3D printing is to complete replication or creation process for the object items through digital modeling for the prototype under the control of a computer. 3D printing can complete the individual replication and achieve accurate simulation for the object shape and structure. Using a variety of medical imaging results to implement data modeling, 3D printing technology has the outstanding advantages in the biomedical field. In recent years, 3D printing technology has been applied in the prevention, treatment, and rehabilitation of cerebrovascular diseases. This article briefly review s the application of 3D printing technology in intracranial aneurysms and arteriovenous malformation, and the prospect of future application is prospected.

Objective:To investigate the cognitive function and its influencing factors among residents in Qingdao city.Methods:The 5 311 research subjects over 65 years old were selected from Qingdao by simple random sampling and stratified sampling. All subjects were investigated by AD8 dementia early screening questionnaire and community screening instrument dementia (CSI-D) to evaluate the prevalence of cognitive decline in this study.The score of AD8 dementia early screening questionnaire ≥2 points or the score of CSI-D ≤7 points was considered to be cognitive decline. Mann-Whitney U test, Chi-square test, Fisher exact probability method, univariate and multivariate Logistic regression analysis and Bootstrap verification were performed using SPSS 26.0 software. Results:Among 5 311 subjects, 1 899 subjects had normal cognitive function (35.76%) and 3 412 subjects had cognitive decline (64.24%). The age of the cognitive decline group was significantly higher than that of the normal group ( P<0.01). There were significant differences in gender, educational level, rural residents, marital status, chronic medical history, hypertension, rheumatoid arthritis, cerebrovascular disease, intervertebral disc disease, ischemic heart disease, monthly household income and alcohol consumption between the two groups (all P<0.05). Univariate Logistic regression analysis showed that female ( β=0.313, OR=1.367, 95% CI=1.221-1.530), age ( β=0.052, OR=1.053, 95% CI=1.043-1.063), rural residents ( β=0.850, OR=2.340, 95% CI=2.042-2.682), widowed ( β=0.557, OR=1.745, 95% CI=1.500-2.029), chronic medical history ( β=0.290, OR=1.336, 95% CI=1.191-1.498), hypertension ( β=0.134, OR=1.143, 95% CI=1.020-1.281), rheumatoid arthritis ( β=0.458, OR=1.581, 95% CI=1.222-2.046), cerebrovascular disease ( β=0.584, OR=1.794, 95% CI=1.352-2.380), intervertebral disc disease ( β=0.578, OR=1.782, 95% CI=1.370-2.319), ischemic heart disease ( β=0.501, OR=1.651, 95% CI=1.272-2.143) were the risk factors for cognitive decline. Higher education level, higher monthly household income and abstinence ( β=-0.244, OR=0.783, 95% CI=0.619-0.992) were protective factors for cognitive decline. Multivariate logistic regression analysis showed that age ( β=0.035, OR=1.036, 95% CI=1.025-1.047), rural residents ( β=0.215, OR=1.239, 95% CI=1.047-1.468), chronic medical history ( β=0.191, OR=1.210, 95% CI=1.067-1.372), cerebrovascular disease ( β=0.480, OR=1.616, 95% CI=1.195-2.187), intervertebral disc disease ( β=0.456, OR=1.578, 95% CI=1.190-2.094) were risk factors for Alzheimer's disease. Higher education level and higher monthly household income were protective factors for Alzheimer's disease. Conclusion:The elderly with chronic diseases, low income and low education level may be at the high risk of cognitive function decline, which should be paid attention to in early screening and intervention.