Decomposition and comparative analysis of health inequities between the male and female older adults in China: a national cross-sectional study.

BACKGROUND: This study aimed to examine the factors influencing self-rated health (SRH) among Chinese older adults by gender differences and provide suggestions and theoretical references to help make policies for older adults' health concerns by government agencies. METHODS: Chinese Longitudinal Health Longevity Survey (CLHLS) in 2018 was adopted, the chi-squared test and the logistic regression analysis were performed to analyse self-rated health reported by Chinese female and male older adults and its influencing factors. In addition, Fairlie decomposition analysis was performed to quantify the contribution level of different influencing factors. RESULTS: Among older adults, males (48.0%) reported a significantly higher level of good self-rated health than females (42.3%). Residence, body mass index (BMI), self-reported income, smoking, drinking, exercise, and social activity were the factors that influenced SRH reported by male and female respondents, with age, marital status and education reaching the significance level only in women. The Fairlie decomposition model can explain the underlying reasons for 86.7% of the gender differences in SRH, with self-reported income (15.3%), smoking (32.7%), drinking (42.5%), exercise (17.4%), social activity (15.1%) and education (-14.6%) being the major factors affecting gender differences in SRH. CONCLUSIONS: The study results can help promote the implementation of the Healthy China Initiative, inform intervention measures, and offer new proposals on creating policies for older adults' health issues by the Chinese government to improve health equity.

Identifying the influence of natural and human factors on seasonal water quality in China: current situation of China's water environment and policy impact.

Agricultural production, urbanization, and other anthropogenic activities, the major causes of surface water pollution in China, have dramatically altered hydrological processes and nutrient cycles. Identifying and quantifying the key factors affecting water quality are essential for the better prevention and management of water pollution. However, due to the limitations of traditional statistical analysis methods, it is difficult to evaluate the spatial changes and interactions of influencing factors on water quality. In addition, research on a national scale is difficult, as it involves large-scale and long-term water quality monitoring work. In this study, we collected and collated the monthly average concentrations of four water quality parameters, dissolved oxygen, ammonia nitrogen, chemical oxygen demand, and total phosphorous, based on data from 1547 water quality monitoring stations in China. The combined pollution level of the water quality was assessed using the water quality index. Based on the water quality characteristics, water quality monitoring sites in the dry and wet seasons were grouped using k-means clustering. Eleven environmental factors were evaluated using geodetector software, including six human factors and five natural factors. The results showed that there are high-risk areas for water quality pollution in the eastern and southeastern coastal regions of China in both the dry and wet seasons and that surface water pollution in China is highly spatial heterogenous in both the dry and wet seasons. Among the anthropogenic factors, urban land area is the main factor of water quality pollution in the dry season, and the explanation rate of spatial heterogeneity of integrated water quality pollution index is 20.3%. The number of poultry farms and the area of farmland explained 12.4% and 12.1% of the integrated water quality pollution index in the wet season. The nonlinear relationship between these three anthropogenic and natural factors and their interaction exacerbated water quality pollution. Based on this analysis, we identified the key factors affecting surface water quality in China during the dry and wet seasons, evaluated the achievements of the water environmental protection policies in China in recent years, and proposed future management measures for the effective prevention and control of water quality pollution in high-risk areas.

Effects of multi-scale landscape pattern changes on seasonal water quality: a case study of the Tumen River Basin in China.

Landscape patterns affect river water quality by influencing hydrological processes. However, with changes in spatial scale and season, landscape factors may have different effects on water pollution. Therefore, quantitative analysis of the scale effect of each landscape index was carried out to determine the mutation point of river water quality and its index relationship, which is of great significance to landscape planning and water quality protection. Based on the water quality monitoring data of 19 sampling points in the Tumen River Basin, we used redundant methods to quantify the spatial scale effects and seasonal dependencies of various landscape indicators on river water quality, then determined the mutation point of the water quality along the landscape-scale gradient. The results showed that different types of landscape indicators have different effects on river water quality, and the spatial-scale effect of landscape composition affects a river's water quality, while landscape configuration indicators had the highest sensitivity. The landscape characteristics of river straps better explained the overall water quality, a phenomenon that is more obvious in the wet season than the dry season. We identified a key landscape indicator of urban area proportion (Urban%) and a contagion index (CONTAG) as the river strap scale. An Urban% < 30% and a CONTAG > 70% suggest effective landscape planning parameters that effectively protect water quality. The results indicated that, to protect water quality, landscape regulation should follow scale-adaptability measures and consider landscape thresholds, which cause abrupt changes in water quality.

High-efficiency secretory expression and characterization of the recombinant type III human-like collagen in Pichia pastoris.

Collagen, the highest content protein in the body, has irreplaceable biological functions, and it is widespread concerned in food, beauty, and medicine with great market demand. The gene encoding the recombinant type III human-like collagen α1 chain fragment was integrated into P. pastoris genome after partial amino acids were substituted. Combined with promoter engineering and high-density fermentation technology, soluble secretory expression with the highest yield of 1.05 g L-1 was achieved using two-stage feeding method, and the purity could reach 96% after affinity purification. The determination of N/C-terminal protein sequence were consistent with the theoretical expectation and showed the characteristics of Gly-X-Y repeated short peptide sequence. In amino acid analysis, glycine shared 27.02% and proline 23.92%, which were in accordance with the characteristics of collagen. Ultraviolet spectrum combined with Fourier transform infrared spectroscopy as well as mass spectrometry demonstrated that the target product conformed to the characteristics of collagen spectrums and existed as homologous dimer and trimer in the broth. This work provided a sustainable and economically viable source of the recombinant type III human-like collagen.