Objective To explore the value of CT signs and quantitative parameters of artificial intelligence (AI) in predicting the efficacy of neoadjuvant chemotherapy for colorectal cancer. Methods A total of 349 colorectal cancer patients who received neoadjuvant chemotherapy at Cangzhou Hospital of Integrated Traditional Chinese and Western Medicine in Hebei Province from January 2022 to January 2025 were selected and and divided into the effective group (n = 267) and the ineffective group (n = 82) according to the evaluation criteria for the efficacy of solid tumors. Conduct a CT examination and extract AI quantitative parameters from the CT images based on the lesion. The data were analyzed using SPSS21.0 software, Logistic regression was used to screen the influencing factors of ineffective neoadjuvant chemotherapy in patients with colorectal cancer, and separate and combined models of CT signs and AI quantitative parameters were established. The predictive effect of the model was verified by using the ROC curve, calibration curve and decision curve. Results Compared with the effective group, the proportion of regular tumor morphology and the proportion of non-enlarged lymph nodesin the ineffective group were smaller. The tumor volume, peak value and entropy value were larger (P < 0.05). Multivariable analysis showed that irregular shape (OR= 4.216), presence of lymph node enlargement (OR = 8.998), larger tumor volume (OR = 1.109), higher average CT value (OR = 1.120), elevated peak value (OR = 2.528), and increased entropy value (OR = 1.390) were independent risk factors for ineffective neoadjuvant chemotherapy in colorectal cancer (P < 0.05). The areas under the ROC curves of the individual and combined models of CT signs and AI quantitative parameters were 0.777, 0.818, and 0.877, respectively(P < 0.05). The calibration curve showed a Brier score of 0.091. The decision curve showed that the threshold was between 0.10 and 0.85, and the combined model achieved a relatively high net clinical benefit. Conclusion CT signs combined with AI quantitative parameters has a predictive value for the efficacy of neoadjuvant chemotherapy in colorectal cancer. To provide evidence-based basis for clinical screening of the population benefiting from chemotherapy and optimization of treatment strategies.

This paper introduces the academic characteristics of Professor ZHANG Ren in treatment with acupuncture for refractory eye diseases in modern times, guided by "homotherapy for heteropathy" (same therapy for different diseases sharing the same pathogenesis). The refractory eye diseases in modern times include a variety of conditions such as glaucoma, macular degeneration, diabetic retinopathy, high myopia and its complications, dry eye, cortical visual impairment and genetic eye diseases. The same therapy is used because these diseases share the similar location and pathogenesis. Professor ZHANG optimizes the methods of acupoint selection and provides the comprehensive prescriptions, "basic prescription, prescription based on disease differentiation, and supplementary prescription". A variety of acupuncture manipulation techniques are operated in clinical practice, such as compound needling methods, penetration needling, manipulations for promoting qi movement and conducting qi flow. "Early, regular and persistent" treatment is the common requirement with "the same acupoints, the same prescription and the same acupuncture method" as well as at "the same time". It is also proposed that the treatment should be provided flexibly according to the different symptoms, "identifying the differences within similarities".
Acupuncture Therapy/methods* ; Humans ; Eye Diseases/history* ; Acupuncture Points ; History, 20th Century ; China ; History, 21st Century

Objective:To analyze the current development status and spatial distribution characteristics of traditional Chinese medicine (TCM) hospitals in China, predict the changing trends of the number of TCM hospitals, the number of beds, and the number of physicians, and provide references for the development of TCM hospitals and the formulation of related policies.Methods:From the official websites of the National Bureau of Statistics and the State Administration of Traditional Chinese Medicine, the total population and number of TCM hospitals of 31 provinces (excluding China′s Hong Kong, Macao, and Taiwan) in China from 2013 to 2022 were included, as well as the number of beds and practicing (assistant) physicians in TCM hospitals from 2013 to 2021. The grey prediction model was applied to predict the changing trends of the number of TCM hospitals, beds and physicians. Using the global Moran′s I index in spatial autocorrelation analysis, the distribution of TCM hospitals per 10 000 people in China was analyzed by spatial correlation analysis, and local G coefficient was analyzed by local hot spots.Results:From 2013 to 2027, the number of TCM hospitals, beds and practicing (assistant) TCM physicians in China all showed an increasing trend year by year. The number of TCM hospitals per 10 000 people in China showed a spatial correlation between 2013 and 2022 ( P<0.05). The hot spots of TCM hospitals were mainly concentrated in North China and Northeast China, while the cold spots were mainly concentrated in southeast coastal areas and Northwest China. Conclusions:The number of TCM hospitals in China is increasing year by year, but it is necessary to control the reasonable increase and avoid blind expansion. It is necessary to formulate regional policies of TCM hospitals according to local conditions and pay attention to the individuation of policies. Focus on hot and cold areas to promote balanced development of TCM hospitals.

