Quantifying the effects of nintedanib treatment on bleomycin-induced pulmonary fibrosis mice model in vivo using a novel synchrotron-based imaging method
Bibliographic record
Abstract
Background: Using our recently developed synchrotron-based x-ray diffraction enhance imaging (DEI)-based multiple-image radiography (MIR) imaging method, we investigated the anti-fibrotic effects of nintedanib (NDN) in bleomycin (BLM)-induced fibrosis in C57BL/6 mice in vivo. The animals were exposed to two treatment protocols: continuous (treatment starting before bleomycin challenge) and therapeutic (starting from day 9 after bleomycin challenge, when fibrosis is established) and studied over a 4 weeks period. Methods: Intratracheal administration of BLM (2.0 unit/kg) was used to trigger pulmonary fibrosis. Daily NDN treatment (30 mg/kg) via oral gavage was administered for both continuous (n=6) and therapeutic (n=7) treatments. The effect of NDN was compared to untreated control groups: BLM (n=16) and phosphate-buffered saline (PBS, n=6) groups. Results: The continuous NDN usage fully prevented the establishment of fibrosis. Similarly, therapeutic NDN treated mice had a reduction in lung parenchyma damage caused by the BLM challenge and lead to faster resolution of fibrosis (Fig 1). Summary: Synchrotron-based DEI-MIR provides a novel tool to quantitatively assess the treatment of idiopathic pulmonary fibrosis (IPF) in an animal model of IPF in vivo, which could be used to evaluate future therapies in IPF. erj;64/suppl_68/PA857/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) | 0.002 | 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".