The COVID-19 pandemic has been the focus of research the past two years. The major breakthrough was made by discovering pathways related to SARS-CoV-2 infection through cellular interaction by angiotensin-converting enzyme (ACE2) and cytokine storm. The presence of ACE2 in lungs, intestines, cardiovascular tissues, brain, kidneys, liver, and eyes shows that SARS-CoV-2 may have targeted these organs to further activate intracellular signalling pathways that lead to cytokine release syndrome. It has also been reported that SARS-CoV-2 can hijack coatomer protein-I (COPI) for S protein retrograde trafficking to the endoplasmic reticulum-Golgi intermediate compartment (ERGIC), which, in turn, acts as the assembly site for viral progeny. In infected cells, the newly synthesized S protein in endoplasmic reticulum (ER) is transported first to the Golgi body, and then from the Golgi body to the ERGIC compartment resulting in the formation of specific a motif at the C-terminal end. This review summarizes major events of SARS-CoV-2 infection route, immune response following host-cell infection as an important factor for disease outcome, as well as comorbidity issues of various tissues and organs arising due to COVID-19. Investigations on alterations of host-cell machinery and viral interactions with multiple intracellular signaling pathways could represent a major factor in more effective disease management.

Chemoinformatics involves integrating the principles of physical chemistry with computer-based and information science methodologies, commonly referred to as “in silico techniques”, in order to address a wide range of descriptive and prescriptive chemistry issues, including applications to biology, drug discovery, and related molecular areas. On the other hand, the incorporation of machine learning has been considered of high importance in the field of drug design, enabling the extraction of chemical data from enormous compound databases to develop drugs endowed with significant biological features. The present review discusses the field of cheminformatics and proposes the use of virtual chemical libraries in virtual screening methods to increase the probability of discovering novel hit chemicals. The virtual libraries address the need to increase the quality of the compounds as well as discover promising ones. On the other hand, various applications of bioinformatics in disease classification, diagnosis, and identification of multidrug-resistant organisms were discussed. The use of ensemble models and brute-force feature selection methodology has resulted in high accuracy rates for heart disease and COVID-19 diagnosis, along with the role of special formulations for targeting meningitis and Alzheimer’s disease. Additionally, the correlation between genomic variations and disease states such as obesity and chronic progressive external ophthalmoplegia, the investigation of the antibacterial activity of pyrazole and benzimidazole-based compounds against resistant microorganisms, and its applications in chemoinformatics for the prediction of drug properties and toxicity—all the previously mentioned—were presented in the current review.

Context Uterine fibroids (UF) are the most common benign tumor of the myometrium (MM) in women of reproductive age. However, the mechanism underlying the pathogenesis of UF is largely unknown. Objective To explore the link between nuclear β-catenin and UF phenotype and β-catenin crosstalk with estrogen and histone deacetylases (HDACs). Design Protein/RNA levels of β-catenin (CTNNB1 gene), its responsive markers cyclin D1 and c-Myc, androgen receptor (AR), p27, and class-I HDACs were measured in matched UF/MM tissues or cell populations. The effects of chemical inhibition/activation and genetic knockdown of CTNNB1 on UF phenotype were measured. The anti-UF effect of 2 HDAC inhibitors was evaluated. Main Outcome Measure β-catenin nuclear translocation in response to β-catenin inhibition/activation, estrogen, and HDAC inhibitors in UF cells. Results UF tissues/cells showed significantly higher expression of nuclear β-catenin, cyclin D1, c-Myc, and HDACs 1, 2, 3, and 8 than MM. Estradiol induced β-catenin nuclear translocation and consequently its responsive genes in both MM and UF cells, while an estrogen receptor antagonist reversed this induction effect. Treatment with β-catenin or HDAC inhibitors led to dose-dependent growth inhibition, while Wnt3a treatment increased proliferation compared with control. Chemical inhibition of β-catenin decreased cyclin D1 and c-Myc expression levels, while β-catenin activation increased expression of the same markers. Genetic knockdown of CTNNB1 resulted in a marked decrease in β-catenin, cyclin D1, c-Myc, and AR expression. Treatment of UF cells with HDAC inhibitors decreased nuclear β-catenin, cyclin D1, and c-Myc expression. Moreover, HDAC inhibitors induced apoptosis of UF cells and cell cycle arrest. Conclusion β-catenin nuclear translocation contributes to UF phenotype, and β-catenin signaling is modulated by estradiol and HDAC activity.