Objective:To understand the development status of maternal and child health care institutions in China from 2012 to 2022, identify the challenges they face, and provide references for further promoting the high-quality development of these institutions.Methods:Data from the China Health Statistics Yearbook (2013—2015), China Health and Family Planning Statistics Yearbook (2016—2017), and China Health and Wellness Statistics Yearbook (2018—2023) were used. Descriptive analysis was conducted on the data related to resource allocation and utilization efficiency, service provision, income and expenditure structure, and operational status of maternal and child health care institutions in China from 2012 to 2022, using methods such as fixed-base growth rate, year-on-year growth rate, and average annual growth rate. Results:From 2012 to 2022, the number of maternal and child health care institutions in China decreased from 3 044 to 3 031. In terms of resource allocation, the average annual growth rates of bed numbers and business-use floor area were 5.404% and 10.923%, respectively, while the average annual growth rate of health professionals was 7.183%. Regarding service provision, the average annual growth rates of outpatient visits and inpatient admissions were 3.954% and 1.572%, respectively. In terms of service efficiency, the bed occupancy rate decreased from 76.9% to 53.9%, and the average number of patients seen per physician per day decreased from 8.85 to 7.30. In terms of income and expenditure and operations, the income-expenditure surplus rate decreased from 9.16% to 5.41%, and the debt-to-asset ratio increased from 27.88% to 33.60%. During the same period, the average annual growth rates of bed numbers and business-use floor area in grassroots maternal and child health care institutions were 4.545% and 10.091%, respectively, lower than the national average. The number of outpatient visits increased from 89.03 million to 126.93 million, with an average annual growth rate of 3.610%, while the number of inpatient admissions decreased from 4.19 million to 3.91 million, with an average annual decline of 0.689%. The income-expenditure surplus rate of grassroots institutions decreased from 7.76% to 4.05%, 1.36 percentage points lower than the national level, and the debt-to-asset ratio increased from 27.53% to 36.37%, higher than the overall level.Conclusions:From 2012 to 2022, maternal and child health care institutions in China achieved certain developments in resource allocation and service scale. However, several challenges remain, including unbalanced resource allocation, decreased utilization efficiency, slowed growth in medical service volume, imbalanced income and expenditure structure, increased asset operation risks, and restricted development of grassroots institutions. It is recommended that relevant management departments and maternal and child health care institutions optimize resource allocation, plan for service transformation and upgrading, expand income sources, strengthen internal financial control, and reinforce the construction of high-quality and efficient maternal and child health care systems to promote the high-quality development of maternal and child health care institutions in China.

