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Enregistrement W2130274483 · doi:10.1111/j.1423-0410.2007.00887.x

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

2007· article· en· W2130274483 sur 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

Notice bibliographique

RevueVox Sanguinis · 2007
Typearticle
Langueen
DomaineMedicine
ThématiqueBlood groups and transfusion
Établissements canadiensResearch Manitoba
Organismes subventionnairesnon disponible
Mots-clésGlycophorinAntigenLoss of heterozygosityAntibodyBiologyMedicineAlleleImmunologyGeneticsGene

Résumé

récupéré en direct d'OpenAlex

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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,017
score de la tête « metaresearch » (Gemma)0,028
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,057
Score d'incertitude au seuil0,113

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0170,028
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0060,007
Études des sciences et des technologies0,0020,002
Communication savante0,0060,002
Science ouverte0,0050,003
Intégrité de la recherche0,0040,005
Charge utile insuffisante (le modèle a refusé de juger)0,0270,015

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,021
Tête enseignante GPT0,283
Écart entre enseignants0,262 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations81
Publié2007
Routes d'admission2
Résumé présentoui

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