Enterovirus 71 (EV71) and Coxsackievirus A16 (CVA16) are two viruses commonly responsible for hand, foot and mouth disease (HFMD) in children. The lack of prophylactic or therapeutic measures against HFMD is a major public health concern. Insect cell-based EV71 and CVA16 virus-like particles (VLPs) are promising vaccine candidates against HFMD and are currently under development. In this paper, the influence of insect cell line, incubation temperature, and serial passaging effect and stability of budded virus (BV) stocks on EV71 and CVA16 VLP production was investigated. Enhanced EV71 and CVA16 VLP production was observed in Sf9 cells compared to High Five™ cells. Lowering the incubation temperature from the standard 27°C to 21°C increased the production of both VLPs in Sf9 cells. Serial passaging of CVA16 BV stocks in cell culture had a detrimental effect on the productivity of the structural proteins and the effect was observed with only 5 passages of BV stocks. A 2.7× higher production yield was achieved with EV71 compared to CVA16. High-resolution asymmetric flow field-flow fractionation couple with multi-angle light scattering (AF4-MALS) was used for the first time to characterize EV71 and CVA16 VLPs, displaying an average root mean square radius of 15±1nm and 15.3±5.8 nm respectively. This study highlights the need for different approaches in the design of production process to develop a bivalent EV71 and CVA16 vaccine.
As the living cytoplasm of laticiferous cells, Hevea brasiliensis latex is a rich blend of organic substances that include a mélange of proteins. A small number of these proteins have given rise to the problem of latex allergy. The salient characteristics of H. brasiliensis latex allergens that are recognized by the International Union of Immunological Societies (IUIS) are reviewed. These are the proteins associated with the rubber particles, the cytosolic C-serum proteins and the B-serum proteins that originate mainly from the lutoids. Procedures for the isolation and purification of latex allergens are discussed, from latex collection in the field to various preparative approaches adopted in the laboratory. As interest in recombinant latex allergens increases, there is a need to validate recombinant proteins to ascertain equivalence with their native counterparts when used in immunological studies, diagnostics, and immunotherapy.
Artificial intelligence (AI), particularly deep learning as a subcategory of AI, provides opportunities to accelerate and improve the process of discovering and developing new drugs. The use of AI in drug discovery is still in its early stages, but it has the potential to revolutionize the way new drugs are discovered and developed. As AI technology continues to evolve, it is likely that AI will play an even greater role in the future of drug discovery. AI is used to identify new drug targets, design new molecules, and predict the efficacy and safety of potential drugs. The inclusion of AI in drug discovery can screen millions of compounds in a matter of hours, identifying potential drug candidates that would have taken years to find using traditional methods. AI is highly utilized in the pharmaceutical industry by optimizing processes, reducing waste, and ensuring quality control. This review covers much-needed topics, including the different types of machine-learning techniques, their applications in drug discovery, and the challenges and limitations of using machine learning in this field. The state-of-the-art of AI-assisted pharmaceutical discovery is described, covering applications in structure and ligand-based virtual screening, de novo drug creation, prediction of physicochemical and pharmacokinetic properties, drug repurposing, and related topics. Finally, many obstacles and limits of present approaches are outlined, with an eye on potential future avenues for AI-assisted drug discovery and design.
Shortened versions of self-reported questionnaires may be used to reduce respondent burden. When shortened screening tools are used, it is desirable to maintain equivalent diagnostic accuracy to full-length forms. This manuscript presents a case study that illustrates how external data and individual participant data meta-analysis can be used to assess the equivalence in diagnostic accuracy between a shortened and full-length form. This case study compares the Patient Health Questionnaire-9 (PHQ-9) and a 4-item shortened version (PHQ-Dep-4) that was previously developed using optimal test assembly methods. Using a large database of 75 primary studies (34,698 participants, 3,392 major depression cases), we evaluated whether the PHQ-Dep-4 cutoff of ≥ 4 maintained equivalent diagnostic accuracy to a PHQ-9 cutoff of ≥ 10. Using this external validation dataset, a PHQ-Dep-4 cutoff of ≥ 4 maximized the sum of sensitivity and specificity, with a sensitivity of 0.88 (95% CI 0.81, 0.93), 0.68 (95% CI 0.56, 0.78), and 0.80 (95% CI 0.73, 0.85) for the semi-structured, fully structured, and MINI reference standard categories, respectively, and a specificity of 0.79 (95% CI 0.74, 0.83), 0.85 (95% CI 0.78, 0.90), and 0.83 (95% CI 0.80, 0.86) for the semi-structured, fully structured, and MINI reference standard categories, respectively. While equivalence with a PHQ-9 cutoff of ≥ 10 was not established, we found the sensitivity of the PHQ-Dep-4 to be non-inferior to that of the PHQ-9, and the specificity of the PHQ-Dep-4 to be marginally smaller than the PHQ-9.