The Combination of Anti-NKG2D and CTLA-4 Ig Therapy Prolongs Islet Allograft Survival in a Murine Model
Bibliographic record
Abstract
Islet transplantation is an effective means of treating severe type 1 diabetes in patients with life-threatening hypoglycemia. Improvements in glycemic control with correction of HbA1C enhance quality of life irrespective of insulin independence. By antagonizing the Natural Killer Group 2, member D (NKG2D) receptor expression on NK and CD8+ T cells, in combination with blocking CTLA-4 binding sites, we demonstrate a significant delay of graft rejection in islet allotransplant. Anti-NKG2D combined with CTLA-4 Ig (n = 15) results in prolonged allograft survival, with 84.6 ± 10% of the recipients displaying insulin independence compared to controls (n = 10, p < 0.001). The effect of combination therapy on graft survival is superior to treatments alone (CTLA-4 Ig vs. combination p = 0.024, anti-NKG2D vs. combination p < 0.001) indicating an interaction between these pathways. In addition, combination treatment also improves glucose tolerance when compared to controls (n = 10, p = 0.018). Histologically, NKG2D+ cells were significantly decreased within the allograft after 7 days of combination treatment (n = 6, p = 0.029). T cell proliferation was significantly reduced with anti-NKG2D therapy and CD8+ T cell daughter fractions were also significantly decreased with mAb and combination treatment when measured by in vitro mixed lymphocyte reaction (n = 5, p = 0.015, p = 0.005 and p = 0.048). These results demonstrate that inhibition of NKG2D receptors and costimulatory pathways enhance islet allograft survival.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".