Detecting donor-specific antibodies: the importance of sorting the wheat from the chaff
Bibliographic record
Abstract
Human leukocyte antigen (HLA) compatibility is very important for successful transplantation of solid organs. In this paper, we focused on the humoral arm of immunity in the clinical setting of organ transplantation: how HLA antibodies develop, how they can be detected, and what they can do to injure organ transplants. Specifically, we explore the technical perspectives of detecting donor-specific antibodies (DSA) in HLA laboratories, and use real-life clinical cases to explain the principles. Currently there are many tools in our HLA antibody detection toolbox: conventional cytotoxicity cross match, flow cross match, and solid phase assays using beads conjugated with single or multiple HLA antigens. Single antigen bead (SAB) assay is the most sensitive tool available for detecting HLA antibodies and assessing the immunological risk for organ transplant. However, there are intrinsic limitations to solid-phase assays and they are prone to both false negativity and importantly, false positivity. Denatured antigens on single antigen beads might be the most prominent source of false positive reactivity, and may have been underestimated by many HLA experts. No single assay is perfect and therefore multiple methods, including the less sensitive assays, should be employed to determine the clinical relevance of detected HLA antibodies. Thoughtful process, including knowledge of HLA systems, cross reactivity, epitopes, and the patient's clinical history should be employed to correctly interpret data. The clinical team should work closely with HLA laboratories to ensure accurate interpretation of information and optimal management of patients before and after organ transplantation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".