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Record W2202073071 · doi:10.1111/trf.13439

The c.64_80del <i>SMIM1</i> allele is segregating in the <scp>H</scp>utterite population

2015· article· en· W2202073071 on OpenAlexaffabout
Gail Coghlan, Teresa Zelinski

Bibliographic record

VenueTransfusion · 2015
Typearticle
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsUniversity of ManitobaChildren's Hospital Research Institute of ManitobaManitoba Health
Fundersnot available
KeywordsAlleleGeneticsPopulationBiologyMedicineGeneEnvironmental health

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.264
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2015
Admission routes2
Has abstractyes

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