Objectives To explore the prognostic factors of patients with pancreatic ductal adenocarcinoma and synchronous liver metastases ( PALM ) receiving palliative treatment .Methods The clinical characteristics , therapeutic approaches and survival outcomes of 108 consecutive patients with PALM who were pathologically diagnosed and received only palliative treatment at Tianjin Medical University Cancer Hospital from January 2001 to December 2015 .were retrospectively analyzed .Survival rates were calculated by Kaplan-Meier method, and factors influencing the survival were analyzed by univariate and multivariate Cox proportional hazard regression model .Results Of these patients, 68 were male and 40 were female, with an average age of 58 years old.Seventy-seven (71.3%) cases or their relatives refused to receive anticancer therapies.Palliative treatments included choledochojejunostomy and /or gastrojejunostomy after exploratory laparotomy for 5 (4.6%) cases, percutaneous transhepatic biliary drainage (n=22, 19.4%), drug analgesia (n=79, 73.1%), drug analgesia combined with percutaneous neurolytic coeliac plexus block (n=17, 15.7%).The median survival time (MS)was 94 days in all patients.Karnofsky performance score (KPS)<80, lymph node metastases, ascites, fasting blood glucose ≥6.1 mmol/L and lactate dehydrogenase ( LDH ) ≥250 U/L were independent risk factors influencing prognosis of PALM . Three groups were categorized according to the number of the above 5 risk factors for 0~1 in low risk group, 2~3 in middle risk group and 4~5 in high risk group, and the MS of 3 groups was 137, 95 and 48 days, respectively, with an extremely statistical significance (P<0.0001).Conclusions KPS, lymph node metastases, ascites, fasting blood glucose and LDH were the risk factors for prognosis of PALM .Patient stratification according to the above factors is more advantageous for judging individualized prognosis and can provide reference for making clinical decision .

Objective:To analyze the influencing factors of anesthesia recovery time after gastric cancer surgery in patients with type 2 diabetes mellitus (T2DM), and establish a nomogram model.Methods:The clinical data of 120 T2DM patients underwent open surgery for gastric cancer from February 2020 to January 2021 in Handan Central Hospital were retrospectively analyzed. Among them, 50 patients had an anesthesia recovery time>30 min (observation group), and 70 patients had an anesthesia recovery time≤30 min (control group). Multivariate Logistic regression was used to analyze the independent risk factors of anesthesia recovery time after gastric cancer surgery in patients with T2DM. Receiver operating characteristic (ROC) curve was drawn to analyze the optimal cut-off value of each influencing factor for predicting anesthesia recovery time after gastric cancer surgery in patients with T2DM. A nomogram model to predict anesthesia recovery time after gastric cancer surgery in patients with T2DM was established with R language software 4.0 "rms" package.Results:The proportion of men, age, decrease amplitude of postoperative temperature, operation time intraoperative blood transfusion rate in observation group were significantly higher than those in control group: 84.00% (42/50) vs. 20.00% (14/70), (60.31 ± 14.23) years vs. (47.58 ± 8.96) years, (0.33 ± 0.09) ℃ vs. (0.28 ± 0.08) ℃, (92.32 ± 8.58) min vs. (84.54 ± 6.24) min and 38.00% (19/50) vs. 10.00% (7/70), the body mass index (BMI), heart rate and operating room temperature were significantly lower than those in control group: (20.11 ± 3.96) kg/m 2 vs. (24.69 ± 4.58) kg/m 2, (103.73 ± 9.57) beats/min vs. (118.32 ± 18.15) beats/min and (28.66 ± 1.58) ℃ vs. (30.25 ± 1.98) ℃, and there were statistical differences ( P<0.01); there were no statistical differences in smoking, drinking, hypertension, hyperlipidemia, coronary heart disease, anesthetic drug types, anesthesiologist job title, muscle relaxant drug types and body temperature between 2 groups ( P>0.05). ROC curve analysis results showed that the optimal cut-off values of age, BMI, heart rate, decrease amplitude of postoperative temperature, operating room temperature and operation time for predicting anesthesia recovery time after gastric cancer surgery in patients with T2DM were 46 years old, 21.8 kg/m 2, 113 beats/min, 0.3 ℃, 30.6 ℃ and 91 min, respectively. Multivariate Logistic regression analysis results showed that gender (male), age (>46 years old), BMI (≤21.8 kg/m 2), decrease amplitude of postoperative temperature (>0.3 ℃), operating room temperature (≤30.6 ℃) and operation time (>91 min) were independent risk factors for predicting anesthesia recovery time after gastric cancer surgery in patients with T2DM ( OR = 1.909, 1.815, 1.606, 1.458, 1.521 and 1.539; 95% CI 1.215 to 3.000, 1.014 to 3.249, 1.252 to 1.941, 1.251 to 1.628, 1.068 to 2.167 and 1.119 to 1.828; P<0.01 or<0.05). When gender, age, BMI, decrease amplitude of postoperative temperature, operating room temperature and operation time were included as predictors for constructing the nomogram model, the internal validation results showed that the c-index of the nomogram model for predicting anesthesia recovery time after gastric cancer surgery in patients with T2DM was 0.701 (95% CI 0.672 to 0.724); calibration curve showed good agreement between observed value and predicted value; the threshold value of the nomogram model for predicting anesthesia recovery time after gastric cancer surgery in patients with T2DM was>0.20, and the nomogram model provided a net clinical benefit; the clinical net benefit of the nomogram model was greater than that of sex, age, BMI, decrease amplitude of postoperative temperature, operating room temperature and operation time. Conclusions:A nomogram model to predict anesthesia recovery time after gastric cancer surgery in patients with T2DM is constructed based on gender, age, BMI, decrease amplitude of postoperative temperature, operating room temperature and operation time, and this model is significant for predicting anesthesia recovery time after gastric cancer surgery in patients with T2DM.

