Objective To investigate the impact of various health education intervention strategies on the proper use of personal respiratory protective equipment (RPE) among workers exposed to dust. Methods Dust-exposed workers were recruited from 60 selected enterprises in Guangdong Province using cluster random sampling method. They were randomly allocated to the control, low-intensity intervention, and high-intensity intervention groups, with 358, 346, and 371 workers in each group, respectively. Workers in the control group received no designed intervention. Workers in the low-intensity intervention group received traditional plus mobile health education on the proper use of RPE. Workers in the high-intensity intervention group received all components of the low-intensity intervention, supplemented with peer education. The intervention lasted for six months. RPE usage was compared among the three groups of workers before and after the intervention. Results Workers in the control, low-intensity intervention, and high-intensity intervention groups showed higher rates of both RPE wearing and correct RPE wearing after the intervention than before it within their respective groups (RPE wearing rate: 94.1% vs 99.2%, 95.7% vs 100.0%, 94.6% vs 100.0%, all P<0.01; correct RPE wearing rate: 66.8% vs 91.1%, 67.3% vs 95.7%, 66.6% vs 96.5%, all P<0.01). Post-intervention correct RPE wearing rates were highest in the high-intensity intervention group, followed by the low-intensity intervention group, and the control group, with the percentage of 96.50%, 95.66% and 91.06%, respectively (P<0.01). Binary logistic regression analysis result showed that different intervention strategies affected the correct use of personal RPE among dust-exposed workers after adjusting for gender, age, and other confounding factors (P<0.05). Compared with the control group, the rates of correct RPE use increased in the low-intensity intervention group and the high-intensity intervention group (odd ratio was 2.14 and 3.01; 95% confidence interval was 1.12 - 4.10 and 1.53 - 5.91, respectively). Conclusion The implementation of traditional plus mobile health education interventions on the proper use of RPE can promote correct RPE utilization among dust-exposed workers, and integrating peer education further enhances the intervention effectiveness.

[Purpose]To analyze the trends of incidence and age at onset of leukemia in Jiangsu cancer registration areas from 2009 to 2019.[Methods]The continuous monitoring data of leukemia from 2009 to 2019 were collected from 16 cancer registries in Jiangsu Province.All datasets were checked and evaluated based on data quality control criteria and were included in the analysis.Crude incidence rate(CIR),age-standardized incidence rate by Chinese standard population(ASIRC),the average annual percentage change(AAPC),the standardized average age at onset,the changes in the age structure of incidence and the changes in the birth cohort by year were calculated.[Results]The incidence rate of leukemia significantly increased from 5.22/105 in 2009 to 7.88/105 in 2019,with a significant upward trend(for CIR,AAPC=4.95％,95％CI:3.82％～6.09％;for ASIRC,AAPC=2.97％,95％CI:1.52％～4.43％).The incidence rates were in-creased in all age groups and increased with the birth cohort by years.There was a tendency of backward shift for the age composition of the population,with the increasing of composition for those over 60 years old.The mean age at onset increased from 48.62 years old in 2009 to 57.96 years old in 2019,with a backward shift in the mean age(β=0.773,P＜0.001),and the mean age at onset increased with the year only in rural areas after standardization(β=0.428,P=0.017).[Conclusion]Leukemia incidence rate in Jiangsu Province increased from 2009 to 2019,and the age at onset has shifted backwards.It's important to strengthen the early prevention and control of leukemia.

