Surgeon-dependent histopathological variations in minor alloantigen-mismatched mouse lung transplantation
Bibliographic record
Abstract
Background: The mouse orthotopic single lung transplant (LTx) model is an important scientific tool to explore LTx immunology. C57BL/10J (B10, H-2b) to C57BL/6J (B6, H-2b) minor alloantigen-mismatched LTx exhibits mild acute rejection and chronic fibrosis, mimicking human LTx, where acute rejection is dampened by immunosuppressants and chronic lung allograft dysfunction (CLAD) develops over time. However, we have observed variations in allograft histology across experiments, which were not explained by animal vendor or experimental conditions. The purpose of this study was to evaluate those variations objectively. Methods: We performed a retrospective review of B10-to-B6 LTx performed in our laboratory 2012-2019. Only LTx without experimental interventions (eg, immunomodulatory agents or genetic modifications) examined at day 28 was eligible for this study. Mice from each surgeon were selected and divided into 3 groups to represent early, middle, and late timepoints in their mouse LTx experience (143 LTx from 5 surgeons). Histology from these LTx was graded in a randomized and blinded manner. Pathological variations and trajectories were graphed; logistic regression analyses were performed for statistical assessment. Results: Distribution and trajectories of pathological outcomes were significantly different across surgeons. In multivariable logistic regression analyses, surgeon was associated with pathological outcomes whereas case number was not. Longer warm ischemia time was associated with more severe pleural fibrosis. Conclusions: The B10 to B6 single LTx model can be a powerful tool to recapitulate CLAD-like histology. However, this is a challenging operation and surgeon-dependent variability in histopathological findings needs to be taken into account when designing experimental protocols.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".