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Purpose: To evaluate whether PTX3 is differentially expressed in the granulosa lutein cells derived from women with PCOS and whether BMP6 can regulate the expression of PTX3 in hGL cells. Methods: The expression levels of BMP6 and PTX3 in granulosa lutein cells were evaluated by RT-qPCR. The correlation between the expression levels of BMP6 /PTX3 and oocyte quality indexes were analyzed using clinical samples. The cells were incubated with BMP6 at different concentrations and times to check the expression of PTX3 in KGN cells. TGF-β type I inhibitors and small interfering RNA targeting ALK2/3/6,SMAD1/5/8 and SMAD4 were used to study the involvement of SMAD dependent pathways in KGN cells. Results: The levels of BMP6 in hGL cells were negatively correlated with the corresponding oocyte maturation rate and high-quality embryo rate, whereas the levels of PTX3 were positively correlated with the corresponding oocyte maturation rate in PCOS. Additionally, the in vitro cell cultured results showed BMP6 significantly inhibited the expression of PTX3 in KGN cells. Furthermore, using a dual inhibition approach (kinase inhibitors and small interfering RNAs), we identified the ALK2/ALK3 type I receptors and BMPR2/ACVR2A type II receptors and the downstream SMAD1/SMAD5-SMAD4 signaling pathway were responsible for the BMP6-induced cellular activities in KGN cells. Conclusions: The suppressive effect of BMP6 on PTX3 was mediated by ALK2/ALK3 type I receptors and BMPR2/ACVR2A type II receptors in granulosa cells through the SMAD1/5-SMAD4 dependent signaling pathway in PCOS.Our findings provides new insights into the understanding of the pathogenesis of PCOS-related ovulatory disorders. [ABSTRACT FROM AUTHOR]

Tumor-associated macrophages (TAMs) play an important role in tumor growth and metastasis. However, the involvement of TAMs infiltration in pulmonary osteosarcoma (OS) metastasis remains poorly understood. Therefore, the effect of OS cells on macrophages migration was investigated by in vivo and in vitro experiments to evaluate the infiltration and mechanism of TAMs in pulmonary OS metastases. The results showed that the zinc finger protein ZIM3 was upregulated in OS cells than in osteoblasts and activated the expression of CCL25, which subsequently promoted the migration of M2 macrophages. CCL25 or ZIM3 silencing in OS cells inhibited the infiltration of M2 macrophages and the formation of pulmonary metastatic nodules in a mouse model of pulmonary OS metastasis and prolonged the survival of the mice. Furthermore, bioinformatics analyses revealed that CCL25 and ZIM3 expressions are negatively correlated with the prognosis of OS patients. In conclusion, this study found that a large number of M2 TAMs were recruited into pulmonary metastatic nodules of OS through the activation of the ZIM3-CCL25 axis in OS cells, thereby facilitating OS metastasis. Therefore, the suppression of ZIM3-CCL25-induced recruitment of M2 TAMs to the metastatic sites might be considered as a therapeutic approach to inhibit the growth of pulmonary OS metastases. [ABSTRACT FROM AUTHOR]

Understanding the impact of land use and ecosystem services on sustainable development goals is a key to achieving sustainable development goals (SDGs). Taking Dianchi Lake Basin as the research area, land use data from five periods, 2001, 2005, 2010, 2015, and 2019, were analyzed using the dynamic equivalent method to determine ecosystem service value (ESV) and hot spot analysis method to explore temporal and spatial changes in ESV in Dianchi Lake Basin. Three sustainable development goals, SDG15.1.1, SDG15.2, and SDG15.3.1, were selected to quantitatively analyze the impact of land use change in Dianchi Lake Basin. The results showed that (1) in the 20-year study period, the main land use types in Dianchi Lake Basin were forest land, cultivated land, construction land, and water area. In the land transfer, the largest amount of land transferred out is cultivated land, accounting for 35.50% of the total transferred out amount. It is transferred to construction land, resulting in significant expansion of construction land, nearly twice as much. (2) The SDG15.1.1 index of three forest land types in Dianchi Lake Basin showed a downward trend, and the total forest land decreased from 45.36 to 41.80%, with a cumulative decrease of 3.56%, of which 2.35% was caused by the transformation from open forest land to other land types. For watershed SDG15.2 and SDG15.3.1 indicators, all were degraded, but the degradation of high forest (SDG15.2) was the most obvious. (3) From 2001 to 2019, the total ESV in Dianchi Lake Basin initially decreased and then increased before decreasing again, with an overall decrease of 3.687 billion yuan. The ESV in the study area was high in the middle and low in the periphery, and the water area dominated by Dianchi Lake was the highest value area. (4) From 2005 to 2019, the spatial displacement relationship between cold and hot spots dominated by Dianchi Lake was corresponding and obvious, during which the ESV fluctuated violently. This study provides a basis for the sustainable development and ecological construction in typical urbanized watershed. [ABSTRACT FROM AUTHOR]

