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:To investigate the distribution of CGG repeat numbers in the FMR1 gene among reproductive-age women in China, providing data reference for carrier screening and genetic counseling of Fragile X syndrome. Methods:This cross-sectional study recruited 16 610 reproductive-age women from 12 medical institutions between July 2022 and October 2023. Peripheral venous blood samples (3 mL) were collected, and genomic DNA was extracted. The number of CGG repeats in the FMR1 gene was determined using the triplet-primed polymerase chain reaction (TP-PCR) combined with capillary electrophoresis technology. Statistical analyses were performed to assess the prevalence and distribution of CGG repeat expansions. Results:Among 16 610 women of childbearing age, 5 684 (34.220%) women had the same number of CGG repeats in the two alleles of FMR1 gene, and 10 926 (65.780%) women had different numbers of repeats in the two alleles. Among the 33 220 FMR1 alleles in 16 610 women of reproductive age, the most common CGG repeat numbers were 29 [48.645% (16 160/33 220)] and 30 [26.276% (8 729/33 220)], while the most frequent CGG genotype was CGG 29/29 [24.726% (4 107/16 610)]. The CGG repeat numbers of FMR1 gene were normal in 16 498 women (99.326%). Among the 112 women (0.674%) with CGG repeat abnormities, 96 (0.578%) women were classified as intermediate carriers, 15 (0.090%) as premutation carriers, and 1 (0.006%) as a full mutation carrier, whose CGG genotype was (36, >200). Conclusion:In the general reproductive-age female population in China, the normal CGG repeat numbers of the FMR1 gene account for 99.326%, while the intermediate carrier rate is 0.578%, and the combined carrier rate of the premutation and full mutation types is 0.096%.

Objective:To analyze four patients with a 16q22 fragile site with miscarriage or infertility by using cytogenetic methods.Methods:Four patients presented at Northwest Women′s and Children′s Hospital between January 2022 and December 2024 were selected as the study subjects. Peripheral blood samples were collected from the patients and subjected to G-banded chromosomal karyotyping, among whom two were also subjected to copy number variation (CNV) sequencing. This study has been approved by the Ethics Committee of the Hospital (Ethics No. 2020-022).Results:The chromosomal karyotypes of the patients were mos 46, XX, fra(16)(q22)[26]/47, XX, del(16)(q22), + chrb(16)(q22)[4]/46, XX, del(16)(q22)[3]/46, XX[91], mos 46, XY, fra(16)(q22)[21]/46, XY, del(16)(q22)[3]/46, XY[76], mos 46, XX, fra(16)(q22)[21]/ 46, XX, del(16)(q22)[4]/46, XX[75] and mos 46, XX, fra(16)(q22)[16]/46, XX, del(16)(q22)[7]/47, XX, del(16)(q22), + chrb(16)(q22)[6]/47, XX, fra(16)(q22), + chrb(16)(q22)[3]/46, XX[68], respectively. CNV sequencing of patients 2 and 4 revealed no deletion or duplication on chromosome 16.Conclusion:Identification of the 16q22 fragile site has facilitated genetic counseling for these patients.

Objective:To explore genomic features associated with gemcitabine sensitivity, patient-derived organoid models of biliary tract cancer (BTC) were established and characterized.Methods:This is an experimental study. The tissue specimens of BTC were collected from patients who underwent surgical resection at the Department of Hepatobiliary and Pancreatic Surgery,the Second Affiliated Hospital of Zhejiang University School of Medicine between January 2020 and December 2023. The tumor organoids were cultured in vitro and histologically characterized. Drug sensitivity testing was performed using gemcitabine,cisplatin,paclitaxel,fluorouracil,and lenvatinib etc. to evaluate cell viability. The correlation between the drug sensitivity of organoids and clinical therapeutic response was analyzed.Results:Thirty-eight patient-derived organoids (PDO) models were successfully established from 43 biliary tract malignancy patients with complete follow-up data,including gallbladder cancer PDO 14 cases,distal bile duct cancer PDO 16 cases,intrahepatic cholangiocarcinoma PDO 8 cases,achieving an overall success rate of 88.4%. Drug sensitivity testing (DST) was performed on the successfully generated PDO,with 35 models successfully completing DST experiments. The overall consistency rate between drug responses in PDOs and clinical survival outcomes in corresponding patients was 8/14. Transcriptomic analysis of gemcitabine-sensitive vs. gemcitabine-resistant PDO identified 71 differentially expressed genes in the resistant group,the significantly up-regulated genes including GLDC, LINC01595, IL-27, ANGPTL3, CYP7A1,and AKR1C1;the significantly down-regulated genes including P2RY2,LIPC,and ECHDC3. Conclusion:A biobank of patient-derived organoids of BTC has been established,which demonstrates its potential as preclinical models and tools for predicting chemotherapy responses for BTC patients.

BACKGROUND:Bone marrow mesenchymal stem cells are one of the sources of adipocytes and express all renin-angiotensin system components,but the effect of angiotensin Ⅱ on bone marrow mesenchymal stem cell differentiation into brown adipose tissue is not clear.OBJECTIVE:To observe the effect of angiotensin Ⅱ on bone marrow mesenchymal stem cell differentiation into brown adipose tissue and investigate the role of angiotensin 1a receptor knockout in effect of angiotensin Ⅱ on bone marrow mesenchymal stem cell differentiation into brown adipocytes and its potential mechanisms.METHODS:After isolation and culture of bone marrow mesenchymal stem cells in wild-type and angiotensin 1a receptor knockout SD rats,the cells were cultured to the third generation and randomly divided into four groups:wild type group,knockout group,wild type+angiotensin Ⅱ group,and knockout+angiotensin Ⅱ group.The differentiation was induced in the brown fat induced differentiation medium for 14 days.Angiotensin Ⅱ(100 nmol/L)was added for intervention when the differentiation medium was changed each time in the latter two groups.Western blot assay,qRT-PCR,immunofluorescence,and other methods were used to detect the expression of induced differentiation,lipolysis,β oxidation,and mitochondrial biogenesis in brown fat.RESULTS AND CONCLUSION:Angiotensin Ⅱ could inhibit the browning of rat bone marrow mesenchymal stem cells.Knockout of angiotensin 1a receptor could improve the inhibitory effect of angiotensin Ⅱ on brown lipid formation of rat bone marrow mesenchymal stem cells by promoting lipolysis,enhancing fatty acid β oxidation,promoting mitochondrial biogenesis,and enhancing mitochondrial function.These findings provide new research directions and potential therapeutic targets for obesity treatment,revealing the important role of renin angiotensin systems in fat metabolism and its potential as a therapeutic target.

Toll-like receptor 5(TLR5)is a pattern recognition receptor,the main extracellular receptor for recognizing flagel-lin,and a bridge between innate and adaptive immunity.It can activate innate immunity and induce the release of a series of down-stream cytokines,as well as play an important role in protecting the host from flagellated pathogens and establishing immune tolerance.These functions are related to the pathogenesis of obesity hypertension in various aspects.This article aims to review the relationship between TLR5 and obese hypertension,as well as the new progress on the interaction between TLR5 and flagellin structure and func-tion,and discuss the future research directions and challenges.