Combining synthetic polymers and biomacromolecules prevents the occurrence of thrombogenicity and intimal hyperplasia in small-diameter vascular grafts (SDVGs). In the present study, an electrospinning poly (L)-lactic acid (PLLA) bilayered scaffold is developed to prevent thrombosis after implantation by promoting the capture and differentiation of endothelial colony-forming cells (ECFCs). The scaffold consists of an outer PLLA scaffold and an inner porous PLLA biomimetic membrane combined with heparin (Hep), peptide Gly-Gly-Gly-Arg-Glu-Asp-Val (GGG-REDV), and vascular endothelial growth factor (VEGF). Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS), and contact angle goniometry were performed to determine successful synthesis. The tensile strength of the outer layer was obtained using the recorded stress/strain curves, and hemocompatibility was evaluated using the blood clotting test. The proliferation, function, and differentiation properties of ECFCs were measured on various surfaces. Scanning electronic microscopy (SEM) was used to observe the morphology of ECFCs on the surface. The outer layer of scaffolds exhibited a similar strain and stress performance as the human saphenous vein via the tensile experiment. The contact angle decreased continuously until it reached 56° after REDV/VEGF modification, and SEM images of platelet adhesion showed a better hemocompatibility surface after modification. The ECFCs were captured using the REDV + VEGF + surface successfully under flow conditions. The expression of mature ECs was constantly increased with the culture of ECFCs on REDV + VEGF + surfaces. SEM images showed that the ECFCs captured by the REDV + VEGF + surface formed capillary-like structures after 4 weeks of culture. The SDVGs modified by REDV combined with VEGF promoted ECFC capture and rapid differentiation into ECs, forming capillary-like structures in vitro. The bilayered SDVGs could be used as vascular devices that achieved a high patency rate and rapid re-endothelialization.

Background It is reported that the adverse impact of nonpharmaceutical interventions (NPIs) on the mental health of children and adolescents may lead to psychologically related disorders during the coronavirus disease 2019 (COVID-19) period. Subject symptoms such as chest pain, chest tightness, and palpitation may be related to increased stress and anxiety in children and adolescents. The present research aimed to determine the number of pediatric consults and etiology of subject symptoms during the COVID-19 pandemic period and compared it with the same timelines in 2019 and 2021 to discuss the impact of different periods on the organic disease onset of children with subject symptoms, especially in cardiac involvement. Methods Children who visited Qingdao Women and Children’s Hospital, Qingdao University between January 23 to April 30, 2019 (pre-COVID-19 period), January 23 to April 30, 2020 (COVID-19 period), and January 23 to April 30, 2021 (post-COVID-19 period) presenting chest pain, chest tightness, and palpitation were recruited. Information to determine gender, age, medical history, department for the initial visit, clinical manifestations, time from the latest onset to the visit, and diagnosis were recorded. Result A total of 891 patients were enrolled in the present study (514 males; median age: 7.72). One hundred twenty-three patients presented during the pre-COVID-19 period while 130 during the COVID-19 period, nevertheless, the number substantially increased during the post-COVID-19 period (n = 638). Cardiac etiology accounted for 1.68% (n = 15) of the patient population, including arrhythmias (n = 10, 1.12%), myocarditis (n = 4, 0.44%), and atrial septal defect (n = 1, 0.11%). There was no significant difference among groups in the distribution of organic etiology. The median time from the latest onset to the visit during the pre-COVID-19 period was 7 days compared to 10 days during the COVID-19 period and 3 days during the post-COVID period. Conclusion During the post-COVID-19 period, the median time from the latest onset to the visit was significantly shorter than that in the pre-COVID-19 period or COVID-19 period. The pediatric consult of children with subject symptoms presented increased substantially during the post-COVID-19 period, while there was no significant difference in the number of patients involving the cardiac disease. Clinicians ought to be more careful to screen heart diseases to prevent missed diagnosis and misdiagnosis during special periods.

Objective The most serious complication of Kawasaki syndrome (KS) is coronary artery lesions (CAL). About 20%-25% of KS will develop into severe CAL without intervention. Machine learning (ML) is a branch of artificial intelligence (AI), which integrates complex data sets on a large scale and uses huge data to predict future events. Besides, computers can reveal new relationships that doctors may not easy to find. The present study presented a model to predict the risk of CAL in KS children by different algorithms to achieve the early diagnosis of CAL. Methods A total of 158 children were enrolled from Women and Children’s Hospital, Qingdao University and divided into 7 to 3 as the training sets and the test sets for modeling and validation studies. The clinical manifestations and auxiliary examinations were collected as input features in our models based on the latest 6th edition diagnostic guidelines. Prior to applying the algorithm to modeling, the principal component analysis (PCA) was used to achieve dimension reduction for eliminating the high correlation between features and the Synthetic Minority Oversampling Technique (SMOTE) for promoting accuracy. There are several classifiers are constructed for models including the Random Forest (RF), the Logical regression (LG), and the eXtreme Gradient Boosting (XGBoost). Results The sensitivity and specificity of RF were 0.8 and 0.906, and the area under the curve (AUC) was 0.972. For LG, the sensitivity and specificity were 0.6 and 0.976. The XGBoost were 0.2 and 0.953, respectively. Conclusion Models are established through three different algorithms to achieve the best sensitivity and specificity. The RF was superior to other methods, which provides a reference for the prevention of CAL.