Objective To predict the quality markers of Alismatis Rhizoma and salted Alismatis Rhizoma based on fingerprints and multivariate statistical analysis combined with network pharmacology.Methods HPLC-DAD method was used to establish fingerprints of Alismatis Rhizoma and salted Alismatis Rhizoma.Based on the peak area data of the fingerprints,clustering analysis,principal component analysis and orthogonal partial least squares-discriminant analysis were employed to evaluate the quality of 10 batches of Alismatis Rhizoma and 12 batches of salted Alismatis Rhizoma.The main components with quality differences were screened.Network pharmacology was used to analyze the targets and related pathways of the screened components,A component-target-pathway network was constructed,and molecular docking was used to verify.Quality markers of Alismatis Rhizoma and salted Alismatis Rhizoma were predicted.Results The HPLC fingerprints of Alismatis Rhizoma and salted Alismatis Rhizoma were established.The similarity evaluation showed that the similarity of 10 batches of Alismatis Rhizoma and 12 batches of salted Alismatis Rhizoma ranges from 0.991 to 0.998,0.992 to 1.000,respectively.Nine components with quality differences were identified through multivariate statistical analysis,and five of them were identified as alismoxide,alisol C,alismol,alisol B,23-acetate alisol B.Network pharmacological analysis suggested 278 targets of action associated with the five components.The main signaling pathways of KEGG pathway enrichment analysis were closely related to the main efficacy and modern pharmacological effects of Alismatis Rhizoma and salted Alismatis Rhizoma.These 5 components were preliminary predicted as quality markers for Alismatis Rhizoma and salted Alismatis Rhizoma.Conclusion This study predicted 5 quality markers for Alismatis Rhizoma and salted Alismatis Rhizoma,which can provide reference for their quality control and further research.

Objective The aim of this study was to develop a method for the determination of nine vulcanizing agents and accelerators in pharmaceutical rubber stoppers by headspace gas chromatography with sulfur chemiluminescence detector(HS-GC-SCD).Methods A DB-1 capillary column(30 m×0.32 mm×0.25 μm)was used with He as the carrier gas.The injection port temperature was 220 ℃,the split ratio was 10∶1,and the carrier gas flow rate was 1.5 mL·min-1.The temperature program was as follows:the starting temperature was 40 ℃,and it was increased to 60 ℃ at a rate of 5 ℃ per minute,and then to 260 ℃ at a rate of 15℃per minute.SCD detector parameters:base temperature was 280℃,combustion chamber temperature was 800 ℃,air flow rate was 60 mL·min-1,and hydrogen flow rate was 40 mL·min-1.Results The sample pretreatment method was simple and convenient,and it effectively reduced the extraction loss.The methodological results showed that all sulfides showed good linear relationships within the concentration range(r2≥ 0.990).The RSD was less than 2％;and the average recovery rate was 86.4％-97.5％.From the analysis of the sample test results,free sulfur is commonly found in rubber stoppers.Among all the samples of the vulcanization system,the free sulfur detected in the sulfur vulcanization system is higher than the free sulfur produced by degradation of other vulcanization systems.Conclusions This method is simple to operate,compared with other existing detection methods.HS-GC-SCD has a smaller matrix effect,higher sensitivity and selectivity,and is appropriate for quantifying the concentrations of sulfide present in various rubber stoppers.

Objective To establish a fingerprint spectrum for Qiangshen Oral Liquid;To predict its quality markers combined with network pharmacology.Methods HPLC-ELSD method was used to establish fingerprint spectrum of Qiangshen Oral Liquid,and common peaks were identified.Its targets and related pathways were predicted through network pharmacology.A component-target-pathway network was constructed,and molecular docking verification was performed to analyze the quality markers of Qiangshen Oral Liquid.Results The HPLC fingerprint spectrum of Qiangshen Oral Liquid was established.20 common peaks were identified and belonged to all prescription drug tastes.13 chromatographic peaks were located using reference standards.The similarity evaluation showed that the similarity of 19 batches of Qiangshen Oral Liquid ranged from 0 to 0.995.Network pharmacology identified 45 key targets related to 13 components,and KEGG enrichment analysis obtained 151 pathways.A component-target-pathway network was constructed,predicted that calycosin-7-O-glucoside,ginsenoside Rg1,ginsenoside Re,ginsenoside Rf,ginsenoside Rb1,astragaloside Ⅳ,ginsenoside Rd,ginsenoside Rb2,astragaloside Ⅱ,astragaloside Ⅰ,schisandrin A and panaxatriol were quality markers for Qiangshen Oral Liquid.Conclusion The fingerprint spectrum of Qiangshen Oral Liquid was established,and quality markers were predicted preliminarily,providing a reference for its standard establishment and quality evaluation.

