Cell death and proliferation-related transcripts in lung allograft airway epithelial cells
Bibliographic record
Abstract
Introduction: For patients with end-stage lung diseases,the only solution is lung transplantation. However, the major barrier to long-term survival following lung transplantation is progressive scarring,termed chronic lung allograft dysfunction (CLAD). Ongoing rejection mediated by alloimmune responses along with loss of the airway lining “club cells” are hallmarks of CLAD fibrotic pathology. To date, whether alloimmune responses induce club cell death and/or aberrant proliferation leading to CLAD is yet to be elucidated. Objective: We aimed to assess whether club cell excessive death and/or reduced proliferation were detected after lung transplantation. Method: We used single cell RNA sequencing (scRNAseq) to establish transcriptomic signatures of allograft airway epithelial cell death and proliferation in one stable lung transplant recipient’s (LTR) airway brushing obtained 3 months post-transplant. Result: We have identified four club cell subclusters in a LTR’s airway brushing. Extrinsic apoptosis-related genes were preferentially upregulated in club cell subcluster 1 compared to other club cell subclusters and other epithelial and immune cells. Proliferation-related genes were downregulated in all club cell subclusters (Fig.1). Conclusion: Our data suggest a relative upregulation of extrinsic apoptosis-related genes and a relative downregulation of proliferation-related genes in club cells found in a LTR’s airway brushing.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".