Development of a small animal model to simulate clinical stereotactic body radiotherapy-induced central and peripheral lung injuries
Bibliographic record
Abstract
Given the tremendous potential of stereotactic body radiotherapy (SBRT), investigations of the underlying radiobiology associated with SBRT-induced normal tissue injury are of paramount importance. This study was designed to develop an animal model that simulates centrally and peripherally located clinical SBRT-induced lung injuries. A 90-Gy irradiation dose was focally delivered to the central and peripheral areas of the left mouse lung with an image-guided small-animal irradiation system. At 1, 2 and 4 weeks after irradiation, micro-computed tomography (micro-CT) images of the lung were taken. Lung function measurements were performed with the Flexivent® system (SCIREQ©, Montreal, Canada). For the histopathological analysis, the lungs were fixed by perfusing with formalin, and paraffin sections were stained with hematoxylin and eosin and Masson's Trichrome. Gross inspection clearly indicated local lung injury confined to the central and peripheral areas of the left lung. Typical histopathological alterations corresponding to clinical manifestations were observed. The micro-CT analysis results appeared to correlate with the histopathological findings. Mouse lung tissue damping increased dramatically at central settings, compared with that at the control or peripheral settings. An animal model to simulate clinical SBRT-induced central and peripheral lung injuries was developed and validated with histopathological, radiological and functional analyses. This model increases our understanding of SBRT-induced central and peripheral lung injuries and will help to improve radiation therapy in the future.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".