ABSTRACT
Chloroxine (5,7-dichloro-8-hydroxyquinoline) is a molecule utilized in some shampoos for the therapy of seborrheic dermatitis of the scalp and dandruff. In this study, we investigated the inhibition effects of 5,7-dichloro-8-hydroxyquinoline and methyl 3,4,5-trihydroxybenzoate compounds on the 3-hydroxy-3-methylglutaryl coenzyme-A (HMG-CoA Reductase) and urease enzymes. We have obtained results for the HMG-CoA Reductase and urease enzymes at the micromolar level. In our study, inhibition result of 5,7-dichloro-8-hydroxyquinoline and Methyl 3,4,5-trihydroxybenzoate on HMG-CoA reductase showed lower values 2.28 ± 0.78 and 33.25 ± 5.04 µg/ml, respectively. Additionally, inhibition result of 5,7-dichloro-8-hydroxyquinoline and methyl 3,4,5-trihydroxybenzoate on urease showed lower values 6.18 ± 1.38 and 8.51 ± 1.35 µg/ml, respectively. Molecular docking calculations were made for their biological activities were compared. In the present work, the structures of the related compounds (1 and 2) were drawn using Gaussian 09 software and done geometry optimization at DFT/B3LYP/6-31G* basis set with aforementioned program. Cytotoxicity potential of these compounds against human lung cancer demonstrated that these compounds had good cytotoxic effects. Both compounds significantly decreased lung cell viability from low doses. In addition, 100 µM dose of all compounds caused significant reductions in lung cell viability. In general, we can say that of the two tested compounds, 5,7-dichloro-8-hydroxyquinoline and methyl 3,4,5-trihydroxybenzoate have cytotoxic effects in all cell types, and this effect is particularly strong in lung cells. Activities were performed at concentrations of 10, 20, 50, 70, and 100 µl and we achieved good results. Lung cell viability (%) value was better at 100 µl concentration and IC50 of them were 54.28 and 48.05 µM.
ABSTRACT
The recent increase in the global incidence of dengue fever resulted in over 2.7 million cases in Latin America and many cases in Southeast Asia and has warranted the development and application of early warning systems (EWS) for futuristic outbreak prediction. EWS pertaining to dengue outbreaks is imperative; given the fact that dengue is linked to environmental factors owing to its dominance in the tropics. Prediction is an integral part of EWS, which is dependent on several factors, in particular, climate, geography, and environmental factors. In this study, we explore the role of increased susceptibility to a DENV serotype and climate variability in developing novel predictive models by analyzing RT-PCR and DENV-IgM confirmed cases in Singapore and Honduras, which reported high dengue incidence in 2019 and 2020, respectively. A random-sampling-based susceptible-infected-removed (SIR) model was used to obtain estimates of the susceptible fraction for modeling the dengue epidemic, in addition to the Bayesian Markov Chain Monte Carlo (MCMC) technique that was used to fit the model to Singapore and Honduras case report data from 2012 to 2020. Regression techniques were used to implement climate variability in two methods: a climate-based model, based on individual climate variables, and a seasonal model, based on trigonometrically varying transmission rates. The seasonal model accounted for 98.5% and 92.8% of the variance in case count in the 2020 Singapore and 2019 Honduras outbreaks, respectively. The climate model accounted for 75.3% and 68.3% of the variance in Singapore and Honduras outbreaks respectively, besides accounting for 75.4% of the variance in the major 2013 Singapore outbreak, 71.5% of the variance in the 2019 Singapore outbreak, and over 70% of the variance in 2015 and 2016 Honduras outbreaks. The seasonal model accounted for 14.2% and 83.1% of the variance in the 2013 and 2019 Singapore outbreaks, respectively, in addition to 91% and 59.5% of the variance in the 2015 and 2016 Honduras outbreaks, respectively. Autocorrelation lag tests showed that the climate model exhibited better prediction dynamics for Singapore outbreaks during the dry season from May to August and in the rainy season from June to October in Honduras. After incorporation of susceptible fractions, the seasonal model exhibited higher accuracy in predicting outbreaks of higher case magnitude, including those of the 2019-2020 dengue epidemic, in comparison to the climate model, which was more accurate in outbreaks of smaller magnitude. Such modeling studies could be further performed in various outbreaks, such as the ongoing COVID-19 pandemic to understand the outbreak dynamics and predict the occurrence of future outbreaks.