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The assumption of the pineal hormone melatonin as a therapeutic use for COVID-19 affected people seems promising. It’s intake has shown significant improvement in the patients’ conditions. Higher melatonin titres in children may provide a protective shield against this disease. The hormone melatonin works as an anti-inflammatory, antioxidant, immunomodulator and strategically slows down the cytokine release which is observed in the COVID-19 disease, thereby improving the overall health of afflicted patients. The medical community is expected shortly to use remedial attributes like anti-inflammatory, anti-oxidant, anti-virals, etc of melatonin in the successful prevention and cure of COVID-19 morbidity. Thus, the administration of melatonin seems auspicious in the cure and prevention of this COVID-19 fatality. Moreover, melatonin doesn’t seem to reduce the efficiency of approved vaccines against the SARS-CoV-2 virus. Melatonin increases the production of inflammatory cytokines and Th1 and enhances both humoral and cell mediated responses. Through the enhanced humoral immunity, melatonin exhibits antiviral activities by suppressing multiple inflammatory products such as IL6, IL1β, and TNFα, which are immmediately released during lung injury of severe COVID-19. Hence, the novel use of melatonin along with other antivirals as an early treatment option against COVID-19 infection is suggested. Here, we have chalked out the invasion mechanisms and appropriate implications of the latest findings concerned with melatonin against the virus SARS-CoV-2. Within the setting of a clinical intervention, the promosing compounds must go through a series of studies before their recommendation. In the clinical field, this is done in a time-ordered sequence, in line with the phase label affixed to proper protocol of trials: phase I - phase II and the final phase III . While medical recommendations can only be made on the basis of reassuring evidence, there are still three issues worth considering before implementation: representativeness, validity, and lastly generalizability.

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The purpose of this study is to investigate the computing capabilities of machine learning algorithms and remotely sensed signals to extract the agricultural information. Many techniques and models have been developed to extract information from the remotely sensed observations, but it remains an exigent problem due to the accuracy, reliability and timeliness parameters. Sugarcane yield estimation based on the temporal profile of multispectral Landsat-8 data has been explored in the proposed work. An initial attempt has been made in this study to select important parameters to be used as input to the machine learning method. Mean Decrease Accuracy and Mean Decrease Gini measures of random forest algorithm have been used to select the important parameters for predictive modelling. The results of the study revealed that Green Normalized Vegetation Index, Normalized Difference Vegetation Index and Land Surface Water Index performed best among other indices. Bands B2, B3, B6 and B7 of Landsat-8 recorded as top scorers. The proposed work focused on ensemble machine learning methods to optimize the correlation of historical crop yield values with spectral information. The Random Forest method exhibits a significant performance (RMSE= 1.51 t/ha and R 2 = 0.94) as compared with other methods such as Classification and Regression Tree, Support Vector Regression and K-Nearest Neighbor. The proposed model based on random forest algorithm is best among all the scenarios and growth stages, whereas model based on classification and regression tree performs worst in all the cases. The proposed study indicates that the numerical value of a single spectral parameter and single-date data is not sufficient for the reliable yield estimation because it is difficult to discriminate some of the crops due to similar phenology in a particular growth period.