Objective To carry out a proficiency testing of content determination of geniposide in Gardeniae fructus,evaluate the content determination ability of index components in traditional Chinese medicine in the laboratory of inspection and detection in drug-related fields,and improve the quality control ability of content determination of related laboratories.Methods The laboratory's capability-verification activities were conducted based on the CNAS-RL02 Rules for Proficiency Testing and ISO/IEC 17043 Conformity Assessment-General Requirements for Proficiency Testing.After preparing the sample,the results of homogeneity and stability tests were analyzed according to CNAS-GL003 Guidance on Evaluating the Homogeneity and Stability of Samples Used for Proficiency Testing.After the test results were qualified,they were used as proficiency testing samples and randomly distributed to participants.The results were collected,and the robust statistical method and the Z scores were used to analyze the results of these laboratories'reports.Results 403 laboratories in this proficiency testing program reported the results,of which 367 results were acceptable,accounting for 91.07％,17(4.22％)laboratories obtained suspicious results,and 19 laboratories gave unsatisfactory results,with the dissatisfaction rate of 4.71％.Conclusion The majority of the 403 participant laboratories have the ability to determine the content of geniposide in Gardeniae fructus by HPLC and the laboratory testing ability and quality management level of the drug monitoring system are high.This proficiency testing provides a basis for understanding the technical reserve capacity and management level of China's pharmaceutical inspection and testing laboratories,and provides technical support for future government supervision.

Polygoni multiflori radix praeparata is a processed product of Polygoni multiflori radix(Polygonum multiflorum Thunb.),and its main components include stilbene glycosides,anthraquinones,flavonoids,alkaloids,phenolic acids,etc.It has antioxidant,antianemic,anti-tumor,hypoglycemic,anti-inflammatory effects,etc,and is widely used in clinical practice.The processing technology is mainly stewinging with black bean juice,steaming,processing for 9 times and braising and simmering.After processing,the color deepens and the content of composition changes.By consulting domestic and foreign literature,the research on Polygoni multiflori radix praeparata is not comprehensive enough compared with Polygoni multiflori radix.Therefore,this paper mainly summarizes the processing technology,chemical composition and pharmacological activity of Polygoni multiflori radix preparata reported in the past 20 years,and provides a reference for further development of Polygoni multiflori radix preparata.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective:To test a new method with thermoluminescent dosimeters (TLDs) to determine the β-ray and γ-ray doses of β-γ mixed radiation fields.Methods:TLDs for personal dose monitoring were irradiated in the reference radiation fields of β-rays ( 90Sr/ 90Y, 85Kr) and γ-rays ( 137Cs). Across the range of 2.0-15.0 mSv, the linearity of TLD response and normalized response with respect to 137Cs were determined at the depths of Hp(10) and Hp(0.07). Using TLD detector readings at the depths of Hp(10) and Hp(0.07), β- and γ-ray doses in the mixed radiation fields were determined, and the result were verified. Results:For Hp(10) and Hp(0.07) under γ-ray exposure and Hp(0.07) under β-ray exposure, the coefficient of determination ( R2) were all >0.998. For the 90Sr/ 90Y source and 85Kr source, the average values of response values normalized with respect to 137Cs at different doses were 1.14 and 0.18, respectively; and the normalized response values derived from the slope values of the dose-response curves for the two sources were 1.17 and 0.18, respectively. The ratios of measurements of Hp(10) to Hp(0.07), kR, for the 85Kr source were close to 0, while the kR values for the 137Cs source were close to 1. Using the average value of kR and the slope value of kR for calculation, the maximum relative deviations between the calculated values and conventional values for Hp(10) γ, Hp(0.07) γ, and Hp(0.07) β were 6.1% and 6.0%, respectively. Conclusions:This method can be applied for the assessment of β-ray and γ-ray doses in β-γ mixed radiation fields of a single β source and single γ source.

