ObjectiveTo establish a rapid analytical method for identifying the differential components in Poria cocos medicinal materials based on ultra performance liquid chromatography-quadrupole-electrostatic field orbital trap high-resolution mass spectrometry（UPLC-Q-Orbitrap-MS）， combined with mass defect filtering（MDF） and molecular network integration techniques. MethodsUPLC-Q-Orbitrap-MS was used for MS data acquisition and identification of P. cocos medicinal materials， with the help of MDF for the study of cleavage behavior and structural identification of triterpenoids. According to the similarity of MS/MS fragmentation patterns of each component， global natural product social molecular network（GNPS） was established， and Cytoscape 3.6.1 was used to screen molecular clusters with similar structures and the the structure of main compound classes were identified and confirmed. Multivariate statistical analyses such as principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA） were used to screen the differential components of the five P. cocos medicinal materials with the variable importance in the projection（VIP） value>1 and P<0.05 as the criteria. ResultsA total of 66 compounds were identified by database comparison， 8 compounds were newly identified by MDF， 28 compounds were newly identified by GNPS， and a total of 102 chemical compounds were identified， including 43 triterpenoids， 16 saccharides， 26 amino acids and peptides， 3 nucleosides， and 14 other compounds. Triterpenoids were predominant in Poriae Cutis and wild Fushen， amino acids and peptides were the most abundant in Poria and cultivated Fushen， carbohydrates were the most abundant in Poriae Cutis. Type Ⅰ and Ⅱ triterpenoids had higher amounts in Poria and cultivated Fushen， type Ⅲ triterpenoids were more abundant in Poriae Cutis， all four types of triterpenoids were higher in Fushenmu， and type Ⅰ， Ⅱ， and Ⅳ triterpenoids were higher in wild Fushen. A total of 12 common differential chemical constituents were screened， including serine， guanosine， gallic acid， 2-octenal， maltotriose， trametenolic acid， dehydroeburicoic acid， dehydrotrametenolic acid， poricoic acid A， poricoic acid B， poricoic acid E and G， but the relative contents of them varied significantly among different medicinal materials. ConclusionAmong the five P. cocos medicinal materials， the types of constituents are generally similar， but their relative contents differed significantly among these medicinal materials， especially in the distribution of triterpenoids. The integration of UPLC-Q-Orbitrap-MS， MDF and GNPS can provide a reference for the rapid qualitative analysis of other Chinese medicines.

ObjectiveTo establish a rapid analytical method for identifying the differential components in Poria cocos medicinal materials based on ultra performance liquid chromatography-quadrupole-electrostatic field orbital trap high-resolution mass spectrometry（UPLC-Q-Orbitrap-MS）， combined with mass defect filtering（MDF） and molecular network integration techniques. MethodsUPLC-Q-Orbitrap-MS was used for MS data acquisition and identification of P. cocos medicinal materials， with the help of MDF for the study of cleavage behavior and structural identification of triterpenoids. According to the similarity of MS/MS fragmentation patterns of each component， global natural product social molecular network（GNPS） was established， and Cytoscape 3.6.1 was used to screen molecular clusters with similar structures and the the structure of main compound classes were identified and confirmed. Multivariate statistical analyses such as principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA） were used to screen the differential components of the five P. cocos medicinal materials with the variable importance in the projection（VIP） value>1 and P<0.05 as the criteria. ResultsA total of 66 compounds were identified by database comparison， 8 compounds were newly identified by MDF， 28 compounds were newly identified by GNPS， and a total of 102 chemical compounds were identified， including 43 triterpenoids， 16 saccharides， 26 amino acids and peptides， 3 nucleosides， and 14 other compounds. Triterpenoids were predominant in Poriae Cutis and wild Fushen， amino acids and peptides were the most abundant in Poria and cultivated Fushen， carbohydrates were the most abundant in Poriae Cutis. Type Ⅰ and Ⅱ triterpenoids had higher amounts in Poria and cultivated Fushen， type Ⅲ triterpenoids were more abundant in Poriae Cutis， all four types of triterpenoids were higher in Fushenmu， and type Ⅰ， Ⅱ， and Ⅳ triterpenoids were higher in wild Fushen. A total of 12 common differential chemical constituents were screened， including serine， guanosine， gallic acid， 2-octenal， maltotriose， trametenolic acid， dehydroeburicoic acid， dehydrotrametenolic acid， poricoic acid A， poricoic acid B， poricoic acid E and G， but the relative contents of them varied significantly among different medicinal materials. ConclusionAmong the five P. cocos medicinal materials， the types of constituents are generally similar， but their relative contents differed significantly among these medicinal materials， especially in the distribution of triterpenoids. The integration of UPLC-Q-Orbitrap-MS， MDF and GNPS can provide a reference for the rapid qualitative analysis of other Chinese medicines.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Threatened abortion is a common disease of obstetrics and gynecology and one of the diseases responding specifically to traditional Chinese medicine （TCM）. The China Association of Chinese Medicine organized experts in TCM obstetrics and gynecology， Western medicine obstetrics and gynecology， and pharmacology to deeply discuss the advantages of TCM and integrated Chinese and Western medicine treatment as well as the medication plans for threatened abortion. After discussion， the experts concluded that chromosome， endocrine， and immune abnormalities were the key factors for the occurrence of threatened abortion， and the Qi and blood disorders in thoroughfare and conception vessels were the core pathogenesis. In the treatment of threatened abortion， TCM has advantages in preventing miscarriages， alleviating clinical symptoms and TCM syndromes， relieving anxiety， regulating reproductive endocrine and immune abnormalities， personalized and diversified treatment， enhancing efficiency and reducing toxicity， and preventing the disease before occurrence. The difficulty in diagnosis and treatment of threatened abortion with traditional Chinese and Western medicine lies in identifying the predictors of abortion caused by maternal factors and the treatment of thrombophilia. Recurrent abortion is the breakthrough point of treatment with integrated traditional Chinese and Western medicine. It is urgent to carry out high-quality evidence-based medicine research in the future to improve the modern diagnosis and treatment of threatened abortion with TCM.

