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As an important way for China to achieve its dual-carbon goal, green finance has become the foundation for promoting high-quality economic development in China. In order to clarify the mechanism of green finance on carbon emissions, this paper puts green finance into the economic model and deduces the relationship between green finance and carbon emission reduction. This paper is based on the panel data of 30 provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan) from 2008 to 2019, using the individual fixed effect model, dynamical model, mediator model, and SDM model to study the impact of green finance on carbon emissions and its impact path of upgrading of the industrial structure and the development of science and technology based on the measurement of the green finance development index of each province by the entropy method. The findings show that the development of green finance can reduce carbon emission significantly, which can be sustained until at least the third phase and generates spatial spillover effects; regional heterogeneity analysis finds that the development of green finance shows geographical discrepancies: compared with the eastern and western regions, the development of green finance in central region can reduce carbon emissions more significantly; not only can the development of green finance directly reduce carbon emission, but also through the upgrading of industrial structure and technological innovation. The research not only provides a new perspective and supplementary empirical evidence for understanding the carbon emission reduction effect of green finance, but also offers some useful references for green finance to contribute to carbon emission reduction. [ABSTRACT FROM AUTHOR]

Purpose. To investigate the factors influencing brachial-ankle pulse wave velocity (baPWV) in an apparently healthy Chinese population, especially the associations between baPWV and indices of blood pressure (BP). Methods. A total of 1123 participants with no history of hypertension were enrolled in this study, and the baPWV and BP of all four limbs were measured along with other covariates. Correlation analyses and multivariate linear regression models were used to identify factors associated with baPWV. Results. A total of 1123 participants (male 43.3%, mean age: 58.4 ± 13.9 years) were included. The average baPWV was 14.87 ± 3.21 m/s, and no difference was found between the sexes. Age was positively correlated with baPWV (r = 0.65 , p < 0.01), especially in females (r = 0.71 versus 0.56 in males). The correlation coefficient between age and baPWV increased markedly after the age of 65 years. In addition, the resting heart rate (RHR), waist-hip ratio, glomerular filtration rate, and plasma glucose level were significantly correlated with baPWV (r = 0.25 , 0.22, -0.43, and 0.25, respectively; p < 0.01). BP parameters were highly positively correlated with baPWV, especially systolic BP (SBP) and pulse pressure (PP). Multivariate regression revealed that age, BP parameters, and RHR were independently correlated with baPWV (p < 0.01) after adjusting for confounding factors. The standardized coefficients of SBP were greater than those of PP, followed by diastolic BP (DBP). Conclusion. BaPWV increased with age, especially after 65 years. Age, BP, and RHR were independent factors associated with baPWV. The effect of SBP on baPWV was more prominent than that of PP. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to examine the relationship between anemia and all-cause mortality in Chinese centenarians., Design: Prospective cohort study., Setting and Participants: We included 1002 Chinese centenarians from the China Hainan Centenarian Cohort Study (CHCCS) MEASUREMENTS: Standard procedures were followed to perform blood analysis, home interviews, and physical examinations. Anemia was defined as a hemoglobin level of less than 130 g/L for men and less than 120 g/L for women., Results: During the 9-year follow-up period, a total of 929 (92.7%) deaths were identified. Cox proportional hazards regression models revealed that anemia (hazard ratio [HR] 1.289, 95% confidence interval [CI]: 1.117-1.489) was significantly associated with all-cause mortality. There was an apparent dose-response relationship between anemia and all-cause mortality. Centenarians with severe anemia had approximately 1.6 times higher likelihood of all-cause mortality than those without anemia (HR 1.662; 95% CI: 1.154-2.394)., Conclusion: Anemia is associated with an increased risk of all-cause mortality in Chinese centenarians. Further research will be needed to collect more comprehensive data on the etiology of anemia and causes of death in centenarians., (Copyright © 2024 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

Background: Biological age (BA) has been used to assess individuals' aging conditions. However, few studies have evaluated BA models' applicability in centenarians., Methods: Important organ function examinations were performed in 1798 cases of the longevity population (80∼115 years old) in Hainan, China. Eighty indicators were selected that responded to nutritional status, cardiovascular function, liver and kidney function, bone metabolic function, endocrine system, hematological system, and immune system. BA models were constructed using multiple linear regression (MLR), principal component analysis (PCA), Klemera and Doubal method (KDM), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and light gradient boosting machine (lightGBM) methods. A tenfold crossover validated the efficacy of models., Results: A total of 1398 participants were enrolled, of whom centenarians accounted for 49.21%. Seven aging markers were obtained, including estimated glomerular filtration rate, albumin, pulse pressure, calf circumference, body surface area, fructosamine, and complement 4. Eight BA models were successfully constructed, namely MLR, PCA, KDM1, KDM2, RF, SVM, XGBoost and lightGBM, which had the worst R 2 of 0.45 and the best R 2 of 0.92. The best R 2 for cross-validation was KDM2 (0.89), followed by PCA (0.62)., Conclusion: In this study, we successfully applied eight methods, including traditional methods and machine learning, to construct models of biological age, and the performance varied among the models., Competing Interests: Declaration of Competing Interest We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome., (Copyright © 2023. Published by Elsevier B.V.)

Background: The disease pathology for diabetes mellitus patients with chronic kidney disease (CKD) may be diabetic nephropathy (DN), non-diabetic renal disease (NDRD), or DN combined with NDRD. Considering that the prognosis and treatment of DN and NDRD differ, their differential diagnosis is of significance. Renal pathological biopsy is the gold standard for diagnosing DN and NDRD. However, it is invasive and cannot be implemented in many patients due to contraindications. This article constructed a new noninvasive evaluation model for differentiating DN and NDRD., Methods: We retrospectively screened 1,030 patients with type 2 diabetes who has undergone kidney biopsy from January 2005 to March 2017 in a single center. Variables were ranked according to importance, and the machine learning methods (random forest, RF, and support vector machine, SVM) were then used to construct the model. The final model was validated with an external group (338 patients, April 2017-April 2019)., Results: In total, 929 patients were assigned. Ten variables were selected for model development. The areas under the receiver operating characteristic curves (AUCROCs) for the RF and SVM methods were 0.953 and 0.947, respectively. Additionally, 329 patients were analyzed for external validation. The AUCROCs for the external validation of the RF and SVM methods were 0.920 and 0.911, respectively., Conclusion: We successfully constructed a predictive model for DN and NDRD using machine learning methods, which were better than our regression methods., Clinical Trial Registration: ClinicalTrial.gov, NCT03865914., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhang, Liu, Dong, Wang, Pei, Chen, Zheng, Wang, Chen, Feng, Sun, Cai and Chen.)