Objective: To assess the association between the metabolic score for insulin resistance index (METS-IR) and overactive bladder (OAB) in the US population，so as to explore the potential of METS-IR as a predictive tool for OAB risk and to provide insights for early screening and intervention strategies. Methods: Based on the data from the National Health and Nutrition Examination Survey (NHANES) 2005-2018，a cross-sectional design was employed，and multivariate logistic regression models were used to analyze the association between METS-IR and OAB. METS-IR was analyzed both as a continuous variable and categorized into quartiles. To further validate the association between METS-IR and OAB across diverse populations，subgroup analyses were conducted in participants stratified by clinical characteristics. Smooth curve fitting was employed to test the linearity of the METS-IR-OAB relationship. Results: Elevated METS-IR was associated with an increased risk of OAB (P<0.001)，and this positive correlation remained stable when METS-IR was categorized into quartiles (P<0.001). Subgroup analyses revealed that the association between METS-IR and OAB was more pronounced in females，participants younger than 55 years，and non-diabetic individuals (P<0.05). Furthermore，smooth curve fitting confirmed a linear positive correlation between METS-IR and OAB，with this linear relationship observed in both diabetic and non-diabetic groups. Conclusion: This study，based on the NHANES 2005-2018 database，found a linear positive correlation between METS-IR and OAB.

BACKGROUND: The American Heart Association (AHA) introduced the concept of cardiovascular-kidney-metabolic (CKM) health and stage, reflecting the interaction among metabolism, chronic kidney disease (CKD), and the cardiovascular system. However, the association between CKM stage and the long-term risk of cardiovascular disease (CVD) has not been validated. This study aimed to evaluate the long-term CVD risk associated with CKM health metrics and CKM stage using data from a population-based cohort study. METHODS: In total, 5293 CVD-free participants were followed up to around 13 years in the Chinese Multi-provincial Cohort Study (CMCS). Considering the pathophysiologic progression of CKM health metrics abnormalities (comprising obesity, central adiposity, prediabetes, diabetes, hypertriglyceridemia, CKD, and metabolic syndrome), participants were divided into CKM stages 0, 1, and 2. The time-dependent Cox regression models were used to estimate the cardiovascular risk associated with CKM health metrics and stage. Additionally, broader CVD outcomes were examined, with a specific assessment of the impact of stage 3 in 2581 participants from the CMCS-Beijing subcohort. RESULTS: Among participants, 91.2% (4825/5293) had at least one abnormal CKM health metric, 8.8% (468/5293), 13.3% (704/5293), and 77.9% (4121/5293) were in CKM stages 0, 1, and 2, respectively; and 710 incident CVD cases occurred during a median follow-up time of 13.3 years (interquartile range: 12.1 to 13.6 years). Participants with each poor CKM health metric exhibited significantly higher CVD risk. Compared with stage 0, the hazard ratio (HR) (95% confidence interval [CI]) for CVD incidence was 1.31 (0.84-2.04) in stage 1 and 2.27 (1.57-3.28) in stage 2. Significant interactive impacts existed between CKM stage and age or sex, with higher CVD risk related to increased CKM stages in participants aged <60 years or females. CONCLUSION These findings highlight the contribution of CKM health metrics and CKM stage to the long-term risk of CVD, suggesting the importance of multi-component recognition and management of poor CKM health in CVD prevention.
Humans ; Female ; Male ; Cardiovascular Diseases/etiology* ; Middle Aged ; Adult ; Cohort Studies ; Renal Insufficiency, Chronic/metabolism* ; Aged ; Risk Factors ; Metabolic Syndrome/metabolism* ; China ; East Asian People

OBJECTIVES: To investigate the active components and possible mechanisms of n-butanol fraction of Periploca forrestii Schltr. ethanol extract for treating Alzheimer's disease (AD). METHODS: The active components of n-butanol fraction of Periploca forrestii Schltr. ethanol extract were analyzed using UPLC-QE-MS technique. In a SD rat model of AD induced by treatment with AlCl₃ and D-gal, the therapeutic effects of low, moderate and high doses of the n-butanol fraction, saline, and donepezil hydrochloride were evaluated using ELISA, HE and Nissl staining, immunohistochemistry and Western blotting. The therapeutic mechanisms of the n-butanol fraction were explored using network pharmacology and molecular docking. RESULTS: Seventeen active components were identified from the n-butanol fraction of Periploca forrestii Schltr. ethanol extract, including phenylpropanoids, flavonoids, anthraquinones, triterpenoids, steroids, and volatile oils. In the rat models of AD, treatment with the n-butanol fraction significantly lowed AChE content in the hippocampus, increased the contents of ACh, SOD, CAT, and GSH-Px, enhanced the expressions of neuronal apoptotic factors Bcl-2, PI3K, Akt, p-PI3K, and p-Akt, and reduced the expressions of Bax and caspase-3 proteins. The treatment also dose-dependently up-regulated hippocampal expressions of Nrf-2, HO-1 and BDNF and down-regulated Keap-1, Aβ and Tau expressions. Bioinformatics analysis identified 14 key intersected targets (including TNF, AKT1 and ESR1) between the n-butanol fraction and AD. CONCLUSIONS The therapeutic effect of n-butanol fraction of Periploca forrestii Schltr. ethanol extract in AD mice is mediated by its multiple active components that regulate multiple targets and pathways.
Animals ; Rats, Sprague-Dawley ; Rats ; 1-Butanol/chemistry* ; Plant Extracts/pharmacology* ; Periploca/chemistry* ; Ethanol/chemistry* ; Alzheimer Disease/drug therapy* ; Male ; Molecular Docking Simulation ; Apoptosis/drug effects*