[Purpose]To analyze the trends of brain tumor incidence and age of onset in cancer registration areas of Jiangsu Province from 2009 to 2019.[Methods]The incidence data of brain tumors from 2009 to 2019 were obtained from 16 national cancer registration areas of Jiangsu Province.The crude incidence rate,age-standardized incidence rate by Chinese standard popula-tion(ASIRC),average age of onset,standardized age-specific incidence rate,and annual average percentage change(AAPC)by sexes and regions were calculated.[Results]The incidence rate of brain tumor in Jiangsu Province from 2009 to 2019 showed an increasing trend(AAPC=4.12％,95％CI:3.31％～4.95％),and the increase in female(AAPC=5.79％,95％CI:4.44％～7.15％)was higher than that in male(AAPC=2.31％,95％CI:1.66％～2.97％);and the increase in urban areas(AAPC=4.84％,95％CI:3.52％～6.18％)was higher than that in rural areas(AAPC=3.04％,95％CI:2.12％～3.98％).The ASIRC also showed an increasing trend(AAPC=2.14％,95％CI:1.27％～3.02％).Birth cohort analysis showed that within the same age group,the incidence rate of brain tumors increased with increasing birth years.The average age of onset for crude incidence showed a delaying trend of 0.43 years old per year(t=7.09,P＜0.001);that for ASIRC was 0.20 years old per year(t=3.33,P=0.009).The proportion of patients in the age group of 60 years old and above increased from 35.79％in 2009 to 44.18％in 2019.[Conclusion]The incidence of brain tumor in cancer registration areas of Jiangsu Province showed an increasing trend from 2009 to 2019 with a delaying trend of average age of onset.

Objective:To explore the clinical efficacy of the sixth generation CyberKnife (M6) in treating patients with brain metastases, and analyze clinical characteristics and dosimetric factors.Methods:Clinical data of patients with brain metastases who received CyberKnife treatment at Sichuan Cancer Hospital from April 2023 to March 2024 were retrospectively analyzed. All patients were treated with CyberKnife with 6 MV X-ray. According to the maximum diameter of brain metastases, the radiation prescription dose of brain metastases was adjusted. The tumor remission, recurrence, 6-month and 1-year overall survival (OS), local control (LC) of intracranial target lesions, progression-free survival (PFS), distant metastasis-free survival (DMFS) of intracranial brain metastases and adverse reactions were evaluated. According to the median biological dose, the survival difference between the groups was compared. Survival analysis was conducted by Kaplan-Meier method. Survival differences among different groups were analyzed by log-rank test.Results:A total of 63 eligible patients with brain metastases were enrolled, with a median age of 59 years (rang: 36-80 years). Among them, 47 patients were diagnosed with primary tumors originating from the lungs, 16 patients with primary tumors originating from other organs; 44 patients with single brain metastases, and 19 patients with 2-3 lesions, respectively. The median biological dose was 67.2 Gy (rang: 47.4-86.4 Gy), and the median single dose was 8 Gy/F (rang: 4-24 Gy/F). The follow-up was conducted until July 15, 2024. The median follow-up time for the entire group was 9 months (rang: 2-15 months). Among the 87 target lesions treated with CyberKnife, 11 patients corresponding to 14 target lesions experienced local recurrence. And the 6-month and 1-year LC rates were 92.5% and 70.9%, respectively. Ten patients corresponding to 16 target lesions died. And the 6-month and 1-year OS rates were 92.7% and 74.8%, respectively. Thirty-five patients corresponding to 50 target lesions experienced disease progression. And the 6-month and 1-year PFS rates were 64.3% and 25.5%, respectively. Thirty-three patients corresponding to 48 target lesions showed distant metastasis outside the target lesions, with a 6-month DMFS of 67.0% and a 1-year DMFS of 33.9%. Group comparison showed that 43 target lesions in the group receiving ≤67.2 Gy irradiation and 44 in the group receiving >67.2 Gy irradiation. The 6-month LC, OS, PFS, and DMFS rates between two groups were 89.8% vs. 97.7% ( P=0.127), 89.8% vs. 95.4% ( P=0.305), 65.4% vs. 68.5% ( P=0.514), 65.4% vs. 68.5% ( P=0.516), respectively. The 1-year LC, OS, PFS, and DMFS rates between two groups were 54.1% vs. 89.5% ( P=0.003), 67.3% vs. 82.9% ( P=0.219), 19.2% vs. 32.7% ( P=0.370) and 23.3% vs. 33.0% ( P=0.533). During the follow-up, only 2 patients (3.2%) were found to have grade 1-2 radiation-induced brain injury (asymptomatic brain injury) by MRI examination, and there were no other radiotherapy related adverse reactions. Conclusions:CyberKnife therapy is clinically effective for brain metastases, with mild adverse reactions. Increasing the tumor irradiation dose can improve local tumor control and is expected to further improve the OS of patients.

