Emergence of a senescent and inflammatory pulmonary CD4 <sup>+</sup> T cell population prior to lung allograft failure
Bibliographic record
Abstract
Abstract Lung transplantation is a life-saving therapy for end-stage pulmonary disease, but its long-term outlook is poor due to a high incidence of chronic lung allograft dysfunction (CLAD). CLAD results from alloimmune-mediated progressive fibrosis and culminates in death or the need for re-transplantation after a median of 6 years. Existing immunosuppression fails to prevent CLAD, suggesting the existence of alloimmune pathways resistant to these drugs. Here, we used mass cytometry to identify cell populations enriched in the bronchoalveolar lavage (BAL) of patients with subsequent allograft dysfunction. We show that CD4 + CD57 + PD1 + T cells emerge in stable lung transplant recipients in the first year post-transplant, conferring heightened risks for CLAD and death or re-transplantation. CD4 + CD57 + PD1 + T cells display features of senescence and secrete inflammatory cytokines. Cellular indexing by transcriptomes and epitopes (CITE-Seq) on BAL CD4 + T cells revealed the existence of 2 oligoclonal CD57 + subsets with putative cytotoxic and follicular helper functions. Finally, we observed that CD4 + CD57 + PD1 + T cells are associated with lung allograft fibrosis in a mouse model and in human explanted CLAD lungs, where they localize near airway epithelium and B cells. Together, our findings reveal the existence of an inflammatory T cell population that predicts future lung allograft dysfunction and may represent a rational therapeutic target in lung transplant recipients.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".