ABSTRACT
COVID-19 and its causative organism SARS-CoV-2 paralyzed the world and was designated a pandemic by the World Health Organization in March 2020. The worldwide health system is trying to discover an effective therapeutic measure since no clinically authorized medications are present. Screening of plant-derived pharmaceuticals may be a viable technique to fight COVID-19 in this vital situation. This review discusses the potential application of in silico approaches in developing new therapeutic molecules related to preventing SARS-CoV-2 infection. Also, it describes the binding affinity of various phytoconstituents with distinct SARS-CoV-2 target sites. In this perspective, an extensive literature survey was carried out to find the potential phytoconstituents to develop new therapeutic entities to treat COVID-19 in different online academic databases and books. Data retrieved from databases were analyzed and interpreted to conclude that many phytochemicals will bind with the 3-chymotrypsin-like (3CLpro) and papain-like proteases (PLpro), spike glycoprotein, ACE-2, NSP15-endoribonuclease, and E protein targets of SARS-CoV-2 main protease using in silico molecular docking approach. The present investigations reveal that phytoconstituents such as curcumin, apigenin, chrysophanol, and gingerol are significantly binding with spike glycoprotein; laurolistine, acetoside, etc. are bound with Mpro for anti-SARS-CoV-2 therapies. Using virtual applications of in silico studies, the current study constitutes a progressive data analysis on the mechanism of binding efficiency of distinct classes of plant metabolites against the active sites of SARS-CoV-2. Furthermore, the current review also demonstrates the fundamental necessity of the alternative and complementary medicine for future therapeutic uses of phytoconstituents by phytochemists in the fight against COVID-19.
ABSTRACT
COVID-19 and its causative organism SARS-CoV-2 paralyzed the world and was designated a pandemic by the World Health Organization in March 2020. The worldwide health system is trying to discover an effective therapeutic measure since no clinically authorized medications are present. Screening of plant-derived pharmaceuticals may be a viable technique to fight COVID-19 in this vital situation. This review discusses the potential application of in silico approaches in developing new therapeutic molecules related to preventing SARS-CoV-2 infection. Also, it describes the binding affinity of various phytoconstituents with distinct SARS-CoV-2 target sites. In this perspective, an extensive literature survey was carried out to find the potential phytoconstituents to develop new therapeutic entities to treat COVID-19 in different online academic databases and books. Data retrieved from databases were analyzed and interpreted to conclude that many phytochemicals will bind with the 3-chymotrypsin-like (3CLpro) and papain-like proteases (PLpro), spike glycoprotein, ACE-2, NSP15-endoribonuclease, and E protein targets of SARS-CoV-2 main protease using in silico molecular docking approach. The present investigations reveal that phytoconstituents such as curcumin, apigenin, chrysophanol, and gingerol are significantly binding with spike glycoprotein;laurolistine, acetoside, etc. are bound with Mpro for anti-SARS-CoV-2 therapies. Using virtual applications of in silico studies, the current study constitutes a progressive data analysis on the mechanism of binding efficiency of distinct classes of plant metabolites against the active sites of SARS-CoV-2. Furthermore, the current review also demonstrates the fundamental necessity of the alternative and complementary medicine for future therapeutic uses of phytoconstituents by phytochemists in the fight against COVID-19.
ABSTRACT
Originating from Wuhan, China, COVID-19 is spreading rapidly throughout the world. The transmission rate is reported to be high for this novel strain of coronavirus, called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as compared to its predecessors. Major strategies in terms of clinical trials of medicines and vaccines, social distancing, use of personal protective equipment (PPE), and so on are being implemented in order to control the spread. The current study concentrates on lockdown and social distancing policy followed by the Indian Government and evaluates its effectiveness using Bayesian probability model (BPM). The change point analysis (CPA) done through the above approach suggests that the states which implemented the lockdown before the exponential rise of cases are able to control the spread of the disease in a much better and efficient way. The analysis has been done for states of Maharashtra, Gujarat, Madhya Pradesh, Rajasthan, Tamil Nadu, West Bengal, Uttar Pradesh, and Delhi as union territory. The highest value of Δ (delta) is reported for Gujarat and Madhya Pradesh with a value of 9.6 weeks, while the lowest value is 4.7, evidently for Maharashtra which is the worst affected. All of the states indicate a significant correlation (p < 0.05, tstat > tcritical) for Δ, i.e., the difference in the time period of CPA and lockdown with cases per population (CPP) and cases per unit area (CPUA), while weak correlation (p < 0.1 and tstat < tcritical) is exhibited by delta and cases per unit population density (CPD). For both CPP and CPUA, tstat > tcritical indicating a significant correlation, while Pearson's correlation indicates the direction to be negative. Further analysis in terms of identification of high-risk areas has been studied from the Voronoi approach of GIS based on the inputs from BPM. All the states follow the above pattern of high population, high case scenario, and the boundaries of risk zones can be identified by Thiessen polygon (TP) constructed therein. The findings of the study help draw strategic and policy-driven response for India, toward tackling COVID-19 pandemic.
