Particulate matter 2.5 (PM2.5) is a widely known atmospheric pollutant which can induce the aging-related pulmonary diseases such as acute respiratory distress syndrome (ARDS), chronic obstructive ...pulmonary disease (COPD) and interstitial pulmonary fibrosis (IPF). In recent years, with the increasing atmospheric pollution, airborne fine PM2.5, which is an integral part of air pollutants, has become a thorny problem. Hence, this study focused on the effect of PM2.5 on cellular senescence in the lung, identifying which inflammatory pathway mediated PM2.5-induced cellular senescence and how to play a protective role against this issue. Our data suggested that PM2.5 induced time- and concentration-dependent increasement in the senescence of A549 cells. Using an inhibitor of cGAS (PF-06928215) and an inhibitor of NF-κB (BAY 11–7082), it was revealed that PM2.5-induced senescence was regulated by inflammatory response, which was closely related to the cGAS/STING/NF-κB pathway activated by DNA damage. Moreover, our study also showed that the pretreatment with selenomethionine (Se-Met) could inhibit inflammatory response and prevent cellular senescence by hindering cGAS/STING/NF-κB pathway in A549 cells exposed to PM2.5. Furthermore, in vivo C57BL/6J mice model demonstrated that aging of mouse lung tissue caused by PM2.5 was attenuated by decreasing cGAS expression after Se-Met treatment. Our findings indicated that selenium made a defense capability for PM2.5-induced cellular senescence in the lung, which provided a novel insight for resisting the harm of PM2.5 to human health.
Display omitted
•PM2.5 inflicted the senescence of lung epithelial cells and lung tissue.•PM2.5 activated the cGAS/STING/NF-κB pathway sensing DNA damage.•PM2.5-induced senescence was regulated by inflammatory response.•Se-Met prevented PM2.5-induced senescence by hindering cGAS/STING/NF-κB pathway.
Nuciferine aporphine alkaloid mainly exists in Nelumbo nucifera Gaertn and is a beneficial to human health, such as anti-obesity, lowering blood lipid, prevention of diabetes and cancer, closely ...associated with inflammation. Importantly, nuciferine may contribute to its bioactivities by exerting intense anti-inflammatory activities in multiple models. However, no review has summarized the anti-inflammatory effect of nuciferine. This review critically summarized the information regarding the structure-activity relationships of dietary nuciferine. Moreover, biological activities and clinical application on inflammation-related diseases, such as obesity, diabetes, liver, cardiovascular diseases, and cancer, as well as their potential mechanisms, involving oxidative stress, metabolic signaling, and gut microbiota has been reviewed. The current work provides a better understanding of the anti-inflammation properties of nuciferine against multiple diseases, thereby improving the utilization and application of nuciferine-containing plants across functional food and medicine.
Display omitted
Abstract
Cholangiocarcinoma (CCA) is a highly malignant disease with a poor prognosis, and mechanisms of initiation and development are not well characterized. It is long noncoding RNAs (lncRNAs) ...acting as miRNA decoys to regulate cancer-related RNAs in competing endogenous RNA (ceRNA) networks that suggest a possible molecular mechanism in CCA. The current study aims to find potential prognosis biomarkers and small molecule therapeutic targets based on the construction of a CCA prognosis-related ceRNA network. A transcriptome dataset for CCA was downloaded from the TCGA database. Differentially expressed lncRNAs (DElncRNAs), DEmiRNAs and DEmRNAs were identified based on the differential expression and a DEceRNA network was constructed using predicted miRNA-lncRNA and miRNA-mRNA interactions. Heat maps, PCA analysis, and Pathway enrichment analysis and GO enrichment analysis were conducted. The prognostic risk model and molecular docking were constructed based on identified key ceRNA networks. A DElncRNA-miRNA-mRNAs network consisting of 434 lncRNA-miRNA pairs and 284 miRNA-mRNA pairs with 200 lncRNAs, 21 miRNAs, and 245 mRNAs was constructed. There were three lncRNAs (AC090772.1, LINC00519, and THAP7-AS1) and their downstream mRNAs (MECOM, MBNL3, RCN2) screened out as prognostic factors in CAA. Three key networks (LINC00519/ hsa-mir-22/ MECOM, THAP7-AS1/hsa-mir-155/MBNL3, and THAP7-AS1/hsa-mir-155/RCN2) were identified based on binding sites prediction and survival analysis. A prognostic risk model was established with a good predictive ability (AUC = 0.66–0.83). Four anticancer small molecules, MECOM and 17-alpha-estradiol (−7.1 kcal/mol), RCN2 and emodin (−8.3 kcal/mol), RCN2 and alpha-tocopherol (−5.6 kcal/mol), and MBNL3 and 17-beta-estradiol (−7.1 kcal/mol) were identified. Based on the DEceRNA network and Kaplan–Meier survival analysis, we identified three important ceRNA networks associated with the poor prognosis of CCA. Four anti-cancer small molecules were screened out by computer-assisted drug screening as potential small molecules for the treatment of CCA. This study provides theoretical support for the development of ceRNA network-based drugs to improve the prognosis of CCA.
