Nondual Antiplatelet Therapy Versus Dual Antiplatelet Therapy Before Transcatheter Aortic Valve Replacement: A Systematic Review and Meta-analysis.

ABSTRACT: Transcatheter aortic valve replacement (TAVR) is an interventional procedure performed in patients with severe aortic stenosis and often required perioperative antiplatelet therapy. Most previous studies have focused on antiplatelet therapy following TAVR. However, few studies have investigated the prognostic effect of preoperative antiplatelet therapy in patients undergoing TAVR. This study aimed to compare the efficacy and safety of nondual antiplatelet therapy (non-DAPT) and DAPT before TAVR. We performed a systematic search of Embase, PubMed, and Web of Science until February 2023. Studies were eligible if they compared non-DAPT (single antiplatelet therapy or no antiplatelet therapy) with DAPT in patients before TAVR. A total of 5 studies, including 2329 patients, met the inclusion criteria and were included in the meta-analysis. Preoperative non-DAPT significantly decreased minor bleeding events compared with preoperative DAPT [odds ratio 0.58; 95% confidence interval: 0.44-0.76]. There were no significant differences in the incidence of other bleeding events, transfusions, stroke, myocardial infarction, or all-cause death. Preoperative single antiplatelet therapy significantly decreased the incidence of major bleeding compared with DAPT (odds ratio 0.14; 95% confidence interval: 0.04-0.48). Preoperative non-DAPT significantly reduced minor bleeding events in patients undergoing TAVR, without increasing the risk of stroke and myocardial infarction.

Efficacy and safety of modified tetracycline dosing in a quadruple therapy for Helicobacter pylori: A retrospective single center study.

BACKGROUND: Although highly effective as a component of Helicobacter pylori (H. pylori) treatment regimen, tetracycline is associated with a high incidence of medication-related adverse events. Modified dosing of tetracycline as part of quadruple therapy may improve safety while providing comparable eradication rates. AIM: To evaluate the efficacy and safety of modified dosing of tetracycline in patients receiving tetracycline and furazolidone-containing quadruple therapy in patients with H. pylori infection. METHODS: Consecutive patients (10/2020-12/2021) who received tetracycline and furazolidone quadruple therapy for H. pylori infection at Sir Run Run Shaw Hospital were identified. All patients received tetracycline, furazolidone, proton pump inhibitor, and bismuth for 14 d as primary or rescue therapy. Modified tetracycline dose group received tetracycline 500 mg twice daily while standard group received 750 mg twice daily or 500 mg three times daily. RESULTS: Three hundred and ninety-four patients [mean age = 46.3 ± 13.9, male = 137 (34.8%), and 309 (78.4%) primary therapy] completed tetracycline and furazolidone quadruple therapy for H. pylori infection including those who received modified tetracycline dose in 157 and standard doses in 118 (750 mg twice daily) and 119 (500 mg three times daily). Eradication rates in the modified tetracycline dose group were 92.40% and in the standard groups, eradication rates were 93.20% for 750 mg twice daily group and 92.43% for 500 mg three times daily group, respectively, without statistical difference (P = 0.959). The incidence of adverse events was lower in the modified tetracycline dose (15.3% vs 32.3% and 29.4%; P = 0.002) compared to the standard dose group. CONCLUSION: In a real-world experience, modified tetracycline dosing as part of tetracycline and furazolidone quadruple therapy for 14 d demonstrated high efficacy, comparable to standard tetracycline dose regimens, with a favorable safety profile.

CS27109, A Selective Thyroid Hormone Receptor-ß Agonist Alleviates Metabolic-Associated Fatty Liver Disease in Murine Models.

