The classical latent structure method does not consider the influence of primary and secondary symptoms,syndromes and symptoms in the analysis and modeling of syndromes.In this paper,based on the data of damp-heat in intestine and stomach syndrome involving 1087 diabetic patients,the classical latent structure analysis was used to obtain the quantitative syndrome diagnostic rules.Then,using Constrained Latent Tree Analysis(CLTA),the quantitative syndrome diagnostic rules containing primary and secondary symptoms were obtained as follows,primary symptoms include halitosis(2.3),yellow tongue coating(2),abdominal distension(2.3),greasy tongue coating(2.1),loose stool or loose stool(1.5),red tongue(1.3),smooth pulse(1.4).Secondary symptoms include epigastric distension(1.1).Compared with the traditional latent structure analysis method,the rules established by CLTA are more compatible with the concept of differentiating primary and secondary symptoms and the common practice of TCM.The quantitative syndrome diagnostic rules of damp-heat in intestine and stomach syndrome constructed by the CLTA method have considerable objectivity in the modeling process.The diagnostic rules established were also compatible with the qualitative concept of TCM theory in stratifying primary and secondary symptoms.Finally,the diagnostic rules are obtained by logistic regression analysis,and the accuracy of the three rules is compared.The results show that the rule recognition accuracy obtained by CLTA is the highest.Therefore,the syndrome diagnostic rules of damp-heat in intestine and stomach obtained from the analysis of CLTA are in line with the constraint semantics of primary and secondary diseases and the theory of traditional Chinese medicine.

Fibrosis is characterized as an aberrant reparative process involving the direct replacement of damaged or deceased cells with connective tissue, leading to progressive architectural remodeling across various tissues and organs. This condition imposes a substantial burden, resulting in considerable morbidity and mortality. Ginseng (Panax ginseng C. A. Meyer), renowned for its medicinal properties, has been incorporated as a key component in Chinese patent medicines to mitigate fibrotic diseases. Ginsenosides, the primary bioactive compounds in ginseng, have garnered significant attention. Over the past five years, extensive research has explored the pharmaceutical potential of ginsenosides in diverse organ fibrosis conditions, including liver, myocardial, renal, and pulmonary fibrosis. Studies have elucidated that ginsenosides demonstrate potential effects on inflammatory responses stemming from parenchymal cell damage, myofibroblast activation leading to extracellular matrix (ECM) production, and myofibroblast apoptosis or inactivation. Additionally, potential downstream targets and pathways associated with these pathological processes have been identified as being influenced by ginsenosides. This review presents a comprehensive overview of the efficacious treatments utilizing ginsenosides for various tissue fibrosis types and their potential anti-fibrotic mechanisms. Furthermore, it offers a reference for the development of novel candidate drugs for future organ fibrosis therapies.
Ginsenosides/pharmacology* ; Humans ; Fibrosis/drug therapy* ; Animals ; Panax/chemistry* ; Drugs, Chinese Herbal/therapeutic use*

