BACKGROUND:Currently, the enzymatic digestion combined with magnetic activated cel sorting for isolating microvascular endothelial cel s are cumbersome and do harm to cel s. Therefore, how to simplify the isolation and culture of human dermal microvascular endothelial cel s to obtain highly purified endothelial cel s in vitro becomes a hotspot.
OBJECTIVE:To explore a simple and effective cultivation method of microvascular endothelial cel s from diabetic patient skins in vitro, and to detect the cel growth.
METHODS:Diabetic patients with chronic foot wounds after amputation were enrol ed to col ect the limb proximal skin and topical skin around the wound superficial dermal tissue. Human dermal microvascular endothelial cel s were obtained using adherent method and trypsin method, fol oewd by purified utilizing trypsin digestion and repeated attachment method when passage culture.
RESULTS AND CONCLUSION:Human dermal microvascular endothelial cells were obtained successfully, Primary cultured endothelial cells completely adhered to the wall at 24 hours, entered the logarithmic phase at the 10th day, and the cell concentration reached 80%at the 12th-13th day. While the passage cells grew more actively than primary cells, and fully covered the bottom in a“cobblestone”arrangement after 5-7 days of culture. Immunohistochemical staining showed that cultured cells were positive for FVIII and CD31-associated antigens with 100%positive rate. MTT assay showed that cell growth curves of 2, 4, and 5 generations of dermal microvascular endothelial presented the invertedSshape. These results suggest that abundant highly purified human dermal microvascular endothelial cells can be obtained through the adherent method and a small amount of short-term trypsin method.

Industry-university-research cooperation is not only the core of technological innovation,but also an important way to enhance industrial competitiveness and achieve high-quality development.Industry-university-research cooperation in Liaoning Province has achieved significant results in promoting technological innovation and economic development,but there are still some problems and challenges.The main problems include insufficient depth of industry-university-research cooperation,scattered innovation resources,lack of long-term stable cooperation mechanisms,as well as talent loss and lack of high-quality innovative talents.Through systematically sorting out the existing models of industry-university-research cooperation,it proposes a series of targeted and operable countermeasures and suggestions.These measures and suggestions provide solid theoretical support for the healthy development of industry-university-research cooperation in Liaoning Province.

Objective:To study the situation of legal regulations for the integration of industry-university-research in Liaoning,improve the concept of legal regulation,optimize the legal regulation methods,so as to promote the deep integration of industry-university-research in Liaoning.Method:Problem-oriented approach was used,and the problems of the current legal regulations for the integration of industry-university-research in Liaoning were reviewed.Result:It was found that legal regulations for the deep integration of industry-university-research in Liaoning were not yet perfect,which was manifested in insufficient coverage of policies and regulations,relatively lagging update of regulations,and insufficient highlighting of local characteristics.Conclusion:In response to the above issues,it is proposed to use measures such as collaborative regulation,incentive regulation and moderate regulation to establish and improve the legal system of the integration of industry-university-research in Liaoning province.

Methods:Two thousand standard sections images werre collected from 2 000 clinical retrospective pediatric hip ultrasound videos from January 2019 to January 2021. All standard sections were annotated by the annotation team through the self-designed software based on Python 3.6 environment for image cross-media data annotation and manual review standardization process with unified standards. Among them, 1 753 were randomly selected for training the deep learning system, and the remaining 247 were used for testing the system. Further, 200 standard sections were randomly selected from the test set, and 8 clinicians independently completed the film reading annotation. The 8 independent results were then compared with the AI results.Results:The testing set consists of 247 patients. Compared with the clinician's measurements, the area under the receiver operating characteristic curve (AUC) of diagnosing hip joint maturity was 0.865, the sensitivity was 76.19%, and the specificity was 96.9%. The AUC of AI system interpretation under Graf detailed typing was 0.575, the sensitivity was 25.90%, the specificity was 89.10%. The 95% LoA of α-angle determined by Bland-Altman method, of -4.7051° to 6.5948° ( Bias -0.94, P<0.001), compared with clinicians' measurements. The 95% LoA of β-angle, of -7.7191 to 6.8777 ( Bias -0.42, P=0.077). Compared with those from 8 clinicians, the results of AI system interpretation were more stable, and the β-angle effect was more prominent. Conclusion:The AI system can quickly and accurately measure the Graf correlation index of standard DDH ultrasonic standard diagnosis plane.