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International Society of Blood Transfusion Committee on Terminology for Red Cell Surface Antigens: Cape Town report

2007· article· en· W2130274483 on OpenAlexaffabout
Geoff Daniels, Willy A. Flegel, Anne Fletcher, George Garratty, C. Levene, Christine Lomas‐Francis, J.M. Moulds, J. J. Moulds, Martin L. Olsson, M.A.M. Overbeeke, Joyce Poole, Marion E. Reid, P. Rouger, C. Ellen van der Schoot, Marion L. Scott, P. Sistonen, E. Smart, Jill R. Storry, Yoshihiko Tani, L.‐C. Yu, Silvano Wendel, Connie M. Westhoff, Teresa Zelinski

Bibliographic record

VenueVox Sanguinis · 2007
Typearticle
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsResearch Manitoba
Fundersnot available
KeywordsGlycophorinAntigenLoss of heterozygosityAntibodyBiologyMedicineAlleleImmunologyGeneticsGene

Abstract

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The Committee met in Cape Town during the 2006 International Society of Blood Transfusion (ISBT) Congress (see Appendix 1 for Committee members). Some changes to the classification documented in Blood Group Terminology 2004 [1] were agreed and are described below. The full updated classification can be found on the Blood Group Terminology website at http://www.blood.co.uk/ibgrl. New antigens were added to the MNS, Kell, Scianna, Cromer, Indian, Knops, and JMH systems (Table 1). In line with convention, amino acid positions are numbered with the translation-initiating methionine as 1, although the more traditional numbering for glycophorin A, with number 1 representing the first amino acid of the mature protein, is also provided. Three new antigens were added to the MNS system: two of high incidence and one of low incidence. MNS44 is defined by an antibody produced by an individual with MNS:−44 red cells, which also express MNS32 (DANE) [2]. The rare MNS:−44 phenotype results from heterozygosity for Mk and for a novel GYP(A-B-A) hybrid gene, which is identical to that encoding GP.Dane apart from lacking the substitution predicted to convert I65 (I46 when amino acid 1 is the first residue of the mature protein) of glycophorin A (GPA) to N64 (N45) in GP.Dane [2, 3]. MNS45, defined by an antibody from a patient with the rare MNS:−45 phenotype, results from homozygosity for a single nucleotide polymorphism (SNP) encoding V81G (V62G) in GPA [4]. Expression of the rare MNS46 antigen results from heterozygosity for an SNP in GYPA encoding a T36R (T17R) substitution in GPA [5]. Anti-MNS46 was found in 0·02% of Japanese blood donors [5]. Two antigens of high incidence and one of low incidence were added to the Kell system. KEL29 and KEL30 are defined by antibodies produced by individuals with the rare KEL:−29 and KEL:−30 phenotypes, which resulted from homozygosity for SNPs in KEL encoding R623K and D305N, respectively [6]. KEL31 expression results from heterozygosity for a KEL SNP encoding R292G [7]. The only example of anti-KEL31 was found in a Japanese blood donor [7]. SC5 (STAR), described in the 2004 report [1], has now been published in full [8]. The two new antigens of the Scianna system, SC6 and SC7, are of high incidence. SC:−6 and SC:−7 phenotypes result from SNPs in ERMAP, encoding R81Q and G35S substitutions in ERMAP, respectively [9]. Anti-SC6 and -SC7 were previously reported as antibodies to high incidence antigens that were absent from SC:−1,—2,—3 red cells [10]. Two new antigens of high incidence were added: CROM14 and CROM15. CROM:−14 arose from homozygosity for an SNP in CD55 encoding E156K in the second complement control protein (CCP) domain of CD55 and CROM:−15 from homozygosity for an SNP encoding Q247R in the fourth CCP domain of CD55 [11, 12]. Antibodies to both antigens were present in the sera of the propositi, whose red cells lacked the corresponding antigens. Two new antigens of the Indian system, IN3 and IN4, are of high incidence. IN:−3 and IN:−4 phenotypes result from homozygosity for SNPs in CD44 encoding H85Q and T163R in CD44, respectively [13]. Anti-IN3 and -IN4 have been produced by three and two individuals, respectively, whose red cells lack the corresponding antigen. KN9 was identified by several antibodies, originally identified incorrectly as anti-KN3 (McCa). KN9, which has an incidence of about 98% in Caucasians and 20% in West Africans, results from an SNP in exon 29 of CR1 encoding I1615 in place of V1615 in CD35 [14]. Four new antigens were added to the JMH system, each defined by alloantibodies to high incidence antigens that do not react with JMH:−1 red cells. Absence of these