Objective:To investigate the expression of placenta expressed transcript 1(Plet1)in ovarian tissue of the letrozole-induced model rats of polycystic ovary syndrome(PCOS)and its effect on the proliferation of rat ovarian granulosa cells,and to clarify the possible mechanism by which Plet1 may contribute to the pathogenesis of PCOS.Methods:The ovarian tissue samples from the rats collected in previous studies were used and divided into control and PCOS groups.Real-time fluorescence quantitative PCR(RT-qPCR)and Western blotting methods were used to detect the expression levels of Plet1 mRNA and protein in ovarian tissue of the rat in two groups.Additionally,twenty-four rats underwent vaginal smear cytology were divided into four groups by estrous cycle phase:proestrus,estrus,metestrus and diestrus.RT-qPCR was used to detect the expression level of Plet1 mRNA in ovarian tissue of the rats in various groups,and immunohistochemistry(IHC)method was used to detect the location of Plet1 expression in the rat ovarian tissue in various groups.The rat ovarian granulosa cells were transfected and divided into control group,si-Plet1-rat-266 group,si-Plet1-rat-383 group,and si-Plet1-rat-554 group.Cell counting kit-8(CCK-8)method was used to assess the cell proliferation activities of rat ovarian granulosa cells in various groups,and RT-qPCR method was used to detect the expression levels of cyclin-d epend ent kinase 6(CDK6)and P53 mRNA in rat ovarian granulosa cells in various groups.Results:The RT-qPCR results revealed that Plet1 mRNA was expressed in the ovaries of normal rats,while no statistically significant difference was observed across estrous cycle phases(P＞0.05).The immunohistochemistry results showed that the expression of Plet1 protein was mainly localized in ovarian granulosa cells and luteal cells in the rat ovarian tissue.Compared with control group,the expression levels of Plet1 mRNA and protein in ovarian tissue of the rats in PCOS group were significantly decreased(P＜0.05).The RT-qPCR results showed that compared with control group,the expression level of Plet1 mRNA in ovarian granulosa cells in si-Plet1-rat-383 group was decreased(P＜0.01),exhibiting the most pronounced reduction.Compared with control group,the proliferation activity of rat ovarian granulosa cells in si-Plet1-rat-383 group was decreased(P＜0.05).Compared with control group,the expression levels of CDK6 and P53 mRNA in rat ovarian granulosa cells in si-Plet1-rat-383 group were significantly decreased(P＜0.05 or P＜0.01).Conclusion:Plet1 protein is predominantly expressed and localized in granulosa cells and luteal cells in normal rat ovarian tissue.Its expression is downregulated in the ovarian tissue of PCOS model rats,and interference with Plet1 gene expression may inhibit the proliferation of rat ovarian granulosa cells.

The classical latent structure method does not consider the influence of primary and secondary symptoms,syndromes and symptoms in the analysis and modeling of syndromes.In this paper,based on the data of damp-heat in intestine and stomach syndrome involving 1087 diabetic patients,the classical latent structure analysis was used to obtain the quantitative syndrome diagnostic rules.Then,using Constrained Latent Tree Analysis(CLTA),the quantitative syndrome diagnostic rules containing primary and secondary symptoms were obtained as follows,primary symptoms include halitosis(2.3),yellow tongue coating(2),abdominal distension(2.3),greasy tongue coating(2.1),loose stool or loose stool(1.5),red tongue(1.3),smooth pulse(1.4).Secondary symptoms include epigastric distension(1.1).Compared with the traditional latent structure analysis method,the rules established by CLTA are more compatible with the concept of differentiating primary and secondary symptoms and the common practice of TCM.The quantitative syndrome diagnostic rules of damp-heat in intestine and stomach syndrome constructed by the CLTA method have considerable objectivity in the modeling process.The diagnostic rules established were also compatible with the qualitative concept of TCM theory in stratifying primary and secondary symptoms.Finally,the diagnostic rules are obtained by logistic regression analysis,and the accuracy of the three rules is compared.The results show that the rule recognition accuracy obtained by CLTA is the highest.Therefore,the syndrome diagnostic rules of damp-heat in intestine and stomach obtained from the analysis of CLTA are in line with the constraint semantics of primary and secondary diseases and the theory of traditional Chinese medicine.

