Anti-idiotypic antibodies from highly sensitized patients stimulate B cells to produce anti-HLA antibodies
Bibliographic record
Abstract
BACKGROUND: Sustained allosensitization increases waiting time for transplantation and increases the risk of rejection. The purpose of this study is to examine the effect of anti-idiotypic antibodies on B-cell responses and to define their role in alloantibody production. METHODS: The Immunoglobulin G (IgG) fraction, or the sera of 19 highly sensitized (HS) patients was absorbed to remove anticlass I antibody and was incubated with B cells. The culture supernatant was assayed for antihistocompatibility leukocyte antigen (HLA) antibody and tested for reactivity against a panel of normal lymphocytes. Similar studies were performed in 5 of the 19 patients who had a fall in alloantibody levels. RESULTS: The IgG (HS) fraction induced anti-HLA antibody from normal and autologous B cells in all 19 HS patients studied. The reactivity to HLA antigens in the culture supernatant was similar to the sera for each patient studied. The in vitro generated anti-HLA antibody bound to the IgG fraction used to stimulate the B cells. The in vitro production of anti-HLA antibodies was absent in the serum of all five patients who became nonsensitized. CONCLUSIONS: All patients who have high levels of alloantibody have anti-idiotypic antibodies in their sera that stimulate B cells to produce anti-HLA class I antibody similar in reactivity to that of their own sera. In the patients who have nondetectable alloantibodies in their sera, the stimulating anti-idiotypes are not measurable. Anti-idiotypic antibodies may act as a vaccine and cause sustained levels of alloantibody production.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".