Immunoglobulin-G subclass antidonor reactivity in transplant recipients
Bibliographic record
Abstract
Outcomes may differ after kidney transplantation compared to combined liver-kidney transplantation. In animal models, distinct patterns of antidonor immunoglobulin (Ig) G subclasses are associated with either rejection or transplant tolerance. Flow cytometry has increased the sensitivity of antidonor immunoglobulin detection. We compared antidonor IgG subclass responses in kidney transplant recipients to those in recipients of liver or multiorgan grafts. In this study of 19 organ (kidney, liver, pancreas) transplantations, recipient serum incubated with donor splenocytes was tested by flow cytometry for the presence of IgM, IgG, or IgG subclass 1-4. Sera before transplantation and 10 days and 100 days after transplantation were used. No differences were seen in antidonor IgM, IgG, or IgG subclass antibodies among recipients of kidney transplants and liver grafts or combination grafts, either before or after transplantation. IgG4 gradually but significantly increased after transplantation in all groups. High levels of antidonor IgG3 either before transplantation or produced after it were found in 3 kidney recipients who experienced acute rejection. No other patients experienced rejection, and no other increase in IgG3 was seen. In conclusion, antidonor IgG subclass profiles may be useful to distinguish populations at risk of rejection but they do not differentiate the immunological response after kidney transplantation from that after liver or combined transplantation. A late rise in antidonor IgG4 is consistent with decreased antidonor reactivity thought to occur late after transplantation.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".