Exploratory metabolomic analysis for characterizing the metabolic profile of the urinary bladder under estrogen deprivation.

Background: Estrogen homeostasis is crucial for bladder function, and estrogen deprivation resulting from menopause, ovariectomy or ovarian dysfunction may lead to various bladder dysfunctions. However, the specific mechanisms are not fully understood. Methods: We simulated estrogen deprivation using a rat ovariectomy model and supplemented estrogen through subcutaneous injections. The metabolic characteristics of bladder tissue were analyzed using non-targeted metabolomics, followed by bioinformatics analysis to preliminarily reveal the association between estrogen deprivation and bladder function. Results: We successfully established a rat model with estrogen deprivation and, through multivariate analysis and validation, identified several promising biomarkers represented by 3, 5-tetradecadiencarnitine, lysoPC (15:0), and cortisol. Furthermore, we explored estrogen deprivation-related metabolic changes in the bladder primarily characterized by amino acid metabolism imbalance. Conclusion: This study, for the first time, depicts the metabolic landscape of bladder resulting from estrogen deprivation, providing an important experimental basis for future research on bladder dysfunctions caused by menopause.

Synthesis of the DEF-Ring Spirocyclic Core of Cyclopamine.

A stereoselective synthesis of the DEF-ring spirocyclic core of cyclopamine was accomplished using commercially available materials. The key steps in the synthesis were (i) the enantioselective vinylogous Mannich reaction, followed by lactamization to generate the piperidine F ring, and (ii) intramolecular oxidative dearomative spiroetherification to construct the DEF-ring spirocyclic core of cyclopamine. We found that the stereochemistry of the spirocyclization was controlled by the configuration of the methyl group (C-20) in the substrate.

Asymmetric Synthesis of Cyclopamine, a Hedgehog (Hh) Signaling Pathway Inhibitor.

Cyclopamine is a teratogenic steroidal alkaloid, which inhibits the Hedgehog (Hh) signaling pathway by targeting the Smoothened (Smo) receptor. Suppression of Hh signaling with synthetic small molecules has been pursued as a therapeutic approach for the treatment of cancer. We report herein the asymmetric synthesis of cyclopamine based on a two-stage relay strategy. Stage-I: total synthesis of veratramine through a convergent approach, wherein a crucial photoinduced excited-state Nazarov reaction was applied to construct the basic [6-6-5-6] skeleton of C-nor-D-homo-steroid. Stage-II: conversion of veratramine to cyclopamine was achieved through a sequence of chemo-selective redox manipulations.

The TFPI2-PPARγ axis induces M2 polarization and inhibits fibroblast activation to promote recovery from post-myocardial infarction in diabetic mice.

BACKGROUND: Diabetes mellitus is one of the causes of poor ventricular remodelling and poor cardiac recovery after myocardial infarction (MI). We previously reported that tissue factor pathway inhibitor-2 (TFPI2) was downregulated in response to hyperglycaemia and that it played a pivotal role in extracellular matrix (ECM) degradation and cell migration. Nonetheless, the function and mechanism of TFPI2 in post-MI remodelling under diabetic conditions remain unclear. Therefore, in the present study, we investigated the role of TFPI2 in post-MI effects in a diabetic mouse model. RESULTS: TFPI2 expression was markedly decreased in the infarcted myocardium of diabetic MI mice compared with that in non-diabetic mice. TFPI2 knockdown in the MI mouse model promoted fibroblast activation and migration as well as matrix metalloproteinase (MMP) expression, leading to disproportionate fibrosis remodelling and poor cardiac recovery. TFPI2 silencing promoted pro-inflammatory M1 macrophage polarization, which is consistent with the results of TFPI2 downregulation and M1 polarization under diabetic conditions. In contrast, TFPI2 overexpression in diabetic MI mice protected against adverse cardiac remodelling and functional deterioration. TFPI2 overexpression also inhibited MMP2 and MMP9 expression and attenuated fibroblast activation and migration, as well as excessive collagen production, in the infarcted myocardium of diabetic mice. TFPI2 promoted an earlier phenotype transition of pro-inflammatory M1 macrophages to reparative M2 macrophages via activation of peroxisome proliferator-activated receptor gamma. CONCLUSIONS: This study highlights TFPI2 as a promising therapeutic target for early resolution of post-MI inflammation and disproportionate ECM remodelling under diabetic conditions.

Association of body fat distribution with all-cause and cardiovascular mortality in US adults: a secondary analysis using NHANES.

