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 investigate the characteristics of thickness changes in peripapillary retinal nerve fiber layer(pRNFL)and identify related risk factors in patients with Moyamoya disease(MMD).Methods A retrospective study was conducted on 150 MMD patients(150 eyes)aged 6-65 years admitted to the Neurosurgery Department of the Fifth Medical Center,Chinese PLA General Hospital from May 2016 to December 2023(observation group),and 150 age-matched healthy volunteers(150 eyes)from the hospital's ophthalmology outpatient department(control group).Both groups were subdivided into pediatric(≤18 years),young adult(18-40 years),and middle-aged(40-65 years)subgroups.The pRNFL thickness in four quadrants was measured by optical coherence tomography(OCT):superior(pRNFL-Sup),inferior(pRNFL-Inf),nasal(pRNFL-Nas),temporal(pRNFL-Tmp),and average thickness(pRNFL-Avg).General clinical data and pRNFL thickness were compared between two groups.Univariate and multivariate logistic regression analyses were performed to identify risk factors for pRNFL thinning in MMD patients.The cohort was randomly divided into training(n=210)and validation(n=90)sets at a 7:3 ratio.A predictive model for pRNFL thinning in MMD patients was constructed based on logistic regression results.Model performance was evaluated using the area under the receiver operating characteristic curve(AUC),and clinical utility was assessed via decision curve analysis.Results Compared with control group,MMD patients exhibited significantly reduced pRNFL-Avg,pRNFL-Sup,pRNFL-Tmp,and pRNFL-Inf thickness(P<0.05 or P<0.001),while pRNFL-Nas showed no significant difference(P>0.05).In the pediatric subgroup,pRNFL-Avg and pRNFL-Inf were thinner(P<0.05).In the young adult subgroup,pRNFL-Avg and pRNFL-Sup were reduced(P<0.001 or P<0.05).In the middle-aged subgroup,pRNFL-Avg,pRNFL-Sup,pRNFL-Inf,and pRNFL-Tmp were all thinner(P<0.05 or P<0.001).Multivariate logistic regression identified visual field defects(OR=15.28,95%CI 2.95-79.10),disease duration(OR=1.11,95%CI 1.05-1.18),and the number of involved cerebral vessels(OR=1.49,95%CI 1.01-2.22)as independent risk factors for pRNFL thinning.The predictive model achieved AUC of 0.94(95%CI 0.91-0.97)and 0.95(95%CI 0.91-0.99)in the training and validation sets,respectively.Decision curve analysis confirmed the model's favorable clinical net benefit.Conclusion Thinning of pRNFL was observed in Moyamoya disease patients with visual field defects,disease duration,and cerebral vascular involvement identified as independent risk factors for pRNFL atrophy.

Objective To investigate the causal relationship between gut microbiota and viral pneumonia,as well as the underlying mechanisms,using two-sample and two-step Mendelian randomization(MR)approaches,thereby providing novel insights for the prevention and treatment of viral pneumonia.Methods All data were obtained from publicly available genome-wide association studies(GWAS)pooled datasets,including gut microbiota data from the MiBioGen Consortium and the Netherlands Microbiome Project,viral pneumonia data from the FinnGen R10 database,and plasma metabolome data from the publicly available GWAS Catalog.Instrumental variables(IVs)were extracted according to the predefined threshold values.MR analyses were conducted using inverse variance weighting(IVW),MR-Egger,weighted median(WME),weighted mode(WM),and Bayesian-weighted Mendelian randomization(BWMR)methods.Reverse MR analysis was performed to determine whether there was a reverse association.Two-step MR analysis was used to explore the potential mediating role of plasma metabolites,and a series of sensitivity analyses were performed to test the stability of the results.Results Among 196 gut microbiota taxa from the MiBioGen consortium GWAS,11 taxa were associated with viral pneumonia.An increase in the abundance of 4 taxa increased the risk of viral pneumonia,while an increase in the abundance of 7 taxa had a protective effect against viral pneumonia.Among the 207 gut microbiota taxa from the Dutch Microbiome Project GWAS data,10 taxa were associated with viral pneumonia,with 6 risk-increasing and 4 protective taxa identified.Mediation analysis results showed that the causal effect of Defluviitaleaceae on viral pneumonia(OR=0.708,95%CI 0.540-0.929,P=0.013)was mediated to some extent by the N6-acetyllysine levels,with a mediation ratio of 18.4%.Sensitivity analyses did not reveal significant heterogeneity or horizontal pleiotropy.Conclusions Specific gut microbiota are causally associated with viral pneumonia and show potential differences across different populations;the protective effect of Defluviitaleaceae against viral pneumonia may be mediated by the N6-acetyllysine levels.Targeting metabolites may become a potential therapeutic approach for viral pneumonia.

Objective To investigate the effect of dorsal repulsive axon guidance protein(Draxin)on the migration of trunk neural crest cells during the early development of embryonic mouse spinal cord.Methods Immunohistochemistry and in situ hybridization were used to detect the expression characteristics of Draxin in early embryonic spinal cord(8 mice each group);In situ hybridization was used to detect the change of migration characteristics of trunk neural crest cells in early embryonic spinal cord of different types of mouse(5 mice each group);in vitro culture method was used to check the effect of Draxin on the migration characteristics of embryonic mouse trunk neural crest cells(16 mice each group).Resultsβ-galactosidase gene Z(LacZ)gene was introduced when Draxin gene was knocked out to produce Draxin gene knockout mice.β-galactosidase staining was used to detect LacZ gene expression in Draxin knockout embryonic mice,and the result showed that Draxin expression was observed in the spinal cord of early embryonic mice since 9.5 days(E9.5).Draxin expression was obvious in the embryonic mice spinal cord in E10.5 period.In situ hybridization was used to detect the expression of Draxin gene in the spinal cord of wild type embryonic mice,and the result further verified the obvious expression of Draxin in the early embryonic mice spinal cord in El0.5 period.Sox10 in situ hybridization was used to detect neural crest cell migration in the spinal cord of embryonic mice in E10.5 period.The result showed that segmental migration of neural crest cells in the early embryonic spinal cord of some Draxin knockout mice was delayed compared with the wild type mice.The effect of Draxin on the migration of wild type early embryonic mice trunk neural crest cells in vitro was tested.The result showed that Draxin reduced the migration distance of neural crest cells in vitro.Conclusion In the early developmental stage of embryonic spinal cord(E9.5-E10.5),neural crest cells migrated exuberant.At the same time,Draxin plays an important inhibitory function in the formation of the specific migration pathways of trunk neural crest cells by promoting neural crest cells migrating away from Draxin expressing regions.

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.