2021
DOI: 10.21203/rs.3.rs-829314/v1
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The Essential Roles of Exosome and CircRNA_101093 to Desensitize Lung Adenocarcinoma to Ferroptosis

Abstract: BackgroundResistance to ferroptosis, a regulated cell death caused by iron-dependent excessive accumulation of lipid peroxides has recently been linked to lung adenocarcinoma (LUAD), the most prevalent lung cancer subtype. Despite intracellular antioxidant system is required for protection against ferroptosis, whether and how extracellular system desensitizes LUAD cells to ferroptosis is incompletely known. MethodsImmunohistochemistry (IHC) and immunoblotting (IB) were used to analyze protein expression, and q… Show more

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Cited by 1 publication
(2 citation statements)
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References 34 publications
(52 reference statements)
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“…An approach that uses a disentangling transformation for losses in detection, along with generating a confidence score based on self-supervised learning is proposed in (Simonelli et al, 2019) which do not need class labels. While (Zhang et al, 2023) introduces a framework that transforms into a depth-aware detection process and represents 3D object candidates through set queries. Then, an attention encoder based on depth is utilized to produce a non-local depth embedding from the image which was provided as input.…”
Section: End-to-end Learning Based Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…An approach that uses a disentangling transformation for losses in detection, along with generating a confidence score based on self-supervised learning is proposed in (Simonelli et al, 2019) which do not need class labels. While (Zhang et al, 2023) introduces a framework that transforms into a depth-aware detection process and represents 3D object candidates through set queries. Then, an attention encoder based on depth is utilized to produce a non-local depth embedding from the image which was provided as input.…”
Section: End-to-end Learning Based Methodsmentioning
confidence: 99%
“…Several transformer-based frameworks, have already been presented in this survey, which include, MonoDTR (Huang et al, 2022), DST3D (Wu et al, 2022), and MonoDETr (Zhang et al, 2023). These frameworks have been used for precise depth inference or serves as feature backbones.…”
Section: Trends In Reliable Three-dimentional Object Detectionmentioning
confidence: 99%