Objective:To explore the correlation between metabolic syndrome (MS), serum high-sensitivity C-reactive protein (hs-CRP) levels, their combination and the risk of digestive system malignancies.Methods:A prospective cohort study was conducted in the participants from the Kailuan cohort who took health examination in July 2006. Anthropometric parameters, epidemiological information, and laboratory test results were collected. Incidence and mortality of digestive system malignant tumors were collected through biennial health examinations and questionnaires. The follow-up period ended on December 31, 2021.According to MS status and hs-CRP levels (hs-CRP≤3 or >3 mg/L), the cohort was divided into 4 groups, induding MS -hs-CRP -, MS -hs-CRP +, MS + hs-CRP -, and MS + hs-CRP + group. Chi-squared test, one analysis of variance, and the Kruskal-Wallis H test were used for inter-group comparison among groups. Kaplan-Meier method was used to calculate the cumulative incidence of digestive system malignant tumors, and log-rank test was performed to compare the cumulative incidence among groups. Multivariable Cox proportional hazards regression models were used to evaluate the effects of MS and hs-CRP levels on the overall risk of digestive system malignant tumors, as well as the effects of their combination on the risk of digestive system malignant tumors of different site, and relevant confounding factors were adjusted.A sensitivity analysis was conducted by excluding individuals diagnosed with digestive system malignancies within one year of follow-up, as well as those taking antihypertensive, antidiabetic, or lipid-lowering medications. Results:A total of 92 916 participants were included in this study. Among them, 57 933 cases were in the MS -hs-CRP - group, 10 949 cases in the MS -hs-CRP + group, 18 412 cases in the MS + hs-CRP - group, and 5 622 cases in the MS + hs-CRP + group.The median follow-up period was 15.01 years (14.66 to 15.20 years). By the end of follow-up, these were 1 992 cases of new-onset digestive system malignant tumors. The cumulative incidence rates of digestive system malignant tumors of MS -hs-CRP -, MS -hs-CRP +, MS + hs-CRP -, and MS + hs-CRP + groups were 2.0%(1 164/57 933), 2.3%(249/10 949), 2.4%(440/18 412), and 2.5%(139/5 622), respectively. The difference in the cumulative incidence among the 4 groups was statistically significant ( χ2=14.09, P=0.003).The results of multivariate Cox analysis showed that, after hs-CRP level and other confounding factors were adjusted, the risk of developing digestive system malignant tumors in participants with MS was 21.4% higher than that in those without MS ( HR=1.214 (95% confidence interval (95% CI): 1.086 to 1.340), P<0.001). After MS status and other confounding factors were adjusted, the risk of developing digestive system malignant tumors in participants with high hs-CRP level (>3 mg/L) was 17.2% higher than those with low hs-CRP level (≤3 mg/L) ( HR=1.172 (95% CI: 1.042 to 1.303), P=0.008). After relevant confounding factors were adjusted, the risks of developing digestive system malignant tumors in the MS -hs-CRP +, MS + hs-CRP -, and MS + hs-CRP + groups increased by 17.2%, 21.4%, and 35.9%, respectively, as compared with that of the MS -hs-CRP - group ( HR=1.172 (95% CI: 1.017 to 1.399), P=0.028; HR=1.214 (95% CI: 1.074 to 1.356), P=0.002; HR=1.359 (95% CI: 1.135 to 1.635), P=0.001). Among the 4 groups, the overall risk of developing digestive system malignant tumors of MS + hs-CRP + group was the highest. After relevant confounding factors were adjusted, the risks of colorectal cancer, liver cancer, and pancreatic cancer of the MS + hs-CRP + group increased by 46.2%, 35.7%, and 88.3%, respectively, as compared with those of the MS -hs-CRP - group ( HR=1.462 (95% CI: 1.088 to 1.956), HR=1.357 (95% CI: 1.132 to 2.089), HR=1.883 (95% CI: 1.052 to 3.342)), suggesting that MS combined with high hs-CRP was a significant risk factor for increased incidences of colorectal cancer, liver cancer, and pancreatic cancer ( P=0.012, 0.016 and 0.033). After participants diagnosed with new digestive system malignancies within one year of follow-up and those taking antihypertensive, antidiabetic, or lipid-lowering medications (108 cases, 10 680 cases, 2 344 cases, 906 cases) were excluded, the results of sensitivity analysis indicated the increased risk of digestive system malignant tumors in the MS -hs-CRP +, MS + hs-CRP -, and MS + hs-CRP + groups were 12.1%, 21.4%, 28.7%; 18.2%, 21.4%, 24.8%; 16.4%, 21.4%, 32.2%; 17.3%, 20.4%, 35.8%. Among the 3 groups, the increased risk of developing digestive system malignant tumors of MS + hs-CRP + group was the highest. Conclusion:MS and hs-CRP >3 mg/L are both independent risk factors for developing digestive system malignant tumors, and their combination further increases the risk of developing digestive system malignant tumors.

