Vaccine-induced donor-specific HLA antibodies: a case report highlighting sensitization risks in renal transplant waitlisted patients
Bibliographic record
Abstract
Background: In renal transplant waitlisted patients, vaccinations remain the standard of care for infection prevention. The vaccine and its adjuvant sensitizer can be potential sources for the induction of donor-specific antibodies (DSA) against human leukocyte antigens (HLA). These novel HLA antibodies can result in a positive flow cell crossmatch (FCXM), which can make a previously compatible live donor incompatible. Case report: We present an adult renal transplant waitlisted patient who has had multiple negative T-cell and B-cell FCXM with no detection of DSA at baseline. The patient then received a single dose of pneumococcal conjugate (PCV13) and a second dose of recombinant zoster vaccine (RZV). After these vaccinations, the patient's FCXM was positive for both T-cells and B-cells and the HLA class I antibodies (A1, 23, 24, 80; B44, 45, 76) showed a calculated panel reactive antibody (cPRA) of 51%. A1 and B44 DSA were detected which predicted incompatibility with the patient's planned live donor renal transplant. The patient had to enter the kidney-paired donation program instead and receive their transplantation after 16 months. Conclusion: RZV or PCV13 vaccines or their adjuvant components can potentially cause allosensitization in renal transplant waitlisted patients. The detection of DSA can result in reduced access to compatible transplants. With advances in HLA immunogenetics, better tools can monitor HLA-specific memory B-cells to provide crucial insights into the primary mechanism of action of HLA DSA antibody formation and suggest interventions to mitigate this memory B-cell 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".