Dendritic Cells in Kidney Transplant Biopsy Samples Are Associated with T Cell Infiltration and Poor Allograft Survival
Bibliographic record
Abstract
Progress in long-term renal allograft survival continues to lag behind the progress in short-term transplant outcomes. Dendritic cells are the most efficient antigen-presenting cells, but surprisingly little attention has been paid to their presence in transplanted kidneys. We used dendritic cell-specific intercellular adhesion molecule-3-grabbing nonintegrin as a marker of dendritic cells in 105 allograft biopsy samples from 105 kidney transplant recipients. High dendritic cell density was associated with poor allograft survival independent of clinical variables. Moreover, high dendritic cell density correlated with greater T cell proliferation and poor outcomes in patients with high total inflammation scores, including inflammation in areas of tubular atrophy. We then explored the association between dendritic cells and histologic variables associated with poor prognosis. Multivariate analysis revealed an independent association between the densities of dendritic cells and T cells. In biopsy samples with high dendritic cell density, electron microscopy showed direct physical contact between infiltrating lymphocytes and cells that have the ultrastructural morphologic characteristics of dendritic cells. The origin of graft dendritic cells was sought in nine sex-mismatched recipients using XY fluorescence in situ hybridization. Whereas donor dendritic cells predominated initially, the majority of dendritic cells in late allograft biopsy samples were of recipient origin. Our data highlight the prognostic value of dendritic cell density in allograft biopsy samples, suggest a new role for these cells in shaping graft inflammation, and provide a rationale for targeting dendritic cell recruitment to promote long-term allograft survival.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".