Objective:To investigate the predictive value of different obesity indicators for colorectal cancer (CRC) risk in different gender populations.Methods:This observational study was conducted within the Kailuan Study (Registration Number: ChiCTR-TNC-11001489). From July 2006 to October 2007, a total of 101,510 employed and retired individuals underwent health examinations, including gastrointestinal disease screening, hematological tests, and questionnaires, at Kailuan General Hospital and its 10 affiliated hospitals. After excluding those with incomplete data, 93,606 participants were included in this study and divided into male (74 852) and female (18 754) groups. CRC incidence was collected through physical examinations and questionnaires every two years. Each participant's follow-up period began at the time of the questionnaire and ended upon CRC diagnosis, death, or December 31, 2021. Body Mass Index (BMI), waist circumference, waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR) were quartiled (Q1, Q2, Q3, Q4), with Q1 serving as the control group. After adjusting for traditional risk factors such as age, total cholesterol, triglycerides, diabetes, hypertension, smoking status, alcohol consumption, and physical exercise, Cox regression models were used to calculate the correlations between BMI, waist circumference, WHR, WHtR, and CRC incidence in both male and female populations.Results:The age of all patients was (51±12) years, BMI was (25.06±3.49) kg/m 2, waist circumference was (86.94±9.97) cm, hip circumference was (97.30±8.81) cm, WHR was 0.89±0.07, and WHtR was 0.52±0.06.Female participants had significantly lower BMI, waist circumference, WHR, and WHtR compared to males, with statistically significant differences (all P<0.05). The mean follow-up duration for all participants was 15.01 (14.10±2.66) years, during which 718 CRC cases were identified, including 626 males (0.83%) and 92 females (0.49%). Cox proportional hazards models for males showed that CRC risk increased with waist circumference from Q3 (HR=1.43, 95%CI: 1.13-1.79, P=0.003) to Q4 (HR=1.45,95%CI: 1.14-1.82, P=0.002). Similarly, CRC risk increased with WHR from Q3 (HR=1.22, 95%CI: 1.01-1.53, P=0.007) to Q4 (HR=1.43, 95%CI: 1.14-1.79, P=0.002) and with WHtR from Q3 (HR=1.37, 95%CI: 1.08-1.74, P=0.009) to Q4 (HR=1.68, 95%CI: 1.33-2.12, P<0.001). For females, CRC risk increased with waist circumference from Q2 (HR=2.37, 95%CI: 1.20-4.67, P=0.012) to Q3 (HR=2.42, 95%CI: 1.21-4.84, P=0.013) but decreased in Q4 ( HR=2.08, 95%CI: 1.02-4.25, P=0.043). CRC risk increased significantly with WHR from Q2 (HR=2.20, 95%CI: 1.11-4.39, P=0.024) to Q3 (HR=2.89, 95%CI: 1.48-5.67, P=0.002) in females but was not statistically significant in Q4 ( P=0.074). Among females, CRC risk also increased significantly with WHtR in Q2 (HR=2.30, 95% CI: 1.16-4.56, P=0.017) and Q4 (HR=2.64, 95%CI: 1.32-5.29, P=0.006). There were no statistically significant differences in CRC risk associated with BMI in either male or female populations (both P>0.05). Conclusion:Waist circumference, WHR, and WHtR were better predictors of CRC risk than BMI in both male and female populations.

Objective:To investigate the influence of diabetes mellitus (DM) and high-sen-sitivity C-reactive protein (Hs-CRP) on the risk of digestive system malignancy.Methods:The pro-spective cohort study was conducted. The clinical data of 93 928 participants who participated health examination in 9 hospitals at Tangshan, including Kailuan General Hospital Affiliated to North China University of Science and Technology et al, in 2006 were selected. According to the presence or absence of DM and the level of Hs-CRP, all participants were divided into 4 groups, including the DM(-)CRP(-) group defined as absence of DM and Hs-CRP ≤3 mg/L, the DM(-)CRP(+) group defined as absence of DM and Hs-CRP>3 mg/L, the DM(+)CRP(-) group defined as presence of DM and Hs-CRP ≤3 mg/L, and the DM(+)CRP(+) group defined as presence of DM and Hs-CRP >3 mg/L. The data of participants were collected by a fixed team of physicians. The first physical examination in 2006 was taken as the starting point for follow-up. The end event of follow-up was defined as the occurrence of digestive system malignancy or death, and the follow-up was up to December 31, 2021. Observation indicators: (1) comparison of clinical data among the 4 groups of participants; (2) the incidence and cumulative incidence rate of digestive system malignancy in participants; (3) influence of DM and Hs-CRP level on the risk of digestive system malignancy; (4) the combined influence of DM and Hs-CRP level on the risk of digestive system malignancy; (5) sensitivity analysis. Comparison of measurement data with normal distribution among multiple groups was conducted using the one-way analysis of variance. For pairwise comparison, least significant difference test was used