Objective: To explore the association between growth patterns and blood pressure in children and adolescents with different nutritional status. Methods: A total of 38 839 children and adolescents aged 6 to 8 years old were included in this study by stratified cluster sampling. The American Academy of Pediatrics 2017 Guideline was used to evaluate the blood pressure, the US 2000 CDC standard was used to determine different growth patterns, and the WHO Child and Adolescent Growth and Development Standard issued in 2007 was used to evaluate nutritional status. Variance analyses were used to compare the levels of systolic and diastolic blood pressure and growth patterns by nutritional status, and χ 2 test was used to compare the difference of prevalence. Multivariate Logistic regression model was used to analyze the association between growth patterns and blood pressure. Results: The proportion of normal growth, catch up growth and catch down growth was 33.2%, 41.6% and 25.2%, respectively. Under different growth patterns, systolic blood pressure (105.17±12.33) mm Hg and diastolic blood pressure (66.55±8.75) mm Hg of catch up growth were higher than those of normal growth and catch down growth. In overweight and obesity, the prevalence of hypertension (24.9%), high systolic blood pressure (18.9%) and high diastolic blood pressure (15.0%) in catch up growth were higher than those in normal growth and catch down growth ( P <0.05). The risk of catch up growth to hypertension was higher in overweight and obesity ( OR =1.18, 95% CI =1.06-1.31) than in normal children and adolescents ( OR =1.15, 95% CI =1.05-1.27). Conclusion In catch up growth children and adolescents, hypertension and high blood pressure are higher than normal growth and catch down growth. Overweight and obesity than normal children and adolescents have a higher risk of hypertension.

Objective: To explore the association between lifestyle and fat mass index (FMI) in different positions of children and adolescents aged 7-18, so as to provide a scientific basis for health promotion in children and adolescents. Methods: A total of 1 531 students aged 7-18 was selected by intentional sampling from 4 schools in Tongzhou District, Beijing from September to December in 2020 and August in 2022. Questionnaire survey was used to collect lifestyle including dietary behavior, moderate to vigorous physical activity, smoke and drink behaviors, sleep time and sleep quality. Dual energy Xray absorptiometry was employed to assess fat mass, and calculated total, android, trunk, hip, gynoid and leg fat mass index (FMI). The ttest and Chisquare test were used to compare the differences of different lifestyle. Logistic regression was used to analysis association between lifestyle and body composition in different positions. Results: Compared with healthy lifestyle, unhealthy lifestyle had higher risk for hightrunk FMI (OR=1.40, P<0.05). After adjusted for sex and age, unhealthy lifestyle had higher risk for hightotal FMI, highandroid FMI, hightrunk FMI (OR=1.37, 1.37, 1.50, P<0.05), compared with healthy lifestyle. Stratified analysis found the associations between unhealthy lifestyle and hightotal FMI, highandroid FMI, hightrunk FMI, and highthigh FMI were only significant in girls with 7-12 years old (OR=2.13, 2.46, 2.13, 2.13, P<0.05). Conclusions Unhealthy lifestyle is associated with hightotal FMI, highandroid FMI and hightrunk FMI. A healthy lifestyle should be maintained during puberty, especially before puberty, to help children and adolescents reduce body fat and promote a balanced distribution of body composition.