Objective:To investigate the characteristics of the endometrial microbiota in patients with varying degrees of intrauterine adhesion (IUA).Methods:This single-center cross-sectional observational study enrolled 115 patients with IUA who were treated at the Hysteroscopic Center of Fuxing Hospital, Capital Medical University, from May 2022 to October 2023. After quality control and data preprocessing, 81 samples met the inclusion criteria for analysis. Patients were grouped according to an established IUA scoring and grading system into mild IUA ( n=38) and moderate-to-severe IUA ( n=43). Endometrial tissue was collected under sterile conditions. Bacterial genomic DNA was extracted, the 16S rRNA V3-V4 region was amplified, and sequencing was performed on an Illumina platform. Differences in endometrial microbiota diversity and composition were compared between the two groups. Results:Patients with varying degrees of IUA exhibited comparable species richness, evenness and diversity of endometrial microbiota. At the phylum level, the endometrial microbiota across all subjects was predominantly composed of Proteobacteria, Firmicutes, Cyanobacteriota, Bacteroidota, and Actinobacteriota, with Proteobacteria (32.29%) and Firmicutes (23.82%) showing the highest mean relative abundances. At the genus level, Ralstonia (16.67%), Lactobacillus (13.45%), and Streptococcus (7.07%) were the most abundant genera. Group comparisons showed that the abundance of Ralstonia was higher in the mild IUA group, whereas Lactobacillus, Vibrio and Pseudoalteromonas were more abundant in the moderate-to-severe IUA group; however, these differences did not reach statistical significance (all P>0.05). LEfSe analysis further indicated that Lactobacillus, Vibrio, Pseudoalteromonas, Aeromonas, Ureaplasma and Acetobacterium were relatively enriched in the moderate-to-severe IUA group, while Geobacillus, Stomatobaculum and Fusicatenibacter were more abundant in the mild IUA group. Conclusion:The composition of the endometrial microbiota differs among patients with varying IUA severity. IUA progression may be associated with alterations in the endometrial microbiota; however, causal relationships and underlying mechanisms require further investigation.

Objective To establish a method for predicting the risk of endometrial cancer(EC)and endometrial atypical hyperplasia(AH)in women with postmenopausal bleeding(PMB)by collecting clinical data on routine medical history.Methods The clinical data of a total of 408 PMB patients admitted to Fuxing Hospital,Capital Medical University were consecutively collected in this retrospective study from December 2013 to December 2023.According to the results of endometrial pathology,patients were divided into case group and control group.EC and AH were included in the malignant group(case group)and the other endometrial pathologies were included in the non-malignant group(control group).Clinical data,including clinical history,high risk factors,and common gynecological ultrasound measurement indicators,were collected and studied by univariate and multivariate Logistic regression analysis.Results The mean age of 408 patients was(60.4±7.8)years.A total of 74 cases(18.1％)were in case group and 334 cases(81.9％)were in control group.Based on Logistic regression analysis,the best predictors of endometrial malignant lesions were selected to create a"LRDNT"(light bleeding,recurrent bleeding,diabetes,non-uniform echogenicity & thickness)model.LRDNT scores range from 0 to 22.The score of LRDNT ≥15 has the largest Yoden index,and the sensitivity to predict endometrial malignant lesions is 79.73％,the specificity is 80.84％,and the prediction accuracy is 80.64％.Conclusions The risk prediction model LRDNT,which combines clinical information and common gynecological ultrasound measurement indicators of PMB patients,can help clinicians classify patients at high and low risk of endometrial malignant lesions,and optimize the strategy of diagnosis and treatment.

