Key comparison study on peptide purity - glycated hexapeptide of HbA1c
Notice bibliographique
Résumé
Main text Under the auspices of the Protein Analysis Working Group (PAWG) of the Comité Consultatif pour la Quantité de Matière (CCQM) a key comparison, CCQM-K115.c, was coordinated by the Bureau International des Poids et Mesures (BIPM), the Health Sciences Authority (HSA) of Singapore and the Chinese National Institute of Metrology (NIM). Nine Metrology Institutes or Designated Institutes and the BIPM participated. Participants were required to assign the mass fraction of the glycated hexapeptide of HbA1c (GE) present as the main component in the comparison sample for CCQM-K115.c. The comparison samples were prepared by HSA/BIPM from synthetic GE purchased from a commercial supplier and used as provided without further treatment or purification. GE was selected to be representative of the performance of a laboratory's measurement capability for the purity assignment of chemically synthesized peptides of known sequence, without cross-links, up to 5 kDa and modification (mono-glycation). It was anticipated to provide an analytical measurement challenge representative for the value-assignment of compounds of broadly similar structural characteristics. The majority of participants used amino acid analysis (PICAA) or quantitative nuclear magnetic resonance (PICqNMR) spectroscopy with a correction for structurally-related peptide impurities approach as the amount of material that has been provided to each participant (25 mg) is insufficient to perform a full mass balance based characterization of the material by a participating laboratory. The coordinators, the BIPM, the HSA and the NIM, were the laboratories to use the mass balance approach as they had more material available. It was decided to propose KCRVs for both the GE mass fraction and the mass fraction of the peptide-related impurities as indispensable contributor regardless of the use of PICAA, PICqNMR or mass balance to determine the GE purity. This allowed participants to demonstrate the efficacy of their implementation of the approaches used to determine the GE mass fraction. In particular, it allows participants to demonstrate the efficacy of their implementation of peptide-related impurity identification and quantification. More detailed studies on the identification/quantification of peptide-related impurities revealed that the integrity of the impurity profile of the related peptide impurities obtained by the participant is crucial for the impact on accuracy of the GE mass fraction assignment. The assessment of the mass fraction of peptide impurities is based on the assumption that that all results are directly taken for the calculation of the KCRVPepImp by use of random-effects meta-analysis (DerSimonian-Laird (DSL) variance-weighted mean). The KCRVPepImp of 45.4 mg/g is associated with a corresponding expanded uncertainty of 9.5 mg/g (k =2.26) providing a more realistic basis of evaluation for the capabilities of the participants to identify/quantify peptide-related impurities. Inspection of the degree of equivalence plots for the mass fraction of peptide impurities and additional information obtained from the peptide-related impurity profile indicates that in most cases the major related peptide impurities have been identified and quantified. The approach selected to obtain a KCRVGE for the mass fraction of GE is based on the DerSimonian-Laird (DSL) variance-weighted mean. The DSL mean takes into account the uncertainties of the results while introducing sufficient excess variance to allow for their observed dispersion resulting in a larger expanded uncertainty U(KCRVGE). The KCRVGE for CCQM-K115.c is 628 mg/g with a corresponding expanded uncertainty of the KCRVGE of 27 mg/g (k =2.26). All GE mass fraction results are in agreement with the KCRVGE. To reach the main text of this paper, click on Final Report . Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/ . The final report has been peer-reviewed and approved for publication by the CCQM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».