Recent pandemic situation of COVID-19 is caused due to SARS-CoV2 and almost all the countries of the world has been affected by this highly contagious virus. Main protease (M pro ) of this virus is a highly attractive drug target among various other enzymes due to its ability to process poly-protein that is the translated product of the SARS-CoV2 RNA. The aim of the present study demonstrates molecular docking study of Glycyrrhiza glabra (Gg) active compounds such as Glycyrrhizic acid (GA), Liquiritigenin (L) and Glabridin (G) against the M pro . Docking studies shows that these active compounds bind strongly with some of the amino acid residues in the active site of M pro and inhibits the enzyme strongly. GA, L, and G are proposed to be strong inhibitors of the enzyme and the amino acids: His 41 , Gly 143 , Gln 189 , Glu 166 , Cys 145 , Thr 25 , Asn 142 , Met 49 , Cys 44 , Thr 45 and pro 168 present in the active site of M pro were shown to make non-covalent interaction with these compounds. In silico ADMET properties prediction also shows that Gg active compounds had good solubility, absorption, permeation, non-toxic, and non- carcinogenic characteristics. Our finding concludes that all of the three active compounds of Gg could have the potential to be strong inhibitors for M pro of SARS-CoV2 but glycyrrhizic acid have a high binding affinity of -8.0 Kcal/mol and glycyrrhizic acid have good ADMET properties than the other two.

The synthetic hydrotalcites with Mg/Al molar ratios of 2.0−3.5 were synthesized by coprecipitation method at low supersaturation conditions followed by hydrothermal treatment under autogenous water vapor pressure at 70−140 °C. These synthesized samples were characterized by powder X-ray diffraction (P-XRD), Fourier transform infrared spectroscopy (FT-IR), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and surface area measurements. The hydrothermal treatment at increasing temperature and longer aging time increased the crystallinity and crystallite size of the hydrotalcite significantly. The crystallinity and crystallite size of the hydrotalcite were observed to decrease on increasing the Mg/Al ratio. The surface area of hydrotalcite was observed to increase on increasing the Mg/Al molar ratio from 2.0 to 3.5. From the kinetic data for crystallization of hydrotalcite at different temperatures, the values of rate constants and activation energy were calculated. The Avrami−Erofeev model (nucleation-growth model) was used for fitting the crystallization data.

Objective:The aim of the study was to evaluate periodontal health status in patients diagnosed with type 1 diabetes mellitus (DM1) and to establish a correlation between metabolic control and periodontal health status.Materials and Methods:Periodontal health parameters namely plaque index (PI), gingival index (GI), probing pocket depth (PPD) and clinical attachment loss (CAL) were recorded in 28 patients diagnosed with type 1 diabetes mellitus (DM1) and 20 healthy controls. Diabetes history was recorded based on the information provided by the physician and it included date of diagnosis, duration, age of diagnosis, latest values of glycosylated haemoglobin and existing diabetic complications. Statistical analysis was performed to evaluate the relationship between periodontal parameters and degree of metabolic control, the duration of the disease and the appearance of complications.Results:The periodontal health in the diabetic group was compromised and they had greater bleeding index (P < 0.001), probing pocket depth (P < 0.001) and clinical attachment level (P = 0.001). Patients diagnosed for diabetes for shorter duration of time (4-7 years) showed bleeding index-disease severity correlation to be 1.760 ± 0.434.Conclusion:Periodontal disease was more evident in type 1 diabetes mellitus patients and periodontal inflammation is greatly increased in subjects with longer disease course, poor metabolic control and diabetic complications.

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Code-mixing is the phenomenon of using more than one language in a sentence. In the multilingual communities, it is a very frequently observed pattern of communication on social media platforms. Flexibility to use multiple languages in one text message might help to communicate efficiently with the target audience. But, the noisy user-generated codemixed text adds to the challenge of processing and understanding natural language to a much larger extent. Machine translation from monolingual source to the target language is a wellstudied research problem. Here, we demonstrate that widely popular and sophisticated translation systems such as Google Translate fail at times to translate code-mixed text effectively. To address this challenge, we present a parallel corpus of the 13,738 code-mixed Hindi-English sentences and their corresponding human translation in English. In addition, we also propose a translation pipeline build on top of Google Translate. The evaluation of the proposed pipeline on P HIN C demonstrates an increase in the performance of the underlying system. With minimal effort, we can extend the dataset and the proposed approach to other code-mixing language pairs.