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One of the health hazards of PM2.5 exposure is to induce pulmonary inflammatory responses. In our previous study, we demonstrated that exposing both the immortalized and primary human bronchial epithelial cells to PM2.5 results in a significant upregulation of VEGF production, a typical signaling event to trigger chronic airway inflammation. Further investigations showed that PM2.5 exposure strongly induces ATR/CHK1/p53 cascade activation, leading to the induction of DRAM1-dependent autophagy to mediate VEGF expression by activating Src/STAT3 pathway. In the current study, we further revealed that TIGAR was another transcriptional target of p53 to trigger autophagy and VEGF upregulation in Beas-2B cells after PM2.5 exposure. Furthermore, LKB1, but not ATR and CHK1, played a critical role in mediating p53/TIGAR/autophagy/VEGF pathway activation also by linking to Src/STAT3 signaling cascade. Therefore, on combination of the previous report, we have identified both ATR/CHK1/p53/DRAM1- and LKB1/p53/TIGAR- dependent autophagy in mediating VEGF production in the bronchial epithelial cells under PM2.5 exposure. Moreover, the in vivo study further confirmed VEGF induction in the airway potentially contributed to the inflammatory responses in the pulmonary vascular endothelium of PM2.5-treated rats. Therefore, blocking VEGF expression or autophagy induction might be the valuable strategies to alleviating PM2.5-induced respiratory injuries.

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The surge of carbon dioxide emission plays a dominant role in global warming and climate change, posing an enormous threat to the development of human being and a profound impact on the global ecosystem. Thus, it is essential to analyze the carbon dioxide emission change trend through an accurate prediction to inform reasonable energy-saving emission reduction measures and effectively control the carbon dioxide emission from the source. This paper proposed a hybrid model by combining the random forest and extreme learning machine together for the carbon dioxide emission forecasting in this paper; the random forest is applied for influential factors analysis and the extreme learning machine for the prediction. To improve the performance of the prediction model, moth-flame optimization is adopted to optimize initial weight and bias in extreme learning machine. A case study whose data is derived from Hebei Province, China, during the period 1995-2015 is conducted to verify the effectiveness of the proposed model. Results show that the novel model outperforms the compared parallel models in carbon dioxide emission prediction and has the potential to improve the accuracy of CO emission forecasting.