In this paper， by referring to ancient and modern literature， the textual research of Inulae Flos has been conducted to clarify the name， origin， production area， quality evaluation， harvesting， processing and others， so as to provide reference and basis for the development and utilization of famous classical formulas containing this herb. After textual research， it could be verified that the medicinal use of Inulae Flos was first recorded in Shennong Bencaojing of the Han dynasty. In successive dynasties， Xuanfuhua has been taken as the official name， and it also has other alternative names such as Jinfeicao， Daogeng and Jinqianhua. The period before the Song and Yuan dynasties， the main origin of Inulae Flos was the Asteraceae plant Inula japonica， and from the Ming and Qing dynasties to the present， I. japonica and I. britannica are the primary source. In addition to the dominant basal species， there are also regional species such as I. linariifolia， I. helianthus-aquatili， and I. hupehensis. The earliest recorded production areas in ancient times were Henan， Hubei and other places， and the literature records that it has been distributed throughout the country since modern times. The medicinal part is its flower， the harvesting and processing method recorded in the past dynasties is mainly harvested in the fifth and ninth lunar months， and dried in the sun， and the modern harvesting is mostly harvested in summer and autumn when the flowers bloom， in order to remove impurities， dry in the shade or dry in the sun. In addition， the roots， whole herbs and aerial parts are used as medicinal materials. In ancient times， there were no records about the quality of Inulae Flos， and in modern times， it is generally believed that the quality of complete flower structure， small receptacles， large blooms， yellow petals， long filaments， many fluffs， no fragments， and no branches is better. Ancient processing methods primarily involved cleaning， steaming， and sun-drying， supplemented by techniques such as boiling， roasting， burning， simmering， stir-frying， and honey-processing. Modern processing focuses mainly on cleaning the stems and leaves before use. Regarding the medicinal properties， ancient texts describe it as salty and sweet in taste， slightly warm in nature， and mildly toxic. Modern studies characterize it as bitter， pungent， and salty in taste， with a slightly warm nature. Its therapeutic effects remain consistent across eras， including descending Qi， resolving phlegm， promoting diuresis， and stopping vomiting. Based on the research results， it is recommended that when developing famous classical formulas containing Inulae Flos， either I. japonica or I. britannica should be used as the medicinal source. Processing methods should follow formula requirements， where no processing instructions are specified， the raw products may be used after cleaning.

In this paper， by referring to ancient and modern literature， the textual research of Inulae Flos has been conducted to clarify the name， origin， production area， quality evaluation， harvesting， processing and others， so as to provide reference and basis for the development and utilization of famous classical formulas containing this herb. After textual research， it could be verified that the medicinal use of Inulae Flos was first recorded in Shennong Bencaojing of the Han dynasty. In successive dynasties， Xuanfuhua has been taken as the official name， and it also has other alternative names such as Jinfeicao， Daogeng and Jinqianhua. The period before the Song and Yuan dynasties， the main origin of Inulae Flos was the Asteraceae plant Inula japonica， and from the Ming and Qing dynasties to the present， I. japonica and I. britannica are the primary source. In addition to the dominant basal species， there are also regional species such as I. linariifolia， I. helianthus-aquatili， and I. hupehensis. The earliest recorded production areas in ancient times were Henan， Hubei and other places， and the literature records that it has been distributed throughout the country since modern times. The medicinal part is its flower， the harvesting and processing method recorded in the past dynasties is mainly harvested in the fifth and ninth lunar months， and dried in the sun， and the modern harvesting is mostly harvested in summer and autumn when the flowers bloom， in order to remove impurities， dry in the shade or dry in the sun. In addition， the roots， whole herbs and aerial parts are used as medicinal materials. In ancient times， there were no records about the quality of Inulae Flos， and in modern times， it is generally believed that the quality of complete flower structure， small receptacles， large blooms， yellow petals， long filaments， many fluffs， no fragments， and no branches is better. Ancient processing methods primarily involved cleaning， steaming， and sun-drying， supplemented by techniques such as boiling， roasting， burning， simmering， stir-frying， and honey-processing. Modern processing focuses mainly on cleaning the stems and leaves before use. Regarding the medicinal properties， ancient texts describe it as salty and sweet in taste， slightly warm in nature， and mildly toxic. Modern studies characterize it as bitter， pungent， and salty in taste， with a slightly warm nature. Its therapeutic effects remain consistent across eras， including descending Qi， resolving phlegm， promoting diuresis， and stopping vomiting. Based on the research results， it is recommended that when developing famous classical formulas containing Inulae Flos， either I. japonica or I. britannica should be used as the medicinal source. Processing methods should follow formula requirements， where no processing instructions are specified， the raw products may be used after cleaning.

