<p>The epidemic of Novel COVID-19 was<b> </b>reported in India in January 2020 and increased day by
day due to the movement of people from abroad to India and then to the
different parts of the country. The COVID-19 has been declared as pandemic
because of its high transmission rate and coved more than 2010 countries of the
world. Under this scenario when there is no medicine for its treatment, the
only solution to this problem is to break the chain of transmission and
restrict the count of infected people. To contain a coronavirus (COVID-19) outbreak,
the Government of India announced the nationwide lockdown with effect from the midnight
of 24<sup>th</sup> March 2020 followed by the extension of the lockdown periods
and presently it is in its 4<sup>th</sup> phase. The various provisions were
made under lockdown for closing the industries, transportation, etc. except the
essential services. It has been very interesting to note that the behavioural
changes in nature are highly positive and atmosphere, hydrosphere, and
biosphere are rejuvenating and it gives an appearance that the earth is under
lockdown for its repairing work. Under this natural recovery, we tried to look
at the improvement in the water quality of the Yamuna River in Delhi, which has
been one of the burst polluted rivers. To study this river, the concentrations of pH, EC, DO<sub>, </sub>BOD,
and COD have been measured which showed a reduction by 1-10%, 33-66%, 51%,
45-90%, and 33-82% respectively during the lockdown phase in comparison to the
pre-lockdown phase. The Nizamuddin Bridge, Okhla U/s, Najafgarh Drain and
Shahdara Drain were the major hotspots responsible for the deterioration of the
water quality of Yamuna River while passing by Delhi region. Five major locations
of Yamuna River have been analysed in this paper that showed a very impressive recovery of the water quality during the lockdown phase as compared to the pre-lockdown
status of water quality. </p>
Breast cancer covers a large area of research because of its prevalence and high frequency all over the world. This study is based on drug discovery against breast cancer from a series of imidazole derivatives. A 3D-QSAR and activity atlas model was developed by exploring the dataset computationally, using the machine learning process of Flare. The dataset of compounds was divided into active and inactive compounds according to their biological and structural similarity with the reference drug. The obtained PLS regression model provided an acceptable r2 = 0.81 and q2 = 0.51. Protein-ligand interactions of active molecules were shown by molecular docking against six potential targets, namely, TTK, HER2, GR, NUDT5, MTHFS, and NQO2. Then, toxicity risk parameters were evaluated for hit compounds. Finally, after all these screening processes, compound C10 was recognized as the best-hit compound. This study identified a new inhibitor C10 against cancer and provided evidence-based knowledge to discover more analogs.
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