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The proper closure of the access cavity between appointments during endodontic treatment is paramount and relies on temporary fillings. This systematic review evaluates the effectiveness of zinc oxide-based materials and glass-ionomer cement (GIC) as temporary coronal sealers after root canal treatment in extracted human teeth. Three databases were searched to identify randomized clinical trials that examined the sealing properties of various temporary sealing materials using dyes or stains as indicators. A total of seven in vitro studies that fulfilled the eligibility criteria were critically analyzed. These indicated significant variations in the relative sealing ability of the coronal breach of endodontically treated teeth, either by zinc oxide or GIC-based materials. While GIC-based material (e.g., Fuji IX and Fuji II) exhibited superior sealing of single-rooted teeth, zinc oxide-based material (e.g., Cavit, Coltosol, Caviton) also showed promising attributes. Resin-modified GIC formulations displayed enhanced physical properties, yet challenges related to adhesive failure and shrinkage during polymerization were observed. Zinc oxide-based materials have demonstrated superior coronal sealing effectiveness over certain GIC in controlled settings. Their premixed nature ensures consistent application and hygroscopic properties improve cavity sealing. However, the focus on dye penetration tests for microleakage in vitro may not fully represent the risk of bacterial infiltration. Thus, in vivo studies are crucial for validating these findings in clinical contexts., Competing Interests: None declared., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/).)

COVID-19 caused by SARS-CoV-2 is a global health issue. It is yet a severe risk factor to the patients, who are also suffering from one or more chronic diseases including different lung diseases. In this study, we explored common molecular signatures for which SARS-CoV-2 infections and different lung diseases stimulate each other, and associated candidate drug molecules. We identified both SARS-CoV-2 infections and different lung diseases (Asthma, Tuberculosis, Cystic Fibrosis, Pneumonia, Emphysema, Bronchitis, IPF, ILD, and COPD) causing top-ranked 11 shared genes (STAT1, TLR4, CXCL10, CCL2, JUN, DDX58, IRF7, ICAM1, MX2, IRF9 and ISG15) as the hub of the shared differentially expressed genes (hub-sDEGs). The gene ontology (GO) and pathway enrichment analyses of hub-sDEGs revealed some crucial common pathogenetic processes of SARS-CoV-2 infections and different lung diseases. The regulatory network analysis of hub-sDEGs detected top-ranked 6 TFs proteins and 6 micro RNAs as the key transcriptional and post-transcriptional regulatory factors of hub-sDEGs, respectively. Then we proposed hub-sDEGs guided top-ranked three repurposable drug molecules (Entrectinib, Imatinib, and Nilotinib), for the treatment against COVID-19 with different lung diseases. This recommendation is based on the results obtained from molecular docking analysis using the AutoDock Vina and GLIDE module of Schrödinger. The selected drug molecules were optimized through density functional theory (DFT) and observing their good chemical stability. Finally, we explored the binding stability of the highest-ranked receptor protein RELA with top-ordered three drugs (Entrectinib, Imatinib, and Nilotinib) through 100 ns molecular dynamic (MD) simulations with YASARA and Desmond module of Schrödinger and observed their consistent performance. Therefore, the findings of this study might be useful resources for the diagnosis and therapies of COVID-19 patients who are also suffering from one or more lung diseases., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Alamin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The edgeR (Robust) is a popular approach for identifying differentially expressed genes (DEGs) from RNA-Seq profiles. However, it shows weak performance against gene-specific outliers and is unable to handle missing observations. To address these issues, we proposed a pre-processing approach of RNA-Seq count data by combining the iLOO-based outlier detection and random forest-based missing imputation approach for boosting the performance of edgeR (Robust). Both simulation and real RNA-Seq count data analysis results showed that the proposed edgeR (Robust) outperformed than the conventional edgeR (Robust). To investigate the effectiveness of identified DEGs for diagnosis, and therapies of ovarian cancer (OC), we selected top-ranked 12 DEGs (IL6, XCL1, CXCL8, C1QC, C1QB, SNAI2, TYROBP, COL1A2, SNAP25, NTS, CXCL2, and AGT) and suggested hub-DEGs guided top-ranked 10 candidate drug-molecules for the treatment against OC. Hence, our proposed procedure might be an effective computational tool for exploring potential DEGs from RNA-Seq profiles for diagnosis and therapies of any disease., Competing Interests: Declaration of competing interest We can confirm that the work entitled “Boosting edgeR (Robust) by dealing with missing observations and gene-specific outliers in RNA-Seq profiles and its application to explore biomarker genes for diagnosis and therapies of ovarian cancer” is an original work and neither this nor any similar manuscript, in whole or in part, is under consideration, in a press, published, or reported elsewhere. All co-authors have seen and agreed with the contents of the manuscript. We would like to declare that we have no any conflict of interest for this work. Anyone can use any information from our manuscript., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

The pandemic of COVID-19 is a severe threat to human life and the global economy. Despite the success of vaccination efforts in reducing the spread of the virus, the situation remains largely uncontrolled due to the random mutation in the RNA sequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which demands different variants of effective drugs. Disease-causing gene-mediated proteins are usually used as receptors to explore effective drug molecules. In this study, we analyzed two different RNA-Seq and one microarray gene expression profile datasets by integrating EdgeR, LIMMA, weighted gene co-expression network and robust rank aggregation approaches, which revealed SARS-CoV-2 infection causing eight hub-genes (HubGs) including HubGs; REL, AURKA, AURKB, FBXL3, OAS1, STAT4, MMP2 and IL6 as the host genomic biomarkers. Gene Ontology and pathway enrichment analyses of HubGs significantly enriched some crucial biological processes, molecular functions, cellular components and signaling pathways that are associated with the mechanisms of SARS-CoV-2 infections. Regulatory network analysis identified top-ranked 5 TFs (SRF, PBX1, MEIS1, ESR1 and MYC) and 5 miRNAs (hsa-miR-106b-5p, hsa-miR-20b-5p, hsa-miR-93-5p, hsa-miR-106a-5p and hsa-miR-20a-5p) as the key transcriptional and post-transcriptional regulators of HubGs. Then, we conducted a molecular docking analysis to determine potential drug candidates that could interact with HubGs-mediated receptors. This analysis resulted in the identification of top-ranked ten drug agents, including Nilotinib, Tegobuvir, Digoxin, Proscillaridin, Olysio, Simeprevir, Hesperidin, Oleanolic Acid, Naltrindole and Danoprevir. Finally, we investigated the binding stability of the top-ranked three drug molecules Nilotinib, Tegobuvir and Proscillaridin with the three top-ranked proposed receptors (AURKA, AURKB, OAS1) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore, the findings of this study might be useful resources for diagnosis and therapies of SARS-CoV-2 infections., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Sarker et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)