Structural elucidation, mechanism of action, antibacterial proficiency and synergistic forte of purified Euphorbia hirta whole plant extract against multi-drug resistant otitis media infection / Elucidación estructural, mecanismo de acción, competencia antibacteriana y fuerza sinérgica del extracto de planta entera de Euphorbia hirta purificado contra la infección por otitis media resistente a múltiples fármacos

This study investigated the antibacterial potential of Euphorbia hirtawhole plant extracts, honey and conventional antibiotics and their synergistic effects against selected multidrug resistant and typed bacterial strains associated with otitis media. E. hirtawhole plant extract was purified using column chromatography technique. The antibacterial assays of extracts were done using standard microbiological procedures. Protein, sodium and potassium ion leakage of the synergistic mixtures was determined using flame-photometry. At 100 mg/ml, acetone extracts presented highest inhibition against S. aureus (NCTC 6571) with 32 ± 0.83 mm zone of inhibition. The fractional inhibitory concentration indices displayed higher synergism in combination of plant extract, honey and ciprofloxacin against P. mirabilisat 0.02 compared to drug combination synergy standard (≤ 0.5). This work revealed augmentation of ciprofloxacin potency when combined with purified E. hirta acetone extract and honey and implies their high potential in the treatment of multidrug resistant infectionof otitis media.

Este estudio investigó el potencial antibacteriano de extractos de plantas enteras de Euphorbia hirta, miel y antibióticos convencionales y sus efectos sinérgicos contra cepas bacterianas seleccionadas multirresistentes y tipificadas asociadas con la otitis media. El extracto de la planta entera de E. hirtase purificó usando la técnica de cromatografía en columna. Los ensayos antibacterianos de extractos se realizaron utilizando procedimientos microbiológicos estándar. La fuga de iones de proteínas, sodio y potasio de las mezclas sinérgicas se determinó mediante fotometría de llama. A 100 mg/ml, los extractos de acetona presentaron la mayor inhibición contra S. aureus (NCTC 6571) con una zona de inhibición de 32 ± 0,83 mm. Los índices de concentración inhibitoria fraccional mostraron un mayor sinergismo en combinación de extracto de planta, miel y ciprofloxacina contra P. mirabilisa 0,02 en comparación con el estándar de sinergia de combinación de fármacos (≤ 0,5). Este trabajo reveló un aumento de la potencia de la ciprofloxacina cuando se combina con extracto de acetona purificado de E. hirtay miel e implica sualto potencial en el tratamiento de infecciones de otitis media resistentes a múltiples fármacos.

Comparison of Predictive Models and Impact Assessment of Lockdown for COVID-19 over the United States.

The novel Coronavirus Disease 2019 (COVID-19) remains a worldwide threat to community health, social stability, and economic development. Since the first case was recorded on December 29, 2019, in Wuhan of China, the disease has rapidly extended to other nations of the world to claim many lives, especially in the USA, the United Kingdom, and Western Europe. To stay ahead of the curve consequent of the continued increase in case and mortality, predictive tools are needed to guide adequate response. Therefore, this study aims to determine the best predictive models and investigate the impact of lockdown policy on the USA' COVID-19 incidence and mortality. This study focuses on the statistical modelling of the USA daily COVID-19 incidence and mortality cases based on some intuitive properties of the data such as overdispersion and autoregressive conditional heteroscedasticity. The impact of the lockdown policy on cases and mortality was assessed by comparing the USA incidence case with that of Sweden where there is no strict lockdown. Stochastic models based on negative binomial autoregressive conditional heteroscedasticity [NB INGARCH (p,q)], the negative binomial regression, the autoregressive integrated moving average model with exogenous variables (ARIMAX) and without exogenous variables (ARIMA) models of several orders are presented, to identify the best fitting model for the USA daily incidence cases. The performance of the optimal NB INGARCH model on daily incidence cases was compared with the optimal ARIMA model in terms of their Akaike Information Criteria (AIC). Also, the NB model, ARIMA model and without exogenous variables are formulated for USA daily COVID-19 death cases. It was observed that the incidence and mortality cases show statistically significant increasing trends over the study period. The USA daily COVID-19 incidence is autocorrelated, linear and contains a structural break but exhibits autoregressive conditional heteroscedasticity. Observed data are compared with the fitted data from the optimal models. The results further indicate that the NB INGARCH fits the observed incidence better than ARIMA while the NB models perform better than the optimal ARIMA and ARIMAX models for death counts in terms of AIC and root mean square error (RMSE). The results show a statistically significant relationship between the lockdown policy in the USA and incidence and death counts. This suggests the efficacy of the lockdown policy in the USA.

