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
Background: 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 in vivo. 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). Methods: 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). Results: 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). Summary: 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. erj;64/suppl_68/PA858/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 | 0.000 | 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 | 0.001 |
| 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".