A deep learning ensemble approach to prioritize antiviral drugs against novel coronavirus SARS-CoV-2 for COVID-19 drug repurposing.

The alarming pandemic situation of Coronavirus infectious disease COVID-19, caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has become a critical threat to public health. The unexpected outbreak and unrealistic progression of COVID-19 have generated an utmost need to realize promising therapeutic strategies to fight the pandemic. Drug repurposing-an efficient drug discovery technique from approved drugs is an emerging tactic to face the immediate global challenge. It​ offers a time-efficient and cost-effective way to find potential therapeutic agents for the disease. Artificial Intelligence-empowered deep learning models enable the rapid identification of potentially repurposable drug candidates against diseases. This study presents a deep learning ensemble model to prioritize clinically validated anti-viral drugs for their potential efficacy against SARS-CoV-2. The method integrates the similarities of drug chemical structures and virus genome sequences to generate feature vectors. The best combination of features is retrieved by the convolutional neural network in a deep learning manner. The extracted deep features are classified by the extreme gradient boosting classifier to infer potential virus-drug associations. The method could achieve an AUC of 0.8897 with 0.8571 prediction accuracy and 0.8394 sensitivity under the fivefold cross-validation. The experimental results and case studies demonstrate the suggested deep learning ensemble system yields competitive results compared with the state-of-the-art approaches. The top-ranked drugs are released for further wet-lab researches.

A Case of Neonatal Diabetes Mellitus Due to INS Gene Mutation with Maternal Mosaicism and Atypical Presentation.

Neonatal diabetes mellitus is a single gene defect that results in diabetes mellitus in the first 6 months of life. We report a child who was diagnosed to be hyperglycemic at 13 months of life and assumed to have type 1 diabetes mellitus and started on insulin. The child came to us at 2 and 1/2 years of age. He had exceptionally good blood glucose control. His history revealed that he was symptomatic with a voracious appetite and poor weight gain since the second half of infancy. Genetic testing revealed a heterozygous mutation of the INS gene (the gene that codes for insulin). The condition has autosomal dominant inheritance. Testing the parents revealed that the mother had 7.8% mosaicism for this variant in her lymphocyte DNA. Though this did not alter the management of the patient, it did help in counseling the parents regarding risk of recurrence in future pregnancies.

Lip repositioning, an alternative treatment of gummy smile - A case report.

The aim of present case report is to discuss surgical lip repositioning technique for the management of excessive gingival display related with vertical maxillary excess and increase mobility of the upper lip. By restricting the muscle pull by elevator lip muscles, this procedure reduces excessive gingival display during smiling. For patient, this procedure is safe, less invasive with minimal side effects and an alternative to orthognathic surgery in the correction of gummy smile.

Comparative Evaluation of Marginal Adaptation of Biodentine(TM) and Other Commonly Used Root End Filling Materials-An Invitro Study.

AIM: The purpose of this investigation was to evaluate the marginal adaptation of three root-end filling materials Glass ionomer cement, Mineral trioxide aggregate and Biodentine(TM). METHODOLOGY: Thirty human single-rooted teeth were resected 3 mm from the apex. Root-end cavities were then prepared using an ultrasonic tip and filled with one of the following materials Glass ionomer cement (GIC), Mineral trioxide aggregate (MTA) and a bioactive cement Biodentine(TM). The apical portions of the roots were then sectioned to obtain three 1 mm thick transversal sections. Confocal laser scanning microscopy (CLSM) was used to determine area of gaps and adaptation of the root-end filling materials with the dentin. The Post hoc test, a multiple comparison test was used for statistical data analysis. RESULTS: Statistical analysis showed lowest marginal gaps (11143.42±967.753m2) and good marginal adaptation with Biodentine(TM) followed by MTA (22300.97±3068.883m(2)) and highest marginal gaps with GIC (33388.17±12155.903m(2)) which were statistically significant (p<0.0001). CONCLUSION: A new root end filling material Biodentine(TM) showed better marginal adaptation than commonly used root end filling materials.