blood through the skin, and studying mosquito feeding behavior could elucidate countermeasures to mitigate biting. Although this type of research has existed for decades, there has yet to be a compelling example of a controlled environment to test the impact of multiple variables on mosquito feeding behavior. In this study, we leveraged uniformly bioprinted vascularized skin mimics to create a mosquito feeding platform with independently tunable feeding sites. Our platform allows us to observe mosquito feeding behavior and collect video data for 30–45 min. We maximized throughput by developing a highly accurate computer vision model (mean average precision: 92.5%) that automatically processes videos and increases measurement objectivity. This model enables assessment of critical factors such as feeding and activity around feeding sites, and we used it to evaluate the repellent effect of DEET and oil of lemon eucalyptus-based repellents. We validated that both repellents effectively repel mosquitoes in laboratory settings (0% feeding in experimental groups, 13.8% feeding in control group, p < 0.0001), suggesting our platform’s use as a repellent screening assay in the future. The platform is scalable, compact, and reduces dependence on vertebrate hosts in mosquito research.
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