AIM To study the chemical constituents from the branches and leaves of Michelia yunnanensis Franch.ex Finet & Gagnep.and their anti-inflammatory activities.METHODS The methanol extract was isolated and purified by silica gel,MCI,Sephadex LH-20 and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.Their anti-inflammatory activities were evaluated by RAW264.7 model.RESULTS Twenty compounds were isolated and identified as dihydrodehydrodiconifenyl alcohol(1),8-hydroxypinoresinol(2),lariciresinol(3),isolariciresinol(4),(7S,8R)-4-hydroxy-3,3',5'-trimethoxy-8',9'-dinor-8,4'-oxyneoligna-7,9-diol-7'-aldehyde(5),thero-2,3-bis-(4-hydroxy-3-methoxypheyl)-3-methoxy-propanol(6),evofolin B(7),(E)-p-coumaryl alcohol γ-O-methyl ether(8),ω-hydroxypropioguaiacone(9),sinapaldehyde(10),isoscopoletin(11),6-hydroxy-5,7-dimethoxycoumarin(12),2α,3α-dihydroxy-2-methylbutyrolactone(13),6-hydroxy-3(1-hydroxy-1-methylethyl)-6-methyl-2-cyclohexen-1-one(14),benzofuran-2-carboxaldehyde(15),3,4-dihydroxy-5-methoxybenzaldehyde(16),3,5-dimethoxy-4-hydroxybenzaldehyde(17),3,4-dihydroxybenzaldehyde(18),3,4-dihydroxybenzoic methyl ester(19),vanillic acid(20).The inhibition rate of compound 1 on NO was 45.39％±0.32％.CONCLUSION Compounds 1-16,18-20 are first isolated from this plant.Compound 1 has anti-inflammatory activity.

OBJECTIVE To observe the effect of early warning mechanisms based on nosocomial infection surveil-lance system on precise prevention and control of spread of multidrug-resistant organisms(MDROs)infections in intensive care unit of neurosurgery department.METHODS The dynamic monitoring was performed in the ICU of neurosurgery department of a three-A general hospital by using real-time surveillance system of nosocomial infec-tion from Jan.2023 to Dec.2023.The warning information was sent to the doctor' station immediately once the MDROs infections were detected,the detection of drug-resistant bacteria was carried out for the relevant personnel and their surroundings,and the precise prevention and control measures were implemented.The impact on the iso-lation rate of MDROs,isolation rate of MDROs from the relevant personnel and surroundings,incidence of noso-comial MDROs infections and incidence of surgical site infection was observed after the measures were taken.RESULTS The carrying rate of MDROs was decreased from 21.80％to 5.74％among the relevant personnel after the precise prevention and control measures were taken(P＜0.05),which was improved remarkably among the doctors(decreasing from 21.43％to 6.07％),nurses(decreasing from 23.68％to 3.98％),nursing workers(decreasing from 25.15％to 8.65％),and cleaning staff(decreasing from 25.49％to 5.05％)(P＜0.001).The isolation rate of MDROs was decreased from 20.77％to 7.12％among the surroundings(P＜0.05),which was decreased most remarkably among head cushions(decreasing from 32.28％to 12.13％),bed rails(decreasing from 16.70％to 3.27％),ventilator panels(decreasing from 23.00％to 6.95％)and bedside floors(decreasing from 31.99％to 9.96％)(P＜0.001).The incidence of nosocomial MDROs infections declined from 3.84％to 1.71％(P＜0.05),with incidence of incision infections decreasing from 4.00％to 1.45％(P＜0.05),the inci-dence of intracranial infections decreasing from 2.38％to 0.56％(P＜0.05).CONCLUSION The detection of drug-resistant bacteria is carried out immediately and precisely for the patients with MDROs infection,relevant personnel and their surroundings by using the real-time surveillance system of nosocomial infection,which achieve remarkable effect on prevention of transmission of MDROs and reduction of incidence of hospital-associated infec-tions and surgical site infections.