Objective:To explore the relationship between obesity indicators and DNA methylation clocks acceleration,and to analyze their temporal sequence.Methods:Data were obtained from two sur-veys conducted in 2013 and 2017-2018 by the Chinese National Twin Registry.Peripheral blood DNA methylation data were measured using the Illumina Infinium Human Methylation 450K BeadChip and EPIC BeadChip.DNA methylation clocks/acceleration metrics(GrimAA,PCGrimAA and Dunedin-PACE)were calculated using the DNA methylation online tool(https://dnamage.genetics.ucla.edu/)or R code provided by researchers.Obesity indicators included weight,body mass index(BMI),waist circumference,waist-hip ratio,and waist-height ratio.A total of 1 070 twin individuals were included in the cross-sectional analysis,comprising 378 monozygotic(MZ)twin pairs and 155 dizygotic(DZ)twin pairs for within-pair analysis.Mixed-effects models were used to examine the associations between obesity indicators and DNA methylation clocks,as well as their acceleration measures.The longitudinal analysis included 314 twin individuals,comprising 95 MZ twin pairs and 62 DZ twin pairs for within-pair analy-sis.Cross-lagged panel models were applied to further explore the temporal relationships between obesity and DNA methylation clock indicators.All analyses were conducted both in the full twin sample and separately within MZ and DZ twin pairs.Results:In the cross-sectional analysis population,monozygotic twins accounted for 71.0％,males for 68.0％,and the mean chronological age was(49.9±12.1)years.In the longitudinal analysis population,monozygotic twins accounted for 60.5％,males for 60.8％,with a mean baseline chronological age of(50.4±10.2)years and a mean follow-up duration of(4.6±0.6)years.Except for the waist-to-hip ratio,which was significantly higher at follow-up com-pared with baseline,no statistically significant differences were observed in the means of other obesity in-dicators between baseline and follow-up.Correlation analysis revealed that weight,BMI,waist circumfe-rence,waist-hip ratio(WHR),and waist-height ratio(WHtR)were positively correlated with Dunedin-PACE in all the twins,with WHtR showing the strongest association(β=0.21,95％CI:0.11 to 0.31).Weight and BMI were negatively associated with GrimAA(β=-0.03,95％CI:-0.05 to-0.01;β=-0.07,95％CI:-0.12 to-0.02),while weight was negatively associated with PCGrim-AA(β=-0.02,95％CI:-0.03 to 0.00).However,within-twin-pair analyses showed no statistically significant correlations.Cross-lagged panel model analysis indicated that higher baseline weight might lead to increased GrimAA at follow-up,while elevated baseline weight,BMI,and waist circumference might increase PCGrimAA.Higher baseline WHR was associated with increased DunedinPACE at follow-up.Conclusion:Obesity indicators correlate with DNA methylation clock acceleration metrics.Baseline obesity may influence changes in certain DNA methylation clock indicators over time,suggesting that obesity could exert long-term health effects by accelerating DNA methylation aging.However,these associations may be confounded by shared genetic or environmental factors among the twins.

