Feasibility study of a novel synchrotron-based imaging method to assess bleomycin-induced pulmonary fibrosis progression in mice in vivo
Bibliographic record
Abstract
Background: Current imaging techniques lack sufficient resolution to detect anatomical changes in the lung parenchyma in animal models in vivo. We have developed a synchrotron-based x-ray diffraction enhance imaging (DEI)-based multiple-image radiography (MIR) method to follow the progression over time of alveolar damage in mice in vivo. This technique provides information on lung x-ray scattering; and the amount of scattering is proportional to the number of air/tissue interfaces. Thus, the larger the amount of scattering, the more air-filled alveoli exist in the parenchyma. Methods: Using DEI-MIR we optimized in vivo imaging of lung damage progression in C57BL/6 mice after intratracheal administration of bleomycin (BLM) (2.0 unit/kg, n=16), or phosphate-buffered saline (PBS) administration was used as control (n=6). At the end of the experimental period, we sacrificed mice and compared synchrotron images to histology. Results: The results show that the scattering signal intensities were significantly decreased after 2 weeks after BLM challenge in the mice, with significant recovery at week 4 (Fig 1). The parenchyma damage was confirmed by histological studies at the same time points. Summary: DEI-MIR provides a non-invasive quantitative assessment tool for longitudinal studies of lung damage progression. erj;64/suppl_68/PA3368/F1 F1 F1
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".