TFNR2 in Ischemia-Reperfusion Injury, Rejection, and Tolerance in Transplantation
Bibliographic record
Abstract
Tumor necrosis factor receptor 2 (TNFR2) has been shown to play a crucial role in CD4+ T regulatory cells (CD4+Tregs) expansion and suppressive function. Increasing evidence has also demonstrated its role in a variety of immune regulatory cell subtypes such as CD8+ T regulatory cells (CD8+ Tregs), B regulatory cells (Bregs), and myeloid-derived suppressor cells (MDSCs). In solid organ transplantation, regulatory immune cells have been associated with decreased ischemia-reperfusion injury (IRI), improved graft survival, and improved overall outcomes. However, despite TNFR2 being studied in the context of autoimmune diseases, cancer, and hematopoietic stem cell transplantation, there remains paucity of data in the context of solid organ transplantation and islet cell transplantation. Interestingly, TNFR2 signaling has found a clinical application in islet transplantation which could guide its wider use. This article reviews the current literature on TNFR2 expression in immune modulatory cells as well as IRI, cell, and solid organ transplantation. Our results highlighted the positive impact of TNFR2 signaling especially in kidney and islet transplantation. However, further investigation of TNFR2 in all types of solid organ transplantation are required as well as dedicated studies on its therapeutic use during induction therapy or treatment of rejection.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".