Background/Aim: Thyroid hormone receptor-ß (THR-ß) agonists play crucial roles in dyslipidemia and metabolic associated fatty liver disease (MAFLD). We developed a novel oral and liver-targeted THR-ß agonist, CS27109, and evaluated its efficacy in the treatment of metabolic disorders. Materials and Methods: We evaluated in vitro and in vivo efficacy and/or safety of CS27109 along with MGL3196 (a phase III THR-ß agonist). Results: CS27109 showed pronounced activity and selectivity to THR-ß and favorable PK properties, which was equivalent to MGL3196. In the hamster model, animals treated with a high dose of CS27109 showed equivalent reductions in serum TC and LDL-c with groups treated with MGL3196. In the rat model, CS27109 and MGL3196 reduced serum ALT, TC, TG, LDL-c, liver weight ratio, and liver steatosis. CS27109 simultaneously decreased liver TG and TC, and MGL3196 additionally reduced AST. In the mouse model, CS27109 dose-dependently reduced serum AST, ALT, liver inflammation, and NAS score, and also downregulated TC, LDL-c, liver steatosis, and fibrosis, but not in a dose-dependent manner. MGL3196 revealed an equivalent effect with CS27109 in that model. CS27109 also exhibited tolerable toxicity to the heart. Conclusions: CS27109 shows comparative in vitro and in vivo efficacy with MGL3196, suggesting its potential therapeutic application in the treatment of MAFLD such as dyslipidemia and steatohepatitis.

Model exploration for discovering COVID-19 targeted traditional Chinese medicine.

In terms of treatment, a particularly targeted drug is needed to combat the COVID-19 pandemic. Although there are currently no specific drugs for COVID-19, traditional Chinese medicine(TCM) is clearly effective. It is recommended that through data analysis and mining of TCM cases (expert experience) and population evidence (RCT and cohort studies), core prescriptions for various efficacy can be obtained. Starting from a multidimensional model of regulating immunity, improving inflammation, and protecting multiple organs, this paper constructs a multidimensional model of targeted drug discovery, integrating molecular, cellular, and animal efficacy evaluation. Through functional activity testing, biophysical detection of compound binding to target proteins, multidimensional pharmacodynamic evaluation systems of cells (Vero E6, Vero, Vero81, Huh7, and caca2) and animals (mice infected with the new coronavirus, rhesus macaques, and hamsters), the effectiveness of effective preparations was evaluated, and various efficacy effects including lung moisturizing, dehumidification and detoxification were obtained. Using modern technology, it is now possible to understand how the immune system is controlled, how inflammation is reduced, and how various organs are protected. Complete early drug characterization and finally obtain effective targeted TCM. This article provides a demonstration resource for the development of new drugs specifically for TCM.

Reporter Molecules Embedded Au@Ag Core-Shell Nanospheres as SERS Nanotags for Cardiac Troponin I Detection.

Rapid and accurate detection of acute myocardial infarction can improve patients' chances of survival. Cardiac troponin I (cTn I) is an important diagnostic biomarker for acute myocardial infarction. However, current immunoassays are insufficient to accurately measure cTn I, as they have limited detection sensitivity and are time-consuming. Surface-enhanced Raman scattering (SERS) is a brilliant fingerprints diagnostic technique characterised by ultrasensitivity, fast response, and qualitative and quantitative analysis capabilities. In this study, reporter molecules (4-Mercaptobenzoic acid, 4-MBA) embedded Au@Ag core-shell nanospheres as SERS nanotags were prepared for the detection of cTn I. As the Raman reporters were embedded between the core and the shell, they could be protected from the external environment and nanoparticle aggregation. Excellent SERS performances were obtained due to the enhanced local electromagnetic field in the gap of core and shell metals. In a standard phosphate buffered saline (PBS) environment, the limit of detection for cTn I was 0.0086 ng mL-1 (8.6 ppt) with a good linear relationship. The excellent Raman detection performance was attributed to the localized surface plasmon resonance effect and strong electromagnetic field enhancement effect produced by the gap between the Au core and the Ag shell. The SERS nanotags we prepared were facile to synthesize, and the analysis procedure could be completed quickly (15 min), which made the detection of cTn I faster. Therefore, the proposed SERS nanotags have significant potential to be a faster and more accurate tool for acute myocardial infarction diagnostics.

