Dura mater, rich in vasculature and immune cells, is the outermost layer of the central nervous system, and thus acts as the first barrier to protect brain. Meningeal lymphatic vessels and immune cells are main components of dural immunity, which respond to a variety of central nervous system diseases. Meanwhile, compared with brain parenchyma, dura mater communicates more with peripheral tissues and is more susceptible to medical interventions. Therefore, dura mater is a promising target to prevent, diagnose and treat intracranial diseases. Here dural immunity is clarified based on meningeal lymphatic vessels and dural immune cells, and current researches inquiring the role of dural immunity in infectious and immune diseases of central nervous system are summarized.

Objective:To assess the effectiveness of a model created using clinical features and preoperative chest CT imaging features in predicting the chronic obstructive pulmonary disease (COPD) among patients diagnosed with lung cancer.Methods:A retrospective analysis was conducted on clinical (age, gender, smoking history, smoking index, etc.) and imaging (lesion size, location, density, lobulation sign, etc.) data from 444 lung cancer patients confirmed by pathology at the Second Affiliated Hospital of Naval Medical University between June 2014 and March 2021. These patients were randomly divided into a training set (310 patients) and an internal test set (134 patients) using a 7∶3 ratio through the random function in Python. Based on the results of pulmonary function tests, the patients were further categorized into two groups: lung cancer combined with COPD and lung cancer non-COPD. Initially, univariate analysis was performed to identify statistically significant differences in clinical characteristics between the two groups. The variables showing significance were then included in the logistic regression analysis to determine the independent factors predicting lung cancer combined with COPD, thereby constructing the clinical model. The image features underwent a filtering process using the minimum absolute value convergence and selection operator. The reliability of these features was assessed through leave-P groups-out cross-validation repeated five times. Subsequently, a radiological model was developed. Finally, a combined model was established by combining the radiological signature with the clinical features. Receiver operating characteristic (ROC) curves and decision curve analysis (DCA) curves were plotted to evaluate the predictive capability and clinical applicability of the model. The area under the curve (AUC) for each model in predicting lung cancer combined with COPD was compared using the DeLong test.Results:In the training set, there were 182 cases in the lung cancer combined with COPD group and 128 cases in the lung cancer non-COPD group. The combined model demonstrated an AUC of 0.89 for predicting lung cancer combined with COPD, while the clinical model achieved an AUC of 0.82 and the radiological model had an AUC of 0.85. In the test set, there were 78 cases in the lung cancer combined with COPD group and 56 cases in the lung cancer non-COPD group. The combined model yielded an AUC of 0.85 for predicting lung cancer combined with COPD, compared to 0.77 for the clinical model and 0.83 for the radiological model. The difference in AUC between the radiological model and the clinical model was not statistically significant ( Z=1.40, P=0.163). However, there were statistically significant differences in the AUC values between the combined model and the clinical model ( Z=-4.01, P=0.010), as well as between the combined model and the radiological model ( Z=-2.57, P<0.001). DCA showed the maximum net benifit of the combined model. Conclusion:The developed synthetic diagnostic combined model, incorporating both radiological signature and clinical features, demonstrates the ability to predict COPD in patients with lung cancer.

Objective: To investigate the expression and molecular mechanism of dickkopf 1 ( DKK1 ) in tongue squamous cell carcinoma (TSCC) by bioinformatics method and molecular biology experiments． Methods: The patients information wasdownload from TCGA-TSCC database，the differentially expressed genes between the cancer and normal tissues were screened by NetworkAnalysed site，the key genes and clinical prognosis were identified through Kaplan-Meier analysis and Lasson regression，the functions and pathways of differentially expressed genes were gained by GO and KEGG database，the expression of DKK1 mRNA and protein in TSCC as well as its relationship with clinicopathological features were analyzed by UALCAN database and immunohistochemistry．Western blot assay was conducted to detect the protein expression of DKK1 in TSCC cells，and siRNA was used to konck down the expression of DKK1 protein in Cal27 cells. Results : The three key genes DKK1，CYP19A1 and IRX4， which were highly expressed in tongue squamous cell carcinoma and the survival rate of TSCC patients with high expression group was poor，were screened through NetworkAnalysed ，Kaplan-Meier analysis and Lasson regression method．UALCAN database showed that the mRNA level of DKK1 in TSCC tissues was higher than that in normal tissues，and its high expression was significantly correlated with clinical stage，histological grade and lymph node metastasis of TSCC patients．The immunohistochemistry assay suggested that the positive rate of DKK1 protein in clinical stage Ⅲ + Ⅳ TSCC tissues was significantly higher than that in stage Ⅰ + Ⅱ TSCC tissues．In addition， the expression level of DKK1 protein in TSCC tissues was significantly higher than that in adjacent tissues．Western blot assay also showed that the protein expression of DKK1 in TSCC cell Cal27 was much higher than normal oral epithelial cell HOEC．When knock down the protein expression of DKK1 in Cal27，the expression of β-catenin、p- p65 和 p65 werealso reduced． Conclusion DKK1 is highly expressed in tongue squamous cell carcinoma tissues and cells and plays an important role．It may be a new target for early diagnosis and drug treatment of TSCC.

