Long-lasting effects of neonatal ionizing radiation exposure on spatial memory and anxiety-like behavior

2009 ◽  
Vol 72 (3) ◽  
pp. 895-904 ◽  
Author(s):  
Lucila Guadalupe Caceres ◽  
Hugo Rios ◽  
Laura Ruth Guelman
2008 ◽  
Vol 63 (1) ◽  
pp. 230-233
Author(s):  
Elżbieta Czekajska-Chehab ◽  
Piotr Przybylski ◽  
Marcin Pankowicz ◽  
Maria Korzec ◽  
Andrzej Drop

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Tarik Emre Sener ◽  
Beste Melek Atasoy ◽  
Ozge Cevik ◽  
Ozlem Tugce Cilingir Kaya ◽  
Sule Cetinel ◽  
...  

AbstractObjectivesTo investigate the possible protective effects of resveratrol against oxidative testicular damage due to scattered radiation during pelvic ionizing radiation exposure in rats.MethodsRats were divided into 5 groups; control, radiation, and radiation + resveratrol therapy in early and late periods. Under anesthesia, 20 Gy ionizing radiation was applied to prostatic region. Resveratrol was administered (10 mg/kg/day) orally before ionizing radiation exposure. Animals were decapitated at the end of 1st and 10th weeks. Biochemical markers of oxidative stress; caspase-3 and sirtuin-1 protein expressions; testosterone levels were evaluated, histological examinations were performed.ResultsSignificant increases in malondialdehyde, 8-hydroxy-deoxyguanosine levels, myeloperoxidase, and caspase-3 activities were observed after ionizing radiation exposure, also superoxide dismutase and glutathione activities were significantly decreased. Radiotherapy increased caspase-3 and decreased sirtuin-1 protein expressions. Resveratrol treatment significantly reversed these parameters and also reversed the decrease in testosterone levels back to control levels in late period.ConclusionResveratrol showed antioxidant and sirtuin-activating properties against oxidative damage caused by scattered radiation to testis and provided hormonal protection. These results suggest that resveratrol may be an alternative protective agent on testicular tissues against the effects of scattered pelvic radiation.


2008 ◽  
Vol 103 (8) ◽  
pp. 2015-2022 ◽  
Author(s):  
Joanna M. Peloquin ◽  
Darrell S. Pardi ◽  
William J. Sandborn ◽  
Joel G. Fletcher ◽  
Cynthia H. McCollough ◽  
...  

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 233
Author(s):  
Jonathan Z.L. Zhao ◽  
Eliseos J. Mucaki ◽  
Peter K. Rogan

Background: Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These signatures may not be sufficiently robust for large scale testing, as their performance has not been adequately validated on external, independent datasets. The present study develops human and murine signatures with biochemically-inspired machine learning that are strictly validated using k-fold and traditional approaches. Methods: Gene Expression Omnibus (GEO) datasets of exposed human and murine lymphocytes were preprocessed via nearest neighbor imputation and expression of genes implicated in the literature to be responsive to radiation exposure (n=998) were then ranked by Minimum Redundancy Maximum Relevance (mRMR). Optimal signatures were derived by backward, complete, and forward sequential feature selection using Support Vector Machines (SVM), and validated using k-fold or traditional validation on independent datasets. Results: The best human signatures we derived exhibit k-fold validation accuracies of up to 98% (DDB2,  PRKDC, TPP2, PTPRE, and GADD45A) when validated over 209 samples and traditional validation accuracies of up to 92% (DDB2,  CD8A,  TALDO1,  PCNA,  EIF4G2,  LCN2,  CDKN1A,  PRKCH,  ENO1,  and PPM1D) when validated over 85 samples. Some human signatures are specific enough to differentiate between chemotherapy and radiotherapy. Certain multi-class murine signatures have sufficient granularity in dose estimation to inform eligibility for cytokine therapy (assuming these signatures could be translated to humans). We compiled a list of the most frequently appearing genes in the top 20 human and mouse signatures. More frequently appearing genes among an ensemble of signatures may indicate greater impact of these genes on the performance of individual signatures. Several genes in the signatures we derived are present in previously proposed signatures. Conclusions: Gene signatures for ionizing radiation exposure derived by machine learning have low error rates in externally validated, independent datasets, and exhibit high specificity and granularity for dose estimation.


2019 ◽  
Vol 87 (2) ◽  
pp. 158-160
Author(s):  
G. A. P. Kartikeswar ◽  
Tushar B. Parikh ◽  
Dhyey Pandya ◽  
Anand Pandit

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