Ginsenoside Rg2 Promotes the Proliferation and Stemness Maintenance of Porcine Mesenchymal Stem Cells through Autophagy Induction.

Mesenchymal stem cells (MSCs) can be used as a cell source for cultivated meat production due to their adipose differentiation potential, but MSCs lose their stemness and undergo replicative senescence during expansion in vitro. Autophagy is an important mechanism for senescent cells to remove toxic substances. However, the role of autophagy in the replicative senescence of MSCs is controversial. Here, we evaluated the changes in autophagy in porcine MSCs (pMSCs) during long-term culture in vitro and identified a natural phytochemical, ginsenoside Rg2, that could stimulate pMSC proliferation. First, some typical senescence characteristics were observed in aged pMSCs, including decreased EdU-positive cells, increased senescence-associated beta-galactosidase activity, declined stemness-associated marker OCT4 expression, and enhanced P53 expression. Importantly, autophagic flux was impaired in aged pMSCs, suggesting deficient substrate clearance in aged pMSCs. Rg2 was found to promote the proliferation of pMSCs using MTT assay and EdU staining. In addition, Rg2 inhibited D-galactose-induced senescence and oxidative stress in pMSCs. Rg2 increased autophagic activity via the AMPK signaling pathway. Furthermore, long-term culture with Rg2 promoted the proliferation, inhibited the replicative senescence, and maintained the stemness of pMSCs. These results provide a potential strategy for porcine MSC expansion in vitro.

Spatial distribution of permafrost degradation and its impact on vegetation phenology from 2000 to 2020.

As global temperatures rise, permafrost is degraded. Permafrost degradation alters vegetation phenology and community composition, thereby affecting local and regional ecosystems. The Xing'an Mountains, located on the southern edge of the Eurasian permafrost region, are very sensitive to the impact of permafrost degradation on ecosystems. Climate change has direct effects on permafrost and vegetation growth, and analysis of the indirect effects of permafrost degradation on vegetation phenology based on the normalized difference vegetation index (NDVI) can explain the internal impact mechanisms of ecosystem components. Based on the temperature at the top of permafrost (TTOP) model, which was used to simulate the spatial distribution of permafrost areas in the Xing'an Mountains from 2000 to 2020, the areas of the three permafrost types showed a decreasing trend. The mean annual surface temperature (MAST) increased significantly at a rate of 0.008 °C year-1 from 2000 to 2020, and the southern boundary of the permafrost region moved north by 0.1-1 degrees. The average NDVI value of the permafrost region increased significantly in 8.34 % of the region. The significant correlations between NDVI and permafrost degradation, temperature and precipitation in the permafrost degradation region were 92.06 % (80.19 % positive, 11.87 % negative), 50.37 % (42.72 % positive, 7.65 % negative), and 81.59 % (36.25 % positive, 45.34 % negative), and were mainly distributed along the southern boundary of the permafrost region. A significance test of phenology in the Xing'an Mountains showed that the end of the growing season (EOS) and the length of the growing season (GLS) were significantly delayed and prolonged in the southern sparse island permafrost region. Sensitivity analysis showed that permafrost degradation was the main factor that affected the start of the growing season (SOS) and GLS. When the effects of temperature, precipitation, and sunshine duration were excluded, the regions with a significant positive correlation between permafrost degradation and SOS (20.96 %) and GLS (28.55 %) were located in both continuous and discontinuous permafrost regions. The regions with a significant negative correlation between permafrost degradation and SOS (21.11 %) and GLS (8.98 %) were mainly distributed on the southern edge of the island permafrost region. In summary, the NDVI changed significantly in the southern boundary of the permafrost region, and this change was mainly attributed to permafrost degradation.

Water Quality Analysis and Evaluation of Eutrophication in a Swamp Wetland in the Permafrost Region of the Lesser Khingan Mountains, China.