Objective To construct 5 machine-learning models and compare their performance in predicting the associations between pre-pregnancy socio-psycho-behavioral exposures of both spouses and preconception outcomes.Methods Based on Chongqing Preconception Reproductive Health and Birth Outcome Cohort of volunteers recruited from Chongqing Health Center for Women and Children during January 2019 and March 2022,5 447 couples were recruited and surveyed through interviewer-interview for the demographic and social-psychological-behavioral data of both spouses(221 variables).According to the inclusion and exclusion criteria,4 097 couples were finally included,and randomly assigned into a training set(n=2 867 spouses)and a validation set(n=1 230 spouses)at a ratio of 7∶3.Feature analysis and collinear screening were applied to select the potential exposure factors.In consideration of difficulty to carry out semen parameters analysis in primary healthcare institutions,feature Set 1 including sperm parameters and feature Set 2 excluding semen parameters were constructed by including or excluding sperm quality simultaneously in the training set and the validation set.Five algorithms,that is,Logistic Regression,Naive Bayes,Random Forest,Gradient Boosting Machine,and Support Vector Machine,were used to construct preconception outcome prediction models,and the parameters of each model were optimized using random search combined with grid search.The predictive performance of each model was compared using precision,recall,F1 score,area under the receiver operating characteristic curve(AUC),and calibration curve.The optimal model was then selected by comparing the changes in the predictive ability of the questionnaire data for fertility outcomes with or without semen parameters.Results There were 24 variables screened out in feature Set 1,and 16 variables in feature Set 2.In feature Set 1,the gradient boosting machine performed better,with a relatively higher AUC value(0.651)and better F1 score(0.61).The logistic regression model performed stably(AUC value=0.647)and was suitable as the reference model.The random forest(AUC value=0.641),Naive Bayes(AUC value=0.641),and support vector machine(AUC value=0.634)performed second-best.By utilizing the gradient boosting machine,comparable results were found between the predictions from feature sets with or without semen parameters,as in feature Set 1,the AUC value of its validation set was 0.651(95%CI:0.629～0.681),the prediction accuracy was 0.63,the recall rate was 0.65,and the average precision value F1 was 0.61;and in feature Set 2,the AUC value of its validation set was 0.649(95%CI:0.624～0.663),and both the calibration curves were close to the ideal curve.The prediction results indicated that in feature Set 1,the features highly negatively correlated with preconception outcomes were female age,male age,and no pregnancy within 1 year without contraception,while the features highly positively correlated with preconception outcomes were female pregnancy history,total sperm vitality,and use of contraceptive measures before enrollment.Conclusion Among the 5 machine-learning algorithms performed in this cohort data,the gradient boosting machine shows slightly better performance.There are 24 factors being associated with preconception outcomes in both spouses,and the performance of the simplified model excluding semen parameters is not significantly declined.It is feasible to use machine-learning methods to predict human preconception outcomes through social-psychological-behavioral questionnaires.

Objective:To analyze the trend of colorectal cancer incidence and age changes in cancer registration areas of Jiangsu Province from 2009 to 2019.Methods:Based on the continuous and complete data of 16 cancer registries with qualified quality control in Jiangsu Province from 2009 to 2019, the crude incidence rate, age-standardized incidence rate by Segi World Standard Population (ASIRW), age-specific incidence rate, mean age at onset, standardized mean age at onset, standardized age-specific incidence proportion, and incidence proportion of the population over 60 years old of colorectal cancer were calculated. Joinpoint software was used to calculate the average annual percentage change (AAPC) of crude incidence rate, ASIRW, age-specific incidence rate, and incidence proportion of the population over 60 years, respectively. Birth cohort models were constructed to analyze the incidence of colorectal cancer and its trends in the population born from 1929 to 2019. Linear regression models were used to analyze the correlation between mean age at onset, standardized mean age at onset and the year of onset.Results:From 2009 to 2019, a total of 48 036 new cases of colorectal cancer were collected from 16 cancer registries in Jiangsu Province, including 27 508 males and 20 528 females. The crude incidence rate and ASIRW of colorectal cancer in Jiangsu Province increased from 19.00/100 000 and 12.32/100 000 in 2009 to 33.49/100 000 and 16.75/100 000 in 2019, respectively, showing a significant upward trend (CR: AAPC=5.99%, ASIRW: AAPC=3.54%, P<0.001). The increase of ASIRW was greater in males than that observed in females (males: AAPC=4.31%, females: AAPC=2.34%, P<0.001), and greater in rural areas than in urban areas (rural areas: AAPC=4.03%, urban areas: AAPC=3.13%, P<0.001). The incidence of people over 50 years old increased significantly by year, with the 60~69 age group exhibiting a more rapid increase ( AAPC=4.97%, P<0.05). In the birth cohort, the incidence increased rapidly in the population over 50 years with the passage of birth year, with AAPCs ranging from 1.76% to 7.05% ( P<0.05). From 2009 to 2019, the standardized mean age at onset of colorectal cancer increased by 0.10 years annually. The proportion of standardized age-specific incidence exhibited a trend of increase in older age groups, and the incidence proportion of the population over 60 years old showed a significant yearly increase ( AAPC=0.86%, P<0.001). Conclusion:From 2009 to 2019, the incidence, mean age at onset and the incidence proportion of the population over 60 years old of colorectal cancer in Jiangsu Province could exhibit a rapid upward trend. The increase is particularly pronounced in males and rural areas. Additionally, the age-specific incidence distribution reveals a trend of increase in older age groups. Therefore, targeted adjustments and comprehensive prevention measures should be strengthened.

