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The simultaneous in-situ growth of carbon nanofibers (CNFs) and densification of a CNFs/CF hybrid multiscale felt are accomplished in a single step by thermal gradient chemical vapor infiltration using Fe as the catalyst and vaporized kerosene under atmospheric pressure. A three-dimensional CNF network which could bridge dissimilar components of composites is formed on carbon fibers (CFs). The length of CNFs can reach several micrometers and the diameters are about 80 nm. Smooth and rough surface densified CNFs can be produced after further higher temperature infiltration. CNFs, anchoring to CFs by the adherence of the catalyst nanoparticles, enhance the bonding between CFs and pyrocarbon as well as promoting the formation of a rough laminar pyrocarbon matrix. The deposition mechanisms and physical model are also discussed. This fast catalytic infiltration process can be applied to other ceramic materials and has significant enlargement potential.

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Background: To explore the significance of phenotype detection of circulating tumor cells (CTCs) based on epithelial-mesenchymal transition (EMT) labeling to evaluate the prognosis of lung cancer. Methods: Database was retrieved from China National Knowledge Infrastructure (CNKI), Chinese Biomedical literature Database (CBM), Chinese Scientific and Journal Database (VIP), Wan Fang database, PubMed, and EMBASE. Based on EMT on overall survival (OS) and disease-free survival (DFS), hazard ratios (HRs) and its 95% of confidence intervals (CIs) were applied to assess the prognostic effect of CTCs. RevMan 5.3 and STATA 16.0 software were adopted to perform the meta-analysis. Results: Based on EMT in terms of the prognosis of patients suffering from lung cancer, this study comprehensively reviewed and evaluated the available evidence of phenotype detection of CTCs. Conclusion: Based on EMT in the prognosis of patients who developed with lung cancer, our findings proved the effect of phenotype detection of CTCs. Such studies may reveal a new prognostic marker for lung cancer patients and help clinicians and health professionals make clinical decisions. OSF Registration Number: DOI 10.17605/OSF.IO/E7KAZ.