Moderation effects of serotype on dengue severity across pregnancy status in Mexico.

BACKGROUND: Pregnancy increases a woman's risk of severe dengue. To the best of our knowledge, the moderation effect of the dengue serotype among pregnant women has not been studied in Mexico. This study explores how pregnancy interacted with the dengue serotype from 2012 to 2020 in Mexico. METHOD: Information from 2469 notifying health units in Mexican municipalities was used for this cross-sectional analysis. Multiple logistic regression with interaction effects was chosen as the final model and sensitivity analysis was done to assess potential exposure misclassification of pregnancy status. RESULTS: Pregnant women were found to have higher odds of severe dengue [1.50 (95% CI 1.41, 1.59)]. The odds of dengue severity varied for pregnant women with DENV-1 [1.45, (95% CI 1.21, 1.74)], DENV-2 [1.33, (95% CI 1.18, 1.53)] and DENV-4 [3.78, (95% CI 1.14, 12.59)]. While the odds of severe dengue were generally higher for pregnant women compared with non-pregnant women with DENV-1 and DENV-2, the odds of disease severity were much higher for those infected with the DENV-4 serotype. CONCLUSION: The effect of pregnancy on severe dengue is moderated by the dengue serotype. Future studies on genetic diversification may potentially elucidate this serotype-specific effect among pregnant women in Mexico.

Pre-to-post lockdown impact on air quality and the role of environmental factors in spreading the COVID-19 cases - a study from a worst-hit state of India.

The present study aims to examine the changes in air quality during different phases of the COVID-19 pandemic, including the lockdown (LD1-4) and unlock period (UL1-2) (post-lockdown) as compared to pre-lockdown (PL1-3) and to establish the relationships of the environmental and demographic variables with COVID-19 cases in the state of Maharashtra, the worst-hit state in India. Atmospheric pollutants such as PM2.5, PM10, NOx, and CO were substantially reduced during the lockdown and unlock phases with the greatest reduction in cities having larger traffic volumes. Compared with the immediate pre-lockdown period (PL3), the averaged PM2.5 and PM10 reduced by up to 51% and 47% respectively during the lockdown periods, which resulted in 'satisfactory' level of air quality index (AQI) as a result of reduced vehicular traffic and industrial closing. These parameters continued to reduce as much as 80% during the unlock periods due to the additive impact of weather (rainfall and temperature) combined with the lockdown conditions. Kendall's correlation matrix showed a significant negative correlation between temperature and air pollutants (r= - 0.35 to - 057). Conversely, SO2 and O3 did not improve, and in some cases, they increased during the lockdown and unlocking. COVID-19 spreading incidences were strongly and positively correlated with temperature (r < 0.62) and dew point (r < 0.73). Thus, this indicates that the increase in temperature and dew point cannot weaken the transmission of this virus. The number of COVID-19 cases relative to air pollutants was negatively correlated (r = - 0.33 to - 0.74), which may be a mere coincidence as a result of lockdown. However, based on pre-lockdown air quality data and demographic factors, it was found that particulate matter (PM2.5 and PM10) and population density are closely linked with higher morbidity and mortality although a more in-depth research is required in this direction to validate this finding. The onset of COVID-19 has allowed us to determine that 'immediate' changes in air quality within densely populated/industrialized areas can improve livelihood based on pollution mitigation. These findings could be used by policymakers to set new benchmarks for air pollution that would improve the quality of life for major sectors of the World's population. COVID-19 has shown us that we can make changes when necessary, and findings may pave the way for future research to inform policy on the tough choices we will have to make between quality of life and survival. Also, our results will enrich the ongoing discussion on the role of environmental factors on the transmission of COVID-19 and will help to take necessary steps for its control.

Elemental analysis and metabolic profiling of medicinally potent members of Zingiberaceae family using FT-IR and LIBS coupled with PLS-DA.

The role of organic and inorganic elemental profiles in the growth, development, and secondary metabolite synthesis of plants is crucial, particularly concerning their medicinal value. However, comprehensive studies addressing both aspects are scarce. Hence, the present manuscript aims to investigate the potential use of Fourier transform infrared spectroscopy (FT-IR) and laser-induced breakdown spectroscopy (LIBS) techniques to obtain the functional groups and organic and inorganic elemental profiles of significant medicinal plants belonging to the Zingiberaceae family collected from two different geographic regions in India. The FT-IR analysis of the methanolic extracts shows the presence of aliphatic and aromatic alcohols, esters, ethers, carboxyl compounds, and their derivatives. In LIBS analysis, the spectral characteristics of atomic and molecular species present in the samples were observed, encompassing both organic and inorganic elements. The presence of heavy metals and trace elements have also been observed in the LIBS spectra of the samples. Furthermore, partial least squares discriminant analysis (PLS-DA) has been used to obtain classification pattern of the samples based on their spectral fingerprints. This study not only helps in reflecting the significance of micronutrients in aiding secondary metabolism thus enhancing the medicinal properties of plants, but also enables the identification of trace elements within plants. This facilitates the determination of the suitable usage and dosage of particular plant components, contributing to the research goal of establishing pharmacological and nutraceutical significance. This study is imperative as it fills a critical gap in research, although further work in this direction is warranted.

Study of different concentric rings inside gallstones with LIBS.

