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COVID-19, the worst pandemic in 100 years, has rapidly spread to the entire world in 2 months since its early report in January 2020. Based on the publicly available data sources, we developed a simple mathematic modeling approach to track the outbreaks of COVID-19 in the US and three selected states New York, Michigan and California. The same approach is applicable to other regions or countries. We hope our work can stimulate more effort in understanding how an outbreak is developing and how big a scope it can be and in what kind of time framework. Such information is critical for outbreak control, resource utilization and re-opening of the normal daily life to citizens in the affected community.The present work is designed to achieve efficient localised skin delivery of folic acid (FA)-loaded nanostructured lipid carriers (NLCs) to infer efficient treatment of skin photoageing conditions induced via excessive exposure to ultraviolet (UV) radiation. FA NLCs were prepared by high-speed homogenisation followed by ultrasonication. The obtained NLCs revealed high encapsulation efficiencies (89.42-99.26%) with nanometric particle sizes (27.06-85.36 nm) of monodisperse distribution (PDI = 0.137-0.442), zeta potential values >|27| mV, pseudoplastic rheological behaviour, good spreadability (2.25-3.30 cm) and promoted occlusive properties throughout 48 h. Optimised NLC formulations appeared as sphere-shaped particles using transmission electron microscopy, showed improved photostability of FA and prolonged in vitro release profile best fitted to Higuchi diffusion model. Ex vivo permeation and deposition of FA, employing Wistar rat skins, depicted enhanced permeability and existence of FA in skin layers after 6 h. Based on the obtained results, FA-loaded NLC formulations demonstrate a promising modality for anti-photoageing therapy.In mammals, DNA methylation patterns are established by various types of DNA methyltransferases and can be stably passed on during cell division, thus creating a paradigm for epigenetic regulation that can mediate long-lasting changes in gene expression even when the initial triggering signal has disappeared. Although functional deficiency of DNMT3A, one of the methyltransferases, leads to abnormal DNA methylation patterns that result in developmental deficits in mammals, the impacts of its overexpression on tissue gene expression and DNA methylation patterns remain unclear. Here, our previously established hDNMT3A transgenic rat model and mRNA sequencing and bisulphite sequencing PCR were used to analyse the impact of hDNMT3A overexpression on tissue transcriptome and methylome, and whether the impact could be inherited intergenerationally was subsequently investigated. Our results revealed that the overexpression of hDNMT3A could induce notable gene expression variations in rat testis and brain. HTH-01-015 in vivo More importantly, 36.02% and 38.89% of these variations could be intergenerationally inherited to offspring without the transmission of the initial endogenic trigger in the brain and testis, respectively. Furthermore, we found that intergenerationally inherited DNA methylation variations in their promoters and exons could be the underlying mechanism. Compared with inheritable variations that were passively induced by environmental factors, these variations were actively induced by endogenous epigenetic modifiers. This study provided evidence for the epigenetic inheritance of endogenous factors that actively induce gene expression and DNA methylation variations; however, more studies are needed to determine the number of generations that these variations can be stably inherited.All but seven U.S. states have laws that govern some aspects of dyslexia screening, intervention, or teacher training in public schools. However, in the three states that mandate child-level reporting, data indicate lower than expected rates of dyslexia identification when compared with commonly accepted dyslexia prevalence rates. To better understand this apparent mismatch, this study explores factors that might predict the school-assigned identification of individuals with dyslexia. Deidentified data on 7,947 second-grade students in 126 schools from one U.S. state included a universal screening measure of literacy skills indicative of dyslexia (i.e., reading and spelling), school-assigned dyslexia classification, and demographic characteristics. As expected, behavioral characteristics of dyslexia from universal screening were associated with school-assigned dyslexia classification. However, dyslexia classification was less likely for minority students and individuals attending schools with a higher percentage of minority students. Students who showed behavioral characteristics of dyslexia and attended schools with a higher proportion of other students with similar poor literacy skills were more likely not to receive a school-assigned dyslexia classification. The findings suggest systematic demographic differences in whether a student is identified with dyslexia by schools even when using universal screening.Background. Reviewing drugs to determine coverage or reimbursement level is a complex process that involves significant time and expertise. Review boards gather evidence from the submission provided, input from clinicians and patients, and results of clinical and economic reviews. This information consists of assessments on multiple criteria that often conflict with one another. Multiple-criteria decision analysis (MCDA) includes methods to address complex decision making problems with conflicting objectives and criteria. We propose an MCDA approach that infers a utility model based on reviews of previously submitted drugs. Methods. We use a recent extension of the UTilitiés Additives DIScriminantes approach, UTADISGMS. This disaggregation approach deconstructs a portfolio of elements such as a set of drugs that have been reviewed and for which a decision has been made. It derives global and marginal utility functions that are consistent with the preferences exhibited by the review boards in their recommendations. We apply the method to oncology drugs reviewed in Canada between 2011 and 2017. We also illustrate how to conduct scenario analyses and predict the coverage decisions for new drugs. Results. Applying the method yields a utility value for each submission along with a set of thresholds that partition the utility values based on the submission outcomes. Scenario analyses illustrate the predictive ability of the method. Conclusion. Preference disaggregation is an indirect way of eliciting an additive global utility value function. It requires less of a cognitive effort from the decision making bodies because it infers preferences from the data rather than relying on direct assessments of model parameters. We illustrate how it can be applied to validate existing decisions and to predict the recommendation of a new drug.