Three soil conditioners were prepared from granulated food waste and decomposed cattle manure combined with desulfurization gypsum, coal gangue, and maifanite, respectively. Field trials were conducted in the saline field growing Cabernet Sauvignon. The effects of soil conditioners on rhizospheric bacterial communities were studied, with the aim of providing a scientific basis for soil amelioration and restoration. Five treatments were designed, including the control (T1), conventional fertilization (T2), reduced chemical fertilization+organic matter-based soil conditioner with calcium additives (T3), reduced chemical fertilization+organic matter-based soil conditioner with silica additives (T4), and reduced chemical fertilization+organic matter-based soil conditioner with magnesium additives (T5), each with three replications. The results indicated that soil conditioners improved the rhizospheric nutrients, yield, and quality of grape (P<0.05), increased relative abundance of Proteobacteria by 17.32%-23.37%, decreased relative abundance of unidentified_Bacteria and Acidobacteriota by 4.22%-28.42% and 20.88%-35.81%, respectively. The bacterial community composition and diversity were different between treatments. Function analysis showed that the expression levels of the genes involved in chromosome and protein synthesis, mRNA biosynthesis, and glyoxylate and dicarboxylate metabolism were up-regulated in the treatments with soil conditioners. The correlation analysis revealed that multiple environmental factors affected the alpha diversity of rhizospheric bacterial communities, and some bacterial taxa were closely related to the grape yield and quality. It is concluded that soil conditioners can effectively alter rhizosphere nutrient levels and bacterial community structures and functions. T5 treatment outperforms other treatments in improving the physico-chemical and biological characteristics of rhizosphere, and the yield, and quality of grape. It has potential for application, and provides an important basis for development of new-type soil conditioners.
Soil Microbiology ; Rhizosphere ; Soil/chemistry* ; Vitis/microbiology* ; Fertilizers ; Bacteria/growth & development* ; Cattle ; Manure ; Animals

Objectives:To develop and validate predictive models for esophageal squamous cell carcinoma (ESCC) using circulating cell-free DNA (cfDNA) terminal motif analysis. The goal was to improve the non-invasive detection of early-stage ESCC and its precancerous lesions.Methods:Between August 2021 and November 2022, we prospectively collected plasma samples from 448 individuals at the Department of Endoscopy, Cancer Hospital, Chinese Academy of Medical Sciences for cfDNA extraction, library construction, and sequencing. We analyzed 201 cases of ESCC, 46 high-grade intraepithelial neoplasia (HGIN), 46 low-grade intraepithelial neoplasia (LGIN), 176 benign esophageal lesions, and 29 healthy controls. Participants, including ESCC patients and control subjects, were randomly assigned to a training set ( n=284) and a validation set ( n=122). The training cohort underwent z-score normalization of cfDNA terminal motif matrices and a selection of distinctive features differentiated ESCC cases from controls. The random forest classifier, Motif-1 (M1), was then developed through principal component analysis, ten-fold cross-validation, and recursive feature elimination. M1's efficacy was then validated in the validation and precancerous lesion sets. Subsequently, individuals with precancerous lesions were included in the dataset and participants were randomly allocated to newly formed training ( n=243), validation ( n=105), and test ( n=150) cohorts. Using the same procedure as M1, we trained the Motif-2 (M2) random forest model with the training cohort. The M2 model's accuracy was then confirmed in the validation cohort to establish the optimal threshold and further tested by performing validation in the test cohort. Results:We developed two cfDNA terminal motif-based predictive models for ESCC and associated precancerous conditions. The first model, M1, achieved a sensitivity of 90.0%, a specificity of 77.4%, and an area under the curve (AUC) of 0.884 in the validation cohort. For LGIN, HGIN, and T1aN0 stage ESCC, M1's sensitivities were 76.1%, 80.4%, and 91.2% respectively. Notably, the sensitivity for jointly predicting HGIN and T1aN0 ESCC reached 85.0%. Both the predictive accuracy and sensitivity increased in line with the cancer's progression ( P＜0.001). The second model, M2, exhibited a sensitivity of 87.5%, a specificity of 77.4%, and an AUC of 0.857 in the test cohort. M2's sensitivities for detecting precancerous lesions and ESCC were 80.0% and 89.7%, respectively, and it showed a combined sensitivity of 89.4% for HGIN and T1aN0 stage ESCC. Conclusions:Two predictive models based on cfDNA terminal motif analysis for ESCC and its precancerous lesions are developed. They both show high sensitivity and specificity in identifying ESCC and its precancerous stages, indicating its potential for early ESCC detection.