TGF-B Regulation of the Mammary Radiation Response

2003 ◽  
Author(s):  
Maria J. Pajares
Keyword(s):  
Radiation Response

scholarly journals Study on multiple thermal radiation response of adjacent tanks based on numerical simulation

2021 ◽  
Vol 1827 (1) ◽  
pp. 012107
Author(s):  
Yawen Zuo ◽  
Mingguang Zhang ◽  
Feng Zheng ◽  
Wenjie Pan ◽  
Sifan Mo
Keyword(s):  
Numerical Simulation ◽  
Thermal Radiation ◽  
Radiation Response

scholarly journals Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Joshua E. Lewis ◽  
Melissa L. Kemp
Keyword(s):  
Machine Learning ◽  
Metabolic Flux ◽  
Metabolic Modeling ◽  
The Cancer Genome Atlas ◽  
Radiation Response ◽  
Metabolomics Data ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Metabolic Biomarkers ◽  
Genome Scale

AbstractResistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning classifiers. Despite the acknowledged role of tumor metabolism in radiation response, metabolomics data is rarely collected in large multi-omics initiatives such as The Cancer Genome Atlas (TCGA) and consequently omitted from algorithm development. In this study, we circumvent the paucity of personalized metabolomics information by characterizing 915 TCGA patient tumors with genome-scale metabolic Flux Balance Analysis models generated from transcriptomic and genomic datasets. Metabolic biomarkers differentiating radiation-sensitive and -resistant tumors are predicted and experimentally validated, enabling integration of metabolic features with other multi-omics datasets into ensemble-based machine learning classifiers for radiation response. These multi-omics classifiers show improved classification accuracy, identify clinical patient subgroups, and demonstrate the utility of personalized blood-based metabolic biomarkers for radiation sensitivity. The integration of machine learning with genome-scale metabolic modeling represents a significant methodological advancement for identifying prognostic metabolite biomarkers and predicting radiosensitivity for individual patients.

Radiation response characteristics of organic–inorganic perovskite-type compounds with a chlorophenethylamine

2021 ◽  
pp. 111409
Author(s):  
Naoki Kawano ◽  
Atsushi Horimoto ◽  
Hiromi Kimura ◽  
Daisuke Nakauchi ◽  
Masaki Akatsuka ◽  
...  
Keyword(s):  
Radiation Response ◽  
Inorganic Perovskite ◽  
Response Characteristics ◽  
Perovskite Type

scholarly journals Deconvoluting the Effect of Chromium and Aluminum on the Radiation Response of Wrought FeCrAl Alloys After Low-Dose Neutron Irradiation

2021 ◽  
Vol 549 ◽  
pp. 152804
Author(s):  
Caleb P. Massey ◽  
Dalong Zhang ◽  
Samuel A. Briggs ◽  
Philip D. Edmondson ◽  
Yukinori Yamamoto ◽  
...  
Keyword(s):  
Neutron Irradiation ◽  
Low Dose ◽  
Radiation Response ◽  
Fecral Alloys

Recent progress of radiation response in nanostructured tungsten for nuclear application

Tungsten ◽  
2021 ◽  
Author(s):  
Hang Xu ◽  
Lan-Li He ◽  
Yong-Feng Pei ◽  
Chang-Zhong Jiang ◽  
Wen-Qing Li ◽  
...  
Keyword(s):  
Recent Progress ◽  
Radiation Response

Optical and radiation response characteristics of Eu2O3-doped K2O–Bi2O3–Ga2O3 glasses

2021 ◽  
Author(s):  
Naoki Kawano ◽  
Kenji Shinozaki ◽  
Masaki Akatsuka ◽  
Hiromi Kimura ◽  
Daisuke Nakauchi ◽  
...  
Keyword(s):  
Radiation Response ◽  
Response Characteristics
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