In this study, we investigate the Pickering emulsion polymerization of styrene using scaled-down chitin nanofibers (SD-ChNFs) as stabilizers to produce nanochitin/polystyrene composite particles. Prior to emulsion polymerization, an SD-ChNF aqueous dispersion was prepared by disintegrating bundles of the parent ChNFs with an upper hierarchical scale in aqueous acetic acid through ultrasonication. After styrene was added to the resulting dispersions, the mixtures at the desired weight ratios (SD-ChNFs to styrene = 0.1:1–1.4:1) were ultrasonicated to produce Pickering emulsions. Radical polymerization was then conducted in the presence of potassium persulfate as an initiator in the resulting emulsions to fabricate the composite particles. The results show that their average diameters decreased to a minimum of 84 nm as the weight ratios of SD-ChNFs to styrene increased. The IR and 1H-NMR spectra of the composite particle supported the presence of both chitin and polystyrene in the material.
Treatment of ynamides with a catalytic amount of TpRuCl(PPh3)2 resulted in the construction of indole scaffolds known as privileged structure motifs. This reaction involved a cascade of 1,2‐rearrangement and cyclization carrying out C−C bond formation via a ruthenium vinylidene intermediate, as revealed by a deuterium‐labeling experiments. Furthermore, the transformation of multi‐functionalized ynamide, derived from a practical drug molecule, showed a high functional group tolerance of this reaction.
Several drugs proposed for the treatment of COVID-19 have reported cases of cardiac adverse events such as ventricular arrhythmias. To properly weigh risks against potential benefits in a timely manner, mathematical modeling of drug disposition and drug action can be useful for predicting patient response. Here we explored the potential effects on cardiac electrophysiology of 4 COVID-19 proposed treatments: lopinavir, ritonavir, chloroquine, and azithromycin, including combination therapy involving these drugs. To address this, we combined simulations of pharmacokinetics (PK) with mechanistic mathematical modeling of human ventricular myocytes to predict adverse events caused by these treatments. We utilized a mechanistic model to construct heterogenous populations of 4 patient groups (healthy male, healthy female, diseased male, and diseased female) each with 1000 members, and studied the varied responses of drugs and combinations on each population. To determine appropriate drug concentrations for recommended COVID-19 regimen, we implemented PK models for each drug and incorporated these values into the mechanistic model. We found that: (1) drug combinations can lead to greater cellular action potential (AP) prolongation, analogous to QT prolongation, compared with drugs given in isolation; (2) simulations of chloroquine with azithromycin caused a significantly greater increase in AP duration (DAPDz190 ms) compared to lopinavir with ritonavir (DAPDz6 ms); (3) drug effects on different patient populations revealed that females with preexisting heart disease are more susceptible to drug-induced arrhythmias as 85 members formed arrhythmias, and less than 20 in each of the other three; and (4) logistic regression analysis performed on the population showed that higher levels of the sodium-calcium exchanger may predispose certain females with heart failure to drug-induced arrhythmias. Overall, these results illustrate how PK and mechanistic modeling can be combined to precisely predict cardiac arrhythmia susceptibility of COVID-19 therapies.
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