The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is responsible for COVID-19, which was declared a global pandemic in March 2020 by the World Health Organization (WHO). Since SARS-CoV-2 main protease plays an essential role in the virus’s life cycle, the design of small drug molecules with lower molecular weight has been a promising development targeting its inhibition. Herein, we evaluated the novel peptidomimetic azatripeptide and azatetrapeptide nitriles against SARS-CoV-2 main protease. We employed molecular dynamics (MD) simulations to elucidate the selected compounds’ binding free energy profiles against SARS-CoV-2 and further unveil the residues responsible for the drug-binding properties. Compound 8 exhibited the highest binding free energy of −49.37 ± 0.15 kcal/mol, followed by compound 7 (−39.83 ± 0.19 kcal/mol), while compound 17 showed the lowest binding free energy (−23.54 ± 0.19 kcal/mol). In addition, the absorption, distribution, metabolism, and excretion (ADME) assessment was performed and revealed that only compound 17 met the drug-likeness parameters and exhibited high pharmacokinetics to inhibit CYP1A2, CYP2C19, and CYP2C9 with better absorption potential and blood-brain barrier permeability (BBB) index. The additional intermolecular evaluations suggested compound 8 as a promising drug candidate for inhibiting SARS-CoV-2 Mpro. The substitution of isopropane in compound 7 with an aromatic benzene ring in compound 8 significantly enhanced the drug’s ability to bind better at the active site of the SARS-CoV-2 Mpro.

The derivative concept is studied in first-year university mathematics. In this study, we focused on students’ ability to correctly apply the rules for derivatives of functions with the different structures that they encounter in their university studies. This was done by investigating the online responses of first-year students at the University of KwaZulu-Natal to online quizzes that contributed to their assessment. Based on this investigation, we then interviewed eight students to gain an insight into the thinking behind their responses. We report on the analysis of students’ responses to five items on the online quizzes based on the derivative concept. The categories in which those items were based are: condition for existence of derivative at a point; rules for derivatives of standard functions; application of chain rule to different function structures; the application of multiple rules; and application of derivatives to optimise a function. Our findings indicate that students had difficulty in detecting that multiple rules for derivatives were required to differentiate certain types of functions represented in symbolic form. Furthermore, students had difficulty in finding the derivative of a function when more than one application of the chain rule was required. However, there were students who had the ability to apply the rules for derivatives of functions without difficulty. In particular, most of the students were able to correctly recall the differentiation rules for functions with standard structures f(x)=xn, h(x)=ekx and y=[g(x)]n, n ≠ 0 and k is a non-zero constant. Students were also able to correctly apply the chain rule to an exponential function with base e, raised to 4x. The majority of students were able to correctly apply the chain rule together with differentiation rules for logarithmic and exponential (with bases a >1) function structures, and function structures that required the application of the product rule together with the chain rule. Most of the students were able to apply derivatives to optimise a function.Significance: A significant percentage of students who took online quizzes experienced difficulties with applying multiple differentiation rules in the context of a single function. The difficulties stemmed from their inability to detect from the structure of the function which rules should be applied and also the order in which those relevant rules should be applied.

The use of vaccinations and antiviral medications have gained popularity in the therapeutic management of avian influenza H7N9 virus lately. Antiviral medicines are more popular due to being readily available. The presence of the neuraminidase protein in the avian influenza H7N9 virus and its critical role in the cleavage of sialic acid have made it a target drug in the development of influenza virus drugs. Generally, the neuraminidase proteins have common conserved amino acid residues and any mutation that occurs around or within these conserved residues affects the susceptibility and replicability of the influenza H7N9 virus. Herein, we investigated the interatomic and intermolecular dynamic impacts of the experimentally reported E119V mutation on the oseltamivir resistance of the influenza H7N9 virus. We extensively employed molecular dynamic (MD) simulations and subsequent post-MD analyses to investigate the binding mechanisms of oseltamivir-neuraminidase wildtype and E119V mutant complexes. The results revealed that the oseltamivir-wildtype complex was more thermodynamically stable than the oseltamivir-E119V mutant complex. Oseltamivir exhibited a greater binding affinity for wildtype (−15.46 ± 0.23 kcal/mol) relative to the E119V mutant (−11.72 ± 0.21 kcal/mol). The decrease in binding affinity (−3.74 kcal/mol) was consistent with RMSD, RMSF, SASA, PCA, and hydrogen bonding profiles, confirming that the E119V mutation conferred lower conformational stability and weaker protein–ligand interactions. The findings of this oseltamivir-E119V mutation may further assist in the design of compounds to overcome E119V mutation in the treatment of influenza H7N9 virus patients.