This study aims to describe the population and regional distribution characteristics of smoking behavior among adult twins in the China Twin Registry (CNTR), as well as the concordance rates for smoking behavior in monozygotic and dizygotic twins, and estimate the heritability. The study population included adult twins in CNTR who had smoking questionnaire data. A random-effects regression model was used to describe the distribution of smoking behavior among different subgroups based on various characteristics. The concordance of smoking behavior between different zygosity groups was calculated, and heritability was estimated. A total of 28 444 twin pairs were included in this study, with an average age of (36.6±12.0) years. Among male twins, 41.2% were current smokers, while only 1.2% of females smoked. Higher smoking rates were observed among male smokers in the 50-59 age group ( z=23.0, P<0.001), northern regions ( z=2.9, P<0.01), rural areas ( z=-5.2, P<0.001), those who were divorced/widowed ( z=3.8, P<0.001), and first-born twins ( z=-4.3, P<0.001), while lower smoking rates were found in those with higher education ( z=-16.1, P<0.001) and unmarried individuals ( z=-16.0, P<0.001). The smoking concordance rate for male monozygotic twins was 69.6%, significantly higher than the 57.3% concordance rate for dizygotic twins ( χ 2=105.0, P<0.05). The heritability of smoking behavior in male twins was estimated at 28.9% (95% CI: 24.3%-33.4%). Stratified analyses showed differences in heritability across regions and age groups: the heritability in northern regions was 32.6% (95% CI: 27.3%-38.0%), higher than the 21.0% (95% CI: 12.4%-29.5%) observed in southern regions; the highest heritability of 35.1% (95% CI: 26.3%-43.9%) was found in the 18-29 age group, with heritability decreasing with age. In conclusion, the smoking rate and influencing factors in the twin population are similar to those in the general population, with unique characteristics, such as higher smoking rates in first-born twins. Genetic factors have a significant impact on smoking behavior.