COVID-19 incidence and mortality in Nigeria: gender based analysis.

BACKGROUND: Coronavirus Disease 2019 (COVID-19) has been surging globally. Risk strata in medical attention are of dynamic significance for apposite assessment and supply distribution. Presently, no known cultured contrivance is available to fill this gap of this pandemic. The aim of this study is to develop a predictive model based on vector autoregressive moving average (VARMA) model of various orders for gender based daily COVID-19 incidence in Nigeria. This study also aims to proffer empirical evidence that compares incidence between male and female for COVID-19 risk factors. METHODS: Wilcoxon signed-rank test is employed to investigate the significance of the difference in the gender distributions of the daily incidence. A VARMA model of various orders is formulated for the gender based daily COVID-19 incidence in Nigeria. The optimal VARMA model is identified using Bayesian information criterion. Also, a predictive model based on univariate autoregressive moving average model is formulated for the daily death cases in Nigeria. Fold change is estimated based on crude case-fatality risk to investigate whether there is massive underreporting and under-testing of COVID-19 cases in Nigeria. RESULTS: Daily incidence is higher in males on most days from 11 April 2020 to 12 September 2020. Result of Wilcoxon signed-rank test shows that incidence among male is significantly higher than female (p-value < 2.22 × 10-16). White neural network test shows that daily female incidence is not linear in mean (p-value = 0.00058746) while daily male incidence is linear in mean (p-value = 0.4257). McLeod-Li test shows that there is autoregressive conditional heteroscedasticity in the female incidence (Maximum p-value = 1.4277 × 10-5) and male incidence (Maximum p-value = 9.0816 × 10-14) at 5% level of significance. Ljung-Box test (Tsay, 2014) shows that the daily incidence cases are not random (p-value=0.0000). The optimal VARMA model for male and female daily incidence is VARMA (0,1). The optimal model for the Nigeria's daily COVID-19 death cases is identified to be ARIMA (0,1,1). There is no evidence of massive underreporting and under-testing of COVID-19 cases in Nigeria. CONCLUSIONS: Comparison of the observed incidence with fitted data by gender shows that the optimal VARMA and ARIMA models fit the data well. Findings highlight the significant roles of gender on daily COVID-19 incidence in Nigeria.

Ticking bomb: Prolonged faecal shedding of novel coronavirus (2019-nCoV) and environmental implications.

The current global coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a tremendous public health challenge globally. While the respiratory transmission of SARS-CoV-2 has been established, evolving reports on the impact of the gastrointestinal system and the prolonged faecal shedding of SARS-CoV-2 show the likelihood of faecally mediated transmission. The increasing evidential presence of SARS-CoV-2 in wastewater and faecal material poses a significant public health threat which may potentiate global vulnerability to high risk of human exposure through environmental drivers especially in less developed countries. While extensively exploring the likelihood of faecally mediated SARS-CoV-2 transmission, infection control and prevention measures aimed at mitigating this pandemic should holistically include environmental drivers.

Impact of COVID-19 prevalence and mode of transmission on mortality cases over WHO regions.

With the current outbreak of coronavirus disease 2019 (COVID-19), countries have been on rising preparedness to detect and isolate any imported and locally transmitted cases of the disease. It is observed that mode of transmission of the disease varies from one country to the other. Recent studies have shown that COVID-19 cases are not influenced by race and weather conditions. In this study, effect of modes of transmission of COVID-19 is considered with respect to prevalence and mortality counts in World Health Organisation (WHO) regions. Also, a negative binomial model is formulated for new death cases in all WHO regions as a function of confirmed cases, confirmed new cases, total deaths and modes of transmission, with the goal of identifying a model that predicts the total new death cases the best. Results from this study show that there is strong linear relationship among the COVID-19 confirmed cases, total new deaths and mode of transmission in all WHO regions. Findings highlight the significant roles of modes of transmission on total new death cases over WHO regions. Mode of transmission based on community transmission and clusters of cases significantly affects the number of new deaths in WHO regions. Vuong test shows that the formulated negative binomial model fits the data better than the null model.