Objective To investigate the anti-proliferation effect of triptolide-eluting stent, as well as its effectiveress, dose-effect relationship and safety. Methods Experimental stents were divided into 4 groups with 10 stents each, namely bare stent group, sirolimus-coated stent group, 5.6 ?g triptolide-coated stent group and 56 ?g triptolide-coated stent group. By morphometric and histopathologic analysis,we compared 5.6 ?g and 56 ?g triptolide-eluting stents with bare metal stents and sirolimus-eluting stents 28 days after stenting in rabbit iliac models of restenosis. Results The neointimal area of 5.6 ?g triptolide-eluting stents was similar to that of bare metal stents, but larger than that of sirolimus-eluting stents. The neointimal area of 56 ?g triptolide-eluting stents was smaller than that of bare metal stents and 5.6 ?g triptolide-eluting stents, but was similar to that of sirolimus-eluting stents. In all the groups, proliferation on both edges of the stents was insignificant. No toxic effect had been found in the experimental animals related to triptolide-eluting stent. Conclusion Triptolide-eluting stents may inhibit neointimal proliferation. The effect of inhibiting neointimal proliferation is dose dependent and with no adverse effect. These results suggest that triptolide-eluting stent can prevent restenosis within four weeks.

OBJECTIVE:To explore the automated intelligent construction and effect of pharmacy intravenous admixture servic-es(PIVAS)in our hospital. METHODS:The main situation of automated intelligent construction and effect of PIVAS in our hospi-tal was introduced,the time and error rate of related links before(Jul.-Sept. 2016)and after(Oct.-Dec. 2016)the automated intelli-gent construction of PIVAS in our hospital were collected,and its effects were evaluated. RESULTS:Our hospital conducted auto-mated intelligent construction in selecting,labeling,sorting and delivering medicines in PIVAS and established related system and process construction. After automated intelligent construction,time for selecting medicines per medical order was shortened from (6.78±0.87)s to(2.65±0.71)s;time for labeling was shortened from(3.24±0.71)s to(1.41±0.55)s;time for sorting a bag of finished infusion was shortened from (13.37 ± 2.84) s to (5.33 ± 1.72) s;and time for delivering the same amount of infusion shortened from (35.64 ± 4.33) min to (18.12 ± 3.57) min (P<0.05). The error rate of selecting medicines was decreased from (2.35 ± 0.59)‰ to (0.26 ± 0.21)‰;error rate of labeling was decreased from (1.51 ± 0.45)‰ to (0.22 ± 0.10)‰;error rate of misclassifying in a wrong ward was decreased from(3.47±1.02)‰ to(0.17±0.10)‰;and error rate of delivering was decreased from(1.33±0.55)‰ to(0.13±0.11)‰(P<0.05). CONCLUSIONS:The automated intelligent construction in PIVAS of our hos-pital has improved work efficiency,reduced error rate and potential medication risk and improved management level in PIVAS.

Amino Acid Sequence ; Genes, Bacterial ; Hydrogen Peroxide ; pharmacology ; Molecular Sequence Data ; Mutation ; genetics ; Mycobacterium ; drug effects ; genetics ; growth & development ; Peptides ; chemistry ; metabolism ; Stress, Physiological ; drug effects