Search

In China, with the largest population and known as the world's factory, the study of air pollution trends can provide an important reference value for the world's population and manufacturing countries. In particular, the polycyclic aromatic hydrocarbon (PAH) pollution status in particulate matter can accurately reflect the dynamic balance between the atmosphere and sediments and the risks to people and ecology. This study combined 85 publications with over 6971 individual samples from 219 sites in 89 Chinese cities. The findings were first integrated into a unified database of the spatial distribution of the PAH concentration. The results revealed that the parent PAH concentration and pollution in atmospheric particulates greatly increased in Chinese cities (> 500 thousand people). The contamination levels, as indicated by the toxic equivalent quantity, incremental lifetime cancer risk, hazard index, and ecological risk, were higher in northern China (NC) than in southern China (SC). The health risk of subpopulations followed the cohort order of juveniles (≤ 18) < adults (> 18). The risk quotient and sediment quality guidelines showed that certain individual PAHs could occasionally pose an ecological risk in some cities, especially in NC. The results of the mean effect range median quotient suggested that the combined negative ecological effects of ∑16PAHs resulted in high (NC) and moderate (SC) risks. The ecological risk level in 73 cities in China was above the moderate level, which is mainly controlled by the source of coal for residential heating and heavy industry, automobile exhaust and light industries in NC and SC, respectively.

Purpose: To establish a model for predicting mild cognitive impairment (MCI) progression to Alzheimer's disease (AD) using morphological features extracted from a joint analysis of voxel-based morphometry (VBM) and surface-based morphometry (SBM). Methods: We analyzed data from 121 MCI patients from the Alzheimer's Disease Neuroimaging Initiative, 32 of whom progressed to AD during a 4-year follow-up period and were classified as the progression group, while the remaining 89 were classified as the non-progression group. Patients were divided into a training set (n= 84) and a testing set (n= 37). Morphological features measured by VBM and SBM were extracted from the cortex of the training set and dimensionally reduced to construct morphological biomarkers using machine learning methods, which were combined with clinical data to build a multimodal combinatorial model. The model's performance was evaluated using receiver operating characteristic curves on the testing set. Results: The Alzheimer's Disease Assessment Scale (ADAS) score, apolipoprotein E (APOE4), and morphological biomarkers were independent predictors of MCI progression to AD. The combinatorial model based on the independent predictors had an area under the curve (AUC) of 0.866 in the training set and 0.828 in the testing set, with sensitivities of 0.773 and 0.900 and specificities of 0.903 and 0.747, respectively. The number of MCI patients classified as high-risk for progression to AD was significantly different from those classified as low-risk in the training set, testing set, and entire dataset, according to the combinatorial model (P&lt; 0.05). Conclusion: The combinatorial model based on cortical morphological features can identify high-risk MCI patients likely to progress to AD, potentially providing an effective tool for clinical screening.

The acquisition of genetic- and epigenetic-abnormalities during transformation has been recognized as the two fundamental factors that lead to tumorigenesis and determine the aggressive biology of tumor cells. However, there is a regularity that tumors derived from less-differentiated normal origin cells (NOCs) usually have a higher risk of vascular involvement, lymphatic and distant metastasis, which can be observed in both lymphohematopoietic malignancies and somatic cancers. Obviously, the hypothesis of genetic- and epigenetic-abnormalities is not sufficient to explain how the linear relationship between the cellular origin and the biological behavior of tumors is formed, because the cell origin of tumor is an independent factor related to tumor biology. In a given system, tumors can originate from multiple cell types, and tumor-initiating cells (TICs) can be mapped to different differentiation hierarchies of normal stem cells, suggesting that the heterogeneity of the origin of TICs is not completely chaotic. TIC’s epigenome includes not only genetic- and epigenetic-abnormalities, but also established epigenetic status of genes inherited from NOCs. In reviewing previous studies, we found much evidence supporting that the status of many tumor-related “epigenetic abnormalities” in TICs is consistent with that of the corresponding NOC of the same differentiation hierarchy, suggesting that they may not be true epigenetic abnormalities. So, we speculate that the established statuses of genes that control NOC’s migration, adhesion and colonization capabilities, cell-cycle quiescence, expression of drug transporters, induction of mesenchymal formation, overexpression of telomerase, and preference for glycolysis can be inherited to TICs through epigenetic memory and be manifested as their aggressive biology. TICs of different origins can maintain different degrees of innate stemness from NOC, which may explain why malignancies with stem cell phenotypes are usually more aggressive.

We have previously shown that elevated expression of mitotic kinase aurora kinase A (AURKA) in cancer cells promotes the development of metastatic phenotypes and is associated clinically with adverse prognosis. Here, we first revealed a clinically positive correlation between AURKA and autophagy-associated protein SQSTM1 in breast cancer and further demonstrated that AURKA regulated SQSTM1 through autophagy. Indeed, depletion by siRNA or chemical inhibition of AURKA by the small molecule VX-680 increased both the level of microtubule-associated protein 1 light chain 3-II (LC3-II) and the number of autophagosomes, along with decreased SQSTM1. Conversely, overexpression of AURKA inhibited autophagy, as assessed by decreased LC3-II and increased SQSTM1 either upon nutrient deprivation or normal conditions. In addition, phosphorylated forms of both RPS6KB1 and mechanistic target of rapamycin (MTOR) were elevated by overexpression of AURKA whereas they were suppressed by depletion or inhibition of AURKA. Moreover, inhibition of MTOR by PP242, an inhibitor of MTOR complex1/2, abrogated the changes in both LC3-II and SQSTM1 in AURKA-overexpressing BT-549 cells, suggesting that AURKA-suppressed autophagy might be associated with MTOR activation. Lastly, repression of autophagy by depletion of either LC3 or ATG5, sensitized breast cancer cells to VX-680-induced apoptosis. Similar findings were observed in cells treated with the autophagy inhibitors chloroquine (CQ) and bafilomycin A1(BAF). Our data thus revealed a novel role of AURKA as a negative regulator of autophagy, showing that AURKA inhibition induced autophagy, which may represent a novel mechanism of drug resistance in apoptosis-aimed therapy for breast cancer.

Cao: Generon (Shanghai) Corporation Ltd.: Research Funding. Yuan:Generon (Shanghai) Corporation Ltd.: Research Funding. Huang:Generon (Shanghai) Corporation Ltd.: Employment. Ji:Generon (Shanghai) Corporation Ltd.: Research Funding. Peng:Generon (Shanghai) Corporation Ltd.: Research Funding. Yan:Generon (Shanghai) Corporation Ltd.: Employment. Tang:Generon (Shanghai) Corporation Ltd.: Employment.