antigens is associated with homozygosity for SNPs in the SEMA7A gene encoding amino acid substitutions in the semaphorin domain of Sema7A (Table 1) [15]. JMH1 retains its original meaning as representing the antigen recognized by antibodies made by individuals apparently lacking the JMH protein, Sema7A. A few changes were made affecting recommended terminology for blood group genes. The commonly used symbols FY*A, FY*B, JK*A, etc. are acceptable alternatives to FY*1, FY*2, JK*1, etc. In the Rh system, the recommended DCE terminology for alleles of RHCE is RHCE*ce, RHCE*Ce, RHCE*CE, etc., and for variants of RHD is RHD*DVI, RHD*DFR, etc. The Human Genome Organization (HUGO) Gene Nomenclature Committee (HGNC) (http://www.gene.ucl.ac.uk/nomenclature/index.html) approves names and symbols for human genes. These symbols are based, where possible, on the functions of the products of the genes and are listed in Table 1 of the 2004 report [1], although three changes have subsequently been made: FY to DARC; DO to ART4; and DAF to CD55. The HGNC symbol (e.g. DARC) should be used in all circumstances except when referring to serologically defined alleles or molecularly defined alleles that represent a serologically defined antigen (e.g. FY*1 or FY*A, but not DARC*1 or DARC*A). The Dombrock glycoprotein, ADP-ribosyltransferase 4 (ART4), is CD297 (http://mpr.nci.nih.gov/PROW). A new collection containing antigens on GPA that are determined primarily by glycosylation of the protein will be established. These will include Hu, M1, Tm, Sj, and Can. Other matters to be discussed are blood group allele terminology and common or consensus alleles for each blood group gene. Dr GL Daniels (Chair): Bristol Institute for Transfusion Sciences, National Blood Service, Bristol, UK. geoff.daniels@nbs.nhs.uk Professor WA Flegel: University Hospital, Ulm, Germany. willy.flegel@uni-ulm.de Dr A Fletcher: Growing your Knowledge, Split Junction, NSW, Australia. af@growingyourknowledge.com.au Professor G Garratty: American Red Cross Blood Services, Southern California Region, Pomona, CA, USA. garratty@usa.redcross.org Dr C Levene: Reference Laboratory for Immunohematology and Blood Groups, Blood Services Center, Magen David Adom, Israel. cyrill@013.net Ms C Lomas-Francis: New York Blood Center, New York, NY, USA. clomas-francis@nybloodcenter.org Mr JJ Moulds: LifeShare Blood Centers, Shreveport, LA, USA. jjmoulds@lifeshare.org Dr JM Moulds: LifeShare Blood Centers, Shreveport, LA, USA. jmmoulds@lifeshare.org Dr ML Olsson: Blood Centre, University Hospital, Lund, Sweden. Martin_L.Olsson@med.lu.se Dr MAM Overbeeke: Sanquin Blood Supply, Diagnostic Services, Amsterdam, The Netherlands. m.overbeeke@sanquin.nl Ms J Poole: IBGRL, National Blood Service, Bristol, UK. joyce.poole@nbs.nhs.uk Dr ME Reid: New York Blood Center, New York, NY, USA. mreid@nybloodcenter.org Professor Ph Rouger: Centre national de Référence pour les Groupes sanguines, Paris, France. tcb_ints@ints.fr Dr CE van der Schoot: Sanquin Research at CLB, Amsterdam, The Netherlands. e.vanderschoot@sanquin.nl Professor M Scott: International Blood Group Reference Laboratory, Bristol, UK. marion.scott@nbs.nhs.uk Dr P Sistonen: Finnish Red Cross Blood Transfusion Service, Helsinki, Finland. pertti.sistonen@bts.redcross.fi Mrs E Smart: South African National Blood Service, East Coast Region, Pinetown, South Africa. smarte@ecr.sansb.org.za Dr JR Storry: Blood Centre, University Hospital, Lund, Sweden. jill.storry@med.lu.se Dr Y Tani: Osaka Red Cross Blood Center, Osaka, Japan. taniy@sannet.ne.jp Dr LC Yu: Mackay Memorial Hospital and National Taiwan University, Taipei, Taiwan. yulc@ntu.edu.tw Dr S Wendel: Blood Bank, Hospital Sirio-Libanes, São Paulo, Brazil. snwendel@uninet.com.br Dr CM Westhoff: American Red Cross and the University of Pennsylvania, Philadelphia, PA, USA. westhoff@usa.redcross.org Dr T Zelinski: Rh Laboratory, Winnipeg, Manitoba, Canada. zelinski@ms.umanitoba.ca

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 imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.057
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.007
Science and technology studies0.0020.002
Scholarly communication0.0060.002
Open science0.0050.003
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0270.015

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 head0.021
GPT teacher head0.283
Teacher spread0.262 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Citations81
Published2007
Admission routes2
Has abstractyes

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