Paclitaxel and sirolimus are the two major drugs for the treatment of coronary arterial disease in current drug eluting stents. The two drugs can effectively inhibit the in-stent restenosis through their independent pathways and show synergistic effect in preventing tumor tissue growth. We hypothesize that the combination of the two drugs in a drug eluting stent (DES) can also effectively suppress the neointima growth in the stented artery. The present work was focused on the investigation of paclitaxel/sirolimus combination release profiles from a novel biodegradable polymer (poly (D, L-lactide -co-glycolide) / amorphous calcium phosphate, PLGA/ACP) coated stent both in vitro and in vivo. For the in vitro, the drug releasing profiles were characterized by measuring the drug concentration in a drug release medium (Dulbecco’s phosphate buffered saline, DPBS, pH 7.4) at predetermined time points. For the in vivo, a rat aorta stenting model was employed. The results showed that both paclitaxel and sirolimus had a two-phase release profile both in vitro and in vivo, which is similar to the drug release profile of their individual coated DESs, and there is no evident of interference between two drugs. The data suggest that paclitaxel and sirolimus can be combined pharmacokinetically in a DES for the treatment of coronary arterial diseases.

The purpose of this study was to optimize a novel biodegradable polymer for drug eluting stent (DES) applications. Degradation profiles of different poly(d, l-lactide-co-glycolide)/amorphous calcium phosphate (PLGA/ACP) composites coated on stents were studied both in vitro and in vivo for three months. For the in vitro study, stents were immersed into the phosphate buffered saline (37°C, pH 7.4) with constant shaking. The polymer weight loss was measured weekly and morphological changes were analyzed. The results demonstrated that approximately 60% of polymer was degraded within the three-month period and there was no significant difference between the different PLGA/ACP composites. However, the composite of 50% PLGA (65/35) with 50% ACP showed a slightly faster degradation rate than other composites. Morphologically, all stent surfaces changed from a micro-porous before degradation to a corrugated solid micro-net-like structure at two months post degradation. Based on in vitro results, 65% PLGA (65/35) with 35% ACP) coated stents were selected and implanted into rat aortas (n = 12) for the in vivo study. Microscopic observation showed that no composite was found on any of the implanted stents at 12 weeks post implantation, which indicated the selected PLGA/ACP composite is desired for DES applications.

Background-Currently, preclinical stent development requires elaborate large animal models which are time consuming and expensive. We herein report a high throughput rat aorta stenting model which could provide a rapid and low-cost platform for preclinical stent development.

BackgroundThe novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a global pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death.ResultsOf the 2,169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed with severe illness, and 75 patients died. Obvious differences in demographics, clinical characteristics and laboratory examinations were found between survivors and non-survivors. A decision tree classifier, including three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase, was developed to predict death outcome in severe patients. This model performed well both in train dataset and test dataset. The accuracy of this model was 0.98 and 0.98, respectively.ConclusionThe machine learning model was robust and effective in predicting the death outcome in severe COVID-19 patients.

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