THE CLINICAL IMPORTANCE OF VARIATION WITHIN THE HLA-Bw4 COMPLEX
Bibliographic record
Abstract
BACKGROUND: Antibody screening of a patient with a failed renal transplant showed positive reactions with most, but not all HLA-Bw4-associated B-locus antigens. However, the patient's serological HLA class I type suggested the presence of HLA-Bw4. METHODS: Standard molecular techniques were used to re-type the patient and donor. ELISA antibody screening helped determine the patient's antibody specificity. RESULTS: The patient's type was HLA-B*1402,4703;Bw6 and the donor HLA-B*4703,51011;Bw4,6. Analysis of ELISA results identified three amino acids (positions 77,80,81) as the most likely epitope recognised by the patient's serum. These corresponded to HLA-B*51011 amino acid mismatches, explaining the lymphocytotoxic reactivity pattern. This epitope is located on a subgroup of the HLA-Bw4 antigen suggesting anti-Bw4 was not a sufficient description of this antibody. CONCLUSIONS: This report identifies an antibody to a sub-group of the Bw4 public specificity and also confirms the need for sequence-level analysis in the tissue-typing laboratory to determine future unacceptable mismatches.
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 | 0.000 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".