Aims To assess knowledge, attitude, and practices (KAP) of young adults with type 1 diabetes mellitus (T1DM) towards COVID-19 amid nationwide lockdown in India. Methods We conducted a cross-sectional web-based survey among young adults with T1DM (aged 18–30 years) in the North, Central, South, and West zones of India. It consisted of fifteen, five and eight questions pertaining to knowledge, attitude, and practices towards COVID-19, respectively. Certain questions relevant to T1DM were also incorporated. Results After exclusion, 212 participants were included (mean age = 25.1 ± 4.3 years; M:F = 10:11). The overall correct rate of the knowledge questionnaire was 83% (mean total knowledge score = 12.4 ± 1.9). Most (74%) had an average knowledge score (mean ± 1SD). Higher educational status, urban residence, and being married were associated with better knowledge scores; however, only urban residence was found to be statistically significant on multinomial logistic regression. Most (88%) felt that being a patient of T1DM, they were at higher risk of getting infected with COVID-19. At the same time, 98% were confident about self-protection. Fifty-one percent of respondents had left home amid lockdown mostly to procure insulin/injection needles/syringes/glucometer strips from the pharmacy. However, all were maintaining proper hand hygiene and majority were following routine dietary advice (95%) and administering prescribed insulin doses (99%). Seventy-two participants (34%) had experienced one or more episodes of hypoglycemia since the commencement of lockdown. Conclusions Young adults with T1DM have average knowledge, positive attitude, and healthy preventive practices towards COVID-19. Awareness campaigns targeted towards rural communities and providing doorstep delivery of insulin/needles/syringes may be more rewarding.

Highlights Young adults with T1DM lacked adequate awareness about COVID-19. Prevailing lockdown had reduced the routine physical activity of people with T1DM. Suboptimal glucose control could be linked to reduction in physical activity.

The larvae and adults of Aedes aegypti were tested for the potential to develop resistance to the synthetic pyrethroid, deltamethrin, alone or a combination of deltamethrin with the synergist, piperonyl butoxide (PBO). Although continuous larval selection for 40 generations resulted in 703-fold resistance, the resistance ratio in the adults was only 1.3. Similarly, adult selections with deltamethrin showed a resistance ratio of less than four after 40 generations, indicating differential response to deltamethrin selection in the two developmental stages of the insect. When the susceptible larvae were subjected to selection pressure of deltamethrin and PBO in the ratio of 1:5 for 20 generations, the speed of selection for deltamethrin resistance slowed down by 60%. The F24 larvae obtained from the strain selected with deltamethrin alone were further subjected to selection pressure with synergized deltamethrin, which resulted in 89% reversal in deltamethrin resistance in just one generation. However, long-term selection with the insecticide-synergist combination returned resistance close to original levels in 15 generations. The data indicate the involvement of cytochrome P450-dependent detoxification as the primary mechanism of development of resistance to deltamethrin in the larvae. Implications of the results on the management of larval and adult stages of Ae. aegypti are discussed.

Proper management of municipal solid waste is one of the prime matters of concern for metropolitan cities. To be able to successfully manage the solid waste generated, we need to plan in advance. A very essential pre-requirement for an efficient solid waste management is an accurate prediction of the garbage generation. Accurate forecasting of the quantity of MSW generation will enable us to design and operate an effective waste collection system. The main objective of this paper is to compare different models of artificial intelligence, viz. artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), discrete wavelet theory-artificial neural network (DWT-ANN), discrete wavelet theory-adaptive neuro-fuzzy inference system (DWT-ANFIS), genetic algorithm-artificial neural network (GA-ANN) and genetic algorithm-adaptive neuro-fuzzy inference system (GA-ANFIS) to examine and evaluate their capability in forecasting the amount of garbage being generated. A case example of the city of New Delhi, India, has been used for better understanding of different models. Root mean square error (RMSE), coefficient of determination (R2) and index of agreement (IA) values for every model were calculated, and the models were compared on the basis of it. The hybrid model of genetic algorithm and artificial neural network was found to have the lowest RMSE, the highest IA value and the highest R2 values, and hence is the most accurate of the above six models.

When the larvae of Anopheles stephensi, a malaria vector, were selected with deltamethrin for 40 successive generations, there was a 60-fold increase in larval resistance to deltamethrin but no increase in the resistance of the adult mosquitoes. This result, and the observation that deltamethrin selection of adults for 40 generations resulted in only a six-fold increase in adult resistance to deltamethrin, indicated some stage specificity. When F(24) deltamethrin-resistant larvae were selected with 1:5 deltamethrin-piperonyl butoxide (deltamethrin-PBO), instead of deltamethrin alone, for 16 generations, the level of resistance to deltamethrin in the F(40) larvae was reduced by 6%-21%. Similarly, selection with deltamethrin-PBO of adults of the parental strain for 20 generations reduced the speed of the development of resistance to deltamethrin, compared with that seen using selection with deltamethrin alone. Deltamethrin selection appears to select initially a monooxygenase-based mechanism. When the monooxygenase-based mechanism is blocked, by treatment with PBO, selection of a kdr-type mechanism is accelerated, as is evident from increased cross-resistance to 1,1,1-trichloro-2,2-bis(p-chlorophenyl)ethane (DDT) in the adults selected with deltamethrin-PBO. The implications of these results are discussed in terms of the management of the larval and adult stages of An. stephensi .