This study explores the main factors influencing international oil price fluctuations, selecting five influential variables: the consumer price index (CPI), industrial production index (IPI), global ...rig count (ADU), economic policy uncertainty index (EPU), and geopolitical risk index (GRI) based on previous literature. Employing the GARCH-MIDAS model, this research analyzes comparative effects on WTI international oil prices. Our findings highlight the varying degrees of influence, with IPI showing a stronger impact and EPU indicating broader economic implications. The GRI index responds primarily to specific geopolitical events with delayed fluctuations. Our study’s novelty lies in the empirical investigation using the GARCH-MIDAS model, offering valuable insights for policymakers to manage oil price volatility effectively, particularly by addressing economic policy uncertainty as a critical factor.
•In Artemisia community, pollen percentages of Artemisia are > 50%, and Poaceae < 3%.•In Poaceae community, pollen percentages of Poaceae are > 3%, and Artemisia < 40%.•When Artemisia and ...Chenopodiaceae coverages are < 5%, their pollen % can reach 30–40%.•Even when Poaceae and Asteraceae have a high coverage, their pollen % are rarely > 10%.
Accurate knowledge of the relationship between modern vegetation and the corresponding surface pollen assemblages is the key to the quantitative reconstruction of paleovegetation and paleoclimate. The pollen assemblages of steppe regions are affected by many factors and distinguishing the pollen assemblages of different plant community types, especially Artemisia and Poaceae communities is a major difficulty in modern pollen studies in these regions. In this study we used RDA analysis to analyze modern vegetation and pollen percentage data from 119 sampling sites in different plant communities in the Bashang region of North China, which has a typical steppe vegetation. We used the results to quantitatively determine the distinguishing features of various plant community types and the relationship between the percentages of the dominant pollen types and the corresponding vegetation cover. The major results are as follows: 1) there is a clear distinction between Artemisia and Poaceae communities; in the Artemisia community, Artemisia pollen percentages are typically > 50%, and Poaceae pollen percentages are typically < 3%. In the Poaceae community, Poaceae pollen percentages are typically > 3%, and Artemisia pollen percentages are typically < 40%. 2) The Artemisia, Chenopodiaceae, Poaceae and Asteraceae are the most common vegetation types and their pollen percentages are the four most abundant types in the pollen assemblages. Their respective pollen percentages and vegetation cover show varying degrees of correlation and representativeness. Artemisia and Chenopodiaceae pollen are over-represented; that is, when the vegetation cover of Artemisia and Chenopodiaceae is < 5%, or even zero %, Artemisia and Chenopodiaceae pollen percentages can still reach 30–40%. By contrast, Poaceae and Asteraceae are under-represented in the pollen assemblages, and even where they have a high vegetation cover, their pollen percentages are rarely > 10%.
The morphology and structural stability of metal/2D semiconductor interfaces strongly affect the performance of 2D electronic devices and synergistic catalysis. However, the structural evolution at ...the interfaces has not been well explored particularly at atomic resolution. In this work, we study the structural evolution of Au nanoparticles (NPs) on few-layer MoS
2
by high resolution transmission electron microscope (HRTEM) and quantitative high-angle annular dark field scanning TEM. It is found that in the transition of Au from nanoparticles to dendrites, a dynamically epitaxial alignment between Au and MoS
2
lattices is formed, and Moiré patterns can be directly observed in HRTEM images due to the mismatch between Au and MoS
2
lattices. This epitaxial alignment can occur in ambient conditions, and can also be accelerated by the irradiation of high-energy electron beam.
In situ
observation clearly reveals the rotation of Au NPs, the atom migration inside Au NPs, and the transfer of Au atoms between neighboring Au NPs, finally leading to the formation of epitaxially aligned Au dendrites on MoS
2
. The structural evolution of metal/2D semiconductor interfaces at atomic scale can provide valuable information for the design and fabrication of the metal/2D semiconductor nano-devices with desired physical and chemical performances.
