An investigation of radiation damage in rat lungs following dual-energy micro-CT imaging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Dual-energy micro-CT imaging techniques have been developed to enable accurate identification and segmentation of different tissues. Using dual-energy techniques for thoracic imaging requires obtaining images at multiple respiratory or cardiac phases, and may require images obtained pre- and post-contrast enhancement for each energy. In this study, we investigated if the multiple images obtained during dual-energy imaging resulted in an x-ray dose sufficiently high to interfere with or mask symptoms of respiratory disease. We performed a dual-energy micro-CT study (5 images in a single session, with a cumulative entrance dose of 0.47 Gy) to image the thorax of healthy male Brown Norway rats at 8 weeks of age. Groups of 5 rats were euthanized at 1 day, 1, 2, 3, and 4-weeks post-exposure and the lungs were excised and examined by histology (H&E stained slides). Positive controls were exposed to an entrance dose of 1.5 Gy and euthanized at 2 weeks and negative controls were not exposed to x-rays. There was no evidence of alveolar damage or inflammation for any of the animals exposed to the dual-energy imaging session compared with the negative control group. Inflammation was evident for the positive controls. This study concludes that the dual-energy imaging protocol developed in this study does not contribute to lung tissue damage. For preclinical respiratory research, these results show that any inflammation and alveolar damage observed in the lungs would be attributed to the disease model under investigation, and not be affected by obtaining 3D dual-energy micro-CT images.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it