Herein, a dual-responsive insulin delivery device by integrating glucose- and HO-responsive polymeric vesicles (PVs) with transcutaneous microneedles (MNs) has been designed. This novel microneedle delivery device achieves a goal of fast response, excellent biocompatibility, and painless administration. The PVs are self-assembled from a triblock copolymer including poly(ethylene glycol), poly(phenylboronic acid) (glucose-sensitive block), and poly(phenylboronic acid pinacol ester) (HO-sensitive block). After loading with insulin and glucose oxidase (GO ), the drug-loaded PVs display a basal insulin release as well as a promoted insulin release in response to hyperglycemic states. The insulin release rate responds quickly to elevated glucose and can be further promoted by the incorporated GO , which will generate the HO at high glucose levels and further break the chemical links of phenylboronic acid pinacol ester group. Finally, the transdermal delivery of insulin to the diabetic rats ((insulin + GO )-loaded MNs) presents an effective hypoglycemic effect compared to that of subcutaneous injection or only insulin-loaded MNs, which indicates the as-prepared MNs insulin delivery system could be of great importance for the applications in the therapy of diabetes.
Multivariate statistical techniques, including cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA), were used to evaluate temporal and spatial variations in and to interpret large and complex water quality datasets collected from the Shuangji River Basin. The datasets, which contained 19 parameters, were generated during the 2 year (2018–2020) monitoring programme at 14 different sites (3192 observations) along the river. Hierarchical CA was used to divide the twelve months into three periods and the fourteen sampling sites into three groups. Discriminant analysis identified four parameters (CODMn, Cu, As, Se) loading more than 68% correct assignations in temporal analysis, while seven parameters (COD, TP, CODMn, F, LAS, Cu and Cd) to load 93% correct assignations in spatial analysis. The FA/PCA identified six factors that were responsible for explaining the data structure of 68% of the total variance of the dataset, allowing grouping of selected parameters based on common characteristics and assessing the incidence of overall change in each group. This study proposes the necessity and practicality of multivariate statistical techniques for evaluating and interpreting large and complex data sets, with a view to obtaining better information about water quality and the design of monitoring networks to effectively manage water resources.
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