This study aimed to investigate the underlying mechanisms of Duhuo Jisheng Decoction(DJD) in the prevention and treatment of knee osteoarthritis(KOA). Forty SPF New Zealand rabbits were randomly divided using SPSS 26.0 software into five groups: blank group, model group, low-dose DJD group, high-dose DJD group, and high-dose DJD+phosphatidylinositol 3-kinase(PI3K)/protein kinase B(Akt)/mammalian target of rapamycin(mTOR) signaling pathway activator group(high-dose DJD+740Y-P group), with eight rabbits in each group. Except for the blank group, the KOA model was established in the other groups using papain injection into the knee joint cavity combined with forced flexion of the knee joint. The day after modeling, the blank group and model group were given normal saline at 10 mL·kg~(-1) by gavage, the low-dose DJD group received DJD at 8.8 g·kg~(-1) by gavage, the high-dose DJD group received DJD at 35.2 g·kg~(-1) by gavage, and the high-dose DJD+740Y-P group received DJD at 35.2 g·kg~(-1) by gavage along with 740Y-P at 0.15 μmoL·kg~(-1) injected via the auricular vein. All groups received treatment continuously for four weeks. After modeling and intervention, behavioral observations were performed for all groups, and after the intervention, imaging assessments of the knee joints were conducted. Cartilage from the knee joints was collected, and gross morphological changes were observed. Pathological changes in cartilage tissue were examined using hematoxylin-eosin(HE) staining. The results of these observations were quantitatively evaluated using the Lequesne MG score, Kellgren-Lawrence(K-L) grading, Pelletier score, and Mankin score. ELISA was used to measure the levels of interleukin-1β(IL-1β), interleukin-18(IL-18), and matrix metalloproteinase 13(MMP13) in cartilage tissue. Real-time RT-PCR was used to detect the mRNA expression levels of PI3K, Akt, mTOR, Nod-like receptor protein 3(NLRP3), cysteine protease 1(caspase-1), and gasdermin D(GSDMD) in cartilage tissue. Western blot was employed to measure the protein expression levels of PI3K, Akt, mTOR, NLRP3, caspase-1, and GSDMD. The results showed that compared with the blank group, the model group exhibited significant knee joint degeneration, increased Lequesne MG score, K-L grading, Pelletier score, and Mankin score, elevated levels of IL-1β, IL-18, and MMP13 in cartilage tissue, activation of PI3K, Akt, and mTOR phosphorylation along with increased mRNA expression levels, and elevated protein and mRNA expression levels of NLRP3, caspase-1, and GSDMD. Compared with the model group, these indicators were reversed in both the low-dose and high-dose DJD groups, with the high-dose group showing greater decline degree than the low-dose DJD group. However, compared with the high-dose DJD group, the improvements in knee joint degeneration were less pronounced in the high-dose DJD+740Y-P group, with increased Lequesne MG score, K-L grading, Pelletier score, Mankin score, elevated levels of IL-1β, IL-18, and MMP13, activation of PI3K, Akt, and mTOR phosphorylation along with increased mRNA expression, and increased protein and mRNA expression levels of NLRP3, caspase-1, and GSDMD. In conclusion, DJD is effective and safe in the treatment of KOA, and its mechanism may be related to the inhibition of PI3K/Akt/mTOR signaling pathway-mediated pyroptosis in cartilage tissue, thereby improving knee joint bone structure, reducing the inflammatory response, and preventing cartilage matrix degradation.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Rabbits ; TOR Serine-Threonine Kinases/genetics* ; Osteoarthritis, Knee/genetics* ; Proto-Oncogene Proteins c-akt/genetics* ; Signal Transduction/drug effects* ; Male ; Disease Models, Animal ; Pyroptosis/drug effects* ; Phosphatidylinositol 3-Kinases/genetics* ; Humans ; Female

Hepatocellular carcinoma(HCC), the third leading cause of cancer-related death worldwide, is characterized by high mortality and recurrence rates. Common treatments include hepatectomy, liver transplantation, ablation therapy, interventional therapy, radiotherapy, systemic therapy, and traditional Chinese medicine(TCM). While exhibiting specific advantages, these approaches are associated with varying degrees of adverse effects. To alleviate patients' suffering and burdens, it is crucial to explore additional treatments and elucidate the pathogenesis of HCC, laying a foundation for the development of new TCM-based drugs. With emerging research on gut microbiota, it has been revealed that microbiota plays a vital role in the development of HCC by influencing intestinal barrier function, microbial metabolites, and immune regulation. TCM, with its multi-component, multi-target, and multi-pathway characteristics, has been increasingly recognized as a vital therapeutic treatment for HCC, particularly in patients at intermediate or advanced stages, by prolonging survival and improving quality of life. Recent global studies demonstrate that TCM exerts anti-HCC effects by modulating gut microbiota, restoring intestinal barrier function, regulating microbial composition and its metabolites, suppressing inflammation, and enhancing immune responses, thereby inhibiting the malignant phenotype of HCC. This review aims to elucidate the mechanisms by which gut microbiota contributes to the development and progression of HCC and highlight the regulatory effects of TCM, addressing the current gap in systematic understanding of the "TCM-gut microbiota-HCC" axis. The findings provide theoretical support for integrating TCM with western medicine in HCC treatment and promote the transition from basic research to precision clinical therapy through microbiota-targeted drug development and TCM-based interventions.
Humans ; Gastrointestinal Microbiome/drug effects* ; Carcinoma, Hepatocellular/microbiology* ; Liver Neoplasms/microbiology* ; Drugs, Chinese Herbal/administration & dosage* ; Animals ; Medicine, Chinese Traditional

Human cytomegalovirus (HCMV) poses a significant risk of neural damage during pregnancy. As the most prevalent intrauterine infectious agent in low- and middle-income countries, HCMV disrupts the development of neural stem cells, leading to fetal malformations and abnormal structural and physiological functions in the fetal brain. This review summarizes the current understanding of how HCMV infection dysregulates the Wnt signaling pathway to induce fetal malformations and discusses current management strategies.
Humans ; Cytomegalovirus Infections/virology* ; Wnt Signaling Pathway ; Pregnancy ; Female ; Cytomegalovirus/physiology* ; Pregnancy Complications, Infectious/virology* ; Congenital Abnormalities/virology* ; Animals

This paper explores the impacts of accelerated rehabilitation protocols following anterior cruciate ligament reconstruction(ACLR)on bone tunnel enlargement(BTE).While accelerated rehabilitation can shorten the recovery time and improve the knee function,it may increase the risk of BTE.In the early rehabilitation phase after ACLR,excessive early weight-bearing and rapid progression of knee flexion angles should be avoided,along with the proper use of braces.Continuous passive motion is not recommended in the early phase post-ACLR to prevent potential effects on BTE.Further research is needed to investigate the mechanisms of BTE and develop more effective rehabilitation strategies.This will help to select appropriate rehabilitation protocols for patients and balance functional recovery with the risk of BTE,thereby reducing the revision rate and improving postoperative outcomes.
Humans ; Anterior Cruciate Ligament Reconstruction/rehabilitation*

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.