Objective To evaluate the changes in the incidence of neural tube defects(NTDs)in Shaanxi province from 2003 to 2022,investigate the diagnosis time and outcomes of defective infants,and pre-dict the incidence of NTDs in Shaanxi province from 2023 to 2025,thereby providing a basis for improving the birth defects surveillance system.Methods Data were collected from all the perinatal infants from 28 weeks of gestation to 7 days after birth in all the hospitals with obstetrical department in Shaanxi province during 2003-2022.The changes in the incidence of NTDs from 2003 to 2022 were analyzed based on the birth defects surveil-lance system.Results A total of 1 106 483 perinatal infants in Shaanxi province from 2003 to 2022 were surveyed,among which NTDs occurred in 848 perinatal infants,with an incidence of 7.66/10 000.The incidence was the highest(48.02/10 000)in 2005 and the lowest(0.57/10 000)in 2022.The NTDs in Shaanxi province were mainly spina bifida(55.90％),which was followed by anencephaly(25.71％)and encephalocele(18.40％).The incidences of the three declined with fluctuations(P＜0.001).The results of the Joinpoint analysis showed that the incidence of NTDs decreased slowly with the annual percentage change of-4.04 from 2003 to 2014 and declined rapidly with the annual percentage change of-28.05 from 2014 to 2022.From 2003 to 2022,the aver-age proportion of prenatal diagnosis of NTDs in Shaanxi province was 72.88％.Dead fetus(61.91％)was the main birth outcome,followed by live birth(26.77％),stillbirth(8.73％),and death within seven days after birth(2.59％).The incidence of NTDs in Shaanxi province from 2023 to 2025 were predicted by the GM(1,1)model as 0.49/10 000,0.41/10 000,and 0.35/10 000,respectively.Conclusions The incidence of NTDs in Shaanxi province declined significantly during 2003-2022,especially in a rapid manner after 2014.Dead fetus was the primary outcome of perinatal infants with NTDs,followed by live birth.

Purpose: To understand the recurrence risk perception of stroke patients and develop a chain mediation model of recurrence risk perception and health behavior. Methods: A cross-sectional study and convenience sampling were used. Stroke survivors were recruited from the neurology departments of three tertiary hospitals. Their recurrence risk perception, behavioral decision-making, social support, self-efficacy, recurrence worry, and health behavior were measured by relevant tools. Data was analyzed through one-way analysis and regression analysis, and the AMOS 21.0 software was used to explore the mediating relationships between variables. Results: Of the 419 participants, 74.7% were aware of stroke recurrence risk. However, only 28.2% could accurately estimate their own recurrence risk. Recurrence risk perception was significantly correlated with behavioral decision-making, social support, self-efficacy, and health behavior (r = .19 ∼ .50, p < .05). Social support and recurrence risk perception could affect health behavior indirectly through self-efficacy, behavioral decision-making, and worry. Behavioral decision-making acted as a main mediator between recurrence risk perception and health behavior, while the path coefficient was .47 and .37, respectively. The chain mediation effect between recurrence risk perception and health behavior was established with a total effect value of .19 (p < .01). Conclusion Most stroke survivors could be aware of recurrence risk but failed to accurately estimate their individual risk. In the mediation model of recurrence risk perception and health behavior, social support seemed to be an important external factor, while self-efficacy, behavioral decision-making, and worry seemed to act as key internal factors.

Purpose: To understand the recurrence risk perception of stroke patients and develop a chain mediation model of recurrence risk perception and health behavior. Methods: A cross-sectional study and convenience sampling were used. Stroke survivors were recruited from the neurology departments of three tertiary hospitals. Their recurrence risk perception, behavioral decision-making, social support, self-efficacy, recurrence worry, and health behavior were measured by relevant tools. Data was analyzed through one-way analysis and regression analysis, and the AMOS 21.0 software was used to explore the mediating relationships between variables. Results: Of the 419 participants, 74.7% were aware of stroke recurrence risk. However, only 28.2% could accurately estimate their own recurrence risk. Recurrence risk perception was significantly correlated with behavioral decision-making, social support, self-efficacy, and health behavior (r = .19 ∼ .50, p < .05). Social support and recurrence risk perception could affect health behavior indirectly through self-efficacy, behavioral decision-making, and worry. Behavioral decision-making acted as a main mediator between recurrence risk perception and health behavior, while the path coefficient was .47 and .37, respectively. The chain mediation effect between recurrence risk perception and health behavior was established with a total effect value of .19 (p < .01). Conclusion Most stroke survivors could be aware of recurrence risk but failed to accurately estimate their individual risk. In the mediation model of recurrence risk perception and health behavior, social support seemed to be an important external factor, while self-efficacy, behavioral decision-making, and worry seemed to act as key internal factors.