OBJECTIVE: To investigate the association of fat and lean mass in specific regions with all-cause and cardiovascular-related mortality. DESIGN: Population based cohort study. SETTING: US National Health and Nutrition Examination Survey (2003-2006 and 2011-2018). PARTICIPANTS: 22 652 US adults aged 20 years or older. EXPOSURES: Fat and lean mass in specific regions obtained from the whole-body dual-energy X-ray absorptiometry. MAIN OUTCOME MEASURES: All-cause and cardiovascular-related mortality. RESULTS: During a median of 83 months of follow-up, 1432 deaths were identified. Associations between body composition metrics and mortality risks were evident above specific thresholds. For all-cause mortality, Android fat mass showed elevated HRs above 2.46 kg (HR: 1.17, 95% CI 1.02 to 1.34), while Android lean mass (ALM) had similar trends above 2.75 kg (HR: 1.17, 95% CI 1.03 to 1.33), and Android total mass above 5.75 kg (HR: 1.08, 95% CI 1.01 to 1.16). Conversely, lower HRs were observed below certain thresholds: Gynoid fat mass (GFM) below 3.71 kg (HR: 0.72, 95% CI 0.56 to 0.93), Gynoid lean mass below 6.44 kg (HR: 0.77, 95% CI 0.64 to 0.92), and Gynoid total mass below 11.78 kg (HR: 0.76, 95% CI 0.70 to 0.84). Notably, below 0.722 kg, the HR of visceral adipose tissue mass (VATM) was 1.25 (95% CI 1.04 to 1.48) for all-cause mortality, and above 3.18 kg, the HR of total abdominal fat mass was 2.41 (95% CI 1.15 to 5.05). Cardiovascular-related mortality exhibited associations as well, particularly for Android fat mass (AFM) above 1.78 kg (HR: 1.22, 95% CI 1.01 to 1.47) and below 7.16 kg (HR: 0.50, 95% CI 0.36 to 0.69). HRs varied for Gynoid total mass below and above 10.98 kg (HRs: 0.70, 95% CI 0.54 to 0.93, and 1.12, 95% CI 1.02 to 1.23). Android per cent fat, subcutaneous fat mass (SFM), AFM/GFM, and VATM/SFM were not statistically associated with all-cause mortality. Android per cent fat, Gynoid per cent fat, AFM/GFM, and VATM/SFM were not statistically associated with cardiovascular-related mortality. Conicity index showed that the ALM/GLM had the highest performance for all-cause and cardiovascular-related mortality with AUCs of 0.785, and 0.746, respectively. CONCLUSIONS: The relationship between fat or lean mass and all-cause mortality varies by region. Fat mass was positively correlated with cardiovascular mortality, regardless of the region in which they located. ALM/GLM might be a better predictor of all-cause and cardiovascular-related mortality than other body components or body mass index.

Comparison of co-administration of amiodarone and rivaroxaban to co-administration of dronedarone and rivaroxaban for hemorrhage risks after atrial fibrillation ablation.

PURPOSE: To investigate whether co-administration of antiarrhythmic dronedarone and anticoagulant rivaroxaban would increase the risks of hemorrhage after atrial fibrillation (AF) ablation. METHODS: A total of 100 patients with AF who underwent radiofrequency catheter ablation (CA) in the Department of Cardiology, the Affiliated Hospital of Qingdao University from 2019-12 to 2020-11 were included. Patients were divided into an oral dronedarone and rivaroxaban group (D-R group, N = 50) and an oral amiodarone and rivaroxaban group (A-R group, N = 50) according to the postoperative antiarrhythmic and anticoagulation strategies. Patients in 2 groups were given propensity score matching (PSM) to obtain a sample with balanced inter-group covariates. A retrospective observational study was conducted. After 3 months of follow-up, the incidence of clinically relevant non-major bleeding (CRNMB), major hemorrhages, and early AF recurrence was observed. RESULTS: After PSM, 41 patients were included in each group. With similarly distributed baseline characteristics and ablation characteristics after PSM, the CRNMB rate after AF ablation was significantly higher in the D-R group than in the A-R group (26.8% versus 7.3%, P = 0.02), and no major hemorrhages were detected in both groups. No significant difference was observed in the sinus rhythm maintenance rate between the D-R group and the A-R group (26.8% vs. 22.0%, P = 0.43). CONCLUSIONS: Compared to co-administration of amiodarone and rivaroxaban, co-administration of dronedarone and rivaroxaban increases the risk of CRNMB but it does not increase the risk of major hemorrhages in blanking period after AF ablation.

Zonation of Landslide Susceptibility in Ruijin, Jiangxi, China.

Landslides are one of the major geohazards threatening human society. The objective of this study was to conduct a landslide hazard susceptibility assessment for Ruijin, Jiangxi, China, and to provide technical support to the local government for implementing disaster reduction and prevention measures. Machine learning approaches, e.g., random forests (RFs) and support vector machines (SVMs) were employed and multiple geo-environmental factors such as land cover, NDVI, landform, rainfall, lithology, and proximity to faults, roads, and rivers, etc., were utilized to achieve our purposes. For categorical factors, three processing approaches were proposed: simple numerical labeling (SNL), weight assignment (WA)-based and frequency ratio (FR)-based. Then 19 geo-environmental factors were respectively converted into raster to constitute three 19-band datasets, i.e., DS1, DS2, and DS3 from three different processes. Then, 155 observed landslides that occurred in the past decades were vectorized, among which 70% were randomly selected to compose a training set (TS1) and the remaining 30% to form a validation set (VS1). A number of non-landslide (no-risk) samples distributed in the whole study area were identified in low slope (<1-3°) zones such as urban areas and croplands, and also added to the TS1 and VS1 in the same ratio. For comparison, we used the FR approach to identify the no-risk samples in both flat and non-flat areas, and merged them into the field-observed landslides to constitute another pair of training and validation sets (TS2 and VS2) using the same ratio of 7:3. The RF algorithm was applied to model the probability of the landslide occurrence using DS1, DS2, and DS3 as predictive variables and TS1 and TS2 for training to obtain the SNL-based, WA-based, and FR-based RF models, respectively. Verified against VS1 and VS2, the three models have similar overall accuracy (OA) and Kappa coefficient (KC), which are 89.61%, 91.47%, and 94.54%, and 0.7926, 0.8299, and 0.8908, respectively. All of them are much better than the three models obtained by SVM algorithm with OA of 81.79%, 82.86%, and 83%, and KC of 0.6337, 0.655, and 0.660. New case verification with the recent 26 landslide events of 2017-2020 revealed that the landslide susceptibility map from WA-based RF modeling was able to properly identify the high and very high susceptibility zones where 23 new landslides had occurred, and performed better than the SNL-based and FR-based RF modeling, though the latter has a slightly higher OA and KC. Hence, we concluded that all three RF models achieve reasonable risk prediction, but WA-based and FR-based RF modeling deserves a recommendation for application elsewhere. The results of this study may serve as reference for the local authorities in prevention and early warning of landslide hazards.