Search

Background: Blonanserin (BNS) had been undergoing post-market surveillance (PMS) since September 2018. Using the surveillance data, we did this analysis to assess the safety and effectiveness of different doses of BNS to explore a sufficient dose range of BNS in Chinese patients with schizophrenia (SZ). Methods: A 12-week, prospective, observational, single-arm, multicenter, open-label PMS was conducted. In this analysis, we divided the patients from PMS into low, medium to high, and high dose groups based on the dose of BNS they received, with medium to high dose group being the focus. The Brief Psychiatric Rating Scale (BPRS) scores at week 2 or 4, 6 or 8, and 12 were calculated to evaluate the effectiveness of BNS in improving psychiatric symptoms. The safety of BNS was reported as the incidence of adverse drug reactions. Results: 364 patients were included in the medium to high dose group, of which 321 completed the surveillance, with a dropout rate of 11.8%. The mean daily dose was 15.1 ± 1.92 mg. The BPRS total score was 50.1 ± 11.95 at baseline and decreased to 26.6 ± 7.43 at 12 weeks (P < 0.001). When compared with other groups, the median to high dose group achieved significantly more reduction in BPRS score at week 12 (P = 0.004 versus low dose and P = 0.033 versus higher dose). Extrapyramidal symptoms [EPS (46.4%)] were the most common adverse reactions in the medium to high group. The average weight gain during the surveillance was 0.5 ± 2.56 kg and prolactin elevation occurred in 2.2% patients. Most adverse reactions were mild. Conclusions: BNS at medium to high doses (mean 15.1 mg/d) significantly improved symptoms of SZ and was well-tolerated. Most ADRs were mild, and the likelihood of causing metabolic side effects and prolactin elevations was low. Medium to high dose of BNS is a more potent treatment choice for SZ. Trial registration number: ChiCTR2100048734. Date of registration: 2021/07/15 (retrospectively registered). [ABSTRACT FROM AUTHOR]

Rationally relieving the population of urban centers in large cities, such as megacities and supercities, is one of the current goals of population development in China. The fringe area of a large city is a potential area to undertake the population of the central area. Studying the relationship between the population and the built environment in this area can help urban planners formulate targeted construction strategies to attract the population of the city center to move to the fringe areas. This paper takes the fringe areas of Wuhan in 2010 and 2020 as its specific research object and puts forward the "5D" index system of built environments that affects the spatial distribution of population based on population data and built environment data. The OLS model is used to screen the influencing factors. This paper analyzes the correlation between population and built environment using a multi-scale geographic weighted regression model as well. According to the results of the regression analysis combined with the development and construction of the fringe areas of remote urban areas in Wuhan over the past 20 years, some suggestions are put forward for the planning and construction of remote urban areas. The results show that the "5D" index system of the built environment covers the influencing factors of the spatial distribution of the population. MGWR reveals the correlation between the influencing factors and the spatial distribution of population in the marginal areas on the global scale and the local scale, respectively, which provides a clear direction for the development of planning and construction to improve the attractiveness of the non-central areas to the population. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Diabetes is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the rapid development of industrialization and a substantial economy, China has become one of the global hotspots of nitrogen (N) and sulfur (S) deposition following Europe and the USA. Here, we developed a dataset with full coverage of N and S deposition from 2005 to 2020, with multiple statistical models that combine ground-level observations, chemistry transport simulations, satellite-derived vertical columns, and meteorological and geographic variables. Based on the newly developed random forest method, the multi-year averages of dry deposition of oxidized nitrogen (OXN), reduced nitrogen (RDN), and S in China were estimated at 10.4, 14.4, and 16.7 kg N/S ha -1 yr -1 , and the analogous numbers for total deposition were respectively 15.2, 20.2, and 25.9 kg N/S ha -1 yr -1 when wet deposition estimated previously with a generalized additive model (GAM) was included. The dry to wet deposition ratio (Rdry/wet) of N stabilized in earlier years and then gradually increased especially for RDN, while that of S declined for over 10 years and then slightly increased. The RDN to OXN deposition ratio (RRDN/OXN) was estimated to be larger than 1 for the whole research period and clearly larger than that of the USA and Europe, with a continuous decline from 2005 to 2011 and a more prominent rebound afterwards. Compared with the USA and Europe, a more prominent lagging response of OXN and S deposition to precursor emission abatement was found in China. The OXN dry deposition presented a descending gradient from east to west, while the S dry deposition a descending gradient from north to south. After 2012, the OXN and S deposition in eastern China declined faster than the west, attributable to stricter emission controls. Positive correlation was found between regional deposition and emissions, while smaller deposition to emission ratios (D/E) existed in developed eastern China, attributed to more intensive human activities and thereby anthropogenic emissions. [ABSTRACT FROM AUTHOR]