Bone grafting is a primary method for treating bone defects. Among various graft materials, xenogeneic bone substitutes are widely used in clinical practice due to their abundant sources, convenient processing and storage, and avoidance of secondary surgeries. With the advancement of domestic production and the limitations of imported products, an increasing number of bone filling or grafting substitute materials isentering clinical trials. Relevant experts have drafted this consensus to enhance the management of medical device clinical trials, protect the rights of participants, and ensure the scientific and effective execution of trials. It summarizes clinical experience in aspects, such as design principles, participant inclusion/exclusion criteria, observation periods, efficacy evaluation metrics, safety assessment indicators, and quality control, to provide guidance for professionals in the field.
Humans ; Bone Substitutes/therapeutic use* ; Randomized Controlled Trials as Topic/methods* ; Consensus ; Bone Transplantation ; Research Design

The biological samples of oral genetic diseases and rare diseases are extremely precious. Collecting and preserving these biological samples are helpful to elucidate the mechanisms and improve the level of diagnose and treatment of oral genetic diseases and rare diseases. The standardized construction of biobanks for oral genetic diseases and rare diseases is important for achieving these goals. At present, there is very little information on the construction of these biobanks, and the standards or suggestions for the classification and coding of biological samples from oral and maxillofacial sources, and this is not conducive to the standardization and information construction of biobanks for special oral diseases. This consensus summarizes the background, necessity, principles, and key points of constructing the biobank for oral genetic diseases and rare diseases. On the base of the group standard "Classification and Coding for Human Biomaterial" (GB/T 39768-2021) issued by the National Technical Committee for Standardization of Biological Samples, we suggest 76 new coding numbers for different of biological samples from oral and maxillofacial sources. We hope the consensus may promote the standardization, and smartization on the biobank construction as well as the overall research level of oral genetic diseases and rare diseases in China.

Objective: This study aimed to analyze the relationship between cerebral ischemia and cerebral blood flow dynamics (cerebrovascular function score), related physiological, and biochemical indexes. Methods: We selected 366 patients who underwent regular physical examination, cerebrovascular hemodynamics test, and head MRI in the health management center of Shandong Electric Power Central Hospital from May 2016 to April 2019. The patients with cerebral ischemia were selected as the case group (264 cases), and those without cerebral ischemia as the control group (102 cases) to analyze the differences in cerebrovascular function scores, blood pressure, body mass index, and related biochemical indexes between the two groups. The risk factors were also analyzed by multiple logistic regression analysis. Results: The average age of cerebral the ischemia group was (53.0±7.6) years, which consisted of 211 men and 53 women. The average age of the control group was (48.7±7.3) years, which consisted of 87 men and 15 women. The comparison of the mean of continuous variables between the two groups of subjects shows that the mean age [(53.0±7.6) years old vs. (48.7±7.3) years old] and systolic blood pressure [(127.70±17.06) mmHg vs. (122.36±14.75) mmHg] (1 mmHg=0.133 kPa) of cerebral ischemia group were significantly higher than that of control group. The high-density lipoprotein level [(1.29±0.26) mmol/L vs. (1.37±0.30) mmol/L] and cerebrovascular function score [(86.55±18.41) vs. （92.16±10.77）] were significantly lower than those in the control group. The exposure rate of systolic blood pressure ≥140 mmHg and cerebrovascular function score <75 [16.7%(44 cases) vs. 2.9%(3 cases)] in the ischemic group was significantly higher than that in the control group, with statistically significant differences (P<0.05). Multivariate logistic regression results showed that the variables included in the regression equation of the numeration data model were age and cerebrovascular function score, and the OR (95%CI) were 1.062 (1.020-1.105） and 6.838 (1.583-29.547). Conclusion Aging, low high-density lipoprotein cholesterol level, and low cerebrovascular function score may be the risk factors affecting MRI-defined cerebral ischemia.