Objective:To discuss the effect of over-expression of solute carrier family 7 member 5(SLC7A5)gene on the apoptosis of ovarian granulosa cells of the rats,and to clarify its related mechanism.Methods:Four 3-week-old SPF grade SD female rats were used to extract the primary ovarian granulosa cells of the rats.These cells were divided into negative control group(NC group)and follicle-stimulating hormone receptor(FSHR)staining group(FSHR group).Immunofluorescence staining was used to detect the expressions of FSHR protein in the ovarian granulosa cells of the rats to identify the successful isolation of the primary ovarian granulosa cells of the rats.The ovarian granulosa cells were divided into control group(transfected with empty vector plasmid)and OE-SLC7A5 group(transfected with SLC7A5 over-expression plasmid).Real-time fluoresscence quantitative PCR(RT-qPCR)and Western blotting methods were used to verify the transfection efficiency of the cells;flow cytometry was used to detect the apoptotic rates and cell cycle percentages of the ovarian granulosa cells in two groups;RT-qPCR method was used to detect the expression levels of SLC7A5,cysteinyl aspartate specific proteinase(Caspase)-3,Caspase-8,and tumor necrosis factor-α(TNF-α)mRNA in the ovarian granulosa cells in two groups;Western blotting method was used to detect the expression levels of SLC7A5,Caspase-3,cleaved Caspase-3,Caspase-8,cleaved Caspase-8,and TNF-α proteins in the ovarian granulosa cells in two groups.Results:The fluorescence microscope observation results showed that the ovarian granulosa cells appeared spindle-shaped or irregular and specifically expressed FSHR.No FSHR green fluorescence was observed in NC group,while FSHR green fluorescence expression was observed in FSHR group,indicating successful isolation of primary ovarian granulosa cells of the rats.Compared with control group,the expression levels of SLC7A5 mRNA and protein in the ovarian granulosa cells in OE-SLC7A5 group were significantly increased(P<0.05),indicating successful transfection of SLC7A5 over-expression plasmid into the ovarian granulosa cells.The flow cytometry results showed that compared with control group,the apoptotic rate of the cells in OE-SLC7A5 group was significantly increased(P<0.05).Compared with control group,the percentage of the ovarian granulosa cells at S phase in OE-SLC7A5 group was significantly decreased(P<0.05).The RT-qPCR results showed that compared with control group,the expression levels of TNF-α,Caspase-3,and Caspase-8 mRNA in the ovarian granulosa cells in OE-SLC7A5 group were significantly increased(P<0.05).The Western blotting results showed that compared with control group,the expression levels of TNF-α,Caspase-8,cleaved Caspase-8,Caspase-3,cleaved Caspase-3,and SLC7A5 proteins in the ovarian granulosa cells in OE-SLC7A5 group were significantly increased(P<0.05).Conclusion:The increased expression of SLC7A5 protein promotes the apoptosis of the granulosa cells by upregulating the expressions of TNF-α,Caspase-8,and Caspase-3 apoptotic pathways.

Suppression of cellular O-linked β-N-acetylglucosaminylation (O-GlcNAcylation) can repress prolifera-tion and migration of various cancer cells,which opens a new avenue for cancer therapy.Based on the regulation of insulin gene transcription,we designed a cell-based fluorescent reporter capable of sensing cellular O-GlcNAcylation in HEK293T cells.The fluorescent reporter mainly consists of a reporter (green fluorescent protein (GFP)),an internal reference (red fluorescent protein),and an operator (neuronal differentiation 1),which serves as a "sweet switch" to control GFP expression in response to cellular O-GlcNAcylation changes.The fluorescent reporter can efficiently sense reduced levels of cellular O-GlcNAcylation in several cell lines.Using the fluorescent reporter,we screened 120 natural products and obtained one compound,sesamin,which could markedly inhibit protein O-GlcNAcylation in HeLa and human colorectal carcinoma-116 cells and repress their migration in vitro.Altogether,the present study demonstrated the development of a novel strategy for anti-tumor drug screening,as well as for con-ducting gene transcription studies.

OBJECTIVETo treat patients with vascular mild cognitive impairment (VMCI) using traditional Chinese medicine (TCM), it is necessary to classify the patients into TCM syndrome types and to apply different treatments to different types. In this paper, we investigate how to properly carry out the classification for patients with VMCI aged 50 or above using a novel data-driven method known as latent tree analysis (LTA).