Objective:To explore the application value of machine learning prediction model for neural invasion in gallbladder cancer based on enhanced computed tomography (CT) and clinical characteristics.Methods:The retrospective cohort study was conducted. The clinical and imaging data of 502 patients with gallbladder cancer who were admitted to The First Affiliated Hospital of Xinxiang Medical University from January 2010 to June 2024 were collected. There were 171 males and 331 females, aged 65(range, 35?91)years. All patients underwent preoperative abdominal enhanced CT and radical resection. The 502 patients were randomly divided into a training set of 351 cases and a test set of 151 cases at a 7:3 ratio. The training set was used to construct prediction model, and the test set was used to validate prediction model. Observation indicators: (1)neural invasion in gallbladder cancer and influencing factor analysis; (2) construction and validation of machine learning prediction models for neural invasion in gallbladder cancer. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the Mann-Whitney U test. Logistic regression model was performed for univariate and multivariate analyses. Independent influencing factors were incor-porated to construct machine learning models using the standard library modules based on Python 3.9. Receiver operating characteristic (ROC) curves were plotted, and the accuracy, sensitivity, specificity, area under the curve (AUC), precision, F1 score, positive predictive value, negative predic-tive value, and Kappa value were calculated to evaluate the predictive performance of the models. The Delong test was used to assess the differences in AUC among different models in the test set. The Hosmer-Lemeshow test and Brier score were used to evaluate the calibration of the models. Results:(1) Neural invasion in gallbladder cancer and influencing factor analysis. Of the 502 patients with gallbladder cancer, 131 cases had neural invasion, and 371 cases had no neural invasion. Results of multivariate analysis showed that total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-lymphocyte ratio, liver invasion detected by CT, vascular invasion detected by CT, hilar or retroperi-toneal lymph node metastasis detected by CT, and tumor stages T3 and T4 were independent influencing factors for neural invasion in patients with gallbladder cancer [ odds ratios=3.747, 2.395, 3.917, 3.596, 2.805, 2.377, 3.523, 2.774, 5.080, 6.809, 95% confidence interval ( CI) as 1.890?7.430, 1.154?4.971, 2.054?7.472, 1.807?7.155, 1.506?5.225, 1.241?4.553, 1.666?7.449, 1.483?5.189, 2.050?12.589, 2.552?18.168, P<0.05]. (2) Construction and validation of machine learning predic-tion models for neural invasion in gallbladder cancer. Based on the independent influencing factors, seven machine learning models were constructed, including logistic regression, K-nearest neighbors, support vector machine, random forest, decision tree, back-propagation neural network, and gradient boosting machine. The ROC curves of seven machine learning models in the test set were plotted, and the AUC were 0.900(95% CI as 0.851?0.948), 0.741(95% CI as 0.646?0.829), 0.836(95% CI as 0.762?0.895), 0.782(95% CI as 0.701?0.855), 0.839(95% CI as 0.770?0.901), 0.817(95% CI as 0.738?0.887), 0.843(95% CI as 0.770?0.909), respectively. Results of Delong test showed that the logistic regression model had the highest AUC. The sensitivity and specificity of the logistic regression model were 0.868 and 0.805 respectively, indicating the best balance. Results of Hosmer-Lemeshow test showed that the logistic regression model had a good goodness-of-fit ( χ2=5.320, P>0.05). The Brier score of the logistic regression model was relatively low, as 0.168, which verified its calibration advantage. Conclusion:Total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-to-lymphocyte ratio, liver invasion detected by enhanced CT, vascular invasion detected by enhanced CT, hilar or retroperitoneal lymph node metastasis detected by enhanced CT, and tumor stages T3 and T4 are independent influencing factors for nerve invasion in patients with gallbladder cancer. Seven machine learning models are constructed based on enhanced CT and clinical characteristics to predict neural invasion in gallbladder cancer, of which the logistic regression model demonstrates good predictive performance.

