Evaluating the dose effects of a longitudinal micro-CT study on pulmonary tissue in C57BL/6 mice
Bibliographic record
Abstract
Background: Micro-computed tomography offers numerous advantages for small animal imaging, including the ability to monitor the same animals throughout a longitudinal study. However, concerns are often raised regarding the effects of x-ray dose accumulated over the course of the experiment. In this study, we scan C57BL/6 mice multiple times per week for six weeks, to determine the effect of the cumulative dose on pulmonary tissue at the end of the study. Methods/Results: C57BL/6 male mice were split into two groups (irradiated group=10, control group=10). The irradiated group was scanned (80kVp/50mA) each week for 6 weeks; the weekly scan session had three scans. This resulted in a weekly dose of 0.84 Gy, and a total study dose of 5.04 Gy. The control group was scanned on the final week. Scans from weeks 1 and 6 were reconstructed and analyzed: overall, there was no significant difference in lung volume or lung density between the control group and the irradiated group. Similarly, there were no significant differences between the week 1 and week 6 scans in the irradiated group. Histological samples taken from excised lung tissue also showed no evidence of inflammation or fibrosis in the irradiated group. Conclusion: This study demonstrates that a 5 Gy x-ray dose accumulated over six weeks during a longitudinal micro-CT study has no significant effects on the pulmonary tissue of C57BL/6 mice. As a result, the many advantages of micro- CT imaging, including rapid acquisition of high-resolution, isotropic images in free-breathing mice, can be taken advantage of in longitudinal studies without concern for negative dose-related effects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".