ABSTRACT
With the outbreak of the Covid-19 pandemic, vaccination has become mandatory. Further, for effective results, the vaccines should be stored within the recommended temperature range, typically between 2°C to 8°C, transported safely without any mishandling and temperature excursion. In order to assure vaccine potency, it is essential to have detailed information on the entire temperature data recorded at user-defined intervals. In this paper, we develop functionality interaction to bring different sensors, memory, and processing units to an integrated platform, providing a compact, power-efficient, and low-cost commercial TemperatuRE, Humidity, and MOvement Data-logger (THERMOD). Moreover, the THERMOD hardware is packed with interactive algorithms that address the aforementioned concerns and log the real-time temperature and jerks (3-dimensional movement) encountered throughout the journey, and the logged data can be retrieved by plugging THERMOD into the host computer/laptop. The THERMOD hardware formulation and algorithm embedding have been done in the institution lab, which enables end-to-end storage and monitoring. Also, the proposed design is built with the defined standards by health organizations, e.g., WHO. Further, to validate the proficiency of the proposed design, comparative analysis has been done; a) a cost analysis has been done to state the cost efficiency of the proposed solution, b) real-time power performance graphs have been plotted which depict that THERMOD outperforms the existing solutions. Moreover, a number of experiments were performed for the validation of the proposed design.
ABSTRACT
The hospital management system in rural areas lacks the proper treatment due to demand of efficient doctors and health care persons. Also, in this situation of COVID-19 pandemic, common people are facing problems in health check up facilities. As per the latest report India has the doctor to patient ratio which is much below the recommended by the WHO. As per WHO guidelines, there should be one doctor for every 1000 patients, in health care environments. India has a ratio of 1:1445 as per the latest records. Also, as per rules PPE kits are essential for the health care persons to handle the corona patients. India still faces the shortage of these PPE kits, which are needed to be manufactured by the Indian ordnance factories. To address this issue, an IoT based system has been developed, which could aid in overcoming the doctor shortage in health care environments. The IoT system designed is a wearable device to be weared by the patient, which could monitor the pulse rate, temperature and SpO2 levels of the concerned patient. The data can be sent to the cloud to be stored on any IoT server like Thingspeak or any other servers like Adafruit.
ABSTRACT
A novel coronavirus disease (COVID-19) continues to challenge the whole world. The disease has claimed many fatalities as it has transcended from one country to another since it was first discovered in China in late 2019. To prevent further morbidity and mortality associated with COVID-19, most of the countries initiated a countrywide lockdown. While physical distancing and lockdowns helped in curbing the spread of this novel coronavirus, it led to massive economic losses for the nations. Positive impacts have been observed due to lockdown in terms of improved air quality of the nations. In the current research, ten tropical and subtropical countries have been analysed from multiple angles, including air pollution, assessment and valuation of health impacts and economic loss of countries during COVID-19 lockdown. Countries include Brazil, India, Iran, Kenya, Malaysia, Mexico, Pakistan, Peru, Sri Lanka, and Thailand. Validated Simplified Aerosol Retrieval Algorithm (SARA) binning model is used on data collated from moderate resolution imaging spectroradiometer (MODIS) for particulate matters with a diameter of less than 2.5 µm (PM2.5) for all the countries for the month of January to May 2019 and 2020. The concentration results of PM2.5 show that air pollution has drastically reduced in 2020 post lockdown for all countries. The highest average concentration obtained by converting aerosol optical depth (AOD) for 2020 is observed for Thailand as 121.9 µg/m3 and the lowest for Mexico as 36.27 µg/m3. As air pollution is found to decrease in the April and May months of 2020 for nearly all countries, they are compared with respective previous year values for the same duration to calculate the reduced health burden due to lockdown. The present study estimates that cumulative about 100.9 Billion US$ are saved due to reduced air pollution externalities, which are about 25% of the cumulative economic loss of 435.9 Billion US$.