Purpose To quantitatively compare the diffusion parameters of mono-and biexponential diffusion-weighted imaging models,and to obtain optimal sets of b-values in diffusion-weighted MRI for obtaining monoexponential apparent diffusion coefficient(ADC)close to perfusion-insensitive intravoxel incoherent motion(IVIM)model ADC(ADCIVIM)in identifying of pulmonary solid benign and malignant lesions.Materials and Methods IVIM was performed in 40 patients with solid nodule and masse in Xi'an Gaoxin Hospital from July 2021 to August 2022 using a 3.0T MR imager.Two experienced diagnostic radiologists subjectively evaluated the IVIM images.A single index model was used to calculate ADC values(ADC0-1 000,ADC20-1 000,ADC50-1 000,ADC80-1 000,ADC150-1 000,ADC300-1 000,ADC500-1 000,ADC300,500,1 000,ADC300,800,1 000,ADC300,500,ADC300,800 and ADC300,1 000).The reference standard ADCIVIM value were calculated using a double-exponential model.The physician's measurements between two physicians were measured.The malignant and benign groups were compared and receiver operator characteristic curve for all parameters were analyzed.Results The measurement consistency of ADC values under b value sets and ADCIVIM was very good,and the intraclass correlation coefficient was more significant than 0.75.The differences between ADCIVIM and ADC values in each b group were statistically significant(t=-6.016--2.500,all P<0.05).The area under the curve(AUC)of ADCIVIM was the largest(0.906),with an optimal threshold of 1.271×10-3 mm2/s,a sensitivity of 80.0%and a specificity of 93.0%.The diagnostic efficacy close to ADCIVIM were ADC300,800(AUC=0.891),ADC50-1 000(AUC=0.827)and ADC300,800,1 000(AUC=0.795),respectively.The optimal threshold of ADC300,800 was 1.140×10-3 mm2/s,the sensitivity and specificity were 80.0%and 93.7%,respectively.Conclusion Combining b-values 300 s/mm2 and 800 s/mm2 is recommended as routine scanning parameters for identifying the insensitive monoexponential ADC between benign and malignant solid pulmonary lesions.

Objective:To explore the application value of machine learning prediction model for neural invasion in gallbladder cancer based on enhanced computed tomography (CT) and clinical characteristics.Methods:The retrospective cohort study was conducted. The clinical and imaging data of 502 patients with gallbladder cancer who were admitted to The First Affiliated Hospital of Xinxiang Medical University from January 2010 to June 2024 were collected. There were 171 males and 331 females, aged 65(range, 35?91)years. All patients underwent preoperative abdominal enhanced CT and radical resection. The 502 patients were randomly divided into a training set of 351 cases and a test set of 151 cases at a 7:3 ratio. The training set was used to construct prediction model, and the test set was used to validate prediction model. Observation indicators: (1)neural invasion in gallbladder cancer and influencing factor analysis; (2) construction and validation of machine learning prediction models for neural invasion in gallbladder cancer. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the Mann-Whitney U test. Logistic regression model was performed for univariate and multivariate analyses. Independent influencing factors were incor-porated to construct machine learning models using the standard library modules based on Python 3.9. Receiver operating characteristic (ROC) curves were plotted, and the accuracy, sensitivity, specificity, area under the curve (AUC), precision, F1 score, positive predictive value, negative predic-tive value, and Kappa value were calculated to evaluate the predictive performance of the models. The Delong test was used to assess the differences in AUC among different models in the test set. The Hosmer-Lemeshow test and Brier score were used to evaluate the calibration of the models. Results:(1) Neural