Purpose: To understand the recurrence risk perception of stroke patients and develop a chain mediation model of recurrence risk perception and health behavior. Methods: A cross-sectional study and convenience sampling were used. Stroke survivors were recruited from the neurology departments of three tertiary hospitals. Their recurrence risk perception, behavioral decision-making, social support, self-efficacy, recurrence worry, and health behavior were measured by relevant tools. Data was analyzed through one-way analysis and regression analysis, and the AMOS 21.0 software was used to explore the mediating relationships between variables. Results: Of the 419 participants, 74.7% were aware of stroke recurrence risk. However, only 28.2% could accurately estimate their own recurrence risk. Recurrence risk perception was significantly correlated with behavioral decision-making, social support, self-efficacy, and health behavior (r = .19 ∼ .50, p < .05). Social support and recurrence risk perception could affect health behavior indirectly through self-efficacy, behavioral decision-making, and worry. Behavioral decision-making acted as a main mediator between recurrence risk perception and health behavior, while the path coefficient was .47 and .37, respectively. The chain mediation effect between recurrence risk perception and health behavior was established with a total effect value of .19 (p < .01). Conclusion Most stroke survivors could be aware of recurrence risk but failed to accurately estimate their individual risk. In the mediation model of recurrence risk perception and health behavior, social support seemed to be an important external factor, while self-efficacy, behavioral decision-making, and worry seemed to act as key internal factors.

Target trail emulation is an observational research method, which can use real-world data (such as observational data and historical data) to carry out research design according to the design principles of randomized controlled trials (RCT) when RCT cannot be carried out. The intervention group and the control group were classified by simulating random grouping. Finally a high-reliable conclusion similar to RCT can be obtained. This paper summarizes the basic concepts and application process of target trail emulation based on the effect of statins on the prognosis of myocardial infarction in diabetic patients to provide reference for the application of this method in real world.

Oral and maxillofacial space infections (OMSI) are common diseases of the facial region involving fascial spaces. Recently, OMSI shows trends of multi drug-resistance, severe symptoms, and increased mortality. OMSI treatment principles need to be updated to improve the cure rate. Based on the clinical experiences of Chinese experts and with the incorporation of international counterparts′ expertise, the principles of preoperative checklist, interpretation of examination results, empirical medication principles, surgical treatment principles, postoperative drainage principles, prevention strategies of wisdom teeth pericoronitis-related OMSI, blood glucose management, physiotherapy principles, Ludwig′s angina treatment and perioperative care were systematically summarized and an expert consensus on the diagnosis and treatment of OMSI was reached. The consensus aims to provide criteria for the diagnosis and treatment of OMSI in China so as to improve the level of OMSI treatment.

Regression discontinuity design (RDD) is a rigorous quasi-experimental method. Based on the randomness of the distribution of samples around the critical value, the data on both sides are used for regression respectively, so as to avoid the interference of potential confounding factors and provide strong evidence for the inference of causal correlation. This study introduces the RDD and its research progress, and expounds its application in detail combined with cases, in order to provide reference for the application of RDD in epidemiological research.

Regression discontinuity design (RDD) is a rigorous quasi-experimental method. Based on the randomness of the distribution of samples around the critical value, the data on both sides are used for regression respectively, so as to avoid the interference of potential confounding factors and provide strong evidence for the inference of causal correlation. This study introduces the RDD and its research progress, and expounds its application in detail combined with cases, in order to provide reference for the application of RDD in epidemiological research.

As an important method to study the phenotype and function of organisms, transcriptome has become one of hot topics in current research. The transcriptomics research usually accompanies with massive data. With the increase of the amount of data, the rules and features hidden in it are not easy to be found. Transforming big data into visual graphics is the most undoubtedly intuitive way to display the hidden information of big data. Several graphs commonly used in transcriptome study were introduced in this paper, such as Venn diagram, heat map, principal component analysis scatter plot, enrichment analysis plot, and time series analysis plot, in order to help readers to choose suitable graphics in future studies.

As an important method to study the phenotype and function of organisms, transcriptome has become one of hot topics in current research. The transcriptomics research usually accompanies with massive data. With the increase of the amount of data, the rules and features hidden in it are not easy to be found. Transforming big data into visual graphics is the most undoubtedly intuitive way to display the hidden information of big data. Several graphs commonly used in transcriptome study were introduced in this paper, such as Venn diagram, heat map, principal component analysis scatter plot, enrichment analysis plot, and time series analysis plot, in order to help readers to choose suitable graphics in future studies.