Gallstones obtained from patients from the north-east region of India (Assam) were studied using laser-induced breakdown spectroscopy (LIBS) technique. LIBS spectra of the different layers (in cross-section) of the gallstones were recorded in the spectral region 200-900 nm. Several elements, including calcium, magnesium, manganese, copper, silicon, phosphorus, iron, sodium and potassium, were detected in the gallstones. Lighter elements, including carbon, hydrogen, nitrogen and oxygen were also detected, which demonstrates the superiority of the LIBS technique over other existing analytical techniques. The LIBS technique was applied to investigate the evolution of C(2) swan bands and CN violet bands in the LIBS spectra of the gallstones in air and an argon atmosphere. The different layers (dark and light layers) of the gallstones were discriminated on the basis of the presence and intensities of the spectral lines for carbon, hydrogen, nitrogen, oxygen and copper. An attempt was also made to correlate the presence of major and minor elements in the gallstones with the common diet of the population of Assam.

Short-term forecasting of the COVID-19 outbreak in India.

As the outbreak of coronavirus disease 2019 (COVID-19) is rapidly spreading in different parts of India, a reliable forecast for the cumulative confirmed cases and the number of deaths can be helpful for policymakers in making the decisions for utilizing available resources in the country. Recently, various mathematical models have been used to predict the outbreak of COVID-19 worldwide and also in India. In this article we use exponential, logistic, Gompertz growth and autoregressive integrated moving average (ARIMA) models to predict the spread of COVID-19 in India after the announcement of various unlock phases. The mean absolute percentage error and root mean square error comparative measures were used to check the goodness-of-fit of the growth models and Akaike information criterion for ARIMA model selection. Using COVID-19 pandemic data up to 20 December 2020 from India and its five most affected states (Maharashtra, Karnataka, Andhra Pradesh, Tamil Nadu and Kerala), we report 15-days-ahead forecasts for cumulative confirmed cases and the number of deaths. Based on available data, we found that the ARIMA model is the best-fitting model for COVID-19 cases in India and its most affected states.

Impact of Environmental Indicators on the COVID-19 Pandemic in Delhi, India.

Currently, there is a massive debate on whether meteorological and air quality parameters play a crucial role in the transmission of COVID-19 across the globe. With this background, this study aims to evaluate the impact of air pollutants (PM2.5, PM10, CO, NO, NO2, and O3) and meteorological parameters (temperature, humidity, wind speed, and rainfall) on the spread and mortality due to the COVID-19 outbreak in Delhi from 14 Mar 2020 to 3 May 2021. The Spearman's rank correlation method employed on secondary data shows a significant correlation between the COVID-19 incidences and the PM2.5, PM10, CO, NO, NO2, and O3 concentrations. Amongst the four meteorological parameters, temperature is strongly correlated with COVID-19 infections and deaths during the three phases, i.e., pre-lockdown (14 March 2020 to 24 March 2020) (r = 0.79), lockdown (25 March 2020 to 31 May 2020) (r = 0.87), and unlock (1 June 2020 to 3 May 2021) (r = -0.75), explaining the variability of about 20-30% in the lockdown period and 18-19% in the unlock period. NO2 explained the maximum variability of 10% and 7% in the total confirmed cases and deaths among the air pollutants, respectively. A generalized linear model could explain 80% and 71% of the variability in confirmed cases and deaths during the lockdown and 82% and 81% variability in the unlock phase, respectively. These findings suggest that these factors may contribute to the transmission of the COVID-19 and its associated deaths. The study results would enhance the ongoing research related to the influence of environmental factors. They would be helpful for policymakers in managing the outbreak of COVID-19 in Delhi, India.

COVID-19 Vaccine Hesitancy and Emerging Variants: Evidence from Six Countries.

As the world tries to cope with the devastating effects of the COVID-19 pandemic and emerging variants of the virus, COVID-19 vaccination has become an even more critical tool toward normalcy. The effectiveness of the vaccination program and specifically vaccine uptake and coverage, however, is a function of an individual's knowledge and individual opinion about the disease and available vaccines. This study investigated the knowledge, attitudes, and resulting community practice(s) associated with the new COVID-19 variants and vaccines in Bangladesh, Colombia, India, Malaysia, Zimbabwe, and the USA. A cross-sectional web-based Knowledge, Attitudes, and Practices (KAP) survey was administered to respondents living in six different countries using a structured and multi-item questionnaire. Survey questions were translated into English, Spanish, and Malay to accommodate the local language in each country. Associations between KAP and a range of explanatory variables were assessed using univariate and multiple logistic regression. A total of 781 responses were included in the final analysis. The Knowledge score mean was 24 (out of 46), Attitude score 28.9 (out of 55), and Practice score 7.3 (out of 11). Almost 65% of the respondents reported being knowledgeable about COVID-19 variants and vaccination, 55% reported a positive attitude toward available COVID-19 vaccines, and 85% reported engaging in practices that supported COVID-19 vaccination. From the multiple logistic models, we found post-graduate education (AOR = 1.83, 95% CI: 1.23-2.74) and an age range 45-54 years (AOR = 5.81, 95% CI: 2.30-14.69) to be significantly associated with reported COVID-19 knowledge. In addition, positive Attitude scores were associated with respondents living in Zimbabwe (AOR = 4.49, 95% CI: 2.04-9.90) and positive Practice scores were found to be associated with people from India (AOR = 3.68, 95% CI: 1.15-11.74) and high school education (AOR = 2.16, 95% CI: 1.07-4.38). This study contributes to the identification of socio-demographic factors associated with poor knowledge, attitudes, and practices relating to COVID-19 variants and vaccines. It presents an opportunity for collaboration with diverse communities to address COVID-19 misinformation and common sources of vaccine hesitancy (i.e., knowledge, attitudes, and practices).