Comparative efficacy and safety of traditional Chinese patent medicine for endometriosis: A Bayesian network meta-analysis protocol

Luoshi Neiyi Prescription inhibits estradiol synthesis and inflammation in endometriosis through the HIF1A/EZH2/SF-1 pathway

ETHNOPHARMACOLOGICAL RELEVANCE

Endometriosis (EMS) is a common gynecological disease that causes dysmenorrhea, chronic pelvic pain and infertility. Luoshi Neiyi Prescription (LSNYP), a traditional Chinese medicine (TCM) formula, is used to relieve EMS in the clinic.

AIMS

This study aimed to examine the active components of LSNYP and the possible mechanism involved in its treatment of EMS.

MATERIALS AND METHODS

Ultrahigh-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS) was used to identify the chemical components of LSNYP. Human primary ectopic endometrial stromal cells (ecESCs) and eutopic endometrial stromal cells (euESCs) were isolated, and the expression levels of hypoxia inducible factor 1A (HIF1A), enhancer of zeste homolog 2 (EZH2) and steroidogenic factor 1 (SF-1) were detected by immunofluorescence and qPCR. Cobalt chloride (CoCl2) was utilized to construct an in vitro hypoxic environment, and lentiviruses were engineered to downregulate HIF1A and EZH2 and upregulate EZH2. Subsequently, the expression levels of HIF1A, EZH2, and SF-1 were measured using qPCR or western blotting. The binding of EZH2 to the SF-1 locus in ESCs was examined via ChIP. Furthermore, the effects of LSNYP on the HIF1A/EZH2/SF-1 pathway were evaluated both in vitro and in vivo.

RESULTS

A total of 185 components were identified in LSNYP. The protein and gene expression levels of HIF1A and SF-1 were increased, whereas those of EZH2 were decreased in ecESCs. After treating euESCs with 50 μmol L-1 CoCl2 for 24 h, cell viability and estradiol (E2) production were enhanced. Hypoxia decreased EZH2 protein expression, while si-HIF1A increased it. SF-1 was increased when EZH2 was downregulated in normal and hypoxic environments, whereas the overexpression of EZH2 led to a decrease in SF-1 expression. ChIP revealed that hypoxia reduced EZH2 binding to the SF-1 locus in euESCs. In vitro, LSNYP-containing serum decreased E2 and prostaglandin E2 (PGE2) production, inhibited cell proliferation and invasion, and reduced the expression of HIF1A, SF-1, steroidogenic acute regulatory protein (StAR), and aromatase cytochrome P450 (P450arom). In vivo, LSNYP suppressed inflammation and adhesion and inhibited the HIF1A/EZH2/SF-1 pathway in endometriotic tissues.

CONCLUSIONS

LSNYP may exert pharmacological effects on EMS by inhibiting E2 synthesis and inflammation through regulation of the HIF1A/EZH2/SF-1 pathway. These results suggest that LSNYP may be a promising candidate for the treatment of EMS.

An artificial intelligence approach for investigating multifactorial pain-related features of endometriosis

Endometriosis is a debilitating, chronic disease that is estimated to affect 11% of reproductive-age women. Diagnosis of endometriosis is difficult with diagnostic delays of up to 12 years reported. These delays can negatively impact health and quality of life. Vague, nonspecific symptoms, like pain, with multiple differential diagnoses contribute to the difficulty of diagnosis. By investigating previously imprecise symptoms of pain, we sought to clarify distinct pain symptoms indicative of endometriosis, using an artificial intelligence-based approach. We used data from 473 women undergoing laparoscopy or laparotomy for a variety of surgical indications. Multiple anatomical pain locations were clustered based on the associations across samples to increase the power in the probability calculations. A Bayesian network was developed using pain-related features, subfertility, and diagnoses. Univariable and multivariable analyses were performed by querying the network for the relative risk of a postoperative diagnosis, given the presence of different symptoms. Performance and sensitivity analyses demonstrated the advantages of Bayesian network analysis over traditional statistical techniques. Clustering grouped the 155 anatomical sites of pain into 15 pain locations. After pruning, the final Bayesian network included 18 nodes. The presence of any pain-related feature increased the relative risk of endometriosis (p-value < 0.001). The constellation of chronic pelvic pain, subfertility, and dyspareunia resulted in the greatest increase in the relative risk of endometriosis. The performance and sensitivity analyses demonstrated that the Bayesian network could identify and analyze more significant associations with endometriosis than traditional statistical techniques. Pelvic pain, frequently associated with endometriosis, is a common and vague symptom. Our Bayesian network for the study of pain-related features of endometriosis revealed specific pain locations and pain types that potentially forecast the diagnosis of endometriosis.

Clinical efficacy of turtle shell decocted pills for endometriosis and their influence on cellular immunity

OBJECTIVE

To explore the therapeutic efficacy of the levonorgestrel-releasing intrauterine system combined with turtle shell decocted pills for endometriosis and their effect on cellular immune function.

METHODS

Clinical data of 118 patients with endometriosis admitted to Taizhou First People's Hospital from January 2019 to January 2020 were retrospectively analyzed. The patients were assigned into a single-drug group (n=68) and a combined traditional Chinese medicine group (n=50) according to treatment methods. The single-drug group was treated with the levonorgestrel-releasing intrauterine system, and the combined traditional Chinese medicine group was treated with additional turtle shell decocted pills for three cycles for a total of 12 weeks. Enzyme-linked immunosorbent assay was adopted to measure the concentration of Th1 cytokines (tumor necrosis factor (TNF)-α and interferon (IFN)-γ) and Th2 cytokines (interleukin (IL)-6 and IL-10), and the protein level of programmed cell death 1 (PD-1) and programmed cell death ligand 1 (PD-L1). The levels of serum luteinizing hormone (LH), follicle stimulating hormone (FSH), and estradiol (E2) were compared between the two groups. The response rate of treatment, the control rate of blood pressure and the incidence of adverse reactions were recorded in both groups.

RESULTS

The response rate of treatment in the combined traditional Chinese medicine group was higher than that in the single-drug group (P<0.05). Compared to before treatment, the TNF-α and IFN-γ increased in both groups after treatment, and the expressions were higher in the combined traditional Chinese medicine group than in the single-drug group (all P<0.05). After treatment, the levels of IL-6, IL-10, PD-1, and PD-L1 decreased, and the decreases in the combined traditional Chinese medicine group were greater than those in the single-drug group (all P<0.05). Serum LH, FSH and E2 levels before and after the treatment in the two groups were not statistically different (all P>0.05). The incidence of treatment-related adverse reactions between the two groups of patients was also not statistically different (P>0.05).

CONCLUSION

Turtle shell decocted pills can increase the clinical efficacy of levonorgestrel-releasing intrauterine system in the treatment of endometriosis, reduce levels of PD-1, and PD-L1 and improve cellular immune function. The pills do not affect the secretion of ovarian hormones or increase adverse reactions.

Network meta-analyses in reproductive medicine: challenges and opportunities

Network meta-analysis allows researchers to synthesise both direct and indirect evidence, thus enabling simultaneous comparisons of multiple treatments. A relatively recent addition to evidence synthesis in reproductive medicine, this approach has become increasingly popular. Yet, the underlying assumptions of network meta-analyses, which drive the validity of their findings, have been frequently ignored. In this article, we discuss the strengths and limitations of network meta-analyses. In addition, we present an overview of published network meta-analyses in reproductive medicine, summarize their challenges and provide insights into future research opportunities.