SP722B CELL CLUSTERS IN T CELL MEDIATED ACUTE KIDNEY ALLOGRAFT REJECTION IN MAN AND MICE
Bibliographic record
Abstract
INTRODUCTION AND AIMS: B-cell activation contributes to an aggravated clinical course of T cell mediated (TCMR) rejection and strategies to combine steroid plus therapy with rituximab for B-cell suppression are ongoing. Here, we analyzed patient kidney transplant (ktx) biopsies and identified characteristic different patterns of B-cell distribution. METHODS: The systemic expression of the B-cell chemoattractant CXCL-13 in patient blood was analyzed. In addition, a translational mouse model of TCMR was investigated and allograft histology was compared to systemic CXCL-13 release over time. RESULTS: In patient biopsies with TCMR B-cell clusters were detected subcapsular, next to atrophic tubuli or as nodular infiltrates in the vicinity of blood vessels. Significant increase of CXCL-13 correlated with Banff 1A rejection. Borderline rejection did cause enhanced CXCL-13 release.In the translational mouse model with fully mismatched BALB/c (H2d) kidney grafts transplanted into C57BL/6 (B6) recipients immunohistochemistry revealed Banff 1A, 2B rejection patterns with dense interstitial CD3+ T-cell infiltrates. Similar to findings in patient ktx biopsies, perivascular CD20+ B-cell clusters with nodular patterns adjacent to middle sized vessels where identified. Flow cytometry of the kidney allografts revealed enhanced T cell receptor (TCR) positive cells but also CD20+ B-cells. In turn, B-cell depletion was seen in the whole blood of allogenic ktx mice compared to isogenic ktx. In addition, CXCL-13 blood levels correlated with the appearance of B-cell clusters in allogenic ktx. CONCLUSIONS: Taken together, we present a translational mouse model for B-cell activation within TCMR with similar characteristics as in patients which offers the possibility to study new treatment approaches.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".