1.Property comparison of various skull repair materials
Ruyu BAI ; Fujun LIU ; Guangyu LU ; Xiaodong WANG ; Zhigang LEI ; Xunmeng ZHANG
Chinese Journal of Tissue Engineering Research 2007;0(23):-
Different materials for skull repair possess varying properties and clinical effects. Metal materials are the first to be applied, but most of them induce corrosion and heat conduction; Non-metal bone substitutes, such as organic glass, have ever been commonly used, but the poor biocompatibility and high infection rate of subcutaneous exudation limit their application; Bone cement shows good histocompatibility, but the repair scale is not complete; Medical silica gel is cheap and effective, but the appearance is not satisfactory resulting from local infections and material exposures; Titanium possesses good biocompatibility and well junctures with the skull, thus it is a promising materials although the shortages still remain. With the development of bioengineering research, the skull repair materials will open up concerning the study of bone tissue engineering, cartilage tissue engineering and cornea tissue engineering. This paper is aimed to search a well-biocompatible and clinically effective material for the skull repair by the comparison on the property and clinical application of varying materials.
2.Value of MRI fat quantification parameter combined with 25-hydroxyvitamin D in predicting fracture risk in patients with osteoporosis
Zhen WANG ; Lianjin GUO ; Jiarong LIANG ; Haoyi YE ; Xunmeng ZHANG
The Journal of Practical Medicine 2024;40(22):3238-3243
Objective To explore the clinical application value of MRI fat quantification parameter[verte-bral bone marrow fat fraction(FF)]combined with 25-hydroxyvitamin D[25(OH)D]in predicting fracture risk in patients with osteoporosis.Methods A total of 90 patients with osteoporosis who were admitted to the hospital from January 2023 to April 2024 were selected as the subjects.Among them,50 patients with osteoporotic vertebral com-pression fractures were included in the fracture group,and 40 patients without fractures were included in the control group.All patients underwent the iterative decomposition of water and fat with echo asymmetry and least-squares estimation(IDEAL-IQ)method of MRI to measure the FF of each vertebra among L1-5 and the average FF of L1-5.Serum 25(OH)D level was detected by electrochemiluminescence method.FF and serum 25(OH)D levels of the two groups were compared.The correlation of FF,25(OH)D and bone mineral density(BMD)was analyzed.Multi-variate logistic regression analysis was conducted to screen the risk factors for fracture in patients with osteoporosis.Receiver operating characteristic(ROC)curves were used to evaluate the predictive value of FF,25(OH)D,and their combination for fracture in patients with osteoporosis.Results Patients in the fracture group were older than those in the control group.BMD and serum 25(OH)D level were lower than those of the control group(P<0.05).The FF of L2 and average FF of L1-5 in the fracture group were higher than those in the control group(P<0.05).Correlation analysis results showed that the FF of L2 and the average FF of L1-5 were negatively correlated with BMD(P<0.05),while serum 25(OH)D level was positively correlated with BMD(P<0.05).Multivariate logistic regression analysis showed that age and FF of L2 were independent risk factors for fracture in patients with osteoporo-sis,while BMD and 25(OH)D were protective factors(P<0.05).ROC curves indicated that the AUC values of FF of L2 and 25(OH)D for predicting fracture were 0.714(95%CI:0.606~0.822)and 0.774(95%CI:0.672~0.876).The AUC of joint prediction was 0.923(95%CI:0.867~0.978),which was significantly larger than that of separate prediction(P<0.05).Conclusions FF of L2 and serum 25(OH)D are related to fracture in patients with osteopo-rosis.Age and BMD are factors influencing the occurrence of fracture in patients with osteoporosis.FF of L2 and 25(OH)D have certain predictive value for fracture risk in patients with osteoporosis,and combined detection of the two can improve predictive efficiency.