invasion in gallbladder cancer and influencing factor analysis. Of the 502 patients with gallbladder cancer, 131 cases had neural invasion, and 371 cases had no neural invasion. Results of multivariate analysis showed that total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-lymphocyte ratio, liver invasion detected by CT, vascular invasion detected by CT, hilar or retroperi-toneal lymph node metastasis detected by CT, and tumor stages T3 and T4 were independent influencing factors for neural invasion in patients with gallbladder cancer [ odds ratios=3.747, 2.395, 3.917, 3.596, 2.805, 2.377, 3.523, 2.774, 5.080, 6.809, 95% confidence interval ( CI) as 1.890?7.430, 1.154?4.971, 2.054?7.472, 1.807?7.155, 1.506?5.225, 1.241?4.553, 1.666?7.449, 1.483?5.189, 2.050?12.589, 2.552?18.168, P<0.05]. (2) Construction and validation of machine learning predic-tion models for neural invasion in gallbladder cancer. Based on the independent influencing factors, seven machine learning models were constructed, including logistic regression, K-nearest neighbors, support vector machine, random forest, decision tree, back-propagation neural network, and gradient boosting machine. The ROC curves of seven machine learning models in the test set were plotted, and the AUC were 0.900(95% CI as 0.851?0.948), 0.741(95% CI as 0.646?0.829), 0.836(95% CI as 0.762?0.895), 0.782(95% CI as 0.701?0.855), 0.839(95% CI as 0.770?0.901), 0.817(95% CI as 0.738?0.887), 0.843(95% CI as 0.770?0.909), respectively. Results of Delong test showed that the logistic regression model had the highest AUC. The sensitivity and specificity of the logistic regression model were 0.868 and 0.805 respectively, indicating the best balance. Results of Hosmer-Lemeshow test showed that the logistic regression model had a good goodness-of-fit ( χ2=5.320, P>0.05). The Brier score of the logistic regression model was relatively low, as 0.168, which verified its calibration advantage. Conclusion:Total bilirubin, carcinoembryonic antigen, CA199, CA125, neutrophil-to-lymphocyte ratio, liver invasion detected by enhanced CT, vascular invasion detected by enhanced CT, hilar or retroperitoneal lymph node metastasis detected by enhanced CT, and tumor stages T3 and T4 are independent influencing factors for nerve invasion in patients with gallbladder cancer. Seven machine learning models are constructed based on enhanced CT and clinical characteristics to predict neural invasion in gallbladder cancer, of which the logistic regression model demonstrates good predictive performance.

Plastics are widely used in all areas of human life,providing convenience while also causing serious en-vironmental pollution problems.Microplastic pollution is one of its derivative problems.Microplastics are plastic parti-cles with a diameter of less than 5 mm.They are currently widely present in the environment,so humans are at considera-ble risk of exposure to microplastics.Humans are mainly exposed to microplastics through the respiratory tract,digestive tract and skin.When exposed to a large number of microplastics,some of them will enter the body and be transported throughout the body via the bloodstream,accumulating in multiple tissues and organs.A significant amount of microplas-tics has also been detected in the cardiovascular system.This paper systematically describes human exposure to and dam-age by microplastics,highlighting the distribution and pathological damage of microplastics in the cardiovascular system.The pathological mechanisms of cardiovascular damage caused by microplastics are analyzed,and relevant clinical research progress is followed.This paper aims to evaluate the pathological risk of microplastics from the perspective of cardiovascu-lar damage,and provide a basis for disease prevention and scientific prevention and control of microplastic pollution.

