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17,307 results on '"Zhang, Qin"'

205. Effect of miR-206 on lower limb ischemia–reperfusion injury in rat and its mechanism

Author

Hui Wang, Meng-Jie Shi, Zhang-Qin Hu, Lin Miao, He-Shi Cai, and Rui-Peng Zhang

Subjects
Medicine, Science
Abstract

Abstract Lower limb ischemia–reperfusion is a common pathological process during clinical surgery. Because lower limb ischemia–reperfusion usually aggravates ischemia-induced skeletal muscle tissue injury after lower limb ischemia–reperfusion, it also causes remote organ heart, intestine, liver, lung and other injuries, and there is no effective clinical treatment for lower limb ischemia–reperfusion injury, so it is urgent to study its injury mechanism. In this study, the rat model of lower limb ischemia–reperfusion was established by clamping the femoral artery with microarterial clips, and the wall destruction such as intimal injury, cell edema, collagen degeneration, neutrophil infiltration, and elastic fiberboard injury of the femoral artery wall was detected. The expression of inflammatory factors was detected by immunohistochemistry. miR-206 preconditioning was used to observe the expression of inflammatory factors, redox status and apoptosis in the vascular wall of rats after acute limb ischemia–reperfusion. Our findings suggest that vascular endothelial cell edema increases, wall thickening, neutrophil infiltration, and elastic fiber layer damage during IRI. Inflammatory factor expression was increased in femoral artery tissue, and miR-206 expression levels were significantly down-regulated. Further studies have found that miR-206 attenuates lower limb IRI by regulating the effects of phase inflammatory factors. In this study, we investigated the effect of miR-206 on inflammatory factors and its possible role in the development of lower limb IRI, providing new research ideas for the regulatory mechanism of lower limb IRI, and providing a certain theoretical basis for the treatment of lower limb ischemia–reperfusion injury after surgery or endovascular intervention.

Published
2023
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226. Phase 2 of extracellular RNA communication consortium charts next-generation approaches for extracellular RNA research

Author

Mateescu, Bogdan, Jones, Jennifer C, Alexander, Roger P, Alsop, Eric, An, Ji Yeong, Asghari, Mohammad, Boomgarden, Alex, Bouchareychas, Laura, Cayota, Alfonso, Chang, Hsueh-Chia, Charest, Al, Chiu, Daniel T, Coffey, Robert J, Das, Saumya, De Hoff, Peter, deMello, Andrew, D’Souza-Schorey, Crislyn, Elashoff, David, Eliato, Kiarash R, Franklin, Jeffrey L, Galas, David J, Gerstein, Mark B, Ghiran, Ionita H, Go, David B, Gould, Stephen, Grogan, Tristan R, Higginbotham, James N, Hladik, Florian, Huang, Tony Jun, Huo, Xiaoye, Hutchins, Elizabeth, Jeppesen, Dennis K, Jovanovic-Talisman, Tijana, Kim, Betty YS, Kim, Sung, Kim, Kyoung-Mee, Kim, Yong, Kitchen, Robert R, Knouse, Vaughan, LaPlante, Emily L, Lebrilla, Carlito B, Lee, L James, Lennon, Kathleen M, Li, Guoping, Li, Feng, Li, Tieyi, Liu, Tao, Liu, Zirui, Maddox, Adam L, McCarthy, Kyle, Meechoovet, Bessie, Maniya, Nalin, Meng, Yingchao, Milosavljevic, Aleksandar, Min, Byoung-Hoon, Morey, Amber, Ng, Martin, Nolan, John, De Oliveira, Getulio P, Paulaitis, Michael E, Phu, Tuan Anh, Raffai, Robert L, Reátegui, Eduardo, Roth, Matthew E, Routenberg, David A, Rozowsky, Joel, Rufo, Joseph, Senapati, Satyajyoti, Shachar, Sigal, Sharma, Himani, Sood, Anil K, Stavrakis, Stavros, Stürchler, Alessandra, Tewari, Muneesh, Tosar, Juan P, Tucker-Schwartz, Alexander K, Turchinovich, Andrey, Valkov, Nedyalka, Van Keuren-Jensen, Kendall, Vickers, Kasey C, Vojtech, Lucia, Vreeland, Wyatt N, Wang, Ceming, Wang, Kai, Wang, ZeYu, Welsh, Joshua A, Witwer, Kenneth W, Wong, David TW, Xia, Jianping, Xie, Ya-Hong, Yang, Kaichun, Zaborowski, Mikołaj P, Zhang, Chenguang, Zhang, Qin, Zivkovic, Angela M, and Laurent, Louise C

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Biochemistry, Biological sciences, Cell biology, Molecular biology
Abstract

The extracellular RNA communication consortium (ERCC) is an NIH-funded program aiming to promote the development of new technologies, resources, and knowledge about exRNAs and their carriers. After Phase 1 (2013-2018), Phase 2 of the program (ERCC2, 2019-2023) aims to fill critical gaps in knowledge and technology to enable rigorous and reproducible methods for separation and characterization of both bulk populations of exRNA carriers and single EVs. ERCC2 investigators are also developing new bioinformatic pipelines to promote data integration through the exRNA atlas database. ERCC2 has established several Working Groups (Resource Sharing, Reagent Development, Data Analysis and Coordination, Technology Development, nomenclature, and Scientific Outreach) to promote collaboration between ERCC2 members and the broader scientific community. We expect that ERCC2's current and future achievements will significantly improve our understanding of exRNA biology and the development of accurate and efficient exRNA-based diagnostic, prognostic, and theranostic biomarker assays.

Published
2022

230. A Two-stage Complex Network using Cycle-consistent Generative Adversarial Networks for Speech Enhancement

Author

Yu, Guochen, Wang, Yutian, Wang, Hui, Zhang, Qin, and Zheng, Chengshi

Subjects
Computer Science - Sound, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Cycle-consistent generative adversarial networks (CycleGAN) have shown their promising performance for speech enhancement (SE), while one intractable shortcoming of these CycleGAN-based SE systems is that the noise components propagate throughout the cycle and cannot be completely eliminated. Additionally, conventional CycleGAN-based SE systems only estimate the spectral magnitude, while the phase is unaltered. Motivated by the multi-stage learning concept, we propose a novel two-stage denoising system that combines a CycleGAN-based magnitude enhancing network and a subsequent complex spectral refining network in this paper. Specifically, in the first stage, a CycleGAN-based model is responsible for only estimating magnitude, which is subsequently coupled with the original noisy phase to obtain a coarsely enhanced complex spectrum. After that, the second stage is applied to further suppress the residual noise components and estimate the clean phase by a complex spectral mapping network, which is a pure complex-valued network composed of complex 2D convolution/deconvolution and complex temporal-frequency attention blocks. Experimental results on two public datasets demonstrate that the proposed approach consistently surpasses previous one-stage CycleGANs and other state-of-the-art SE systems in terms of various evaluation metrics, especially in background noise suppression., Comment: Accepted by Speech Communication

Published
2021

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