Objective:To investigate the predictive efficacy of Delta radiomics for the patholo-gical complete remission (pCR) of pancreatic cancer after total neoadjuvant therapy (TNT).Methods:The retrospective cohort study was conducted. The clinicopathological data of 263 patients with pancreatic cancer who were admitted to Henan Provincial People′s Hospital (Zhengzhou University People's Hspital) from January 2019 to September 2024 were collected. There were 166 males and 97 females, aged (56±12)years. All patients underwent TNT. The 263 patients were randomly divided into a training set of 184 cases and a test set of 79 cases using a 7∶3 random seed count. The training set was used to construct the prediction model, and the test set was used to validate the performance of the prediction model. Observation indicators: (1) postoperative and follow-up condi-tions; (2) imaging feature selection and model construction; (3) evaluation of predictive efficacy of different radiomic models. Comparison of measurement data with normal distribution between groups was conducted using the t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. The Kaplan-Meier method was used to calculate the survival rate and draw survival curve. The Log-rank test was used for survival analysis. The perfor-mance of the prediction model for pCR after TNT was evaluated using the receiver operator charac-teristic (ROC) curve, precision-recall (P-R) curve and Bootstrap method, along with the calculation of area under the curve (AUC), precision rate, recall rate, F1-score. Results:(1) Postoperative and follow-up conditions. All 263 patients underwent surgery after TNT, with pathological examination revealing 124 cases of pCR (86 cases in the training set, 38 cases in the test set) and 139 cases of non-pCR (98 cases in the training set, 41 cases in the test set), respectively. All 263 patients were followed up for 6(range, 3-12) months after surgery, of which 15 cases (4 cases of pCR and 11 cases of non-pCR) were lost to follow-up or died due to non-tumor reasons within 6 months after surgery. The postoperative 6-month recurrence-free survival rate of 124 pCR patients and 139 non-pCR patients were 80% and 50%, respectively, showing a significant difference between the two groups of patients ( χ2=22.84, P<0.05). (2) Imaging feature selection and model construction. Construction of the traditional radiology model: based on the response evaluation criteria in solid tumors 1.1, the Logistic regression model was constructed using the relative shrinkage (D%) as a predictive variable. The AUC of traditional radiology model was 0.72 [95% confidence interval ( CI) as 0.63?0.81] in the training set and 0.75 (95% CI as 0.66?0.84) in the test set, respectively. Construction of the Delta radiomics model: 10 non-zero coefficient features were selected. The Delta radiomics models were constructed by using the regularized Logistic regression, random forest, gradient boosting machine, and support vector machine algorithms through using selected features as input variables. (3) Evaluation of predictive efficacy of different radiomic models. The AUC of Delta radiomics model constructed by regularized Logistic regression algorithm in the test set for predicting pCR in pancreatic cancer after TNT was 0.90, higher than that of the random forest algorithm, gradient boosting machine algorithm, support vector machine algorithm (AUC as 0.81, 0.81, 0.83), and higher than that of the traditional radiology model (AUC as 0.72). Results of Bootstrap method revealed significant differences in the predictive efficacy of Delta radiomics model constructed by regularized Logistic regression algorithm compared to the Delta radiomics model constructed by random forest algorithm, gradient boosting machine algorithm, support vector machine algorithm and the tradi-tional radiology model (95% CI as 0.03?0.16, 0.03?0.16, 0.03?0.13, 0.08?0.29, P<0.05). The regularized Logistic regression algorithm within the Delta radiomics model demonstrated the best overall performance among the above models evaluated. Conclusion:Compared to the traditional radiology model, the Delta radiomics model offers superior efficacy in predicting pCR of pancreatic cancer after TNT, in which the regularized Logistic regression algorithm demonstrates the best overall performance metrics.

Objective:To investigate the optimization of inferior vena cava imaging using dual-layer spectral detector CT (DLCT) virtual monoenergetic images (VMI) combined with multiphase scanning.Methods:A retrospective analysis was conducted on the imaging data of 184 patients who underwent inferior vena cava imaging using dual-layer detector spectral CT at Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, from January 2021 to October 2024. Each patient underwent multiphase scanning (60, 80, and 120 s after contrast injection were referred to as the first, second, and third phases, respectively). The images were reconstructed into conventional 120 kVp polyenergetic image (PI) and VMIs at 40, 50, 60, 70, and 80 keV. Image quality of 120 kVp PI and VMI for each phase was evaluated. The objective image quality indicators included CT value, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and noise. Comparisons of the above indictors within the same phase were performed using repeated measures ANOVA or the Friedman test, while comparisons between different phases were conducted using one-way ANOVA or the Kruskal-Wallis test.Results:At the same phase, the CT value, SNR, and CNR of the 40 keV VMI were higher than those of other energy level VMIs and PI (all P<0.001). The SNR of the 40 keV VMI in the third phase was significantly higher than in the first phase ( P<0.05), while there was no significant difference between the first and second phases ( P>0.05). The standard deviation (SD) of the 40 keV VMI in the third phase was significantly lower than that in the first and second phases (all P<0.05). The subjective scores for the 40 keV VMI were higher than those for other energy level VMIs and PI at the same phase ( P<0.001). The subjective scores for the 40 keV VMI in the third phase were higher than those in the second and first phases ( P<0.001). The percentage of scores≥4 in the third phase (77.17%,142/184) was significantly higher than those in the first phase (28.26%,52/184) and second phase (61.96%,114/184) ( P<0.001). Conclusion:In inferior vena cava imaging, the 40 keV VMI, combined with the optimal phase (120 s delay), effectively optimizes image quality.