EARLY BUD-BREAK 1 and EARLY BUD-BREAK 3 control resumption of poplar growth after winter dormancy.

Bud-break is an economically and environmentally important process in trees and shrubs from boreal and temperate latitudes, but its molecular mechanisms are poorly understood. Here, we show that two previously reported transcription factors, EARLY BUD BREAK 1 (EBB1) and SHORT VEGETATIVE PHASE-Like (SVL) directly interact to control bud-break. EBB1 is a positive regulator of bud-break, whereas SVL is a negative regulator of bud-break. EBB1 directly and negatively regulates SVL expression. We further report the identification and characterization of the EBB3 gene. EBB3 is a temperature-responsive, epigenetically-regulated, positive regulator of bud-break that provides a direct link to activation of the cell cycle during bud-break. EBB3 is an AP2/ERF transcription factor that positively and directly regulates CYCLIND3.1 gene. Our results reveal the architecture of a putative regulatory module that links temperature-mediated control of bud-break with activation of cell cycle.

Quantitative Structure-Activity Relationship (QSAR) Model for the Severity Prediction of Drug-Induced Rhabdomyolysis by Using Random Forest.

Drug-induced rhabdomyolysis (DIR) is a rare and potentially life-threatening muscle injury that is characterized by low incidence and high risk. To our best knowledge, the performance of the current predictive models for the early detection of DIR is suboptimal because of the scarcity and dispersion of DIR cases. Therefore, on the basis of the curated drug information from the Drug-Induced Rhabdomyolysis Atlas (DIRA) database, we proposed a random forest (RF) model to predict the DIR severity of the marketed drugs. Compared with the state-of-art methods, our proposed model outperformed extreme gradient boosting, support vector machine, and logistic regression in distinguishing the Most-DIR concern drugs from the No-DIR concern drugs (Matthews correlation coefficient (MCC) and recall rate of our model were 0.46 and 0.81, respectively). Our model was subsequently applied to predicting the potentially serious DIR for 1402 drugs, which were reported to cause DIR by the postmarketing DIR surveillance data in the FDA Spontaneous Adverse Events Reporting System (FAERS). As a result, 62.7% (94) of drugs ranked in the top 150 drugs with the Most-DIR concerns in FAERS can be identified by our model. The top four drugs (odds ratio >30) including acepromazine, rapacuronium, oxyphenbutazone, and naringenin were correctly predicted by our model. In conclusion, the RF model can well predict the Most-DIR concern drug only based on the chemical structure information and can be a facilitated tool for early DIR detection.

Corrigendum: Basis and Design of a Randomized Clinical Trial to Evaluate the Effect of Jinlida Granules on Metabolic Syndrome in Patients With Abnormal Glucose Metabolism.

[This corrects the article DOI: 10.3389/fendo.2020.00415.].

Improving Model Performance on the Stratification of Breast Cancer Patients by Integrating Multiscale Genomic Features.

In clinical cancer research, it is a hot topic on how to accurately stratify patients based on genomic data. With the development of next-generation sequencing technology, more and more types of genomic features, such as mRNA expression level, can be used to distinguish cancer patients. Previous studies commonly stratified patients by using a single type of genomic features, which can only reflect one aspect of the cancer. In fact, multiscale genomic features will provide more information and may be helpful for clinical prediction. In addition, most of the conventional machine learning algorithms use a handcrafted gene set as features to construct models, which is generally selected by a statistical method with an arbitrary cut-off, e.g., p value < 0.05. The genes in the gene set are not necessarily related to the cancer and will make the model unreliable. Therefore, in our study, we thoroughly investigated the performance of different machine learning methods on stratifying breast cancer patients with a single type of genomic features. Then, we proposed a strategy, which can take into account the degree of correlation between genes and cancer patients, to identify the features from mRNAs and microRNAs, and evaluated the performance of the models with the new combined features of the multiscale genomic features. The results showed that, compared with the models constructed with a single type of features, the models with the multiscale genomic features generated by our proposed method achieved better performance on stratifying the ER status of breast cancer patients. Moreover, we found that the identified multiscale genomic features were closely related to the cancer by gene set enrichment analysis, indicating that our proposed strategy can well reflect the biological relevance of the genes to breast cancer. In conclusion, modelling with multiscale genomic features closely related to the cancer not only can guarantee the prediction performance of the models but also can effectively provide candidate genes for interpreting the mechanisms of cancer.

