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Zhao, Pengcheng

Publications and source records attributed to Zhao, Pengcheng.

HMMR is a downstream target of FOXM1 in enhancing proliferation and partial epithelial-to-mesenchymal transition of bladder cancer cells

Our previous that HMMR upregulation independently predicts poor survival in patients with papillary muscle-invasive bladder cancer (MIBC). In this study, we explored its downstream regulations and the potential transcriptional factors activating its expression. MIBC derived T24 cells, and non-MIBC (NMIBC) derived RT4 cells were used for in vitro and in vivo studies. HMMR expression enhanced cell proliferation, the expression of mesenchymal markers, and cell invasion. It induced the nuclear entry of β-catenin, increased its active form in the nuclear part, and elevated the relative TOP/FOP activity. The promoter region of HMMR has a canonical FKH motif. FOXM1 bound to this site and activated HMMR transcription. HMMR knockdown significantly weakened FOXM1 overexpression induced bladder cancer growth, invasion, partial epithelial-to-mesenchymal transition (pEMT), as well as the activation of the Wnt/β-catenin signaling pathway. In conclusion, the findings in this study expanded our understanding of the mechanisms underlying HMMR dysregulation and the functional role of the FOXM1-HMMR axis in bladder cancer.

60 APPLIED LIFE SCIENCES↗

Spatial Mode Correction of Single Photons Using Machine Learning

Abstract Spatial modes of light constitute valuable resources for a variety of quantum technologies ranging from quantum communication and quantum imaging to remote sensing. Nevertheless, their vulnerabilities to phase distortions, induced by random media, impose significant limitations on the realistic implementation of numerous quantum‐photonic technologies. Unfortunately, this problem is exacerbated at the single‐photon level. Over the last two decades, this challenging problem has been tackled through conventional schemes that utilize optical nonlinearities, quantum correlations, and adaptive optics. In this article, the self‐learning and self‐evolving features of artificial neural networks are exploited to correct the complex spatial profile of distorted Laguerre–Gaussian modes at the single‐photon level. Furthermore, the potential of this technique is used to improve the channel capacity of an optical communication protocol that relies on structured single photons. The results have important implications for real‐time turbulence correction of structured photons and single‐photon images.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