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Techniques for dose reduction in computed tomography (CT) are receiving increasing attention. Lowering the tube current in front of the patient, known as risk-organ-based tube current modulation (RTM), represents a new approach. Physical dose parameters can determine the exposure but are not able to assess the biological-X-ray interactions. The purpose of this study was to establish a biological phantom model to evaluate the effect of RTM on X-ray-induced DNA double-strand breaks (DSBs). In breast phantoms and in the location of the spine in an Alderson phantom, isolated human blood lymphocytes were irradiated using a 128-slice CT scanner. A standard thoracic CT protocol (120 kV, 110 ref. mAs, anatomy-based tube current modulation, pitch 0.6, scan length 30 cm) with and without RTM was used. X-ray-induced DSBs were quantified in isolated blood lymphocytes using immunofluorescence microscopy after staining for the phosphorylated histone variant γ-H2AX. Using RTM, the resulting DNA damage reduction was 41% in superficial breast locations (P = 0.0001), 28% in middle breast locations (P = 0.0003) and 29% in lower breast locations (P = 0.0001), but we found a DNA damage increase of 36% in superficial spine locations (P = 0.0001) and of 26% in deep spine locations (P = 0.0001). In summary, we established a biological phantom model that is suitable for detecting DNA damage in distinct organs. In addition, we were able to show that, using RTM, X-ray-induced DNA damage in the breast can be significantly reduced; however, there is a significant increase in DSBs in the location of the spine.
Pubmed ID: 30239765
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THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.
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