for homogeneity of variance, and Dunnett′s T3 test was used for heterogeneity of variance. Comparison of measurement data with skewed distribution among multiple groups was conducted using the Kruskal-Wallis rank sum test, and Dunn-Bonferroni test was used for pairwise comparison. Comparison of count data among multiple groups was conducted using the chi-square test, and Bonferroni test was used among multiple comparisons. The Kaplan-Meier method was used to plot cumulative incidence curve, and Log-rank test was used for cumulative incidence rate analysis. The Cox proportional risk model was used for multivariate analysis. All models were adjusted for relevant confounders. Results:(1) Comparison of clinical data among the 4 groups of participants. Of the 93 928 participants, there were 70 743 cases in the DM(-)CRP(-) group, 14 644 cases in the DM(-)CRP(+) group, 6 425 cases in the DM(+)CRP(-) group, and 2 116 cases in the DM(+)CRP(+) group. There were significant differences in gender, age, fasting blood glucose, Hs-CRP, triglyceride, alanine aminotransferase, body mass index, marrital status, smoking, drinking, high school degree or above, physical exercise, high salt diet, high fat diet, positive hepatitis B virus surface antigen, fatty liver, liver cirrhosis, gallstone, taking hypoglycemic drugs, taking lipid-lowering drugs among the 4 groups of participants ( P<0.05). (2) The incidence and cumulative incidence rate of digestive system malignancy in participants. At the end-up of follow-up, 2 008 cases developed digestive system malignancy in the 93 928 participants, including 717 cases of colorectal cancer, 456 cases of liver cancer, 396 cases of gastric cancer, 195 cases of esophageal cancer, 144 cases of pancreatic cancer, 65 cases of gallbladder cancer or extrahepatic cholangiocarcinoma, 35 cases of small bowel cancer. The cumulative incidence rates of digestive system malignancy were 2.19%, 2.42%, 2.86%, 3.59% in participants of the DM(-)CRP(-) group, DM(-)CRP(+) group, DM(+)CRP(-) group, DM(+)CRP(+) group, respectively, showing a significant difference among the 4 groups ( χ2=31.72, P<0.05). (3) Influence of DM and Hs-CRP level on the risk of digestive system malignancy. After adjusting for the confounding factors of the participants, results of multivariate analysis showed that DM and Hs-CRP >3 mg/L were independent influencing factors for the incidence of digestive system malignancy ( hazard ratio=1.32, 1.19, 95% confidence interval as 1.13-1.56, 1.06-1.33, P<0.05). Futher analysis showed that there was a significant difference in interaction between DM and Hs-CRP >3 mg/L ( P<0.05). (4) The combined influence of DM and Hs-CRP level on the risk of digestive system malign-ancy. After adjusting for confounding factors, results of multivariate analysis showed that using the DM(-)CRP(-) group as the control group, the risk of incidence of digestive system malignancy increased in the DM(-)CRP(+) group, DM(+)CRP(-) group, and DM(+)CRP(+) group, respectively ( hazard ratio=1.14, 1.23, 1.79, 95% confidence interval as 1.01-1.29, 1.02-1.48, 1.38-2.31, P<0.05). In the site-specific analysis of digestive system malignancy, using the DM(-)CRP(-) group as the control group, the risk of incidence of liver cancer increased in the DM(-)CRP(+) group ( hazard ratio=1.37, 95% confidence interval as 1.07-1.75, P<0.05), the risk of incidence of liver cancer and pancrea-tic cancer increased in the DM(+)CRP(-) group ( hazard ratio=1.60, 1.74, 95% confidence interval as 1.16-2.21, 1.00-3.02, P<0.05), the risk of incidence of small bowel cancer, pancreatic cancer and colorectal cancer increased in the DM(+)CRP(+) group ( hazard ratio=5.05, 2.31, 2.23, 95% confidence interval as 1.57-16.21, 1.00-5.31, 1.54-3.24, P<0.05). (5) Sensitivity analysis. After adjusting for confounding factors of excluding 3 types of participants (103 cases of digestive system malignancy within 1 year of follow-up, 2 370 cases of taking glucose-lowering drugs, and 915 cases of taking lipid-lowering drugs), results of multivariate analysis showed that using the DM(-)CRP(-) group as the control group, the risk of incidence of digestive system malignancy increased in the DM(+)CRP(-) group, and DM(+)CRP(+) group, respectively ( hazard ratioexcluding cases of digestive system malignancy within 1 year of follow-up=1.26, 1.66, 95% confidence interval as 1.04-1.52, 1.26-2.18, P<0.05; hazard ratioexcluding cases taking glucose-lowering drugs=1.23, 1.75, 95% confidence interval as 1.02-1.49, 1.31-2.33, P<0.05; hazard ratioexcluding cases taking lipid-lowering drugs=1.24, 1.80, 95% confidence interval as 1.03-1.49, 1.39-2.34, P<0.05). Conclusions:DM and Hs-CRP >3 mg/L are independent influencing factors for the incidence of digestive system malignancy. There is an interation and synergistic effect between DM and Hs-CRP to promote the incidence of digestive system malignancy.