Quality control of Radix Bupleuri (RB) can be challenging due to the complexity of origin, the similar morphological characteristics, and the diversity of the multiple components. In this study, an integrated strategy for extensive identification of metabolites in plants based on multiple data processing methods was proposed to distinguish four commercially available RB species. First, the pre-processed mass spectrometry data was uploaded to Global Natural Products Social Molecular Networking (GNPS) for spectral library search and molecular network analysis, which can effectively differentiate isomers and reduce molecular redundancy. Second, the possible cleavage mode was summarized from the characteristic MS/MS fragment ions of saikoside standard, and then the possible structure of saikoside in the sample was deduced according to the cleavage patterns. Third, collected all kinds of RB components reported in the literature and matched the information in the samples to obtain more comprehensive information about metabolites. Finally, chemical markers were found employing chemometrics. This strategy not only increases the variety and number of identified components, but also improves the accuracy of the data. Based on this strategy, a total of 132 components were identified from different species of RB, and 14 chemical constituents were considered to be potential chemical markers to distinguish four kinds of RB. Among them, saikogenin a, hydroxy-saikosaponin a, hydroxy-saikosaponin d, and rutinum were of great significance for identification. The method proposed in this study not only successfully identified and distinguished four species of RB, but also laid a good theoretical foundation for regulating the RB market. This strategy provides promising perspectives in the accurate analysis of the ingredients of traditional Chinese medicine. [ABSTRACT FROM AUTHOR]

The monitoring of harmful phytoplankton is very important for the maintenance of the aquatic ecological environment. Traditional algae monitoring methods require professionals with substantial experience in algae species, which are time-consuming, expensive, and limited in practice. The automatic classification of algae cell images and the identification of harmful phytoplankton images were realized by the combination of multiple convolutional neural networks (CNNs) and deep learning techniques based on transfer learning in this work. Eleven common harmful and 31 harmless phytoplankton genera were collected as input samples; the five CNNs classification models of AlexNet, VGG16, GoogLeNet, ResNet50, and MobileNetV2 were fine-tuned to automatically classify phytoplankton images; and the average accuracy was improved 11.9% when compared to models without fine-tuning. In order to monitor harmful phytoplankton which can cause red tides or produce toxins severely polluting drinking water, a new identification method of harmful phytoplankton which combines the recognition results of five CNN models was proposed, and the recall rate reached 98.0%. The experimental results validate that the recognition performance of harmful phytoplankton could be significantly improved by transfer learning, and the proposed identification method is effective in the preliminary screening of harmful phytoplankton and greatly reduces the workload of professional personnel. [ABSTRACT FROM AUTHOR]

Zerovalent iron (ZVI) has been a major focus of research and has attracted great attention during the last 2 decades by international researchers because of its excellent pollutant removal performance and several other merits in environmental remediation. Based on Web of Science Core Collection data, we present a comprehensive bibliometric analysis of ZVI research from 2000 to 2019. We analyze 4472 publications assuming three stages of growth trend of annual publication totals. We find that "The Chemical Engineering Journal" has been the most productive journal; Noubactep C is identified as the most productive author; China has been the most active country in this field and the Chinese Academy of Science the most productive institution. The timeline of keywords shows seven distinct co-citation clusters. In addition, the top 38 keywords with strong citation bursts are also detected, suggesting that the innovation of green composite synthesis of ZVI and nanoscale ZVI and its efficient removal capacity might be the prevailing research directions in the future. [ABSTRACT FROM AUTHOR]