Successful Usage of Extracorporeal Plasma Perfusion Adsorption Devices Columns for Desensitization Through Immunoadsorption in End-Stage Renal Disease Patients for ABO-Incompatible Kidney Transplant
Bibliographic record
Abstract
Background: ABO-incompatible kidney transplantation (ABOiKT) has emerged as a viable solution to overcome donor shortages, particularly in countries with underdeveloped deceased donor programs. This study evaluated the efficacy of the SECORIM ABO immunoadsorption (IA) column (Vitrosorb AB, Malmo, Sweden) desensitization protocol in 21 end-stage renal disease (ESRD) patients undergoing live donor ABOiKT. Methods: Patients underwent rituximab induction (375 - 500 mg), followed by individualized IA (1 - 3 sessions) using SECORIM ABO columns. Pre-transplant isoagglutinin IgG titers ranged from 1:32 to 1:1,024, successfully reduced to ≤ 1:8 before transplantation. Post-operative immunosuppression included tacrolimus, mycophenolate mofetil, and corticosteroids. Results: All recipients demonstrated stable graft function with no early rejection. The mean serum creatinine at discharge was 1.2 mg/dL (range 0.6 - 2.61 mg/dL), and tacrolimus trough levels varied between 6.19 and 24.9 ng/mL. There were no incidences of hyperacute rejection or graft loss. Conclusion: The IA protocol proved effective in facilitating safe ABOiKT, ensuring optimal immunological modulation with favorable short-term outcomes, offering a reproducible framework for resource-constrained healthcare settings.
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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".