Objective To compare the therapeutic effects of manual reduction and machine reduction in patients with benign paroxysmal positional vertigo(BPPV)at different ages.Methods Randomly select 300 patients clinically diagnosed with BPPV who visited the Hearing Center of the First Affiliated Hospital of Wenzhou Medical University from April 2022 to December 2023.They were divided into young and middle-aged group of 170 cases(18-59 years old)and elderly group of 130 cases(≥60 years old)according to age.Each group was further divided equally into experimental group and control group based on the diagnosis and treatment methods adopted by the patients.The experimental group patients were treated with a benign paroxysmal positional vertigo diagnosis and treatment system(model/specification:SRM-Ⅳ)for machine reduction.The control group patients were treated with manual reduction.Compare the effective rate of reduction,reduction frequency,incidence of residual dizziness symptoms,and duration of each group.Results In young and middle-aged patients,the incidence and duration of residual dizziness symptoms after reduction in experimental group were significantly lower than those in control group(P<0.05),and there was no statistically significant difference in the effective rate and number of reductions(P>0.05).In elderly group,the reduction efficiency of experimental group was significantly higher than that of control group,and the reduction frequency was lower than that of the control group(P<0.05).There was no statistically significant difference in the incidence and duration of residual dizziness symptoms after reduction(P>0.05).Conclusion Machine reduction can significantly improve the reduction efficiency of elderly BPPV patients,significantly shorten the duration of residual dizziness after reduction treatment in young and middle-aged patients,and reduce its incidence.

Objective:To construct a leadership evaluation indicator system for managers in primary healthcare institutions.Methods:This study was a qualitative research. Based on the Chinese Academy of Sciences′ Five Forces Model of Leadership, the framework of the leadership evaluation indicator system for primary healthcare institution managers was initially constructed through literature review, expert interviews, surveys, and group discussions. From November 2020 to June 2021, 19 experts were invited to participate in two rounds of Delphi expert consultations. Based on the consultation results, the leadership evaluation indicator system for primary healthcare institution managers was developed, and the analytic hierarchy process (AHP) was used to determine the weight of each indicator.Results:Nineteen consulting experts were included in the study, 13 of whom were aged 40 years or older, and seven of whom had 20 years or more of professional experience. The response rates of the two rounds of Delphi expert consultation questionnaires were 18/19 and 19/19, respectively; the authority coefficients of the experts were 0.92 and 0.91, respectively. In the second round of consultation, the coordination coefficients of each level of indicators were 0.640, 0.557, and 0.512 ( P<0.05), respectively. The constructed leadership indicator system for managers of primary healthcare institutions included five primary indicators, 16 secondary indicators, and 44 tertiary indicators. The primary indicators, in order of their weight, were "influence"(0.299),"decisiveness"(0.245),"control"(0.156),"inspiration"(0.150), and "foresight"(0.150). Conclusions:This study is a preliminary construction of a leadership evaluation indicator system for managers of primary healthcare institutions, which can be a reference for the training and evaluation of relevant talents.