Postmenopausal osteoporosis (PMOP) is a chronic metabolic bone disease caused by a decrease in estrogen levels. With the acceleration of population aging process, the public health burden caused by it is becoming increasingly severe. The prevalence rate of osteoporosis in people over 65 years old in China is as high as 32%, which is especially prominent after menopause, which is about 5 times that of elderly men. About 40% of postmenopausal women are at risk of osteoporotic fractures, with a disability rate of up to 50% and a fatality rate of about 20%. The prevention and treatment of osteoporosis has become a major public health issue of global concern, and it is particularly urgent to develop reasonable and effective prevention and treatment programs and explore their scientific basis. Exercise is an important non-drug means for the prevention and treatment of PMOP, it can improve estrogen levels and the expression of bone formation transcription factors, and inhibit the levels of proinflammatory factors and bone resorption markers, macroscopically manifested by the improvement of bone microstructure and bone density. However, the effectiveness of exercise in improving bone mineral density (BMD) remains controversial. Some studies revealed significant changes of bone to mechanical stimulation, while others showed no significant effect of mechanical training, this heterogeneity in bone adapt to mechanical stimulation is particularly evident in postmenopausal women. Although the evidence that a wide range of exercise programs can improve osteoporosis, the optimal solution to address bone mineral loss remains unclear. The most effective exercise type, dosage and personalized adaptation are still being determined. This study will fully consider the differences in gender and hormone levels, searching and screening randomized controlled trials of PubMed, CNKI and other databases regarding exercise improving bone mineral density in women with PMOP. Strictly following the PRISMA guidelines to reviewed and compared the effects of different types of exercise modalities on BMD at different sites in women with PMOP by network Meta-analysis, to provide theoretical guidance to maintain or improve BMD in women with PMOP.

OBJECTIVE: To identify phytochemical constituents present in the extract of flowers of Xanthoceras sorbifolia and evaluate their anti-oxidant and anti-hyperglycemic capacities. METHODS: The AlCl₃ colorimetric method and Prussian Blue assay were used to determine the contents of total flavonoids and total phenolic acids in extraction layers, and the bioactive layers was screened through anti - oxidative activity in vitro. The Waters ACQUITY UPLC system and a Waters ACQUITY UPLC BEH C₁₈ column (2.0 mm × 150 mm, 5 μm) were used to identify the ingredients. And anti-oxidative ingredients were screened by off-line UPLC-QTOF-MS/MS-free radical scavenging. The ameliorative role of it was further evaluated in a high-fat, streptozotocin-induced type 2 diabetic rat model and the study was carried out on NADPH oxidase (PDB ID: 2CDU) by molecular docking. RESULTS: Combined with the results of activity screening in vitro, the anti - oxidative part was identified as the ethyl acetate layer. A total of 24 chemical constituents were identified by liquid chromatography-mass spectrometry in the ethyl acetate layer and 13 main anti-oxidative active constituents were preliminarily screened out through off-line UPLC-QTOF-MS/MS-free radical scavenging. In vivo experiments showed that flowers of X. sorbifolia could significantly reduce the blood glucose level of diabetic mice and alleviate liver cell damage. Based on the results of docking analysis related to the identified phytocompounds and oxidase which involved in type 2 diabetes, quercetin 3-O-rutinoside, kaempferol-3-O-rhamnoside, isorhamnetin-3-O-glucoside, and isoquercitrin showed a better inhibitory profile. CONCLUSION The ethyl acetate layer was rich in flavonoids and phenolic acids and had significant anti-oxidant activity, which could prevent hyperglycemia. This observed activity profile suggested X. sorbifolia flowers as a promising new source of tea to develop alternative natural anti-diabetic products with a high safety margin.

This study aimed to investigate the role and mechanism of sappanone A (SA) in regulating renal ischemia-reperfusion injury (IRI) in rats. The animal experiment has been approved by the Ethics Committee of Suzhou Wujiang District Children's Hospital (approval number: 2022010). First, hematoxylin-eosin (H&E) staining was used to evaluate the effects of SA on IRI, and renal damage was scored. Serum creatinine (SCr), blood urea nitrogen (BUN) and cystatin C (Cystatin C) were analyzed. The effect of sappanone A on the apoptosis of renal tubular epithelial cells induced by IRI was analyzed by TUNEL staining. Protein expression levels of p-JNK/JNK, p-ERK/ERK, Bcl2, Bax and cleaved-caspase 3 in renal tissues were detected by Western blot. Finally, H&E staining, serological analysis, TUNEL staining and Western blot were used to determine whether JNK activator anisomycin could reverse the effect of SA on IRI in rats. The results showed SA significantly reduced the renal tubule injury caused by ischemia-reperfusion, and decreased the level of SCr, BUN and Cys C in serum. TUNEL staining showed that SA significantly reduced the apoptosis of renal tubular epithelial cells induced by IRI. Western blot analysis of kidney tissue showed that SA significantly promoted the expression of apoptosis inhibiting protein Bcl2 and inhibited the expression of apoptosis-promoting proteins Bax and cleaved-caspase 3. Further analysis elucidated that SA did not affect the phosphorylation of ERK but decreased the phosphorylation of JNK. Finally, H&E staining, serological analysis, TUNEL staining and Western blot confirmed that JNK activator anisomycin could reverse the alleviating effect of SA on IRI in rats. The above findings suggest that SA could alleviate IRI in rats by inhibiting JNK phosphorylation.

This paper summarized the clinical experience of Professor GE Linyi in treating ulcerative colitis （UC） by stages with the method of clearing. Professor GE believes that the core pathogenesis of UC is dampness and heat in the intestines， and by taking the method of clearing as the basis， he proposed four methods for treatment of UC including clearing and transforming， clearing and dispersing，clearing and moisterning， clearing and nourishing. The pathogenesis of UC in its active stage is dampness and heat in the intestines， congestion and stagnation of qi and blood， and accumulation of stasis toxins， for which the treatment method is to clear and transform， accompanied by clearing and dispersing method. In terms of the clearing and transforming method， Bai Tou Weng Decoction （白头翁汤） combined with Haungqin （Radix Scutellariae）， Machixian （Herba Portulacae） and Pugongying （Herba Taraxaci） is taken as the basic prescription to clear and transform dampness and heat， cool blood， resolve toxins and stop dysentery. For the clearing and dispersing method， medicinals to rectify qi such as Chaihu （Radix Bupleuri）， Cuxiangfu （Vingar Rhizoma Cyperi）， Muxiang （Radix Aucklandiae）， Zhiqiao （Fructus Aurantii）， and Binlang （Semen Arecae）， as well as those to regulate blood such as Danggui （Radix Angelicae Sinensis）， Cebaiye （Cacumen Platycladi） and Diyutan （Radix Sanguisorbae Carbonisatus） are recommended. The pathogenesis of the remission stage is healthy qi depletion and lingering pathogen of dampness and heat stasis toxin in the intestines， for which the method of clearing and nourishing， clearing and moistening can be used； the latter is mainly for people with yin fluids injury， and self-made Qingrun Yichang Decoction （清润益肠汤） is recommended， while the former is for those with spleen and stomach weakness， and self-made Qingyang Jianpi Decoction （清养健脾汤） can be used.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.