BACKGROUND:Drug eluting stents and endothelium stents for clinical treatment of vascular stenosis can lead to delayed endothelialization and restenosis. A rapamycin eluting stent combined with CD34 antibody can play a synergistic role to offset delayed endothelialization and intimal hyperplasia due to antiproliferative drugs, but it is stil in the pilot phase. OBJECTIVE:To observe the ability of rapamycin eluting stent combined with CD34 antibody to capture endothelial progenitor cels, and to observe the differentiation characteristics of the captured cels. METHODS:Scanning electron microscope and indirect immunofluorescence were used to observe the morphology and differentiation characteristics of captured endothelial progenitor cels. Under a fluorescence microscope, we observed the captured endothelial progenitor cels and the degree of endothelialization after implantation of the rapamycin eluting stent combined with CD34 antibody into rabbit ear vein. RESULTS AND CONCLUSION:Under the scanning electron microscope, fusiform-like cels with a diameter of 6-8 μm were captured by the composite stent, and 24 hours later, the cels became ful-shaped. The captured cels had the appearance characteristics of endothelial progenitor cels. Results from indirect immunofluorescence observation showed that there were a lot of red fluorescent spots on the coating which represented adherent cels positive for vascular endothelial growth factor receptor-2; the composite stent was largely covered with vascular endothelial cels at 24 hours after stent implantation, and fuly covered at 48 hours, but there was no abnormal cel cluster. These findings indicate that the rapamycin eluting stent combined with CD34 antibody can be specific to rapidly capture endothelial progenitor cels in the peripheral blood, and the stent can be completely covered with vascular endothelial cels at 48 hours after stent implantation, thereby achieving rapid endothelialization and promoting the repair of endothelial cels.

Schizophrenia(SZ)is a serious mental illness,and traditional diagnostic methods are prone to missed and misdiagnosis.Using machine learning(ML)algorithms can extract SZ relative features from functional MRI(fMRI)data,hence being helpful for diagnosing and predicting treatment response of SZ.The research progresses of ML based on fMRI for diagnosis and treatment of SZ were reviewed in this article.