Objective:To assess the impact of urban-rural health insurance integration on the non-agricultural employment of rural labor in China.Methods:Using CHFS data(2011-2019)and a DID model,this study analyzed the impact on non-agricultural employment probability and quality(wage income,job stability)and examined the effect of different integration models.Results:The integration of urban and rural health insurance significantly increases the probability and quality of non-agricultural employment among rural labor.This effect operates through three key mechanisms:easing household liquidity constraints,improving health-related human capital,and expanding job search scope.Moreover,the"unified urban-rural tier"model proves more effective than the"one-system-multi-tiers"model in promoting non-agricultural employment and enhancing job quality.Conclusion:The integration positively contributes to improving rural labor allocation efficiency and mobilizing surplus rural labor capacity.Policy recommendations include accelerating the adoption of the"unified urban-rural tier"model and further refining the urban-rural resident health insurance system.

AIM To study the chemical constituents from the branches and leaves of Michelia yunnanensis Franch.ex Finet & Gagnep.and their anti-inflammatory activities.METHODS The methanol extract was isolated and purified by silica gel,MCI,Sephadex LH-20 and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.Their anti-inflammatory activities were evaluated by RAW264.7 model.RESULTS Twenty compounds were isolated and identified as dihydrodehydrodiconifenyl alcohol(1),8-hydroxypinoresinol(2),lariciresinol(3),isolariciresinol(4),(7S,8R)-4-hydroxy-3,3',5'-trimethoxy-8',9'-dinor-8,4'-oxyneoligna-7,9-diol-7'-aldehyde(5),thero-2,3-bis-(4-hydroxy-3-methoxypheyl)-3-methoxy-propanol(6),evofolin B(7),(E)-p-coumaryl alcohol γ-O-methyl ether(8),ω-hydroxypropioguaiacone(9),sinapaldehyde(10),isoscopoletin(11),6-hydroxy-5,7-dimethoxycoumarin(12),2α,3α-dihydroxy-2-methylbutyrolactone(13),6-hydroxy-3(1-hydroxy-1-methylethyl)-6-methyl-2-cyclohexen-1-one(14),benzofuran-2-carboxaldehyde(15),3,4-dihydroxy-5-methoxybenzaldehyde(16),3,5-dimethoxy-4-hydroxybenzaldehyde(17),3,4-dihydroxybenzaldehyde(18),3,4-dihydroxybenzoic methyl ester(19),vanillic acid(20).The inhibition rate of compound 1 on NO was 45.39％±0.32％.CONCLUSION Compounds 1-16,18-20 are first isolated from this plant.Compound 1 has anti-inflammatory activity.

OBJECTIVE To observe the effect of early warning mechanisms based on nosocomial infection surveil-lance system on precise prevention and control of spread of multidrug-resistant organisms(MDROs)infections in intensive care unit of neurosurgery department.METHODS The dynamic monitoring was performed in the ICU of neurosurgery department of a three-A general hospital by using real-time surveillance system of nosocomial infec-tion from Jan.2023 to Dec.2023.The warning information was sent to the doctor' station immediately once the MDROs infections were detected,the detection of drug-resistant bacteria was carried out for the relevant personnel and their surroundings,and the precise prevention and control measures were implemented.The impact on the iso-lation rate of MDROs,isolation rate of MDROs from the relevant personnel and surroundings,incidence of noso-comial MDROs infections and incidence of surgical site infection was observed after the measures were taken.RESULTS The carrying rate of MDROs was decreased from 21.80％to 5.74％among the relevant personnel after the precise prevention and control measures were taken(P＜0.05),which was improved remarkably among the doctors(decreasing from 21.43％to 6.07％),nurses(decreasing from 23.68％to 3.98％),nursing workers(decreasing from 25.15％to 8.65％),and cleaning staff(decreasing from 25.49％to 5.05％)(P＜0.001).The isolation rate of MDROs was decreased from 20.77％to 7.12％among the surroundings(P＜0.05),which was decreased most remarkably among head cushions(decreasing from 32.28％to 12.13％),bed rails(decreasing from 16.70％to 3.27％),ventilator panels(decreasing from 23.00％to 6.95％)and bedside floors(decreasing from 31.99％to 9.96％)(P＜0.001).The incidence of nosocomial MDROs infections declined from 3.84％to 1.71％(P＜0.05),with incidence of incision infections decreasing from 4.00％to 1.45％(P＜0.05),the inci-dence of intracranial infections decreasing from 2.38％to 0.56％(P＜0.05).CONCLUSION The detection of drug-resistant bacteria is carried out immediately and precisely for the patients with MDROs infection,relevant personnel and their surroundings by using the real-time surveillance system of nosocomial infection,which achieve remarkable effect on prevention of transmission of MDROs and reduction of incidence of hospital-associated infec-tions and surgical site infections.

