Alloantibody Production is Regulated by CD4+ T Cells' Alloreactive Pathway, Rather Than Precursor Frequency or Th1/Th2 Differentiation
Bibliographic record
Abstract
Although CD4(+) T cells play an important role in the regulation of allograft rejection, the exact mechanisms by which they operate and the actual contribution of direct and indirect alloreactivity pathways remain to be fully characterized. Previous studies have established a possible relationship between the indirect alloreactivity pathway and antibody production, but interpretation of these results have been complicated by shortcomings inherent to the models used in these studies. To address this issue, we have developed a model based on TCR transgenic mice derived from a CD4(+) T-cell clone which recognize specific alloantigens by both alloreactivity pathways. Skin allografts on alphabeta T-cell deficient mice adoptively transferred with transgenic CD4(+) T cells were rejected without significant delay between the two alloreactivity pathways. No IgG alloantibody was produced following allograft rejection by the direct alloreactivity pathway alone. Importantly, production of antibodies against alloantigens of the direct pathway was shown to require help from CD4(+) T cells activated by the indirect pathway. These results indicate that the events leading to the initiation of immune responses responsible for graft rejection are clearly dependent on the population of antigen-presenting cells involved in T- and B-lymphocyte activation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".