Basis and Design of a Randomized Clinical Trial to Evaluate the Effect of Jinlida Granules on Metabolic Syndrome in Patients With Abnormal Glucose Metabolism.

Background: Metabolic syndrome (MS) is a powerful risk factor for cardiovascular and cerebrovascular diseases. Although lifestyle intervention reduces several of the symptoms of the syndrome and cardiovascular risks, the lifestyle intervention that yields the benefits is restrictive. Jinlida is a Chinese patent medicine that has shown activity in type 2 diabetes, which has been approved in China. Preclinical studies in Jinlida granules support an improved role of abnormal glucose and lipids metabolism as well as reducing weight. Here, we describe the protocol of an ongoing clinical trial investigating a new therapy for metabolic syndrome in patients with abnormal glucose metabolism. Methods: This study will enroll 880 subjects (aged 18-70 years) who have metabolic syndromes with abnormal glucose metabolism. All the participants in a double-blind, parallel, randomized, placebo-controlled trial, will receive Jinlida or placebo, orally, 9 g/time, three times daily for 2-4 years period on the basis of lifestyle intervention. The primary outcome measure (Incidence of type 2 diabetes) will be assessed during intervention cycles. Adverse events were monitored. All statistical tests will be performed using a two-sided test, and a p ≤ 0.05 (two-sided test) will be considered to be statistically significant results. Discussion: Results from this study will provide evidence on whether incorporating oral Jinlida granules treatment into lifestyle intervention can delay or inhibit the development of diabetes mellitus in metabolic syndrome subjects with abnormal glucose metabolism. Clinical trial registration: Registered at http://www.chictr.org.cn/enIndex.aspx. Trial registration number: ChiCTR1900023241.

Uncovering the prognostic gene signatures for the improvement of risk stratification in cancers by using deep learning algorithm coupled with wavelet transform.

BACKGROUND: The aim of gene expression-based clinical modelling in tumorigenesis is not only to accurately predict the clinical endpoints, but also to reveal the genome characteristics for downstream analysis for the purpose of understanding the mechanisms of cancers. Most of the conventional machine learning methods involved a gene filtering step, in which tens of thousands of genes were firstly filtered based on the gene expression levels by a statistical method with an arbitrary cutoff. Although gene filtering procedure helps to reduce the feature dimension and avoid overfitting, there is a risk that some pathogenic genes important to the disease will be ignored. RESULTS: In this study, we proposed a novel deep learning approach by combining a convolutional neural network with stationary wavelet transform (SWT-CNN) for stratifying cancer patients and predicting their clinical outcomes without gene filtering based on tumor genomic profiles. The proposed SWT-CNN overperformed the state-of-art algorithms, including support vector machine (SVM) and logistic regression (LR), and produced comparable prediction performance to random forest (RF). Furthermore, for all the cancer types, we firstly proposed a method to weight the genes with the scores, which took advantage of the representative features in the hidden layer of convolutional neural network, and then selected the prognostic genes for the Cox proportional-hazards regression. The results showed that risk stratifications can be effectively improved by using the identified prognostic genes as feature, indicating that the representative features generated by SWT-CNN can well correlate the genes with prognostic risk in cancers and be helpful for selecting the prognostic gene signatures. CONCLUSIONS: Our results indicated that gene expression-based SWT-CNN model can be an excellent tool for stratifying the prognostic risk for cancer patients. In addition, the representative features of SWT-CNN were validated to be useful for evaluating the importance of the genes in the risk stratification and can be further used to identify the prognostic gene signatures.