METHODA cross-sectional survey on VMCI was carried out in several regions in Northern China between February 2008 and February 2012 which resulted in a data set that involves 803 patients and 93 symptoms. LTA was performed on the data to reveal symptom co-occurrence patterns, and the patients were partitioned into clusters in multiple ways based on the patterns. The patient clusters were matched up with syndrome types, and population statistics of the clusters are used to quantify the syndrome types and to establish classification rules.

RESULTSEight syndrome types are identified: Qi deficiency, Qi stagnation, Blood deficiency, Blood stasis, Phlegm-dampness, Fire-heat, Yang deficiency, and Yin deficiency. The prevalence and symptom occurrence characteristics of each syndrome type are determined. Quantitative classification rules are established for determining whether a patient belongs to each of the syndrome types.

CONCLUSIONA solution for the TCM syndrome classification problem for patients with VMCI and aged 50 or above is established based on the LTA of unlabeled symptom survey data. The results can be used as a reference in clinic practice to improve the quality of syndrome differentiation and to reduce diagnosis variances across physicians. They can also be used for patient selection in research projects aimed at finding biomarkers for the syndrome types and in randomized control trials aimed at determining the efficacy of TCM treatments of VMCI.

The efficacy of traditional Chinese medicine (TCM) treatments for Western medicine (WM) diseases relies heavily on the proper classification of patients into TCM syndrome types. The authors developed a data-driven method for solving the classification problem, where syndrome types were identified and quantified based on statistical patterns detected in unlabeled symptom survey data. The new method is a generalization of latent class analysis (LCA), which has been widely applied in WM research to solve a similar problem, i.e., to identify subtypes of a patient population in the absence of a gold standard. A well-known weakness of LCA is that it makes an unrealistically strong independence assumption. The authors relaxed the assumption by first detecting symptom co-occurrence patterns from survey data and used those statistical patterns instead of the symptoms as features for LCA. This new method consists of six steps: data collection, symptom co-occurrence pattern discovery, statistical pattern interpretation, syndrome identification, syndrome type identification and syndrome type classification. A software package called Lantern has been developed to support the application of the method. The method was illustrated using a data set on vascular mild cognitive impairment.
Data Collection ; Data Interpretation, Statistical ; Diagnosis, Differential ; Humans ; Medicine, Chinese Traditional

Metabolomics,a hot field of research of life science in recent years,is to analyze endogenous smallmolecule metabolites in biological samples for an overall understanding of the characteristics of metabolic disorders.A growing number of studies have confirmed the application of metabolomics for clinical diagnosis and treatment of diseases."Metabolic fingerprint" of biological fluids can be employed for disease prevention,timely diagnosis,accurate treatment,prognostic assessment and drug discovery.Clinical metabolomics is to measure low-molecule-weight metabolites' alterations of individuals in response to physiological stressors,disease processes,or drug therapy,aiming to discover potential biomarkers and drug targets.Coronary artery disease (CAD) is characterized as complex molecular events.Metabolic disturbances are involved in CAD progression.The application of metabolomics to CAD is an emerging field.Advances in metabolomics improve our knowledge on CAD in early diagnosis,prognostic prediction,and personalized therapy.

Objective: In China, doctors at TCM hospitals and clinics often divide patients with a Western medicine (WM) disease into several syndrome classes from the TCM perspective and treat patients in different classes using different principles. A key problem is how to carry out the classification properly. We propose an evidence-based ap-proach for solving the problem where evidence is obtained by analyzing unlabeled symptom data using latent tree models.Method: In previous work, we have shown how latent tree analysis of symptom data can be used to identify TCM syndrome classes among patients with a WM disease. In the paper, we investigate how to establish classification rules for distinguishing between the classes.Results: We have applied the method to a data set about Vascular Mild Cognitive Impairment that involves 93 symptoms and 803 patients. Nine syndrome types are identified, along with the corresponding classification rules. Conclusions: An evidence-based approach to the TCM patient classification prob-lem has been developed. The approach can be used to answer the following questions about a WM disease: What TCM syndrome classes are there? What are the sizes of the classes? What are the statistical characteristics of each class? How can one differentiate between the different classes?