Our findings suggest that the prevalence of UI is high in China, with SUI as the most common subtype. Age, vaginal delivery, and others are risk factors for SUI.

Background: Hospital preparedness is critical for the early detection and management of public health emergency (PHE). Understanding the current status of PHE preparedness is the first step in planning to enhance hospitals' capacities for emergency response. The objective of this study is to understand the current status of hospital PHE preparedness in China.

Aims/IntroductionThe present meta‐analysis was carried out to assess the association between exposure to the level of atmospheric particulate matter 2.5 (PM2.5; fine particulate matter with aerodynamic diameter less than 2.5 μm) and type 2 diabetes mellitus or gestational diabetes mellitus (GDM).Materials and MethodsWe searched the Medline, EMBASE, Cochrane and Web of Science databases to obtain articles according to the responding literature search strategies. Among a total of 279 identified articles, 55 were reviewed in depth, of which 10 articles (11 cohort studies) satisfied the inclusion criteria. Only cohort studies that disclosed the association between PM2.5 and type 2 diabetes mellitus or GDM were included in this article. A fixed‐effects model was selected if P > 0.1 and I 2 < 50%; otherwise, a random‐effects model would be used to calculate the total effect value. Subgroup analysis was further carried out according to the types of diabetes mellitus (type 2 diabetes mellitus and GDM). The relative risk was used to estimate the association between PM2.5 and diabetes mellitus.ResultsThe positive associations between PM2.5 and the incidence of type 2 diabetes mellitus were found in the long‐term exposure period (relative risk 1.25, 95% confidence interval 1.10–1.43), which showed that with every 10‐μg/m3 increase in PM2.5, the risk of type 2 diabetes mellitus would increase by 25% in the long‐term exposure. Although the significant associations were not identified between maternal exposure to PM2.5 and GDM in the first trimester, the second trimester and the entire pregnancy periods, we could conclude that maternal exposure to PM2.5 in the entire pregnancy period would be more likely to lead to developing GDM (relative risk 1.162, 95% confidence interval 0.806–1.675) than the other two periods.ConclusionsLong‐term exposure to PM2.5 would be more likely to lead to developing type 2 diabetes mellitus, but more studies would be required to confirm the association between PM2.5 and GDM. It might be a wise to take effective measures to reduce PM2.5 exposure in vulnerable populations, especially for pregnant women.

Over the wire and back again: The reversible hydrogenation–oxidative dehydrogenation of quinolines is reported by using Pt nanowire (NW) as the catalyst under mild reaction conditions. Pt NW shows high activity and selectivity in the hydrogenation of quinolines under H2 pressure (1 bar=100 kPa), and these hydrogenation products can be easily oxidized under the same conditions in an atmosphere of oxygen (1 bar) or in air.

Our results suggest that the training status and needs of China's rural doctors are still disjointed; measures including the introduction of remote education and clinical further education, extended training time and more clinical skills training should be adopted.

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Promoting tourism in China using sustainable practices has become a very important issue. In order to analyze temporal characteristics and spatial regularities of green total factor productivity (GTFP), carbon emissions and the consumption of energy related to tourism in China were estimated using a "bottom-up" method. The construction of a measurement framework (including carbon emissions and energy consumption) of GTFP for the tourism industry was also undertaken. The data envelopment analysis (DEA) model and the Malmquist–Luenberger (ML) index were used to measure and calculate tourism GTFP in China between 2007 and 2018, as well as analyze spatio-temporal differences. Results indicate that: (1) carbon emissions and the consumption of energy are increasing, and they have not yet peaked, with traffic associated with tourism accounting for the largest proportion among tourism sectors; the spatial distribution of carbon emissions and the consumption of energy is not balanced; (2) green development of tourism in China has achieved a good level of performance during the study period, driven by technical efficiency. Since 2014, pure technical efficiency (PE) has been >1, indicating that the tourism industry in China has entered a stage of change and promotion; (3) significant spatial differences exist in tourism GTFP in China. For example, the overall pattern of being strongest in the east and weakest in the west has not changed. Currently, eastern, central, and western regions in China rely on different dynamic mechanisms to promote tourism green development. In addition, some provinces have become the core or secondary growth poles of tourism green development in China.