The H7N9 virus attaches itself to the human cell receptor protein containing the polysaccharide that terminates with sialic acid. The mutation of neuraminidase at residue E119 has been explored experimentally. However, there is no adequate information on the substitution with E119V in peramivir at the intermolecular level. Therefore, a good knowledge of the interatomic interactions is a prerequisite in understanding its transmission mode and subsequent effective inhibitions of the sialic acid receptor cleavage by neuraminidase. Herein, we investigated the mechanism and dynamism on the susceptibility of the E119V mutation on the peramivir–neuraminidase complex relative to the wildtype complex at the intermolecular level. This study aims to investigate the impact of the 119V substitution on the neuraminidase–peramivir complex and unveil the residues responsible for the complex conformations. We employed molecular dynamic (MD) simulations and extensive post-MD analyses in the study. These extensive computational investigations were carried out on the wildtype and the E119V mutant complex of the protein for holistic insights in unveiling the effects of this mutation on the binding affinity and the conformational terrain of peramivir–neuraminidase E119V mutation. The calculated total binding energy (ΔG_bind) for the peramivir wildtype is −49.09 ± 0.13 kcal/mol, while the E119V mutant is −58.55 ± 0.15 kcal/mol. The increase in binding energy (9.46 kcal/mol) is consistent with other post-MD analyses results, confirming that E119V substitution confers a higher degree of stability on the protein complex. This study promises to proffer contributory insight and additional knowledge that would enhance future drug designs and help in the fight targeted at controlling the avian influenza H7N9 virus. Therefore, we suggest that experimentalists collaborate with computational chemists for all investigations of this topic, as we have done in our previous studies.

Background: AI has quickly become integrated into higher education, transforming how students access information, complete academic tasks, and engage with learning. Universities of technology, with their focus on applied learning, technological skills, and graduate employability, face both challenges and opportunities in incorporating AI in ways that improve learning without sacrificing conceptual understanding, critical thinking, or academic integrity. While current research highlights widespread student use of AI tools, there remains limited empirical evidence on how undergraduate students in applied and health-related fields interact with AI, perceive its educational impact, and manage risks associated with overreliance. Objective: The study aims to examine undergraduate students’ AI use patterns, their perceptions of how AI affects conceptual understanding and critical thinking, and their views on risks such as dependency and ethical issues within a university of technology setting. An additional goal is to investigate students’ experiences with AI literacy programs and gather evidence to support responsible, educationally grounded AI integration that aligns with institutional strategies and graduate attributes. Methods: The study adopts a convergent mixed methods, cross-sectional, exploratory-descriptive design. Quantitative data will be collected using a structured questionnaire administered to undergraduate students across the mathematics, somatology, and Bachelor of Health Sciences (biomedical and clinical technology) programs within the Faculties of Applied Sciences and Health Sciences at Durban University of Technology. Qualitative data will be generated through semistructured interviews. Quantitative data will be analyzed using descriptive and inferential statistical methods, whereas qualitative data will be analyzed thematically using the method by Tesch. Integration of findings will occur at the interpretation stage to enable triangulation and comprehensive insight. Integration will follow the joint display approach described by Guetterman et al. Quantitative and qualitative results for each objective will be displayed side by side in matrices that compare statistical patterns with thematic insights. Convergence, complementarity, or discordance will be explicitly noted. Meta-inferences will be generated through team deliberation using an integration codebook. A triangulation protocol will document how discrepancies are resolved (eg, prioritizing qualitative depth for mechanism explanation and quantitative breadth for prevalence estimation). Results: A pilot study conducted within the mathematics department identified emerging patterns of AI dependence among students, helping refine research tools and focus interventions. This study was funded by the Department of Higher Education and Training on January 1, 2025, and was conducted between August 2025 and November 2025, with preliminary results available at the end of that period. Data collection is ongoing and is expected to conclude by the end of November 2026, after which the data will be analyzed in 2027 and the findings will be published in a separate results paper. Conclusions: The study is expected to provide evidence on the use of AI in applied and health-related fields. The findings will inform institutional policy, curriculum development, and the development of a short course on the responsible use of AI, thereby supporting broader discussions on the sustainable integration of AI in higher education.