The interaction between the gut Microbiota and herbal medicines.

As technologies used to study the gut microbiota have improved, the relationship between the gut microbiota and health has become increasingly obvious. Herbal medicines have been used for thousands of years, and are known to be "simple, convenient, cheap, and effective". However, due to many factors, such as their complex composition, unclear active compounds, and poor knowledge of their underlying mechanisms, the clinical applications of herbal medicines are not widely recognized. Recently, there have been an increasing number of studies which have investigated the interaction between the gut microbiota and herbal medicines. We have found that interactions between the gut microbiota and herbal medicines occur primarily through two pathways. One pathway is that the gut microbiota "digests" the herbal medicines into absorbable active small molecules, which enter the body and induce physiological changes. The other is that herbal medicines regulate the composition of the gut microbiota and its secretions, thereby changed gut microbiota and its secretions inducing physiological changes. In summary, the interactions between the gut microbiota and herbal medicines can be attributed to absorbable active small molecules and changed gut microbiota and its secretions. Our findings will aid the exploration of the mechanisms and pathways underlying the function of herbal medicines in the future. This review also summarizes the direction of future research and the main problems faced by the current researchers.

Comparative Analysis for the Performance of Variant Calling Pipelines on Detecting the de novo Mutations in Humans.

Despite of the low occurrence rate in the entire genomes, de novo mutation is proved to be deleterious and will lead to severe genetic diseases via impacting on the gene function. Considering the fact that the traditional family based linkage approaches and the genome-wide association studies are unsuitable for identifying the de novo mutations, in recent years, several pipelines have been proposed to detect them based on the whole-genome or whole-exome sequencing data and were used for calling them in the rare diseases. However, how the performance of these variant calling pipelines on detecting the de novo mutations is still unexplored. For the purpose of facilitating the appropriate choice of the pipelines and reducing the false positive rate, in this study, we thoroughly evaluated the performance of the commonly used trio calling methods on the detection of the de novo single-nucleotide variants (DNSNVs) by conducting a comparative analysis for the calling results. Our results exhibited that different pipelines have a specific tendency to detect the DNSNVs in the genomic regions with different GC contents. Additionally, to refine the calling results for a single pipeline, our proposed filter achieved satisfied results, indicating that the read coverage at the mutation positions can be used as an effective index to identify the high-confidence DNSNVs. Our findings should be good support for the committees to choose an appropriate way to explore the de novo mutations for the rare diseases.

Combined Targeting of Estrogen Receptor Alpha and XPO1 Prevent Akt Activation, Remodel Metabolic Pathways and Induce Autophagy to Overcome Tamoxifen Resistance.

A majority of breast cancer specific deaths in women with ERα (+) tumors occur due to metastases that are resistant to endocrine therapy. There is a critical need for novel therapeutic approaches to resensitize recurrent ERα (+) tumors to endocrine therapies. The objective of this study was to elucidate mechanisms of improved effectiveness of combined targeting of ERα and the nuclear transport protein XPO1 in overcoming endocrine resistance. Selinexor (SEL), an XPO1 antagonist, has been evaluated in multiple late stage clinical trials in patients with relapsed and /or refractory hematological and solid tumor malignancies. Our transcriptomics analysis showed that 4-Hydroxytamoxifen (4-OHT), SEL alone or their combination induced differential Akt signaling- and metabolism-associated gene expression profiles. Western blot analysis in endocrine resistant cell lines and xenograft models validated differential Akt phosphorylation. Using the Seahorse metabolic profiler, we showed that ERα-XPO1 targeting changed the metabolic phenotype of TAM-resistant breast cancer cells from an energetic to a quiescent profile. This finding demonstrated that combined targeting of XPO1 and ERα rewired the metabolic pathways and shut down both glycolytic and mitochondrial pathways that would eventually lead to autophagy. Remodeling metabolic pathways to regenerate new vulnerabilities in endocrine resistant breast tumors is novel, and given the need for better strategies to improve therapy response in relapsed ERα (+) tumors, our findings show great promise for uncovering the role that ERα-XPO1 crosstalk plays in reducing cancer recurrences.

