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PurposeTo explore the effects of hypoxic non-small-cell lung cancer (NSCLC)-derived exosomes on NSCLC resistance to cisplatin.Materials and methodsExosomes were isolated by differential centrifugation and characterized by transmission electron microscope and Western blotting. Quantitative real-time PCR was used to measure miR-21 levels. MTT assays and colony formation assays were performed to investigate the effects of hypoxia-induced exosomes on cisplatin resistance.ResultsHypoxic NSCLC cell-derived exosomes facilitate normoxic cell resistance to cisplatin. In addition, hypoxia enhanced the miR-21 expression in NSCLC cells and cell-derived exosomes. Interestingly, changes in miR-21 levels in the hypoxia-induced exosomes affected the sensitivity of recipient cells to cisplatin. Mechanically, exosomal miR-21 promoted cisplatin resistance by downregulating phosphatase and tensin homolog (PTEN). The expression of miR-21 in tumor cell lines and clinical NSCLC tumor samples was positively correlated with hypoxia-inducible factor-1α and negatively correlated with PTEN. Moreover, high miR-21 expression was associated with shorter median survival period in patients undergoing pharmacotherapy, but no association was observed in patients who were not under pharmacotherapy.ConclusionExosomal miR-21 derived from hypoxic NSCLC cells may promote cisplatin resistance, which indicates that exosomal miR-21 might be a potential biomarker and therapeutic target to address NSCLC chemoresistance.

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In helicopter-borne transient electromagnetic (HTEM) signal processing, removal of motion-induced noise is one of the most important steps. A special type of short-term noise, which could be classified as high-frequency motion-induced noise (HFM noise) based on its cause and time-frequency features, was observed in the field data of the Chinese Academy of Sciences-HTEM system. Because the HFM noise is an in-band noise for the HTEM response, it usually remains after the normal denoising procedure developed for the conventional motion-induced noise. To solve this problem, we have developed a three-stage workflow to remove the HFM noise using the wavelet neural network (WNN). In the first stage, the WNN training is performed, and the data segment in which the HFM noise is dominant is selected as the sample set. In the second stage, the HFM noise corresponding to the data segment in which the earth’s response coexisted with the HFM noise is predicted using the well-trained WNN. In the last stage, the predicted HFM noise is removed from the corresponding original data. As an example, we applied our workflow in the field data observed in Inner Mongolia, the HFM noise is removed effectively, and the results provide a strong data foundation for the subsequent processing procedures.