Technology empowers students but can also entice them to plagiarise. To tackle this problem, plagiarism detection tools are especially useful, not only in popular thinking as a deterrent for students, but also as an educational tool to raise students' awareness of the offence and to improve their academic skills. Commercial text matching tools (e.g. Turnitin) are at a high level of maturity. These tools offer the ability to interact with students, making them suitable for an educational objective. Additionally, they can be readily integrated into learning environments enabling uniform application at an institutional level. On the other hand, computer source code matching tools, despite their successful detection performance, are mostly used as standalone tools that are difficult to adopt at an institutional level. The research presented in this paper describes the trial and evaluation of a tool that is seamlessly integrated into the Moodle virtual learning environment. The tool provides code similarity scanning capability within Moodle so that institutions using this learning environment could apply this tool easily at an enterprise level. Additionally, the educational aspects available in text matching tools have been added into the tool capability. The tool relies on two popular code matching services, MOSS and JPlag, as underlying engines to provide good code similarity scanning performance. The evaluation of the tool from both academics' and students' perspectives indicates a considerable level of interest in using the tool, and supports the suitability of this tool for wider institutional adoption in the computing education community.

Monoamine oxidase B (MAO-B) is a key target in Parkinson's disease treatment due to its role in dopamine metabolism. This study applied a multi-stage in silico workflow - combining 3D-pharmacophore modelling, 2D-QSAR, ADMET filtering, docking, molecular dynamics (MD), and MM/PBSA analysis - to identify selective MAO-B inhibitors. From four datasets including ZINC, DrugBank, TCM, and UNPD, 22 top candidates were selected based on docking scores and predicted selectivity over MAO-A. MD simulations (200 ns) and binding free energy calculations identified four promising compounds - ZINC21285023, ZINC79651118, ZINC58283019, and UNPD89644 (crotafuran E)- that exhibited stable binding and favourable interactions with key residues such as Cys172 and Tyr435. These compounds demonstrated performance comparable to or better than safinamide and are strong candidates for further experimental validation as selective MAO-B inhibitors.

With the escalation of viral infections in recent decades, including the COVID- 19 pandemic, viral infectious diseases have increasingly become a global concern, attracting significant attention. Among many viral epidemics, the dengue virus, an RNA virus from the Flaviviridae family, has been reported by the WHO as one of the most prevalent mosquito-borne diseases, infecting roughly 400 million people yearly and spreading across all continents worldwide. In the last two decades, researchers from academia and industry have diligently studied many aspects of the virus, including its structure, life cycle, potential therapeutic agents, and vaccines. Dengvaxia® and Qdenga®, approved vaccines for DENV-4, have been a milestone in dengue prevention and treatment. However, these vaccines have some noticeable drawbacks, including Dengvaxia® being a monovalent vaccine against DENV-4 with a risk of severe dengue infection following the first use, Qdenga® being mainly effective for all 4 serotypes only in the cases of previously infected patients while being effective against only DENV-1 and DENV-2 in dengue-naïve patients. Additionally, no drug against dengue has been approved. Thus, numerous screening campaigns have been conducted on both natural and synthesized substances to search for anti-dengue agents, especially those targeting the virus's key protease (NS2B/NS3), to mitigate the dengue fever epidemic. As hit screening is only the first step in the drug discovery and development cycle, subsequent in-depth analyses (using a wide range of approaches from computational simulations to protein-ligand co-crystallization) have been conducted to provide more insights into the characteristics of optimal DENV NS2B/NS3 protease inhibitors. This review discusses recent discoveries in the search for novel inhibitors and highlights the importance of understanding the structural relationship between hits and the NS2B/NS3 protease for effective lead optimization.