Objective To establish a fingerprint spectrum for Qiangshen Oral Liquid;To predict its quality markers combined with network pharmacology.Methods HPLC-ELSD method was used to establish fingerprint spectrum of Qiangshen Oral Liquid,and common peaks were identified.Its targets and related pathways were predicted through network pharmacology.A component-target-pathway network was constructed,and molecular docking verification was performed to analyze the quality markers of Qiangshen Oral Liquid.Results The HPLC fingerprint spectrum of Qiangshen Oral Liquid was established.20 common peaks were identified and belonged to all prescription drug tastes.13 chromatographic peaks were located using reference standards.The similarity evaluation showed that the similarity of 19 batches of Qiangshen Oral Liquid ranged from 0 to 0.995.Network pharmacology identified 45 key targets related to 13 components,and KEGG enrichment analysis obtained 151 pathways.A component-target-pathway network was constructed,predicted that calycosin-7-O-glucoside,ginsenoside Rg1,ginsenoside Re,ginsenoside Rf,ginsenoside Rb1,astragaloside Ⅳ,ginsenoside Rd,ginsenoside Rb2,astragaloside Ⅱ,astragaloside Ⅰ,schisandrin A and panaxatriol were quality markers for Qiangshen Oral Liquid.Conclusion The fingerprint spectrum of Qiangshen Oral Liquid was established,and quality markers were predicted preliminarily,providing a reference for its standard establishment and quality evaluation.

Objective To predict the quality markers of Alismatis Rhizoma and salted Alismatis Rhizoma based on fingerprints and multivariate statistical analysis combined with network pharmacology.Methods HPLC-DAD method was used to establish fingerprints of Alismatis Rhizoma and salted Alismatis Rhizoma.Based on the peak area data of the fingerprints,clustering analysis,principal component analysis and orthogonal partial least squares-discriminant analysis were employed to evaluate the quality of 10 batches of Alismatis Rhizoma and 12 batches of salted Alismatis Rhizoma.The main components with quality differences were screened.Network pharmacology was used to analyze the targets and related pathways of the screened components,A component-target-pathway network was constructed,and molecular docking was used to verify.Quality markers of Alismatis Rhizoma and salted Alismatis Rhizoma were predicted.Results The HPLC fingerprints of Alismatis Rhizoma and salted Alismatis Rhizoma were established.The similarity evaluation showed that the similarity of 10 batches of Alismatis Rhizoma and 12 batches of salted Alismatis Rhizoma ranges from 0.991 to 0.998,0.992 to 1.000,respectively.Nine components with quality differences were identified through multivariate statistical analysis,and five of them were identified as alismoxide,alisol C,alismol,alisol B,23-acetate alisol B.Network pharmacological analysis suggested 278 targets of action associated with the five components.The main signaling pathways of KEGG pathway enrichment analysis were closely related to the main efficacy and modern pharmacological effects of Alismatis Rhizoma and salted Alismatis Rhizoma.These 5 components were preliminary predicted as quality markers for Alismatis Rhizoma and salted Alismatis Rhizoma.Conclusion This study predicted 5 quality markers for Alismatis Rhizoma and salted Alismatis Rhizoma,which can provide reference for their quality control and further research.

Objective To investigate the influencing factors of pregnancy outcomes after transcervical resection of polyp(TCRP)in infertility patients.Methods The data of 442 patients with infertility complicated with endometrial polyp(EP)underwent TCRP from March 2021 to March 2022 were retrospectively analyzed,and the postoperative follow-up time was 12-24 months,and they were divided into postoperative pregnancy group(pregnancy group)and non-pregnant group(control group).The two groups were compared with age,body mass index(BMI),infertility type,abnormal uterine bleeding,adverse pregnancy history,number of EP,EP size,intraoperative negative pressure uterine aspiration,endometritis,TCRP operation time,and uterine cavity depth,and multivariate analysis was conducted on the influencing factors of pregnancy outcomes after TCRP in infertility patients.Results Among 442 infertility patients with EP,274 cases(62.0%)were in the pregnancy group and 168 cases(38%)in the control group.The factors influencing pregnancy after TCRP were age(OR=0.925,95%CI:0.881-0.972,P<0.05),abnormal uterine bleeding before surgery(OR=0.646,95%CI:0.432-0.967,P<0.05)and primary infertility(OR=2.105,95%CI:1.295-3.423,P<0.05).Conclusion TCRP can improve pregnancy outcomes in infertility patients with EP.Age and abnormal uterine bleeding before operation were the protective factors to increase pregnancy after TCRP in infertility patients.Primary infertility is a risk factor for pregnancy after TCRP in infertility patients.

