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Positive association also emerged from the healthy stage to the pre-frailty and from the pre-frailty stage to the frailty stage (once a month to once in a two-week period).The extended and diverse interference of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in multiple host functions and the diverse associated symptoms implicate its involvement in fundamental cellular regulatory processes. The activity of ten-eleven translocase 2 (TET2) responsible for selective DNA demethylation, has been recently identified as a regulator of endogenous virus inactivation and viral invasion, possibly by proteasomal deregulation of the TET2/TET3 activities. In a recent report, we presented a detailed list of factors that can be affected by TET activity, including recognition of zinc finger protein binding sites and bimodal promoters, by enhancing the flexibility of adjacent sequences. In this review, we summarize the TET-associated processes and factors that could account for SARS-CoV-2 diverse symptoms. Moreover, we provide a correlation for the observed virus-induced symptoms that have been previously associated with TET activities by in vitro and in vitro studies. These include early hypoxia, neuronal regulation, smell and taste development, liver, intestinal, and cardiomyocyte differentiation. Finally, we propose that the high mortality of SARS-CoV-2 among adult patients, the different clinical symptoms of adults compared to children, the higher risk of patients with metabolic deregulation, and the low mortality rates among women can all be accounted for by the complex balance of the three enzymes with TET activity, which is developmentally regulated. This activity is age-dependent, related to telomere homeostasis and integrity, and associated with X chromosome inactivation via (de)regulation of the responsible XIST gene expression.Evidence suggests that countries with neoliberal political and economic philosophical underpinnings have greater health inequalities compared to less neoliberal countries. But few studies examine how neoliberalism specifically impacts health inequalities involving highly vulnerable populations, such as Indigenous groups. Even fewer take this perspective from an oral health viewpoint. From a lens of indigenous groups in five countries (the United States, Canada, Australia, Aotearoa/New Zealand and Norway), this commentary provides critical insights of how neoliberalism, in domains including colonialism, racism, inter-generational trauma and health service provision, shapes oral health inequalities among Indigenous societies at a global level. We posit that all socially marginalised groups are disadvantaged under neoliberalism agendas, but that this is amplified among Indigenous groups because of ongoing legacies of colonialism, institutional racism and intergenerational trauma.The effects such as warpage, dimensional instability and environmental stress corrosion, due to the presence of residual stresses in polymeric products, are strongly dependent on injection molding conditions. The holding time and holding pressure belongs to most important processing parameters, determining the dimensional stability and properties of injected goods. A new procedure based on a visualization technique was applied, where the levels of residual stresses of the samples were estimated. The experiments were performed for samples produced of translucent methacrylate acrylonitrile butadiene styrene (MABS), a commodity polymer with a high transparency, necessary for the optical visualization of the stress whitening. The samples produced by injecting molding were deformed to a constant elongation, to observe the dependent stress whitening effect subsequently used to evaluate the stress distribution. It was found that depending on the value of the injection holding pressure, various levels of residual stress and its distribution may be observed in MABS samples. These measurements conformed that the applied optical method is an easy-to-perform technique. The possibility to detect the residual stresses over the whole cross-section of the transparent product, without the necessity for local stress determination, is another significant advantage of this investigation procedure.The aim of the study was to develop a novel buccal dosage form to transport rhodamine 123 and human insulin as models for poorly water-soluble and biological drugs, using lipid-core micelles (LCMs)-loaded mucoadhesive films. LCMs were synthesized by a low-energy hot emulsification process, yielding spherically shaped, small-sized, monodispersed and negatively charged carriers with high entrapment efficiency. In vitro release studies demonstrated a higher release of insulin rather than rhodamine from LCMs in simulated physiological conditions, due to an initial burst release effect; however, both release profiles are mainly explained by a diffusion mechanism. Furthermore, LCMs-loaded mucoadhesive films were manufactured and preserved with similar mechanical properties and optimal mucoadhesive behavior compared to nonloaded films. Ex vivo permeation experiments using excised porcine buccal epithelium reveal that both rhodamine and insulin-loaded LCM films elicited a significantly enhanced permeation effect compared to LCMs in suspension and free drugs in solution as controls. Hence, LCMs-loaded mucoadhesive films are suitable as buccal dosage form for the transport and delivery of rhodamine 123 and insulin, as models for poorly water-soluble and biological drugs, respectively.Multimodal learning analytics (MMLA), which has become increasingly popular, can help provide an accurate understanding of learning processes. However, it is still unclear how multimodal data is integrated into MMLA. By following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper systematically surveys 346 articles on MMLA published during the past three years. For this purpose, we first present a conceptual model for reviewing these articles from three dimensions data types, learning indicators, and data fusion. selleck Based on this model, we then answer the following questions 1. What types of data and learning indicators are used in MMLA, together with their relationships; and 2. What are the classifications of the data fusion methods in MMLA. Finally, we point out the key stages in data fusion and the future research direction in MMLA. Our main findings from this review are (a) The data in MMLA are classified into digital data, physical data, physiological data, psychometric data, and environment data; (b) The learning indicators are behavior, cognition, emotion, collaboration, and engagement; (c) The relationships between multimodal data and learning indicators are one-to-one, one-to-any, and many-to-one.