Objective: To analyze the cumulative effect of preadolescent lifestyles on the prevalence and incidence of elevated blood pressure (EBP) in children, and to provide evidence for early hypertensive intervention. Methods: In 2017, a cohort study on pubertal development was conducted in Xiamen, Fujian Province. Participants underwent a physical examination and questionnaire survey. Between 2017 and 2019, this study carried out a 2-year follow-up of 1 316 students whose complete data were obtained. Preadolescent lifestyles were assessed by referring to baseline survey results obtained in 2017, and the prevalence and incidence of EBP were determined according to blood pressure levels that were determined in 2019. The logistic regression model was used to analyze the influence of healthy lifestyles on the prevalence and incidence of EBP after adjusted for confounding factors. Results: The prevalence of EBP in 2017 and 2019 was 25.0% and 23.3%, respectively, and its incidence was 18.9% after the 2-year follow-up. Children with a healthy lifestyle had a lower prevalence and incidence of EBP. The regression analysis showed that children who have more healthy lifestyle factors were at a lower risk of EBP (P<0.05). Compared with students with 0-2 healthy lifestyle factors, the OR value of EBP prevalence was 0.52 (95% CI=0.30-0.90) and the RR value of EBP incidence was 0.60(95%CI=0.29-1.22) in children with 4-5 healthy lifestyle factors. Among them, children who were overweight or obese were at a significantly lower risk of EBP prevalence (OR=0.29, 95%CI=0.10-0.81). Conclusion Preadolescent lifestyles seemed to have a cumulative effect on the occurrence of EBP. Maintaining a healthy lifestyle had a positive effect on reducing the risk of EBP, particularly in overweight and obese children, and this was due to their adoption of a comprehensive healthy lifestyle.

Objective: To analyze the association between masculine and feminine traits, gender role in students and family factors, and to provide a scientific basis for promoting the healthy development of children and adolescents gender role. Methods: Cluster random sampling method was used to selected 823 students from 2 schools of Xiamen in May, 2018, participants were asked to report individual masculine and feminine traits through the Child s Sex Role Inventory, and then gender role was divided into four types (masculine, feminine, androgynous, undifferentiated). Non parametric tests, generalized linear model, and Logistic regression model were used to explore the association between masculine and feminine traits, gender role and family factors. Results: Compared with the boys from the nuclear family, the masculine traits were lower in boys of other families by 0.282( 95% CI = -0.452 --0.110), and the feminine traits were also lower by 0.192 (95% CI =-0.369--0.014). Compared with boys of middle socioeconomic status (SES), the masculine traits of boys from low SES decreased by 0.157 (95% CI =-0.286--0.029), and the feminine traits decreased by 0.140 (95% CI =-0.274--0.005). The proportion of androgynous in boys from other families was significantly lower than boys from the nuclear family ( OR =0.45, 95% CI =0.21-0.94), with the proportion of undifferentiated significantly higher than nuclear family 2.33 (95% CI =1.22-4.44). The proportion of androgynous in boys from low SES was significantly lower than boys from middle SES ( OR =0.59, 95% CI =0.35-0.99), with the proportion of undifferentiated significantly higher than middle SES 1.62 (95% CI =1.00-2.65). Among girls, the differences in masculine and feminine traits and gender role of different family factors were not statistically significant ( P >0.05). Conclusion The masculine and feminine traits and gender role in students are associated with family structures and socioeconomic status, especially in boys. Therefore, the family environment is an important factor affecting children s gender role, and the healthy development of children s gender role needs parents attention and correct guidance.

Objective: To analyze the correlation between the scores of masculinity and femininity and parental rearing pattern in lower grade primary school students in Xiamen, and to provide a policy suggestions and reference basis for establishing a correct concept of gender role among children and adolescents. Methods: A cluster random sampling method was used to select 823 students from two primary schools in Xiamen. Masculinity and femininity scores were assessed by Children s Sex Role Inventory(CSRI), while attitudes and behaviors of parental rearing pattern were obtained through EMBU. Regression analyses were used to analyze the correlation between masculinity and femininity scores and parental rearing pattern. Results: Masculinity scored 2.82 ( 2.41 ,3.24) and 2.82 (2.47,3.18), femininity scored 2.87 (2.40,3.20) and 3.13 (2.73,3.47) among boys and girls, with no significant gender difference ( P >0.05). Masculinity and femininity scores varied significantly by parental emotional warmth and understanding(father: Z/H =44.61, 37.24；mother: Z/H=41.68, 46.64, P <0.05). Among boys, increasing parental emotional warmth and understanding and paternal excessive interference were associated with higher masculinity and femininity scores. Increasing rejection and maternal deny were associated with lower masculinity scores. Among girls, increasing the understanding of emotional warmth of fathers was associated with masculinity and femininity scores, increasing excessive interference from mothers was associated with lower masculinity scores ( P <0.05). Conclusion There are gender differences in the relationship between parental rearing pattern and masculinity and femininity scores. In particular, increasing parents emotional warmth and understanding and reducing mothers punishment and harshness, rejection and denial, and other negative parenting styles could facilitate healthy development of masculinity and femininity among primary school students.

