Chronic Airway Fibrosis in Orthotopic Mouse Lung Transplantation Models—An Experimental Reappraisal
Bibliographic record
Abstract
BACKGROUND: Several mouse lung transplantation (Tx) models have been proposed for the study of chronic airway fibrosis (CAF), the most prevalent complication seen in human lung transplant recipients, termed chronic lung allograft dysfunction. Alternatively, it has been called for to establish an experimental animal model for restrictive allograft syndrome, another phenotype of chronic lung allograft dysfunction. However, these mouse transplant models exhibit significant heterogeneity in consistency and reproducibility. We therefore aimed at reevaluating current available models. METHODS: Four different Tx combinations were used that manifest CAF: 2 minor antigen-mismatched Tx combinations (MINOR, donor: C57BL/10, recipient: C57BL/6J); or MINOR-N using recipient C57BL/6N, major histocompatibility antigen-mismatched immunosuppressed Tx (MAJOR, donor: BALB/c, recipient: C57BL/6J), and syngeneic Tx (donor and recipient: C57BL/6J) as control. The recipients were harvested and analyzed at week 8. Oxygenation, histology, reverse transcription polymerase chain reaction, and magnetic resonance imaging were performed to analyze outcome of those models. RESULTS: The most prominent manifestation of CAF, thickest subepithelial fibrotic changes, worst oxygenation, and the most severe acute rejection were detected in the MAJOR group compared with all other (P < 0.05). Gene expressions of TNF-α and TGF-β1 were higher, and IL-10 was lower in the MAJOR group. Immunohistochemistry found pleuroparenchymal fibrotic change in both the MAJOR and MINOR-J groups. CONCLUSIONS: We propose the major mismatch model under mild immunosuppression as the most suitable model for studying posttransplant CAF, and both the major and minor mismatch models for the restrictive phenotype.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".