Objective:To assess the impact of urban-rural health insurance integration on the non-agricultural employment of rural labor in China.Methods:Using CHFS data(2011-2019)and a DID model,this study analyzed the impact on non-agricultural employment probability and quality(wage income,job stability)and examined the effect of different integration models.Results:The integration of urban and rural health insurance significantly increases the probability and quality of non-agricultural employment among rural labor.This effect operates through three key mechanisms:easing household liquidity constraints,improving health-related human capital,and expanding job search scope.Moreover,the"unified urban-rural tier"model proves more effective than the"one-system-multi-tiers"model in promoting non-agricultural employment and enhancing job quality.Conclusion:The integration positively contributes to improving rural labor allocation efficiency and mobilizing surplus rural labor capacity.Policy recommendations include accelerating the adoption of the"unified urban-rural tier"model and further refining the urban-rural resident health insurance system.

Objective:To investigate the predictive efficacy of Delta radiomics for the patholo-gical complete remission (pCR) of pancreatic cancer after total neoadjuvant therapy (TNT).Methods:The retrospective cohort study was conducted. The clinicopathological data of 263 patients with pancreatic cancer who were admitted to Henan Provincial People′s Hospital (Zhengzhou University People's Hspital) from January 2019 to September 2024 were collected. There were 166 males and 97 females, aged (56±12)years. All patients underwent TNT. The 263 patients were randomly divided into a training set of 184 cases and a test set of 79 cases using a 7∶3 random seed count. The training set was used to construct the prediction model, and the test set was used to validate the performance of the prediction model. Observation indicators: (1) postoperative and follow-up condi-tions; (2) imaging feature selection and model construction; (3) evaluation of predictive efficacy of different radiomic models. Comparison of measurement data with normal distribution between groups was conducted using the t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. The Kaplan-Meier method was used to calculate the survival rate and draw survival curve. The Log-rank test was used for survival analysis. The perfor-mance of the prediction model for pCR after TNT was evaluated using the receiver operator charac-teristic (ROC) curve, precision-recall (P-R) curve and Bootstrap method, along with the calculation of area under the curve (AUC), precision rate, recall rate, F1-score. Results:(1) Postoperative and follow-up conditions. All 263 patients underwent surgery after TNT, with pathological examination revealing 124 cases of pCR (86 cases in the training set, 38 cases in the test set) and 139 cases of non-pCR (98 cases in the training set, 41 cases in the test set), respectively. All 263 patients were followed up for 6(range, 3-12) months after surgery, of which 15 cases (4 cases of pCR and 11 cases of non-pCR) were lost to follow-up or died due to non-tumor reasons within 6 months after surgery. The postoperative 6-month recurrence-free survival rate of 124 pCR patients and 139 non-pCR patients were 80% and 50%, respectively, showing a significant difference between the two groups of patients ( χ2=22.84, P<0.05). (2) Imaging feature selection and model construction. Construction of the traditional radiology model: based on the response evaluation criteria in solid tumors 1.1, the Logistic regression model was constructed using the relative shrinkage (D%) as a predictive variable. The AUC of traditional radiology model was 0.72 [95% confidence interval ( CI) as 0.63?0.81] in the training set and 0.75 (95% CI as 0.66?0.84) in the test set, respectively. Construction of the Delta radiomics model: 10 non-zero coefficient features were selected. The Delta radiomics models were constructed by using the regularized Logistic regression, random forest, gradient boosting machine, and support vector machine algorithms through using selected features as input variables. (3) Evaluation of predictive efficacy of different radiomic models. The AUC of Delta radiomics model constructed by regularized Logistic regression algorithm in the test set for predicting pCR in pancreatic cancer after TNT was 0.90, higher than that of the random forest algorithm, gradient boosting machine algorithm, support vector machine algorithm (AUC as 0.81, 0.81, 0.83), and higher than that of the traditional radiology model (AUC as 0.72). Results of Bootstrap method revealed significant differences in the predictive efficacy of Delta radiomics model constructed by regularized Logistic regression algorithm compared to the Delta radiomics model constructed by random forest algorithm, gradient boosting machine algorithm, support vector machine algorithm and the tradi-tional radiology model (95% CI as 0.03?0.16, 0.03?0.16, 0.03?0.13, 0.08?0.29, P<0.05). The regularized Logistic regression algorithm within the Delta radiomics model demonstrated the best overall performance among the above models evaluated. Conclusion:Compared to the traditional radiology model, the Delta radiomics model offers superior efficacy in predicting pCR of pancreatic cancer after TNT, in which the regularized Logistic regression algorithm demonstrates the best overall performance metrics.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.