Objective:To investigate the relationship between intracerebral high-density foci and progressive stroke (PS) morbidity by using dual-energy CT, which can quantify the intracerebral high-density foci of patients with acute ischemic stroke after endovascular treatment.Methods:Ninety-two patients with acute ischemic stroke who received interventional treatment in Gaozhou People′s Hospital from May 2019 to August 2020, and underwent dual-energy CT scan immediately after intervention, were analyzed. The patients were divided into PS group ( n=35) and non-PS group ( n=57) according to the National Institutes of Health Stroke Scale (NIHSS) score, and the patients whose NIHSS score increased≥4 points within 72 hours of stroke were included in the PS group, while the patients whose NIHSS score increased<4 points were included in the non-PS group. The clinical data, volume of high-density foci and CT values were compared between the 2 groups. Logistic regression analysis was used to adjust for confounding factors and screen for risk factors. The correlations of the admission NIHSS score, presence and volume of high-density lesions, maximum CT (CTmax) value and average CT (CTave) value with the onset of PS were analyzed, and the receiver operating characteristic curve was used to screen predictive indicators of PS. Results:In the PS group, the NIHSS score (18.80±8.50 vs 14.40±9.58, t=2.229, P=0.028), proportion of high-density foci [29/35(82.9%) vs 32/57 (56.1%), χ 2=6.928, P=0.008], high-density focal volume [13.23 (39.33) cm 3vs 0.76 (9.82) cm 3, U=1 440.000, P<0.001], CTmax value [80.00 (92.00) HU vs 65.00 (87.50) HU, U=1 337.000, P=0.005] and CTave value [53.48 (23.79) HU vs 45.94 (55.11) HU, U=1 345.000, P=0.004] were higher than those in the non-PS group. The NIHSS score ( OR=1.054, 95% CI 1.004-1.106, P=0.033; rs=0.255, 95% CI 0.051-0.447, P=0.014), presence of high-density foci ( OR=3.776, 95% CI 1.358-10.503, P=0.011; rs=0.274, 95% CI 0.093-0.460, P=0.008), high-density focal volume ( OR=1.026, 95% CI 1.003-1.049, P=0.027; rs=0.381, 95% CI 0.183-0.560, P<0.001), CTmax value ( OR=1.006, 95% CI 1.001-1.011, P=0.014; rs=0.292, 95% CI 0.088-0.475, P=0.005) and CTave value ( OR=1.021, 95% CI 1.007-1.035, P=0.004; rs=0.299, 95% CI 0.092-0.484, P=0.004) were all risk factors affecting PS morbidity and were positively correlated with PS morbidity. The area under the receiver operating characteristic curve of NIHSS score, high-density lesion volume, CTmax value, and CTave value to predict the onset of PS was 0.652, 0.722, 0.670 and 0.674, respectively. The volume of high-density lesions had moderate predictive value for the onset of PS. Conclusions:For AIS patients, CT examination should be performed immediately after interventional operation. The volume, CTmax value and CTave value of high-density lesions newly appeared in the ischemic area are positively correlated with the onset of PS. Quantifying the volume of high-density lesions can help to predict the onset of PS.

