Biology subjects

Erraguntla, M.

Publications and source records attributed to Erraguntla, M..

3 recordsLinked to original sources

bioRxiv2024

Exposure to cooler temperatures during pupal development increases Aedes aegypti vector competence and the R0 for Zika virus

Temperature profoundly affects various aspects of ectotherm biology. Notably, in mosquito species that spread viral diseases, temperature influences not only vector biology, but also the dynamics of pathogen-vector interactions. However, research attempting to address the role of the thermal environment in disease transmission often employs constant temperatures, which do not reflect the natural diurnal fluctuations these organisms experience. Additionally, most studies focus on adult mosquitoes in the period following virus infection. Much less attention has been paid to evidence indicating that temperatures experienced during earlier developmental stages may also affect the ability of disease vectors to be infected with and transmit viruses. Here, we show that Aedes aegypti exposed to temperatures below 25{degrees}C, specifically during the pupal stage of development, exhibit heightened susceptibility to Zika virus (ZIKV), which increases transmission efficiency. Modeling suggests that exposing mosquitoes to cooler fluctuating diurnal temperature ranges only during the relatively short pupal stage increases the R0 or reproductive number of ZIKV. Data loggers placed near Harris County Mosquito Control trap sites consistently recorded temperatures below 25{degrees}C, indicating natural exposure to such conditions. These results highlight the significance of thermal heterogeneity in the microhabitats where container-breeding mosquitoes undergo development. Such heterogeneity may play a more important role in the transmission of mosquito-borne diseases than previously recognized. Author SummaryA paucity of information regarding how natural heterogeneity in thermal environments influences the spread of viral pathogens by mosquito species hinders our ability to decipher current and future patterns of disease transmission under changing climatic conditions. Here, we show that Ae. aegypti pupae exposed to cooler fluctuating diurnal temperature ranges, mimicking realistic field conditions, increases vector competence for ZIKV. Modeling cooler temperatures during immature life stages predicts slower development and increased mortality, counterbalancing increases in disease transmission. However, exposing mosquitoes to lower temperatures only during the relatively short pupal stage was predicted to increase the R0 value of ZIKV. These results suggest that environmental temperatures specifically experienced during the pupal stage may have an important role in disease transmission.

microbiology↗

bioRxiv2019

Predicting long-term Type 2 Diabetes with Support Vector Machine using Oral Glucose Tolerance Test

Diabetes is a large healthcare burden worldwide. There is substantial evidence that lifestyle modifications and drug intervention can prevent diabetes, therefore, an early identification of high risk individuals is important to design targeted prevention strategies. In this paper, we present an automatic tool that uses machine learning techniques to predict the development of type 2 diabetes mellitus (T2DM). Data generated from an oral glucose tolerance test (OGTT) was used to develop a predictive model based on the support vector machine (SVM). We trained and validated the models using the OGTT and demographic data of 1,492 healthy individuals collected during the San Antonio Heart Study. This study collected plasma glucose and insulin concentrations before glucose intake and at three time-points thereafter (30, 60 and 120 min). Furthermore, personal information such as age, ethnicity and body-mass index was also a part of the dataset. Using 11 oral glucose tolerance test (OGTT) measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using Minimum Redundancy Maximum Relevance feature selection algorithm. All possible combinations of the 10 best ranked features were used to generate SVM based prediction models. This research shows that an individuals plasma glucose levels, and the information derived therefrom have the strongest predictive performance for the future development of T2DM. Significantly, insulin and demographic features do not provide additional performance improvement for diabetes prediction. The results of this work identify the parsimonious clinical data needed to be collected for an efficient prediction of T2DM. Our approach shows an average accuracy of 96.80 % and a sensitivity of 80.09 % obtained on a holdout set.

scientific communication and education↗

bioRxiv2018

Predicting Aquatic Development and Mortality Rates of Aedes Aegypti

Mosquito-borne pathogens continue to be a significant burden within human populations, with Aedes aegypti continuing to spread dengue, chikungunya, and Zika virus throughout the world. Using data from a previously conducted study, a linear regression model was constructed to predict the aquatic development rates based on the average temperature, temperature fluctuation range, and larval density. Additional experiments were conducted with different parameters of average temperature and larval density to validate the model. Using a paired t-test, the model predictions were compared to experimental data and showed that the prediction models were not significantly different for average pupation rate, adult emergence rate, and juvenile mortality rate. The models developed will be useful for modeling and estimating the number of Aedes aegypti in the environment under different temperature, diurnal temperature variations, and larval densities.\n\nAuthor SummaryUsing experimental data from experiments conducted on Aedes aegypti, we formulated regression models to predict pupation, adult emergence, and juvenile mortality rates based on average temperature, temperature fluctuation range, and larval density. The prediction models produced were shown to account for high levels of variation within the data. Validation was performed by comparing omitted data sets to the predictions generated by our models. Our results show that the models produce results that are not significantly different from the experimental results and are capable of predicting aquatic development rates of Ae. aegypti.

ecology↗