Objective: To analyze the association between sleep duration and social anxiety in Chinese children and adolescents to provide evidence for promoting healthy lifestyle and mental health in children and adolescents. Methods: A total of 1 145 children and adolescents aged 7-16 were recruited by cluster random sampling in Beijing in 2020, and received a series of body measurements and questionnaire survey. Social Anxiety Scale for Children (SASC) and Pittsburgh Sleep Quality Index (PSQI) were used to evaluate the social anxiety symptoms and sleep duration of children and adolescents. T test was used to compare the differences of social anxiety level in different groups, and multivariate linear regression analysis was used to analyze the relationship between sleep duration and social anxiety. Results: The average score of social anxiety was (5.47±4.18). The social anxiety score of girls, participants aged 13-16, with insufficient physical activity and insufficient sleep duration were higher ( t =-4.34, -6.14, 3.35, 2.93, P < 0.05). The results of multivariate linear regression model showed that after adjusting confounding factors, social anxiety decreased by 0.78 for each additional hour of sleep duration ( β =-0.78, 95% CI =-1.03--0.54, P <0.01), with 0.60 in boys (95% CI = -0.95 --0.25), 0.90 in girls (95% CI =-1.24--0.56), 0.75 among participants aged 7-12 (95% CI =-1.11--0.40) and 0.76 among participants aged 13~16 (95% CI =-1.11--0.41)( P <0.01), respectively. Social anxiety among participants who were not over weight or obese decreased by 0.78(95% CI =-1.09--0.48) and 0.81 among overweight and obese group (95% CI =-1.22- -0.41 )( P <0.01) for each additional hour of sleep duration, respectively. Conclusion Substantial differences in social anxiety are observed in children and adolescents by gender, age group and nutritional status. Sufficient sleep duration is significantly related to the decrease of social anxiety, and improve the overall level of student mental health.

Objective: To analyze the trend of percentile changes in body composition among children and adolescents aged 7 to 18 by gender and age, and to provide a scientific basis for reference value. Methods: A stratified cluster random sampling method was used to select 1 585 children and adolescents from September to December 2020. Use the GAMLSS to estimate the P 25 , P 50 and P 75 percentile value of gender, age specific body fat percentage (BF%), fat mass index (FMI) and fat free mass (FFM). Results: In different age groups, body fat percentage of boys with normal nutritional status peaked at around 10-year old, among them, body fat percentage of 10-year old P 25 , P 50 and P 75 were 22.2%, 26.1% and 30.1%, respectively. Body fat percentage of obese boys was at a high level. Among girls, body fat percentage under different nutritional status did not change. With the increase of age, fat mass index plateaued slightly in normal and overweight boys. However, in obese boys, with the increase of age, FMI gradually increased with the increase of percentile, while the value of FMI gradually decreased in girls. Whether in boys or girls, with the increase of age, fat free mass index shows an increasing trend, and boys increase faster. Conclusion Under different nutritional status, there are differences in the percentile value of body fat percentage, fat mass index and fat free mass by gender and age, which can provide a scientific basis for reference value of body composition in children and adolescents.

Objective: To explore the association between digital devices usage and body weight overestimation in children and adolescents aged 7-18, in order to provide a scientific basis for body weight overestimation prevention in children and adolescents. Methods: Based on the data of the Research Special Project for Public Welfare Industry of Health using stratified cluster sampling method in 2012, a tatal of 40 073 children and adolescents from 7 provinces with complete information were chosen. Ordinal multivariable Logistic regression model estimated the association between digital devices usage and body weight overestimation. Results: A total of 4 276(11.8%) students with overestimation of body weight were detected, who spent >300 min/d time in digital devices(5.12%) than others (3.84%)( χ 2=19.14, P <0.01). Univariate analysis showed that students with time spent on digital devices >300 min/d had a higher risk in overestimation of body weight ( OR=1.36,95%CI=1.18-1.57,P <0.01) compared with students who spent on digital devices≤120 min/d. There was still a significant association after confounder adjustment ( OR=1.28, 95%CI= 1.10-1.48,P <0.05). Stratified analysis showed that the association between digital devices usage and overestimation of body weight were only observed in girls, 11-18 years old and non single child( P <0.05). Conclusion The time usage of digital devices is associated with overestimation of body weight in children and adolescents. It may helpful for children and adolescents to prevent overestimation of body weight by reducing time spent on digital devices.