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Enregistrement W4310107990 · doi:10.1182/blood-2022-160274

Mirages - REDS Explorer: An Online Portal for Longitudinal Metabolomics Data from the REDS-III RBC Omics Study

2022· article· en· W4310107990 sur OpenAlexaff
Travis Nemkov, Kyle W. Bartsch, Mars Stone, Matthew D. Galbraith, Rachel Culp‐Hill, Joaquı́n M. Espinosa, Tamir Kanias, Steven Kleinman, Michael P. Busch, Philip L Norris, Angelo D’Alessandro

Notice bibliographique

RevueBlood · 2022
Typearticle
Langueen
DomaineMedicine
ThématiqueNeonatal Health and Biochemistry
Établissements canadiensUniversity of VictoriaUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésMetabolomicsOmicsBiologyMedicineBioinformatics

Résumé

récupéré en direct d'OpenAlex

Over one hundred million units of packed red blood cells (pRBCs) are stored and transfused annually around the world. Storage of pRBCs in the blood bank contributes to a series of biochemical and morphological changes in pRBCs, collectively referred to as the "storage lesion(s)", that ultimately impact RBC capacity to circulate in the bloodstream of recipients, carry and deliver oxygen. A growing body of studies has shown that the metabolism of pRBCs is affected by storage duration, processing strategies, as well as donor exposures ("exposome"). The latter include diet; consumption of alcohol, caffeinated beverages; smoking; and use of medications that do not result in donor deferral. Furthermore donor genetics (e.g., sex, ethnicity, G6PD status and many other genetic traits in RBC proteins) and other biological factors (e.g., age, body mass index [BMI]) impact pRBC storage and transfusion efficacy. Despite significant advances in the characterization of the storage lesion, most omics studies have been limited in scale to tens of pRBC units. These studies thus failed to grasp the extent to which donor biology, processing strategies or other factors (exposures) impact the metabolic age of stored units (as opposed to the chronological age - i.e., days elapsed since donation). As such, it remains unclear whether or to what extent the molecular make up of a unit correlates to its hemolytic propensity and post-transfusion efficacy. To bridge this gap, we leveraged the Recipient Epidemiology and Donor Evaluation Study-IV-Pediatric (REDS-IV-P), a research program aimed at improving blood donor safety and optimizing transfusion outcomes. Overall, 97% (13,403) of the whole blood donations provided by 13,758 donors age 18+ who provided informed consent at four different blood centers across the US were evaluable for hemolysis parameters, including spontaneous and stress (oxidative and osmotic) hemolysis analysis in ~42-day stored RBC derived from 8,502 whole blood donations. A total of 643 donors scoring in the 5th and 95th percentile for hemolysis parameters were invited to donate a second unit of pRBCs, which were assayed at storage days 10, 23 and 42 for hemolytic parameters and mass spectrometry-based high-throughput metabolomics. A pilot study was performed on 599 samples as part of the REDS-III RBC Omics project, while the whole recalled donor cohort was assayed as part of REDS-IV-P, for a total of serial 1,929 samples. As part of the MIRAGES project (Metabolic Investigation of Red blood cells, as a function of Aging, Genetics, Environment and Storage), we generated the REDS Explorer portal, for real time data processing and visualization. The portal not only serves as the largest online metabolomics data repository in transfusion medicine, but also affords data elaborations, including correlations to biological characteristics (donor sex, age, BMI, blood group, Rh status), processing strategies (additive solutions, blood center, irradiation) and functional readouts (ferritin levels, hemolytic parameters). The user can adjust for relevant covariates, select specific ranges for variables such as age, and filter based on donor sex, additive solution or storage duration - while choosing between the REDS III pilot data or the REDS-IV-P full recalled donor cohort. The portal generates publication quality figures for free, direct download. For example, here we show that L-citrulline is a previously unappreciated marker of blood donor age increasing in pRBCs as a function of donor age (Figure 1.A-B - q=3.84 e-24), with opposite trends observed for hydroxyisovaleryl-carnitine (q = 3.55 e-11 Figure 1.C). The latter was then identified as the top positive correlate to donor ferritin levels at the time of donation (q = 1.47 e-16 - Figure 1.D). The portal facilitates generation and investigation of new data-driven hypotheses, enabling the rapid dissemination and/or further mechanistic testing, thus maximizing the value of large-scale initiatives such as REDS-III/IV-P. The blood donor population as a window on the larger healthy population, high-throughput metabolomics applications in transfusion medicine, and the MIRAGES: REDS Explorer portal are directly relevant to advances in the fields of epidemiology and hematology. Figure 1 - Example of an output from the MIRAGES - REDS Explorer portal - based on donor age and ferritin levelsFigure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,200
Score d'incertitude au seuil0,550

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,141
Tête enseignante GPT0,357
Écart entre enseignants0,216 · 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 tête enseignante, pas un consensus.

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

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

En bref

Citations1
Publié2022
Routes d'admission1
Résumé présentoui

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