Osteopontin-expressing macrophages in dogs affected with canine idiopathic pulmonary fibrosis, a model for human idiopathic pulmonary fibrosis
Bibliographic record
Abstract
Canine idiopathic pulmonary fibrosis (CIPF) affects West Highland white terriers (WHWTs) and mimics idiopathic pulmonary fibrosis. In this study, single cell RNA-sequencing (scRNA-seq) was used to compare transcriptomic expression of broncho-alveolar lavage fluid (BALF) macrophages sampled from CIPF and healthy WHWTs. Osteopontin (SPP1) transcript, encoding a glycoprotein associated with fibrosis in human, was further analyzed at the protein level by immunohistochemistry in lung tissue and ELISA in BALF and serum from CIPF WHWTs, healthy WHWTs and healthy terriers other than WHWTs. Two pro-fibrotic macrophages clusters overexpressing SPP1 were identified by scRNA-seq: one in a larger proportion of CIPF (13.5±4.7%) compared with healthy WHWTs (2.9±0.2%, P-value:0.009); one enriched in pro-fibrotic transcripts in CIPF compared with healthy WHWTs (enrichment score: 2.01; q-value:0.008). SPP1 immunolabelling was found in all dogs in ciliated cells, smooth muscular cells and macrophages. In CIPF WHWTs, pneumocytes II and lung interstitium were also labelled, while it was the case in half of healthy WHWTs and absent in terriers. SPP1 serum concentration was higher in CIPF (2.15ng/mL [0.87-5.13]) compared with healthy WHWTs (0.63ng/mL [0.41-1.63]; P-value:0.013) and terriers (0.31ng/mL [0.19-0.51]; P-value:<0.0001), and in healthy WHWTs compared with terriers (P-value:0.002). Higher SPP1 BALF level was found in CIPF (0.34ng/mL [0.15-0.52]; P-value:<0.0001) and healthy WHWTs (0.25ng/mL [0.14-0.40]; P-value:0.002) compared with terriers (0.02ng/mL [0.01-0.08]). Our results support that profibrotic macrophage clusters contribute to CIPF pathogenesis. The overexpression of SPP1 by macrophages but also by other lung cells might trigger increased SPP1 concentrations in BALF and serum, making SPP1 a possible biomarker for CIPF.
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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 | 0.000 |
| 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".