Long‐read <scp>DNA</scp> sequencing resolves a rare case of alloimmune hemolysis mimicking autoimmune hemolysis
Bibliographic record
Abstract
BACKGROUND: Immune hemolytic anemia poses a significant challenge in transfusion medicine, as identification of underlying alloantibodies can be masked by warm and/or cold autoantibodies. This increases the risk of transfusing incompatible blood, which can precipitate or exacerbate hemolysis. Identifying alloantibodies in the presence of autoantibodies remains difficult with standard serologic and genotypic methods, often delaying accurate diagnosis and appropriate transfusion strategies. CASE REPORT: We describe a 63-year-old woman with autoimmune hemolytic anemia who suffered near-fatal hemolysis following transfusion. Despite extensive serologic and genotypic testing, the cause of her hemolytic transfusion reactions remained elusive. Given her clinical course and transfusion history, we hypothesized that her acute hemolytic transfusion reactions could be due to immune sensitization to a high-incidence RBC antigen. Research whole-genome long-read sequencing (LRS) revealed homozygosity for a rare KEL*02N.16 allele, consistent with a rare Ko phenotype, which was validated by Sanger sequencing. Retrospective serologic testing with Ko RBCs further confirmed alloimmunization within the Kell system. CONCLUSION: This case highlights the limitations of conventional serologic and genotypic methods in detecting rare blood group phenotypes, and emphasizes the diagnostic power of long-read sequencing in transfusion medicine. Early molecular testing in complex hemolytic cases can facilitate targeted transfusion strategies, reduce the risk of severe hemolysis, and improve patient outcomes. As sequencing technologies become more accessible, they have the potential to revolutionize blood group typing and alloimmunization risk assessment in clinical practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".