[Purpose]To analyze the trends of incidence and age of onset of ovarian cancer in can-cer registration areas of Jiangsu Province from 2009 to 2019.[Methods]Based on data reported by 16 cancer registries in Jiangsu Province,the average annual change percentage(AAPC)of ovarian cancer crude rate(CR),age-standardized incidence rate by Chinese standard population(ASIRC),proportion of cases in people over 60 years old of age were calculated by Joinpoint software.The linear regression model was used to calculate the trend of the mean age of onset and the standardi-zed average age of onset.The change trend of ovarian cancer incidence rate among people born in different periods using the birth cohort from 1929 to 2019.[Results]The CR(AAPC=4.18％,P＜0.001)and ASIRC(AAPC=2.11％,P=0.010)of ovarian cancer from 2009 to 2019 showed a significant increasing trend.The CR of ovarian cancer in urban areas was higher than that in rural areas,however,the CR in rural areas showed a significant upward trend(AAPC=4.85％,P＜0.05),while it was not significantly changed in urban areas.The incidence of ovarian cancer first increased and then decreased with age,and peaked at the age group of 60～69 years old.From 2009 to 2019,the incidence trend of different age groups varied,but the age group over 50 years showed an up-ward trend,and the upward trend became more significantly as age increased.From 2009 to 2019,the crude mean age of onset and the standardized mean age of onset in Jiangsu Province showed a significant upward trend,with an average annual increase of 0.572 years and 0.380 years,respectively(P＜0.05).The average age of onset in both urban and rural areas showed a sig-nificant upward trend,and the annual increase in urban areas was higher than that in rural areas.However,there was no significant change in rural areas after adjusting the population composi-tion.Compared with 2009,the standardized age proportion of ovarian cancer in Jiangsu Province in 2019 showed a backward trend.Standardized proportion of ovarian cancer among people over 60 years old showed a significant upward trend(AAPC=2.78％,P=0.010).[Conclusion]From 2009 to 2019,the incidence rate of ovarian cancer in Jiangsu Province showed an upward trend.Compared with urban areas,the increase in rural areas was more pronounced.The average age of onset of ovarian cancer has shifted later with the increasing cases in older individuals.

Objective: To investigate the trend in incidence and change in age at onset of malignant tumor in cancer registration areas of Jiangsu Province from 2009 to 2021, so as to provide the evidence for formulating cancer prevention and control strategies and optimizing the allocation of healthcare resources. Methods: Incidence data of malignant tumor cases from 2009 to 2021 were collected from the aggregated database of 16 qualified cancer registries of Jiangsu Province. The crude incidence, age-specific incidence, average age at onset, proportion of age-specific incidence, and proportion of incidence in cases aged ≥60 years were calculated by genders and urban/rural areas, and age-standardized using the Segi's world standard population. The trend in incidence of malignant tumor from 2009 to 2021 was evaluated using average annual percent change (AAPC). The trend in average age at onset of malignant tumor from 2009 to 2021 was evaluated using the linear regression model. Results: From 2009 to 2021, a total of 703 185 cases of malignant tumor were reported in Jiangsu Province, comprising 400 970 males and 302 215 females. The crude incidence of malignant tumor increased from 268.26/100 000 in 2009 to 380.97/100 000 in 2021 (AAPC=2.880%, P<0.05). From 2009 to 2021, the world population-standardized incidence of malignant tumor showed upward trends in the total population, females, and urban and rural areas (AAPC=0.635%, 2.332%, 0.795%, and 0.385%, all P<0.05), while a downward trend was observed in males (AAPC=-0.608%, P<0.05). From 2009 to 2021, the crude incidence of malignant tumor in the groups aged 0-<30 years, 30-<40 years, 40-<50 years, 60-<70 years, and ≥80 years showed upward trends (AAPC=3.160%, 4.462%, 1.295%, 0.569%, and 1.496%, all P<0.05), a downward trend was found in the group aged 50-<60 years (AAPC=-0.860%, P<0.05), while no statistically significant trend was observed in the group aged 70-<80 years (P>0.05). The world population-standardized average age at onset showed downward trends in the total population, females, and urban areas, with average annual decreases of 0.085, 0.223, and 0.136 years, respectively (all P<0.05). Conversely, an upward trend was observed in males, with an average annual increase of 0.081 years (P<0.05). No statistically significant trend was found in rural areas (P>0.05). Compared with 2009, the proportion of malignant tumor incidence cases increased in all age groups between 20-<50 years in 2021. Additionally, the proportion of malignant tumor cases aged over 60 years showed a downward trend from 2009 to 2021 (AAPC=-0.322%, P<0.05). Conclusions From 2009 to 2021, the overall incidence of malignant tumor in registration areas of Jiangsu Province showed an upward trend, with the age at onset tending to become younger. There were differences in the incidence trends across genders and urban/rural areas.