Objective:To construct of a computed tomography (CT) based radiomics model for predicting the prognosis of patients with gastric neuroendocrine neoplasm (GNEN) and inves-tigate its application value.Methods:The retrospective cohort study was conducted. The clinico-pathological data of 182 patients with GNEN who were admitted to 2 medical centers, including the First Affiliated Hospital of Zhengzhou University of 124 cases and the Affiliated Cancer Hospital of Zhengzhou University of 58 cases, from August 2011 to December 2020 were collected. There were 130 males and 52 females, aged 64(range, 56-70)years. Based on random number table, all 182 patients were divided into the training dataset of 128 cases and the validation dataset of 54 cases with a ratio of 7:3. All patients underwent enhanced CT examination. Observation indicators: (1) construction and validation of the radiomics prediction model; (2) analysis of prognostic factors for patients with GNEN in the training dataset; (3) construction and evaluation of the prediction model for prognosis of patients with GNEN. Measurement data with skewed distribution were represented as M(range), and comparison between groups was conducted using the Mann-Whitney U test. Count data were described as absolute numbers, and the chi-square test, corrected chi-square test or Fisher exact probability were used for comparison between groups. The Kaplan-Meier method was used to calculate survival rate and draw survival curve, and the Log-rank test was used for survival analysis. The COX regression model was used for univariate and multivariate analyses. The R software (version 4.0.3) glmnet software package was used for least absolute shrinkage and selection operator (LASSO)-COX regression analysis. The rms software (version 4.0.3) was used to generate nomogram and calibration curve. The Hmisc software (version 4.0.3) was used to calculate C-index values. The dca.R software (version 4.0.3) was used for decision curve analysis. Results:(1) Construction and valida-tion of the radiomics prediction model. One thousand seven hundred and eighty-one radiomics features were finally extracted from the 182 patients. Based on the feature selection using intra-group correlation coefficient >0.75, and the reduce dimensionality using LASSO-COX regression analysis, 14 non zero coefficient radiomics features were finally selected from the 1 781 radiomics features. The radiomics prediction model was constructed based on the radiomics score (R-score) of these non zero coefficient radiomics features. According to the best cutoff value of the R-score as -0.494, 128 patients in the training dataset were divided into 64 cases with high risk and 64 cases with low risk, 54 patients in the validation dataset were divided into 35 cases with high risk and 19 cases with low risk. The area under curve (AUC) of radiomics prediction model in predicting 18-, 24-, 30-month overall survival rate of patients in the training dataset was 0.83[95% confidence interval ( CI ) as 0.76-0.87, P<0.05], 0.84(95% CI as 0.73-0.91, P<0.05), 0.91(95% CI as 0.78-0.95, P<0.05), respectively. The AUC of radiomics prediction model in predicting 18-, 24-, 30-month overall survival rate of patients in the validation dataset was 0.84(95% CI as 0.75-0.92, P<0.05), 0.84 (95% CI as 0.73-0.91, P<0.05), 0.86(95% CI as 0.82-0.94, P<0.05), respectively. (2) Analysis of prognostic factors for patients with GNEN in the training dataset. Results of multivariate analysis showed gender, age, treatment method, tumor boundary, tumor T staging, tumor N staging, tumor M staging, Ki-67 index, CD56 expression were independent factors influencing prognosis of patients with GNEN in the training dataset ( P<0.05). (3) Construction and evaluation of the prediction model for prognosis of patients with GNEN. The clinical prediction model was constructed based on the independent factors influen-cing prognosis of patients with GNEN including gender, age, treatment method, tumor boundary, tumor T staging, tumor N staging, tumor M staging, Ki-67 index, CD56 expression. The C-index value of clinical prediction model in the training dataset and the validation dataset was 0.86 (95% CI as 0.82-0.90) and 0.80(95% CI as 0.72-0.87), respectively. The C-index value of radiomics prediction model in the training dataset and the validation dataset was 0.80 (95% CI as 0.74-0.86, P<0.05) and 0.75(95% CI as 0.66-0.84, P<0.05), respectively. The C-index value of clinical-radiomics combined prediction model in the training dataset and the validation dataset was 0.88(95% CI as 0.85-0.92) and 0.83 (95% CI as 0.77-0.89), respectively. Results of calibration curve show that clinical prediction model, radiomics prediction model and clinical-radiomics combined prediction model had good predictive ability. Results of decision curve show that the clinical-radiomics combined prediction model is superior to the clinical prediction model, radiomics prediction model in evaluating the prognosis of patients with GNEN. Conclusions:The predection model for predicting the prognosis of patients with GNEN is constructed based on 14 radiomics features after selecting. The prediction model can predict the prognosis of patients with GNEN well, and the clinical-radiomics combined prediction model has a better prediction efficiency.

