Interactions of Co(II)- and Zn(II)porphyrin of 5,10,15,20-tetrakis(1-methyl-4-pyridinio)porphyrin with DNA in Aqueous Solution and Their Antimicrobial Activities.

Antibiotics are frequently used to treat, prevent, or control bacterial infections, but in recent years, infections resistant to all known classes of conventional antibiotics have significantly grown. The development of novel, nontoxic, and nonincursive antimicrobial methods that work more quickly and efficiently than the present antibiotics is required to combat this growing public health issue. Here, Co(II) and Zn(II) derivatives of tetrakis(1-methylpyridinium-4yl)porphyrin [H2TMPyP]4+ as a tetra(ρ-toluenesulfonate) were synthesized and purified to investigate their interactions with DNA (pH 7.40, 25 °C) using UV-vis, fluorescence techniques, and antimicrobial activity. UV-vis results showed that [H2TMPyP]4+ had a high hypochromicity (∼64%) and a substantial bathochromic shift (Δλ, 14 nm), while [Co(II)TMPyP]4+ and [Zn(II)TMPyP]4+ showed little hypochromicity (∼37%) and a small bathochromic shift (Δλ, 3-6 nm). Results reveal that [H2TMPyP]4+ interacts with DNA via intercalation, while Co(II)- and [Zn(II)TMPyP]4+ interact with DNA via outside self-stacking. Fluorescence results also confirmed the interaction of [H2TMPyP]4+ and the metalloporphyrins with DNA. Results of the antimicrobial activity assay revealed that the metalloporphyrins showed inhibitory effects on Gram-positive and Gram-negative bacteria and fungi, but that neither the counterions nor [H2TMPyP]4+ exhibited any inhibitory effects. Mechanism of antimicrobial activities of metalloporphyrins are discussed.

Does climate change affect the transmission of COVID-19? A Bayesian regression analysis.

Aim: Coronavirus is an airborne and infectious disease and it is crucial to check the impact of climatic risk factors on the transmission of COVID-19. The main objective of this study is to determine the effect of climate risk factors using Bayesian regression analysis. Methods: Coronavirus disease 2019, due to the effect of the SARS-CoV-2 virus, has become a serious global public health issue. This disease was identified in Bangladesh on March 8, 2020, though it was initially identified in Wuhan, China. This disease is rapidly transmitted in Bangladesh due to the high population density and complex health policy setting. To meet our goal, The MCMC with Gibbs sampling is used to draw Bayesian inference, which is implemented in WinBUGS software. Results: The study revealed that high temperatures reduce confirmed cases and deaths from COVID-19, but low temperatures increase confirmed cases and deaths. High temperatures have decreased the proliferation of COVID-19, reducing the virus's survival and transmission. Conclusions: Considering only the existing scientific evidence, warm and wet climates seem to reduce the spread of COVID-19. However, more climate variables could account for explaining most of the variability in infectious disease transmission.

Effects of climate variables on the COVID-19 mortality in Bangladesh.

Infectious diseases such as severe acute respiratory syndrome (SARS) and influenza are influenced by weather conditions. Climate variables, for example, temperature and humidity, are two important factors in the severity of COVID-19's impact on the human respiratory system. This study aims to examine the effects of these climate variables on COVID-19 mortality. The data are collected from March 08, 2020, to April 30, 2022. The parametric regression under GAM and semiparametric regression under GAMLSS frameworks are used to analyze the daily number of death due to COVID-19. Our findings revealed that temperature and relative humidity are commencing to daily deaths due to COVID-19. A positive association with COVID-19 daily death counts was observed for temperature range and a positive association for humidity. In addition, one-unit increase in daily temperature range was only associated with a 1.08% (95% CI: 1.06%, 1.10%), and humidity range was only associated with a 1.03% (95% CI: 1.02%, 1.03%) decrease in COVID-19 deaths. A flexible regression model within the framework of Generalized Additive Models for Location Scale and Shape is used to analyze the data by adjusting the time effect. We used two adaptable predictor models, such as (i) the Fractional polynomial model and (ii) the B-spline smoothing model, to estimate the systematic component of the GAMLSS model. According to both models, high humidity and temperature significantly (and drastically) lessened the severity of COVID-19 death. The findings on the epidemiological trends of the COVID-19 pandemic and weather changes may interest policymakers and health officials.

Do Temperature and Humidity Affect the Transmission of SARS-CoV-2?-A Flexible Regression Analysis.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly transmissible virus that causes Coronavirus disease 2019 (COVID-19). Temperature and humidity are two essential factors in the transmission of SARS-CoV-2 affect the respiratory system of human. This study aimed to investigate the effects of temperature and humidity on the transmission of SARS-CoV-2 and the Spread Covid-19. The daily number of SARS-CoV-2 infected new cases, and the number of death due to Covid-19 are considered the response variables. Data are collected from March 08, 2020 to January 31, 2021. A flexible regression model under the Generalized Additive Models for Location Scale and Shape framework is used to analyze data. The temperature and humidity have a significant impact on the transmission of SARS-CoV-2. The temperature is highly significant in the number of SARS-CoV-2 infected new cases and number of death due to COVID-19. In contrast, the humidity is significant on the number of SARS-CoV-2 infected new cases, but it is insignificant on the number of death due to COVID-19 at a 5% level of significance. The analysis revealed that both the temperature and humidity inversely affected the daily number of deaths and new cases of COVID-19.