Quantifying the effect of pirfenidone treatment on bleomycin-induced pulmonary fibrosis mice model in vivo using a novel synchrotron-based imaging method
Bibliographic record
Abstract
<bold>Background:</bold> We have developed a synchrotron-based x-ray diffraction enhance imaging (DEI)-based multiple-image radiography (MIR) imaging method to follow the progression of alveolar damage over time in bleomycin (BLM)-induced fibrosis in C57BL/6 mice <italic>in vivo</italic>. Here we investigated the anti-fibrotic effect of pirfenidone (PFD) under two conditions: treatment starting before BLM challenge (i.e. continuous), or starting from day 9 after BLM challenge, when fibrosis is established (i.e. therapeutic). <bold>Methods:</bold> Pulmonary fibrosis was induced by intratracheal administration of BLM (2.0 unit/kg). PFD treatment (30 mg/kg) was administered daily via oral gavage (n=6 for continuous, and n=7 for therapeutic); the effectiveness of the treatment was compared to a BLM, no treatment (n=16) and a phosphate-buffered saline (PBS) control group (n=6). <bold>Results:</bold> The continuous PFD treatment significantly prevented fibrosis formation. The therapeutic PFD treatment reduced the lung damage cause by BLM at week 2 and 3 after BLM challenge and accelerated the recovery of the mice (Fig 1). <bold>Summary:</bold> Synchrotron-based DEI-MIR provides a non-invasive quantitative assessment tool for testing the effect of current treatment for idiopathic pulmonary fibrosis (IPF) in small rodent models; and this imaging technique could be used for developing new treatments for the IPF patients. <fig><object-id>erj;64/suppl_68/PA858/F1</object-id><object-id>F1</object-id><object-id>F1</object-id><graphic></graphic></fig>
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".