Objective:To investigate the predictive value of different obesity indicators for colorectal cancer (CRC) risk in different gender populations.Methods:This observational study was conducted within the Kailuan Study (Registration Number: ChiCTR-TNC-11001489). From July 2006 to October 2007, a total of 101,510 employed and retired individuals underwent health examinations, including gastrointestinal disease screening, hematological tests, and questionnaires, at Kailuan General Hospital and its 10 affiliated hospitals. After excluding those with incomplete data, 93,606 participants were included in this study and divided into male (74 852) and female (18 754) groups. CRC incidence was collected through physical examinations and questionnaires every two years. Each participant's follow-up period began at the time of the questionnaire and ended upon CRC diagnosis, death, or December 31, 2021. Body Mass Index (BMI), waist circumference, waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR) were quartiled (Q1, Q2, Q3, Q4), with Q1 serving as the control group. After adjusting for traditional risk factors such as age, total cholesterol, triglycerides, diabetes, hypertension, smoking status, alcohol consumption, and physical exercise, Cox regression models were used to calculate the correlations between BMI, waist circumference, WHR, WHtR, and CRC incidence in both male and female populations.Results:The age of all patients was (51±12) years, BMI was (25.06±3.49) kg/m 2, waist circumference was (86.94±9.97) cm, hip circumference was (97.30±8.81) cm, WHR was 0.89±0.07, and WHtR was 0.52±0.06.Female participants had significantly lower BMI, waist circumference, WHR, and WHtR compared to males, with statistically significant differences (all P<0.05). The mean follow-up duration for all participants was 15.01 (14.10±2.66) years, during which 718 CRC cases were identified, including 626 males (0.83%) and 92 females (0.49%). Cox proportional hazards models for males showed that CRC risk increased with waist circumference from Q3 (HR=1.43, 95%CI: 1.13-1.79, P=0.003) to Q4 (HR=1.45,95%CI: 1.14-1.82, P=0.002). Similarly, CRC risk increased with WHR from Q3 (HR=1.22, 95%CI: 1.01-1.53, P=0.007) to Q4 (HR=1.43, 95%CI: 1.14-1.79, P=0.002) and with WHtR from Q3 (HR=1.37, 95%CI: 1.08-1.74, P=0.009) to Q4 (HR=1.68, 95%CI: 1.33-2.12, P<0.001). For females, CRC risk increased with waist circumference from Q2 (HR=2.37, 95%CI: 1.20-4.67, P=0.012) to Q3 (HR=2.42, 95%CI: 1.21-4.84, P=0.013) but decreased in Q4 ( HR=2.08, 95%CI: 1.02-4.25, P=0.043). CRC risk increased significantly with WHR from Q2 (HR=2.20, 95%CI: 1.11-4.39, P=0.024) to Q3 (HR=2.89, 95%CI: 1.48-5.67, P=0.002) in females but was not statistically significant in Q4 ( P=0.074). Among females, CRC risk also increased significantly with WHtR in Q2 (HR=2.30, 95% CI: 1.16-4.56, P=0.017) and Q4 (HR=2.64, 95%CI: 1.32-5.29, P=0.006). There were no statistically significant differences in CRC risk associated with BMI in either male or female populations (both P>0.05). Conclusion:Waist circumference, WHR, and WHtR were better predictors of CRC risk than BMI in both male and female populations.