Objective:To investigate the characteristics of the endometrial microbiota in patients with varying degrees of intrauterine adhesion (IUA).Methods:This single-center cross-sectional observational study enrolled 115 patients with IUA who were treated at the Hysteroscopic Center of Fuxing Hospital, Capital Medical University, from May 2022 to October 2023. After quality control and data preprocessing, 81 samples met the inclusion criteria for analysis. Patients were grouped according to an established IUA scoring and grading system into mild IUA ( n=38) and moderate-to-severe IUA ( n=43). Endometrial tissue was collected under sterile conditions. Bacterial genomic DNA was extracted, the 16S rRNA V3-V4 region was amplified, and sequencing was performed on an Illumina platform. Differences in endometrial microbiota diversity and composition were compared between the two groups. Results:Patients with varying degrees of IUA exhibited comparable species richness, evenness and diversity of endometrial microbiota. At the phylum level, the endometrial microbiota across all subjects was predominantly composed of Proteobacteria, Firmicutes, Cyanobacteriota, Bacteroidota, and Actinobacteriota, with Proteobacteria (32.29%) and Firmicutes (23.82%) showing the highest mean relative abundances. At the genus level, Ralstonia (16.67%), Lactobacillus (13.45%), and Streptococcus (7.07%) were the most abundant genera. Group comparisons showed that the abundance of Ralstonia was higher in the mild IUA group, whereas Lactobacillus, Vibrio and Pseudoalteromonas were more abundant in the moderate-to-severe IUA group; however, these differences did not reach statistical significance (all P>0.05). LEfSe analysis further indicated that Lactobacillus, Vibrio, Pseudoalteromonas, Aeromonas, Ureaplasma and Acetobacterium were relatively enriched in the moderate-to-severe IUA group, while Geobacillus, Stomatobaculum and Fusicatenibacter were more abundant in the mild IUA group. Conclusion:The composition of the endometrial microbiota differs among patients with varying IUA severity. IUA progression may be associated with alterations in the endometrial microbiota; however, causal relationships and underlying mechanisms require further investigation.

Objective To investigate the influencing factors of pregnancy outcomes after transcervical resection of polyp(TCRP)in infertility patients.Methods The data of 442 patients with infertility complicated with endometrial polyp(EP)underwent TCRP from March 2021 to March 2022 were retrospectively analyzed,and the postoperative follow-up time was 12-24 months,and they were divided into postoperative pregnancy group(pregnancy group)and non-pregnant group(control group).The two groups were compared with age,body mass index(BMI),infertility type,abnormal uterine bleeding,adverse pregnancy history,number of EP,EP size,intraoperative negative pressure uterine aspiration,endometritis,TCRP operation time,and uterine cavity depth,and multivariate analysis was conducted on the influencing factors of pregnancy outcomes after TCRP in infertility patients.Results Among 442 infertility patients with EP,274 cases(62.0%)were in the pregnancy group and 168 cases(38%)in the control group.The factors influencing pregnancy after TCRP were age(OR=0.925,95%CI:0.881-0.972,P<0.05),abnormal uterine bleeding before surgery(OR=0.646,95%CI:0.432-0.967,P<0.05)and primary infertility(OR=2.105,95%CI:1.295-3.423,P<0.05).Conclusion TCRP can improve pregnancy outcomes in infertility patients with EP.Age and abnormal uterine bleeding before operation were the protective factors to increase pregnancy after TCRP in infertility patients.Primary infertility is a risk factor for pregnancy after TCRP in infertility patients.