RESUMO
Red doctor’s culture is a characteristic cultural system in the field of red and health during the revolutionary period， as well as an important part of China’s revolutionary culture. It differs from general medical culture， and its formation and development has unique historical implications. Therefore， it is necessary to deeply analyze the basic connotation of red doctor’s culture and explore the contemporary value it contains. In the new era， inheriting the red doctor’s culture is of great significance， which can effectively promote the construction of a healthy China in the new era， provide high-quality resources for ideological and political education in the new era， and become a new approach to inheriting the red gene. The successful inheritance of the red doctor’s culture is inseparable from the joint efforts of all sectors of society. Strengthening scientific research， optimizing the educational environment， and increasing cultural propaganda， can assist in the inheritance of red doctor’s culture， open a new chapter of it， make the red doctor’s culture deeply penetrate people’s hearts， and let the red bloodline be passed down from generation to generation.
RESUMO
Objective:To establish and preliminarily validate a nomogram model for predicting the risk of retinal vein occlusion (RVO).Methods:A retrospective clinical study. A total of 162 patients with RVO (RVO group) diagnosed by ophthalmology examination in The Second Affiliated Hospital of Xi'an Jiaotong University from January 2017 to April 2022 and 162 patients with age-related cataract (nRVO group) were selected as the modeling set. A total of 45 patients with branch RVO, 45 patients with central RVO and 45 patients with age-related cataract admitted to Xi'an Fourth Hospital from January 2022 to February 2023 were used as the validation set. There was no significant difference in gender composition ratio ( χ2=2.433) and age ( Z=1.006) between RVO group and nRVO group ( P=0.120, 0.320). Age, gender, blood routine (white blood cell count, hemoglobin concentration, platelet count, neutrophil count, monocyte count, lymphocyte count, erythrocyte volume, mean platelet volume, platelet volume distribution width), and four items of thrombin (prothrombin time, activated partial thrombin time, fibrinogen, and thrombin time) were collected in detail ), uric acid, blood lipids (total cholesterol, triglyceride, high-density lipoprotein, low-density lipoprotein, lipoprotein a), hypertension, diabetes mellitus, coronary heart disease, and cerebral infarction. Neutrophil/lymphocyte ratio and platelet/lymphocyte ratio were calculated. The single logistic regression was used to analyze the clinical parameters of the two groups of patients in the modeling set, and the stepwise regression method was used to screen the variables, and the column graph for predicting the risk of RVO was constructed. The Bootstrap method was used to repeated sample 1 000 times for internal and external verification. The H-L goodness-of-fit test and receiver operating characteristic (ROC) curve were used to evaluate the calibration and discrimination of the nomogram model. Results:After univariate logistic regression and stepwise regression analysis, high density lipoprotein, neutrophil count and hypertension were included in the final prediction model to construct the nomogram. The χ2 values of the H-L goodness-of-fit test of the modeling set and the validation set were 0.711 and 4.230, respectively, and the P values were 0.701 and 0.121, respectively, indicating that the nomogram model had good prediction accuracy. The area under the ROC curve of the nomogram model for predicting the occurrence of post-stroke depression in the modeling set and the verification set was 0.741 [95% confidence interval ( CI) 0.688-0.795] and 0.741 (95% CI 0.646-0.836), suggesting that the nomogram model had a good discrimination. Conclusions:Low high density lipoprotein level, high neutrophil count and hypertension are independent risk factors for RVO. The nomogram model established based on the above risk factors can effectively assess and quantify the risk of post-stroke depression in patients with cerebral infarction.