RESUMO
Harnessing the inflammation and angiogenesis is extremely important in wound healing. In this study, we developed bioactive elastin-based hydrogels which can recruit and modulate the innate immune cells and accelerate angiogenesis in the wound site and subsequently improve wound regeneration. These hydrogels were formed by visible-light cross-linking of acryloyl-(polyethylene glycol)-N-hydroxysuccinimide ester modified elastin with methacrylated gelatin, in order to mimic dermal microenvironment. These hydrogels showed highly tunable mechanical properties, swelling ratios and enzymatic degradation profiles, with moduli within the range of human skin. To mimic the in vivo degradation of the elastin by elastase from neutrophils, in vitro co-culture of the hydrogels and neutrophils was conducted. The derived conditioned medium containing elastin derived peptides (EDP-conditioned medium) promoted the expression of both M1 and M2 markers in M1 macrophages in vitro. Additionally, the EDP-conditioned medium induced superior tube formation of endothelia cells in Matrigel. In mice wound model, these elastin-based hydrogels attracted abundant neutrophils and predominant M2 macrophages to the wound and supported their infiltration into the hydrogels. The outstanding immunomodulatory effect of the elastin-based hydrogels resulted in superior angiogenesis, collagen deposition and dermal regeneration. Hence, these elastin-based hydrogels can be a promising regenerative platform to accelerate wound repair.
RESUMO
Background The early mortality after surgery for infective endocarditis is high. Although risk models help identify patients at high risk, most current scoring systems are inaccurate or inconvenient. The objective of this study was to construct an accurate and easy-to-use prediction model to identify patients at high risk of early mortality after surgery for infective endocarditis. Methods and Results A total of 476 consecutive patients with infective endocarditis who underwent surgery at 2 centers were included. The development cohort consisted of 276 patients. Eight variables were selected from 89 potential predictors as input of the XGBoost model to train the prediction model, including platelet count, serum albumin, current heart failure, urine occult blood ≥(++), diastolic dysfunction, multiple valve involvement, tricuspid valve involvement, and vegetation >10 mm. The completed prediction model was tested in 2 separate cohorts for internal and external validation. The internal test cohort consisted of 125 patients independent of the development cohort, and the external test cohort consisted of 75 patients from another center. In the internal test cohort, the area under the curve was 0.813 (95% CI, 0.670-0.933) and in the external test cohort the area under the curve was 0.812 (95% CI, 0.606-0.956). The area under the curve was significantly higher than that of other ensemble learning models, logistic regression model, and European System for Cardiac Operative Risk Evaluation II (all, P<0.01). This model was used to develop an online, open-access calculator (http://42.240.140.58:1808/). Conclusions We constructed and validated an accurate and robust machine learning-based risk model to predict early mortality after surgery for infective endocarditis, which may help clinical decision-making and improve outcomes.