[Systemic review of Jintiange Capsules in treatment of postmenopausal osteoporosis].

To systemically evaluate the efficacy and safety of Jintiange Capsules in the treatment of postmenopausal osteoporosis(PMOP).Seven literature databases were retrieved systematically,and two reviewers independently searched and screened studies,extracted data,and included all the randomized controlled trials on Jintiange Capsules in the treatment of PMOP.Interventions included comparison of Jintiange Capsules with placebo and routine treatment,and the studies on Jintiange Capsules combined with routine treatment versus conventional treatment were also included.The evaluation indicators of the study included at least one of the followings:fracture,quality of life,daily living ability,clinical symptoms,death,adverse events/adverse reactions,bone density,and bone metabolism indexes.The original study quality evaluation was conducted by following the Cochrane Handbook standard and statistical analysis was performed by using Rev Man 5.2.A total of 7 randomized controlled trials were included and the study quality was low.Meta-analysis showed that as compared with conventional treatment alone,Jintiange Capsules combined with conventional treatment showed more obvious effects in pain relief(MD=-0.98,95% CI[-1.55,-0.41],P=0.000 8),increasing blood calcium levels(MD=0.05,95% CI[0.02,0.09],P=0.003) and lowering serum alkaline phosphatase levels(MD=-12.92,95% CI[-24.09,-1.75],P=0.02).In addition,the Chinese patent medicine alone or in combination with conventional treatment was relatively safe.In conclusion,Jintiange Capsules has a certain effect in treating PMOP,but the quality of evidence is low.It is necessary to conduct well designed randomized controlled trials and select recognized evaluation indicators,especially the end outcomes in order to further improve the clinical evidence.

Free Fatty Acids Rewire Cancer Metabolism in Obesity-Associated Breast Cancer via Estrogen Receptor and mTOR Signaling.

Obesity is a risk factor for postmenopausal estrogen receptor alpha (ERα)-positive (ER+) breast cancer. Molecular mechanisms underlying factors from plasma that contribute to this risk and how these mechanisms affect ERα signaling have yet to be elucidated. To identify such mechanisms, we performed whole metabolite and protein profiling in plasma samples from women at high risk for breast cancer, which led us to focus on factors that were differentially present in plasma of obese versus nonobese postmenopausal women. These studies, combined with in vitro assays, identified free fatty acids (FFA) as circulating plasma factors that correlated with increased proliferation and aggressiveness in ER+ breast cancer cells. FFAs activated both the ERα and mTOR pathways and rewired metabolism in breast cancer cells. Pathway preferential estrogen-1 (PaPE-1), which targets ERα and mTOR signaling, was able to block changes induced by FFA and was more effective in the presence of FFA. Collectively, these data suggest a role for obesity-associated gene and metabolic rewiring in providing new targetable vulnerabilities for ER+ breast cancer in postmenopausal women. Furthermore, they provide a basis for preclinical and clinical trials where the impact of agents that target ERα and mTOR signaling cross-talk would be tested to prevent ER+ breast cancers in obese postmenopausal women. SIGNIFICANCE: These findings show that obesity-associated changes in certain blood metabolites rewire metabolic programs in cancer cells, influence mammary epithelial cell tumorigenicity and aggressiveness, and increase breast cancer risk.

Effectiveness of traditional Chinese medicine Jinlida granules as an add-on therapy for type 2 diabetes: A system review and meta-analysis of randomized controlled trials.