Objective:To evaluate the value of preoperative prediction of vessel invasion (VI) of locally advanced gastric cancer by machine learning model based on the venous phase enhanced CT radiomics features.Methods:A retrospective analysis of 296 patients with locally advanced gastric cancer confirmed by pathology in the First Affiliated Hospital of Zhengzhou University from July 2011 to December 2020 was performed. The patients were divided into VI positive group ( n=213) and VI negative group ( n=83) based on pathological results. The data were divided into training set ( n=207) and test set ( n=89) according to the ratio of 7∶3 with stratification sampling. The clinical characteristics of patients were recorded, and the independent risk factors of gastric cancer VI were screened by multivariate logistic regression. Pyradiomics software was used to extract radiomic features from the venous phase enhanced CT images, and the minimum absolute shrinkage and selection algorithm (LASSO) was used to screen the features, obtain the optimal feature subset, and establish the radiomics signature. Four machine learning algorithms, including extreme gradient boosting (XGBoost), logistic, naive Bayes (GNB), and support vector machine (SVM) models, were used to build prediction models for the radiomics signature and the screened clinical independent risk factors. The efficacy of the model in predicting gastric cancer VI was evaluated by the receiver operating characteristic curve. Results:The degree of differentiation (OR=13.651, 95%CI 7.265-25.650, P=0.003), Lauren′s classification (OR=1.349, 95%CI 1.011-1.799, P=0.042) and CA199 (OR=1.796, 95%CI 1.406-2.186, P=0.044) were independent risk factors for predicting the VI of locally advanced gastric cancer. Based on the venous phase enhanced CT images, 864 quantitative features were extracted, and 18 best constructed radiomics signature were selected by LASSO. In the training set, the area under the curve (AUC) of XGBoost, logistic, GNB and SVM models for predicting gastric cancer VI were 0.914 (95%CI 0.875-0.953), 0.897 (95%CI 0.853-0.940), 0.880 (95%CI 0.832-0.928) and 0.814 (95%CI 0.755-0.873), respectively, and in the test set were 0.870 (95%CI 0.769-0.971), 0.877 (95%CI 0.788-0.964), 0.859 (95%CI 0.755-0.961) and 0.773 (95%CI 0.647-0.898). The logistic model had the largest AUC in the test set. Conclusions:The machine learning model based on the venous phase enhanced CT radiomics features has high efficacy in predicting the VI of locally advanced gastric cancer before the operation, and the logistic model demonstrates the best diagnostic efficacy.

Inhibitory cells derived from bone marrow are a kind of inhibitory cells derived from bone marrow.These cells are not only related to tumor growth,but also participate in the inflammatory immune process.Therefore,we established a rat model of chronic osteomyelitis,and used gemcitabine to inhibit the cell growth ratio of MDSCs.We detected the ratio of MDSCs in bone marrow and spleen of rats by flow cytometry and immunofluorescence,detected the changes of inflammatory factors in peripheral blood by ELISA,and analyzed the inflammatory factors(TNF-α,PCT,IL-4,IL-10,IL-11)in peripheral blood of normal rats,osteomyelitis rats and rats after gemcitabine inhibition.The results showed that the proportion of MDSCs cells in bone marrow and spleen of osteomyelitis model rats was increased,but it was significantly decreased in gemcitabine group(P<0.05).Levels of inflammatory factors(TNF-α,PCT,IL-4,IL-10,IL-17,IFN-γ,TGF-β)were positively correlated with the change of MDSCs cell proportion(P<0.05).From the results,it can be inferred that the change of the proportion of MDSCs cells in rat osteomyelitis is positively related to the inflammatory factors,and gemcitabine can reduce inflammatory factors by inhibiting MDSCs.

