Identifying factors influencing local governments' adoption of comprehensive smoke-free policies: an event history analysis based on panel data from 36 key cities in China (2013-2021).

Introduction: The issue of tobacco control remains a significant concern for public health worldwide. In recent years, remarkable progress has been made toward adopting smoke-free measures in indoor public places. Although China has yet to introduce a national regulation, specifically for smoke-free public places, more than a dozen cities have successively approved and implemented comprehensive smoke-free regulations. Different cities in China have diverse attitudes and behaviors toward smoke-free policies; however, the reasons for these policy differences and the influencing factors have not received sufficient attention and research. Methods: On the basis of the multiple streams framework, this study selects 36 key Chinese cities as research samples and uses a directed dyad-year event history analysis method to analyze the factors influencing the implementation of comprehensive smoke-free policies in cities. Results: Results show that the adoption of such policies is positively influenced by scientific evidence, focal events, media coverage, institutional foundations, economic comparisons, and the influence of health departments and of tobacco control groups. By contrast, policy adoption is negatively affected by the differences in administrative levels, central policy signals, and the influence of the tobacco industry. Discussion: This study contributes to understanding the internal logic behind local governments' adoption of comprehensive smoke-free policies, offering insights for further advocacy at the city and national levels in China and providing experiences that can promote the global tobacco control movement.

Ethanol templated synthesis of microporous/mesoporous nanozinc oxide with multi-level structure and its outstanding photo-catalytic properties.

Zinc oxide has been of interest because of its efficient redox capacity in the UV spectral region. However, the high bandwidth limits its application in the visible region. Although synthesizing heterojunctions and doping with other elements have become the focus of the problem, it inevitably has an impact on the environment. In contrast, the template method is not only environmentally friendly but also can be used to increase the degradation rate by changing the nanoparticle mesoporous structure. Microporous/mesoporous zinc oxide with multi-level structure was synthesized using anhydrous ethanol as a green templating agent in a mild and energy-efficient method. The prepared nZnO was characterized using XRD, SEM, BET, and HR-TEM. XRD confirmed that the formation of hexagonal wurtzite zincite nZnO with good crystallinity. SEM results showed that the products were flower-like structures composed of nanosheets with a thickness of 20 nm and an average diameter of 400 nm. TEM and BET confirmed the presence of pits with diameters ranging from about 1 nm to 20 nm existed on the surface of the nanosheets, while the specific surface area of 28.05 m2/g and the pore volume of 0.069 cm3/g also provide advantages for nZnO as a photocatalytic material. The synthesized nZnO overcame the disadvantage of responding only in the UV region, and the photocatalytic degradation efficiency of MB reached 93.2% after 60 min of xenon lamp irradiation, and stabilized at 86.15% after five photocycling tests. Compared with other kinds of templates, anhydrous ethanol has the advantages of environmental friendliness and simple post-processing, and it also provides ideas for the synthesis of multilevel structures of other nanomaterials.

Bayesian Inference for Mixed Gaussian GARCH-Type Model by Hamiltonian Monte Carlo Algorithm.

MCMC algorithm is widely used in parameters' estimation of GARCH-type models. However, the existing algorithms are either not easy to implement or not fast to run. In this paper, Hamiltonian Monte Carlo (HMC) algorithm, which is easy to perform and also efficient to draw samples from posterior distributions, is firstly proposed to estimate for the Gaussian mixed GARCH-type models. And then, based on the estimation of HMC algorithm, the forecasting of volatility prediction is investigated. Through the simulation experiments, the HMC algorithm is more efficient and flexible than the Griddy-Gibbs sampler, and the credibility interval of forecasting for volatility prediction is also more accurate. A real application is given to support the usefulness of the proposed HMC algorithm well.

A New Prognostic Index PDPI for the Risk of Pneumonia Among Patients With Diabetes.

Objective: Risk factors for the development of pneumonia among patients with diabetes mellitus are unclear. The aim of our study was to elucidate the potential risk factors and attempt to predict the probability of pneumonia based on the history of diabetes. Methods: We performed a population-based, prospective multicenter cohort study of 1,043 adult patients with diabetes in China during 2017-2019. Demographic information, comorbidities, or laboratory examinations were collected. Results: The study included 417 diabetic patients with pneumonia and 626 no-pneumonia-onset diabetic patients. The predictive risk factors were chosen on the basis of a multivariate logistic regression model to predict pneumonia among patients with diabetes including male sex [odds ratio (OR) = 1.72, 95% confidence interval (CI): 1.27-2.33, p < 0.001], age ≥ 75 years (OR = 2.31, 95% CI: 1.61-3.31, p < 0.001), body mass index < 25 (OR = 2.59, 95% CI: 1.92-3.50, p < 0.001), chronic obstructive pulmonary disease (OR = 6.58, 95% CI: 2.09-20.7, p = 0.001), hypertension (OR = 4.27, 95% CI: 3.12-5.85, p < 0.001), coronary heart disease (OR = 2.98, 95% CI: 1.61-5.52, p < 0.001), renal failure (OR = 1.82, 95% CI: 1.002-3.29, p = 0.049), cancer (OR = 3.57, 95% CI: 1.80-7.06, p < 0.001), use of insulin (OR = 2.28, 95% CI: 1.60-3.25, p < 0.001), and hemoglobin A1c ≥ 9% (OR = 2.70, 95% CI: 1.89-3.85, p < 0.001). A predictive nomogram was established. This model showed c-statistics of 0.811, and sensitivity and specificity were 0.717 and 0.780, respectively, under cut-off of 125 score. Conclusion: We designed a clinically predictive tool for assessing the risk of pneumonia among adult patients with diabetes. This tool stratifies patients into relevant risk categories and may provide a basis for individually tailored intervention for the purpose of early prevention.

The complete chloroplast genome of Vitis heyneana Roem. et Schult., an economic plant to China.

The chloroplast (cp) genome sequence of Vitis heyneana has been characterized from Illumina pair-end sequencing. The complete cp genome was 160,830 bp in length, containing a large single-copy region (LSC) of 89,049 bp and a small single-copy region (SSC) of 19,071 bp, which were separated by a pair of 26,355 bp inverted repeat regions (IRs). The genome contained 131 genes, including 88 protein-coding genes, 37 tRNA genes, and 8 rRNA genes. The overall GC content is 37.4%, while the corresponding values of the LSC, SSC, and IR regions are 35.3, 31.7, and 43.0%, respectively. Further, the phylogenetic analysis suggested that V. heyneana was closely related to Vitis ficifolia.