The c.64_80del <i>SMIM1</i> allele is segregating in the <scp>H</scp>utterite population
Bibliographic record
Abstract
BACKGROUND: The high-incidence red blood cell (RBC) antigen Vel is coded by SMIM1 (small-membrane molecule 1 gene), where a homozygous 17 base pair deletion underlies the majority of Vel- phenotypes. Because anti-Vel has been reported to cause severe hemolytic transfusion reactions and periodically hemolytic disease of the newborn and fetus, identification of individuals negative for Vel is clinically important. STUDY DESIGN AND METHODS: RBCs from the members of a large three-generation Hutterite family were serologically determined to be Vel+(w) . Genomic DNA from these family members was polymerase chain reaction amplified and analyzed for SMIM1 polymorphisms by either Sanger sequencing or restriction fragment length polymorphisms. SMIM1 genotyping was also conducted on DNA from an additional 104 Hutterites. RESULTS: All family members whose RBCs weakly expressed the Vel antigen were found to be heterozygous for the c.64_80del mutation in SMIM1. Of the 104 additional Hutterite samples, four were found to be heterozygous for the same SMIM1 mutation. CONCLUSION: After emigrating to the United States and Canada, the Hutterite population has expanded dramatically. Alleles that initially entered the population have been maintained within the population. The c.64_80del null allele of SMIM1 is one such allele, thus having implications for transfusion medicine and child or maternal health.
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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) | 0.004 | 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".