Objective:To investigate the influence of diabetes mellitus (DM) and high-sen-sitivity C-reactive protein (Hs-CRP) on the risk of digestive system malignancy.Methods:The pro-spective cohort study was conducted. The clinical data of 93 928 participants who participated health examination in 9 hospitals at Tangshan, including Kailuan General Hospital Affiliated to North China University of Science and Technology et al, in 2006 were selected. According to the presence or absence of DM and the level of Hs-CRP, all participants were divided into 4 groups, including the DM(-)CRP(-) group defined as absence of DM and Hs-CRP ≤3 mg/L, the DM(-)CRP(+) group defined as absence of DM and Hs-CRP>3 mg/L, the DM(+)CRP(-) group defined as presence of DM and Hs-CRP ≤3 mg/L, and the DM(+)CRP(+) group defined as presence of DM and Hs-CRP >3 mg/L. The data of participants were collected by a fixed team of physicians. The first physical examination in 2006 was taken as the starting point for follow-up. The end event of follow-up was defined as the occurrence of digestive system malignancy or death, and the follow-up was up to December 31, 2021. Observation indicators: (1) comparison of clinical data among the 4 groups of participants; (2) the incidence and cumulative incidence rate of digestive system malignancy in participants; (3) influence of DM and Hs-CRP level on the risk of digestive system malignancy; (4) the combined influence of DM and Hs-CRP level on the risk of digestive system malignancy; (5) sensitivity analysis. Comparison of measurement data with normal distribution among multiple groups was conducted using the one-way analysis of variance. For pairwise comparison, least significant difference test was used for homogeneity of variance, and Dunnett′s T3 test was used for heterogeneity of variance. Comparison of measurement data with skewed distribution among multiple groups was conducted using the Kruskal-Wallis rank sum test, and Dunn-Bonferroni test was used for pairwise comparison. Comparison of count data among multiple groups was conducted using the chi-square test, and Bonferroni test was used among multiple comparisons. The Kaplan-Meier method was used to plot cumulative incidence curve, and Log-rank test was used for cumulative incidence rate analysis. The Cox proportional risk model was used for multivariate analysis. All models were adjusted for relevant confounders. Results:(1) Comparison of clinical data among the 4 groups of participants. Of the 93 928 participants, there were 70 743 cases in the DM(-)CRP(-) group, 14 644 cases in the DM(-)CRP(+) group, 6 425 cases in the DM(+)CRP(-) group, and 2 116 cases in the DM(+)CRP(+) group. There were significant differences in gender, age, fasting blood glucose, Hs-CRP, triglyceride, alanine aminotransferase, body mass index, marrital status, smoking, drinking, high school degree or above, physical exercise, high salt diet, high fat diet, positive hepatitis B virus surface antigen, fatty liver, liver cirrhosis, gallstone, taking hypoglycemic drugs, taking lipid-lowering drugs among the 4 groups of participants ( P<0.05). (2) The incidence and cumulative incidence rate of digestive system malignancy in participants. At the end-up of follow-up, 2 008 cases developed digestive system malignancy in the 93 928 participants, including 717 cases of colorectal cancer, 456 cases of liver cancer, 396 cases of gastric cancer, 195 cases of esophageal cancer, 144 cases of pancreatic cancer, 65 cases of gallbladder cancer or extrahepatic cholangiocarcinoma, 35 cases of small bowel cancer. The cumulative incidence rates of digestive system malignancy were 2.19%, 2.42%, 2.86%, 3.59% in participants of the DM(-)CRP(-) group, DM(-)CRP(+) group, DM(+)CRP(-) group, DM(+)CRP(+) group, respectively, showing a significant difference among the 4 groups ( χ2=31.72, P<0.05). (3) Influence of DM and Hs-CRP level on the risk of digestive system malignancy. After adjusting for the confounding factors of the participants, results of multivariate analysis showed that DM and Hs-CRP >3 mg/L were independent influencing factors for the incidence of digestive system malignancy ( hazard ratio=1.32, 1.19, 95% confidence interval as 1.13-1.56, 1.06-1.33, P<0.05). Futher analysis showed that there was a significant