Protective anti-donor IgM production after crossmatch positive liver-kidney transplantation
Bibliographic record
Abstract
The mechanism by which a liver transplantation might protect a simultaneous kidney transplant in a crossmatch-positive recipient is unknown. Flow cytometry crossmatch (FCXM) has increased the sensitivity of donor-specific antibody (DSA) detection compared with complement-dependant cytotoxicity (CDC). Here we compare the outcome of a liver-kidney transplantation (LKT), which was CDC and FCXM positive, to the mate-isolated kidney transplantation (KT), which was CDC negative but FCXM positive, from the same donor. Immunoglobulin G (IgG) and immunoglobulin M (IgM) DSAs were measured by FCXM using splenocytes and purified T cells. The KT graft was hyperacutely rejected and removed, but the LKT graft survived without episodes of rejection. Both the KT and the LKT recipients had high levels of circulating antidonor IgG, but not IgM, before transplantation. By day 3, antidonor IgG and IgM in the LKT recipient increased 2 and 7 fold respectively, whereas the KT recipient maintained the high IgG level but did not increase IgM. Histology of the KT graft showed IgG and complement (C1q) deposition, but in the LKT grafts, IgM was deposited without IgG or C1q. Circulating IgG and IgM DSAs returned to background by day 10 and were still at background on day 100. We report a crossmatch-positive LKT where posttransplantation production of IgM DSA, which failed to fix complement, appeared to protect the grafts.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".