Investigating In Situ Expression of c-MYC and Candidate Ubiquitin-Specific Proteases in DLBCL and Assessment for Peptidyl Disruptor Molecule against c-MYC-USP37 Complex.

Diffuse Large B-Cell Lymphoma (DLBCL) is the most common form of non-Hodgkin's lymphoma (NHL). Elevated expression of c-MYC in DLBCL is associated with poor prognosis of the disease. In different cancers, c-MYC has been found regulated by different ubiquitin-specific proteases (USPs), but to date, the role of USPs in c-MYC regulation has not been investigated in DLBCL. In this study, in situ co expression of c-MYC and three candidates USPs, USP28, USP36 and USP37, have been investigated in both the ABC and GCB subtypes of DLBCL. This shows that USP37 expression is positively correlated with the c-MYC expression in the ABC subtype of DLBCL. Structurally, both c-MYC and USP37 has shown large proportion of intrinsically disordered regions, minimizing their chances for full structure crystallization. Peptide array and docking simulations has shown that N-terminal region of c-MYC interacts directly with residues within and in proximity of catalytically active C19 domain of the USP37. Given the structural properties of the interaction sites in the c-MYC-USP37 complex, a peptidyl inhibitor has been designed. Molecular docking has shown that the peptide fits well in the targeted site of c-MYC, masking most of its residues involved in the binding with USP37. The findings could further be exploited to develop therapeutic interventions against the ABC subtype of DLBCL.

Design and Immunoinformatic Assessment of Candidate Multivariant mRNA Vaccine Construct against Immune Escape Variants of SARS-CoV-2.

To effectively counter the evolving threat of SARS-CoV-2 variants, modifications and/or redesigning of mRNA vaccine construct are essentially required. Herein, the design and immunoinformatic assessment of a candidate novel mRNA vaccine construct, DOW-21, are discussed. Briefly, immunologically important domains, N-terminal domain (NTD) and receptor binding domain (RBD), of the spike protein of SARS-CoV-2 variants of concern (VOCs) and variants of interest (VOIs) were assessed for sequence, structure, and epitope variations. Based on the assessment, a novel hypothetical NTD (h-NTD) and RBD (h-RBD) were designed to hold all overlapping immune escape variations. The construct sequence was then developed, where h-NTD and h-RBD were intervened by 10-mer gly-ala repeat and the terminals were flanked by regulatory sequences for better intracellular transportation and expression of the coding regions. The protein encoded by the construct holds structural attributes (RMSD NTD: 0.42 Å; RMSD RBD: 0.15 Å) found in the respective domains of SARS-CoV-2 immune escape variants. In addition, it provides coverage to the immunogenic sites of the respective domains found in SARS-CoV-2 variants. Later, the nucleotide sequence of the construct was optimized for GC ratio (56%) and microRNA binding sites to ensure smooth translation. Post-injection antibody titer was also predicted (~12000 AU) to be robust. In summary, the construct proposed in this study could potentially provide broad spectrum coverage in relation to SARS-CoV-2 immune escape variants.

Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores.

Several challenges are associated with e-learning systems, the most significant of which is the lack of student motivation in various course activities and for various course materials. In this study, we used machine learning (ML) algorithms to identify low-engagement students in a social science course at the Open University (OU) to assess the effect of engagement on student performance. The input variables of the study included highest education level, final results, score on the assessment, and the number of clicks on virtual learning environment (VLE) activities, which included dataplus, forumng, glossary, oucollaborate, oucontent, resources, subpages, homepage, and URL during the first course assessment. The output variable was the student level of engagement in the various activities. To predict low-engagement students, we applied several ML algorithms to the dataset. Using these algorithms, trained models were first obtained; then, the accuracy and kappa values of the models were compared. The results demonstrated that the J48, decision tree, JRIP, and gradient-boosted classifiers exhibited better performance in terms of the accuracy, kappa value, and recall compared to the other tested models. Based on these findings, we developed a dashboard to facilitate instructor at the OU. These models can easily be incorporated into VLE systems to help instructors evaluate student engagement during VLE courses with regard to different activities and materials and to provide additional interventions for students in advance of their final exam. Furthermore, this study examined the relationship between student engagement and the course assessment score.