BACKGROUND: Jinlida granules are a commonly prescribed oral medication in China used in combination with antidiabetic drugs to lower blood glucose. The aim of this study was to systematically identify and pool the findings of randomized controlled trials evaluating the effectiveness and safety of Jinlida granules as add-on therapy for glycemic control in type 2 diabetes (T2D). METHODS: The China National Knowledge Infrastructure (CNKI), Wang Fang, PubMed, China biology medicine (CBM), and VIP Database for Chinese Technical Periodicals (VIP) databases were searched for papers regarding the effects of Jinlida granules in T2D published before 1 July 2018. A pooled analysis of extracted data was performed using random-effects models. RESULTS: In all, data were retrieved for 15 studies including 1810 individuals. Decreases in HbA1c were greater in groups receiving Jinlida granules as add-on therapy compared with control groups (n = 1820; mean difference - 0.66; 95% confidence interval - 0.72, -0.60; P < 0.00001; I2 = 38%). In addition, Jinlida granules reduced body mass index and had beneficial effects on homeostatic model assessment of ß-cell function and homeostasis model assessment of insulin resistance. No obvious adverse events were reported. CONCLUSIONS: Findings from this meta-analysis demonstrate additional benefits of Jinlida granules as an add-on therapy for T2D and that Jinlida granules are generally safe. Treatment with Jinlida granules provided clinically and statistically significant reductions in fasting plasma glucose, 2-hour post-load glucose, and HbA1c levels in patients with T2D. However, the findings should be interpreted with caution due to the small sample size and study limitations.

Long-Term Administration of Conjugated Estrogen and Bazedoxifene Decreased Murine Fecal ß-Glucuronidase Activity Without Impacting Overall Microbiome Community.

Conjugated estrogens (CE) and Bazedoxifene (BZA) combination is used to alleviate menopause-associated symptoms in women. CE+BZA undergo first-pass-metabolism in the liver and deconjugation by gut microbiome via ß-glucuronidase (GUS) enzyme inside the distal gut. To date, the impact of long-term exposure to CE+BZA on the gut microbiome or GUS activity has not been examined. Our study using an ovariectomized mouse model showed that CE+BZA administration did not affect the overall cecal or fecal microbiome community except that it decreased the abundance of Akkermansia, which was identified as a fecal biomarker correlated with weight gain. The fecal GUS activity was reduced significantly and was positively correlated with the abundance of Lactobacillaceae in the fecal microbiome. We further confirmed in Escherichia coli K12 and Lactobacillus gasseri ADH that Tamoxifen-, 4-hydroxy-Tamoxifen- and Estradiol-Glucuronides competed for GUS activity. Our study for the first time demonstrated that long-term estrogen supplementation directly modulated gut microbial GUS activity. Our findings implicate that long-term estrogen supplementation impacts composition of gut microbiota and microbial activity, which affects estrogen metabolism in the gut. Thus, it is possible to manipulate such activity to improve the efficacy and safety of long-term administered estrogens for postmenopausal women or breast cancer patients.

Bazedoxifene and conjugated estrogen combination maintains metabolic homeostasis and benefits liver health.

The bazedoxifene and conjugated estrogens (CE+BZA) combination has been shown to prevent visceral adiposity and weight gain after ovariectomy. However, its impact on the liver transcriptomes associated with prevention of hepatosteatosis is yet to be determined. In the present study, we use liver transcriptomics and plasma metabolomics analysis to characterize the effects of various estrogens on liver. The CE+BZA combination was very effective at preventing ovariectomy-induced weight gain in mice fed a high-fat diet (HFD). In CE+BZA treated animals, liver weight and hepatic lipid deposition were significantly lower than in Vehicle (Veh) treated animals. Additionally, CE+BZA induced unique liver transcriptome and plasma metabolome profiles compared to estradiol, conjugated estrogens alone, and bazedoxifene alone. Blood plasma metabolite analysis identified several metabolites similar to and distinct from other estrogen treatments. Integrated pathway analysis showed that gene networks that were associated with inflammation, reactive oxygen species pathway and lipid metabolism and their relevant metabolites were regulated significantly by CE+BZA treatment. Thus, long-term CE+BZA treatment modulated hepatic metabolic gene networks and their associated metabolites and improves hepatic health without stimulating the uterus.