Objective:To investigate the effect of prospective intervention on emergence agitation and postoperative recovery in patients with chronic sinusitis during preoperative visits.Methods:A total of 80 patients with chronic sinusitis who underwent general anesthesia in Dayi County People′s Hospital of Chengdu City from December 2019 to October 2020 were selected and randomly divided into group D and group G, with 40 patients in each group. Group D received preoperative visit with conventional methods and group G received preoperative visit with prospective intervention methods. The hemodynamic changes of patients in the two groups at 30 min before the operation (T 1) and 1 (T 2), 5 (T 3), 10 (T 4) and 30 min (T 5) after tracheal tube extraction were recorded. The anxiety and depression scores of patients before the intervention and 1 d after the operation were compared between the two groups. The incidence of emergence agitation after the operation and complications during anesthesia awakening period were observed in the two groups, sino-nasal outcome test-20 (SNOT-20) was used to assess the postoperative recovery. Results:The incidence of emergence agitation in group G was lower than that in group D: 7.5%(3/40) vs. 25.0%(10/40), the difference was statistically significant ( χ2 = 4.50, P<0.05). There was no significant difference in systolic blood pressure, diastolic blood pressure and heart rate between the two groups at T 1 and T 5 ( P>0.05), but the level of above indicators in group G at T 2, T 3 and T 4 were significantly higher than those in group D ( P<0.05). The scores of State-Trait Anxiety Inventory(S-AI) and Self-Rating Depression Scale (SDS) in group G at the first day after the operation were significantly lower than those in group D: (35.45 ± 5.32) scores vs. (39.35 ± 4.91) scores, (35.42 ± 7.82) scores vs. (38.76 ± 5.21) scores, the differences were statistically significant ( P<0.05). The incidence of complications during anesthesia awakening period in group G was slightly lower than that in group D ( P>0.05). After the operation, the scores of sinusitis symptoms and nasal symptoms in the two groups were significantly decreased compared with those before the operation, and the scores of group G were significantly lower than those in group D ( P<0.05). Conclusions:Prospective intervention before anesthesia in patients with chronic sinusitis surgery can reduce stress response, improve bad mood, reduce the incidence of emergence agitation, and promote the postoperative recovery.

Objective:To describe the baseline characteristics of the subjects enrolled in the China Quantitative CT (QCT) big data program in 2018—2019.Methods:Based on baseline data from the Chinese health big data project from January 2018 to December 2019 from the eligible enrolled population, measurements of bone mineral density (BMD) and visceral adipose tissue (VAT) were performed using Mindways′ QCT Pro Model 4 system. The baseline data of age, gender, regional distribution, height, weight, abdominal circumference, blood pressure, blood routine and blood biochemical tests were analyzed. And the single factor analysis of variance (ANOVA) was used to check the age related trend of BMD and VAT in both genders.Results:After screening the inclusion exclusion criteria and outliers of the main indicators, 86 113 people were enrolled in the project. The enrollment rate was 92.47%, including 35 431 (41.1%) women and 50 682 (58.9%) men, and the ratio of men to women was 1.43. The mean age was (50.3±12.7) years in all the subjects, and it was (50.2±12.8) years and (50.4±12.5) years in men and women, respectively, and there was no statistical difference between the two genders ( P>0.05). Total of 43 833 people were enrolled in east China, it was the largest group by region (50.90%), it was followed by central China (16 434 people, 19.08%), and the number of people enrolled in Northeast China was the lowest (2 914 people, 3.38%). The rate of completing of health information indicators related to the main outcome of the study were all above 70%, and there were significant differences between men and women (all P<0.05). The mean BMD was (139.33±46.76) mg/cm 3 in women, (135.90±36.48) mg/cm 3 in men, which showed a decreasing trend with age in both gender (both P<0.001); the mean intra-abdominal fat area was (116.39±56.23) cm 2 in women, (191.67±77.07) cm 2 in men, and there was an increasing trend with age in both men and women (both P<0.001). Conclusions:There are gender differences in BMD and VAT measured by QCT with different age tendency, and there are gender differences in health information index. Regional factors should also be taken into account for regional differences in the inclusion of data.