Purpose To quantitatively compare the diffusion parameters of mono-and biexponential diffusion-weighted imaging models,and to obtain optimal sets of b-values in diffusion-weighted MRI for obtaining monoexponential apparent diffusion coefficient(ADC)close to perfusion-insensitive intravoxel incoherent motion(IVIM)model ADC(ADCIVIM)in identifying of pulmonary solid benign and malignant lesions.Materials and Methods IVIM was performed in 40 patients with solid nodule and masse in Xi'an Gaoxin Hospital from July 2021 to August 2022 using a 3.0T MR imager.Two experienced diagnostic radiologists subjectively evaluated the IVIM images.A single index model was used to calculate ADC values(ADC0-1 000,ADC20-1 000,ADC50-1 000,ADC80-1 000,ADC150-1 000,ADC300-1 000,ADC500-1 000,ADC300,500,1 000,ADC300,800,1 000,ADC300,500,ADC300,800 and ADC300,1 000).The reference standard ADCIVIM value were calculated using a double-exponential model.The physician's measurements between two physicians were measured.The malignant and benign groups were compared and receiver operator characteristic curve for all parameters were analyzed.Results The measurement consistency of ADC values under b value sets and ADCIVIM was very good,and the intraclass correlation coefficient was more significant than 0.75.The differences between ADCIVIM and ADC values in each b group were statistically significant(t=-6.016--2.500,all P<0.05).The area under the curve(AUC)of ADCIVIM was the largest(0.906),with an optimal threshold of 1.271×10-3 mm2/s,a sensitivity of 80.0%and a specificity of 93.0%.The diagnostic efficacy close to ADCIVIM were ADC300,800(AUC=0.891),ADC50-1 000(AUC=0.827)and ADC300,800,1 000(AUC=0.795),respectively.The optimal threshold of ADC300,800 was 1.140×10-3 mm2/s,the sensitivity and specificity were 80.0%and 93.7%,respectively.Conclusion Combining b-values 300 s/mm2 and 800 s/mm2 is recommended as routine scanning parameters for identifying the insensitive monoexponential ADC between benign and malignant solid pulmonary lesions.

Cardiovascular disease is one of the most common causes of death in the world,and related western drugs usually have obvious side effects.Traditional Chinese medicine,characterized by its multi-component and multi-target,has unique advantage in the prevention and treatment of cardiovascular diseases.Cornus Officinal is a histor-ically used plants with medicinal and edible properties.Ancient books and modern literatures have shown that Cor-nus Officinal has the potential to prevent and treat cardiovascular diseases.With the in-depth study of the chemical composition and pharmacological effect of Cornus Officinal,its pharmacodynamic substances and mechanism in the prevention and treatment of cardiovascular diseases are gradually clear.This article reviews the pathways and targets of Cornus Officinal in the prevention and treatment of cardiovascular diseases in recent years,and summarizes its potential effective components,in order to provide theoretical support for the clinical application and traditional Chinese medicine development.

Objective: To investigate the association of sleep and circadian rhythm disruption indicators (including chronotype, sleep duration, and social jetlag) with co-occurring depressive and anxiety symptoms among primary and secondary school students, so as to provide a reference for promoting their mental health. Methods: In October 2023, a total of 15 944 primary and secondary school students were recruited from Nanjing, using a stratified cluster random sampling method. The Morning and Evening Questionnaire-5, Center for Epidemiological Studies Depression, and Generalized Anxiety Disorder-7 were used for the survey. Chi-square test was employed for intergroup comparisons, and Logistic regression model was applied to analyze the independent and joint effects of sleep related factors on comorbid symptoms of depressive and anxiety among primary and middle school students. Results: The prevalence of co-occurring depressive and anxiety symptoms among primary and secondary school students in Nanjing was 16.9%. After adjusting for covariates, Logistic regression analysis revealed significant independent associations between evening chronotype ( OR=6.55, 95%CI =5.59-7.68), insufficient sleep duration ( OR=3.05, 95%CI =2.60-3.59), and social jetlag ≥2 h ( OR= 2.09 , 95%CI =1.85-2.37) with comorbid symptoms of depressive and anxiety among students (all P <0.05). Concurrent of evening chronotype and insufficient sleep ( OR=7.54, 95%CI =3.55-16.01), as well as evening chronotype and social jetlag ≥2 h ( OR=4.18, 95%CI =3.01-5.81), were associated with an increased risk of co-occurring depressive and anxiety symptoms (both P < 0.05 ). In the female and high school student subgroups, the combination of evening chronotype and insufficient sleep or social jetlag ≥2 h showed stronger joint effects on co-occurring depressive and anxiety symptoms [ OR (95% CI )=8.46(3.25-22.04) and 15.90(3.66-69.08); 7.87(4.90-12.65) and 4.85(3.10-7.59), respectively; all P <0.05]. Conclusions Evening chronotype, insufficient sleep, and social jetlag≥2 h may serve as risk factors for comorbid symptoms of depressive and anxiety in school aged populations. Paying attention to the coexistence of multiple sleep related risk factors may help mitigate the occurrence of emotional disorders in this demographic.