Wuyiling Nature Reserve is located at the edge of a frozen soil area and has abundant vegetation resources. It is an important area for evaluating the impact of frozen soil degradation on the environment. Analyzing the water quality and eutrophication characteristics of different swamps and water bodies can provide a basis for protecting the water environment of frozen soil areas. The pollution characteristics of different swamps and water bodies were analyzed and the Levenberg-Marquardt back-propagation neural network was used to evaluate water quality and eutrophication. Finally, the eutrophication evaluation was compared with the nutritional status index. The results demonstrated that (1) the highest concentrations of the total phosphorus (TP), total nitrogen, ammonia nitrogen (AN), permanganate index (PI), chemical oxygen demand, and chlorophyll were all present in the Tangwang River, which could be caused by the effects of human and agricultural activities along the river. The maximum average concentrations of TP, AN, and PI in the four wetland swamps appeared in the shrub swamp; (2) The water quality category of shrub, grass, and forest swamps in Wuyiling Nature Reserve was Class II, and the water quality category of floating swamps and the Tangwang River was Class I. The nutrient levels of the four marsh swamp wetlands and the Tangwang River were in the mid-range for many nutrients; and (3) the LM-BP neural network model, which was used to evaluate water eutrophication, and the results of a comprehensive evaluation of a nutritional status index were similar, further demonstrating the credibility of the evaluation results.

Analysis of temporal spatial distribution characteristics of PM2.5 pollution and the influential meteorological factors using Big Data in Harbin, China.

Based on the monitoring data of atmospheric pollutants and the meteorological data in Harbin in 2017, the temporal spatial distribution characteristics of PM2.5 pollution and the relationships between PM2.5 concentration and meteorological factors in this region were analyzed. The PM2.5 concentration data and the meteorological data in 2017 were comprehensively analyzed by using ArcGIS and R. The results show that spatially, the PM2.5 concentration in the central districts of Harbin are high in the southeast and low in the northwest; temporally, PM2.5 pollution is most serious in autumn and winter, with multiple spells of heavy pollution and an obvious "weekend effect", while the air quality is better in spring and summer; overall, relative humidity is positively correlated to PM2.5 concentration, while temperature, wind direction, and wind speed are negatively correlated to PM2.5 mass concentration, and low wind speed and high relative humidity are major contributors to increase of PM2.5 concentration.Implications: Highlight: The use of big data to deal with the data of air pollution and meteorology.Key points: The air pollution data of Harbin in autumn and winter is more serious than that in spring and summer, and is closely related to meteorological factors. Attraction: Big data is used to process air pollution data and meteorological data, and R language is used to describe the relationship between them.

Common cytokine polymorphisms and predisposition to polycystic ovary syndrome: a meta-analysis.

The results of studies on the relationship between cytokine polymorphisms and polycystic ovary syndrome (PCOS) have been controversial. This meta-analysis was thus designed to more precisely assess the relationship between TNF-α/IL-1/IL-6/IL-10 polymorphisms and PCOS by pooling the results of published studies. A search of PubMed, Embase, Web of Science, and CNKI databases turned up 23 studies that were pooled and analyzed in this meta-analysis. The overall results showed that the distributions of TNF-α -238 G/A, TNF-α -857 C/T, and IL-1B -51 C/T polymorphisms among patients and controls differed significantly. Additionally, the distributions of TNF-α -308 G/A and IL-1B -51 C/T polymorphisms among patients and controls from Asian populations differed significantly, whereas the distributions of IL-6 -174 G/C and IL-1A -889 C/T polymorphisms among patients and controls from Caucasian populations also differed significantly. In conclusion, our meta-analysis demonstrated that TNF-α -238 G/A, TNF-α -857 C/T, and IL-1B -51 C/T polymorphisms might influence susceptibility to PCOS in the overall pooled population. Moreover, TNF-α -308 G/A and IL-1B -51 C/T polymorphisms might influence susceptibility to PCOS in Asians, whereas IL-6 -174 G/C and IL-1A -889 C/T polymorphisms might influence susceptibility to PCOS in Caucasians.