3D bioprinting technology is a rapidly developing technique that employs bioinks containing biological materials and living cells to construct biomedical products. However, 3D-printed tissues are static, while human tissues are in real-time dynamic states that can change in morphology and performance. To improve the compatibility between in vitro and in vivo environments, an in vitro tissue engineering technique that simulates this dynamic process is required. The concept of 4D printing, which combines "3D printing + time" provides a new approach to achieving this complex technique. 4D printing involves applying one or more smart materials that respond to stimuli, enabling them to change their shape, performance, and function under the corresponding stimulus to meet various needs. This article focuses on the latest research progress and potential application areas of 4D printing technology in the cardiovascular system, providing a theoretical and practical reference for the development of this technology.
Humans ; Tissue Engineering/methods* ; Bioprinting/methods* ; Printing, Three-Dimensional ; Cardiovascular System ; Tissue Scaffolds

Objective: To investigate the incidence and risk factors of peripartum mood disorder (PPMD) in order to improve clinical prevention and intervention of this condition. Methods: This was a prospective cohort study recruiting first-trimester pregnant women (<13 gestational weeks) from Beijing Daxing Maternal and Child Care Hospital from October 1, 2016 to December 31, 2017. Zung Self-rating Anxiety Scale (SAS) and Zung Self-rating Depression Scale (SDS) were used to evaluate the anxiety and depression status in the second and third trimesters, respectively. Their life styles, social and environmental factors exposure during pregnancy were also collected. Statistical analysis was conducted using Chi-square test, student's t test, Mann-Whitney U test and multivariate logistic regression. Results: (1) A total of 478 subjects were enrolled in early pregnancy and 460 (96.2%) and 431 (90.2%) of them completed SAS and SDS assessment in the second and third trimesters, respectively, and 413 (86.4%) of the subjects finished both assessments on two occasions. (2) The prevalence of anxiety and depression was 7.1% (30/425) and 13.9% (59/425) in the second trimester, and 11.2% (44/392) and 21.5% (84/390) in the third. The incidence of anxiety and depression in the third trimester was 7.7% (26/336) and 9.6% (30/313). (3) Cross-sectional data analysis showed that prolonged television watching was a risk factor of anxiety in the second (OR=1.216, 95%CI: 1.055-1.402) and third (OR=1.166, 95%CI: 1.044-1.303) trimester, while exercise was a protect factor (OR=0.238, 95%CI: 0.105-0.541; OR=0.432, 95%CI: 0.212-0.879). Pregnant women with longer sleeping time had lower risks of depression in the second trimester (OR=0.725, 95%CI: 0.554-0.950); those who did exercise had lower risks of depression in the third trimester (OR=0.450, 95%CI: 0.252-0.803). (4) Longitudinal-data analysis revealed that longer television watching time in the second trimester was a risk factor for anxiety (OR=1.264, 95%CI: 1.117-1.432) and depression (OR=1.119, 95%CI: 1.005-1.246) in the third trimester. Conclusions The prevalence of anxiety and depression in the third trimester is higher than that in the second trimester. The incidence of depression is higher than that of anxiety in the third trimester. Prolonged television viewing time in the second trimester is a risk factor for both anxiety and depression in the third trimester.