Chemokines and Their Receptors in Islet Allograft Rejection and as Targets for Tolerance Induction
Bibliographic record
Abstract
Graft rejection is a major barrier to successful outcome of transplantation surgery. Islet transplantation introduces insulin secreting tissue into type 1 diabetes mellitus recipients, relieving patients from exogenous insulin injection. However, insulitis of grafted tissue and allograft rejection prevent long-term insulin independence. Leukocyte trafficking is necessary for the launch of successful immune responses to pathogen or allograft. Chemokines, small chemotactic cytokines, direct the migration of leukocytes through their interaction with chemokine receptors found on cell surfaces of immune cells. Unique receptor expression of leukocytes, and the specificity of chemokine secretion during various states of immune response, suggest that the extracellular chemokine milieu specifically homes certain leukocyte subsets. Thus, only those leukocytes required for the current immune task are attracted to the inflammatory site. Chemokine blockade, using antagonists and monoclonal antibodies directed against chemokine receptors, is an emerging and specific immunosuppressive strategy. Importantly, chemokine blockade may potentiate tolerance induction regimens to be used following transplantation surgery, and prevent the need for life-long immunosuppression of islet transplant recipients. Here, the role for chemokine blockade in islet transplant rejection and tolerance is reviewed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".