Cytokines Regulate the Pattern of Rejection and Susceptibility to Cyclosporine Therapy in Different Mouse Recipient Strains After Cardiac Allografting
Bibliographic record
Abstract
We determined the role of cytokines in regulating the pattern of rejection and recipient susceptibility to cyclosporine (CsA) in a mouse cardiac allograft model. Hearts from C3H mice transplanted into untreated BALB/c (Th2-dominant) and C57BL/6 (Th1-dominant) mice showed different patterns of rejection. C3H allografts in BALB/c mice showed typical acute vascular rejection (AVR) with strong intragraft deposition and high serum levels of anti-donor IgG with predominant IgG1, while C3H allografts in C57BL/6 mice showed typical acute cellular rejection (ACR) with massive intragraft infiltration of CD4(+) and CD8(+) lymphocytes and low serum levels of anti-donor IgG with predominant IgG2a. Elevated intragraft mRNA expression of IL-2, IFN-gamma, and IL-12 mRNA was present in C57BL/6 recipients, whereas allografts in BALB/c mice displayed increased IL-4 and IL-10 mRNA levels. CsA therapy completely inhibited ACR and induced indefinite allograft survival in C57BL/6 recipients, while the same therapy failed to prevent AVR, and only marginally prolonged graft survival in BALB/c recipients. In contrast, rapamycin blocked AVR, achieving indefinite survival in BALB/c recipients, but was less effective at preventing ACR in C57BL/6 recipients. The disruption of the IL-12 or IFN-gamma genes in C57BL/6 mice shifted ACR to AVR, and resulted in concomitant recipient resistance to CsA therapy. Conversely, disruption of IL-4 gene in BALB/c mice markedly attenuated AVR and significantly prolonged allograft survival. These data suggest that the distinct cytokine profiles expressed by different mouse strains play an essential role in regulating the pattern of rejection and outcome of CsA/rapamycin therapy.
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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 | 0.001 | 0.000 |
| 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".