Confined groundwater is important for the domestic water supply in arid and semiarid regions that have salty phreatic water. A systematic investigation was conducted in the Wuyi region, a typical ...central area of the North China Plain (NCP), regarding the confined groundwater geochemistry. A total of 59 samples were collected from confined aquifers across the region for in situ parameter determination and laboratory analysis. The results showed the confined groundwater was neutral to slightly alkaline, and dominantly soft fresh. The moderately hard brackish water and very hard brackish water accounted for 1.69% and 6.78% of the total samples, respectively. The hydro-chemical faces are mainly SO4·Cl–Na type with a few of the HCO3–Na type. The entropy-weighted water quality index assessment demonstrated that 21.3% of the groundwater samples came under the medium to extremely poor quality, and were unsuitable for drinking purposes due to the high content of major ions. Various populations are at a chronic health risk at some local sites by high levels of F- and Fe in groundwater, with susceptibility in the order of adult females < adult males < children < infants. The poor groundwater quality and health threats result from the natural water–rock interactions (including mineral dissolution and cation exchange) rather than anthropogenic inputs. This research can provide references for groundwater resource development and management in the NCP and other similar regions worldwide.
Measles-containing vaccine (MCV) has been effective in controlling the spread of measles. Some countries have declared measles elimination. But recently years, the number of cases worldwide has ...increased, posing a challenge to the global goal of measles eradication. This study estimated the relationship between meteorological factors and measles using spatiotemporal Bayesian model, aiming to provide scientific evidence for public health policy to eliminate measles.
Descriptive statistical analysis was performed on monthly data of measles and meteorological variables in 136 counties of Shandong Province from 2009 to 2017. Spatiotemporal Bayesian model was used to estimate the effects of meteorological factors on measles, and to evaluate measles risk areas at county level. Case population was divided into multiple subgroups according to gender, age and occupation. The effects of meteorological factors on measles in subgroups were compared.
Specific meteorological conditions increased the risk of measles, including lower relative humidity, temperature, and atmospheric pressure; higher wind velocity, sunshine duration, and diurnal temperature variation. Taking lowest value (Q1) as reference, RR (95%CI) for higher temperatures (Q2-Q4) were 0.79 (0.69-0.91), 0.54 (0.44-0.65), and 0.48 (0.38-0.61), respectively; RR (95%CI) for higher relative humidity (Q2-Q4) were 0.76 (0.66-0.88), 0.56 (0.47-0.67), and 0.49 (0.38-0.63), respectively; RR (95%CI) for higher wind velocity (Q2-Q4) were 1.43 (1.25-1.64), 1.85 (1.57-2.18), 2.00 (1.59-2.52), respectively. 22 medium-to-high risk counties were identified, mainly in northwestern, southwestern and central Shandong Province. The trend was basically same in the effects of meteorological factors on measles in subgroups, but the magnitude of the effects was different.
Meteorological factors have an important impact on measles. It is crucial to integrate these factors into public health policies for measles prevention and control in China.
The early warning model of infectious diseases plays a key role in prevention and control. This study aims to using seasonal autoregressive fractionally integrated moving average (SARFIMA) model to ...predict the incidence of hemorrhagic fever with renal syndrome (HFRS) and comparing with seasonal autoregressive integrated moving average (SARIMA) model to evaluate its prediction effect.
Data on notified HFRS cases in Weifang city, Shandong Province were collected from the official website and Shandong Center for Disease Control and Prevention between January 1, 2005 and December 31, 2018. The SARFIMA model considering both the short memory and long memory was performed to fit and predict the HFRS series. Besides, we compared accuracy of fit and prediction between SARFIMA and SARIMA which was used widely in infectious diseases.
Model assessments indicated that the SARFIMA model has better goodness of fit (SARFIMA (1, 0.11, 2)(1, 0, 1)
: Akaike information criterion (AIC):-631.31; SARIMA (1, 0, 2)(1, 1, 1)
: AIC: - 227.32) and better predictive ability than the SARIMA model (SARFIMA: root mean square error (RMSE):0.058; SARIMA: RMSE: 0.090).
The SARFIMA model produces superior forecast performance than the SARIMA model for HFRS. Hence, the SARFIMA model may help to improve the forecast of monthly HFRS incidence based on a long-range dataset.
PDF