difference in interaction between DM and Hs-CRP >3 mg/L ( P<0.05). (4) The combined influence of DM and Hs-CRP level on the risk of digestive system malign-ancy. After adjusting for confounding factors, results of multivariate analysis showed that using the DM(-)CRP(-) group as the control group, the risk of incidence of digestive system malignancy increased in the DM(-)CRP(+) group, DM(+)CRP(-) group, and DM(+)CRP(+) group, respectively ( hazard ratio=1.14, 1.23, 1.79, 95% confidence interval as 1.01-1.29, 1.02-1.48, 1.38-2.31, P<0.05). In the site-specific analysis of digestive system malignancy, using the DM(-)CRP(-) group as the control group, the risk of incidence of liver cancer increased in the DM(-)CRP(+) group ( hazard ratio=1.37, 95% confidence interval as 1.07-1.75, P<0.05), the risk of incidence of liver cancer and pancrea-tic cancer increased in the DM(+)CRP(-) group ( hazard ratio=1.60, 1.74, 95% confidence interval as 1.16-2.21, 1.00-3.02, P<0.05), the risk of incidence of small bowel cancer, pancreatic cancer and colorectal cancer increased in the DM(+)CRP(+) group ( hazard ratio=5.05, 2.31, 2.23, 95% confidence interval as 1.57-16.21, 1.00-5.31, 1.54-3.24, P<0.05). (5) Sensitivity analysis. After adjusting for confounding factors of excluding 3 types of participants (103 cases of digestive system malignancy within 1 year of follow-up, 2 370 cases of taking glucose-lowering drugs, and 915 cases of taking lipid-lowering drugs), results of multivariate analysis showed that using the DM(-)CRP(-) group as the control group, the risk of incidence of digestive system malignancy increased in the DM(+)CRP(-) group, and DM(+)CRP(+) group, respectively ( hazard ratioexcluding cases of digestive system malignancy within 1 year of follow-up=1.26, 1.66, 95% confidence interval as 1.04-1.52, 1.26-2.18, P<0.05; hazard ratioexcluding cases taking glucose-lowering drugs=1.23, 1.75, 95% confidence interval as 1.02-1.49, 1.31-2.33, P<0.05; hazard ratioexcluding cases taking lipid-lowering drugs=1.24, 1.80, 95% confidence interval as 1.03-1.49, 1.39-2.34, P<0.05). Conclusions:DM and Hs-CRP >3 mg/L are independent influencing factors for the incidence of digestive system malignancy. There is an interation and synergistic effect between DM and Hs-CRP to promote the incidence of digestive system malignancy.

Objective:To analyze the current development status and spatial distribution characteristics of traditional Chinese medicine (TCM) hospitals in China, predict the changing trends of the number of TCM hospitals, the number of beds, and the number of physicians, and provide references for the development of TCM hospitals and the formulation of related policies.Methods:From the official websites of the National Bureau of Statistics and the State Administration of Traditional Chinese Medicine, the total population and number of TCM hospitals of 31 provinces (excluding China′s Hong Kong, Macao, and Taiwan) in China from 2013 to 2022 were included, as well as the number of beds and practicing (assistant) physicians in TCM hospitals from 2013 to 2021. The grey prediction model was applied to predict the changing trends of the number of TCM hospitals, beds and physicians. Using the global Moran′s I index in spatial autocorrelation analysis, the distribution of TCM hospitals per 10 000 people in China was analyzed by spatial correlation analysis, and local G coefficient was analyzed by local hot spots.Results:From 2013 to 2027, the number of TCM hospitals, beds and practicing (assistant) TCM physicians in China all showed an increasing trend year by year. The number of TCM hospitals per 10 000 people in China showed a spatial correlation between 2013 and 2022 ( P<0.05). The hot spots of TCM hospitals were mainly concentrated in North China and Northeast China, while the cold spots were mainly concentrated in southeast coastal areas and Northwest China. Conclusions:The number of TCM hospitals in China is increasing year by year, but it is necessary to control the reasonable increase and avoid blind expansion. It is necessary to formulate regional policies of TCM hospitals according to local conditions and pay attention to the individuation of policies. Focus on hot and cold areas to promote balanced development of TCM hospitals.