Rabbit antithymocyte globulin–induced serum sickness disease and human kidney graft survival
Bibliographic record
Abstract
BACKGROUND: Rabbit-generated antithymocyte globulins (ATGs), which target human T cells, are widely used as immunosuppressive agents during treatment of kidney allograft recipients. However, ATGs can induce immune complex diseases, including serum sickness disease (SSD). Rabbit and human IgGs have various antigenic differences, including expression of the sialic acid Neu5Gc and α-1-3-Gal (Gal), which are not synthesized by human beings. Moreover, anti-Neu5Gc antibodies have been shown to preexist and be elicited by immunization in human subjects. This study aimed to assess the effect of SSD on long-term kidney allograft outcome and to compare the immunization status of grafted patients presenting with SSD following ATG induction treatment. METHODS: We analyzed data from a cohort of 889 first kidney graft recipients with ATG induction (86 with SSD [SSD(+)] and 803 without SSD [SSD(-)]) from the Données Informatisées et Validées en Transplantation data bank. Two subgroups of SSD(+) and SSD(-) patients that had received ATG induction treatment were then assessed for total anti-ATG, anti-Neu5Gc, and anti-Gal antibodies using ELISA assays on sera before and after transplantation. RESULTS: SSD was significantly associated with long-term graft loss (>10 years, P = 0.02). Moreover, SSD(+) patients exhibited significantly elevated titers of anti-ATG (P = 0.043) and anti-Neu5Gc (P = 0.007) IgGs in late post-graft samples compared with SSD(-) recipients. CONCLUSION: In conclusion, our data indicate that SSD is a major contributing factor of late graft loss following ATG induction and that anti-Neu5Gc antibodies increase over time in SSD(+) patients. FUNDING: This study was funded by Société d'Accélération du Transfert de Technologies Ouest Valorisation, the European FP7 "Translink" research program, the French National Agency of Research, Labex Transplantex, the Natural Science and Engineering Research Council of Canada, and the Canadian Foundation for Innovation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".