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Enregistrement W4324129135 · doi:10.1002/jev2.12286

The quick reference card “Storage of urinary EVs” – A practical guideline tool for research and clinical laboratories

2023· letter· en· W4324129135 sur OpenAlexaff
Martin E. van Royen, Carolina Soekmadji, Cristina Grange, Jason Webber, Tobias Tertel, Marvin Droste, Anja Buescher, Bernd Giebel, Guido Jenster, Alicia Llorente, Charles J. Blijdorp, Dylan Burger, Uta Erdbrügger, Elena S. Martens‐Uzunova

Notice bibliographique

RevueJournal of Extracellular Vesicles · 2023
Typeletter
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueExtracellular vesicles in disease
Établissements canadiensOttawa HospitalUniversity of Ottawa
Organismes subventionnairesNational Heart, Lung, and Blood InstituteHelse Sør-Øst RHFNorges ForskningsrådNational Institutes of HealthCancer Research WalesNierstichtingKWF KankerbestrijdingKreftforeningen
Mots-clésMedicineExtracellular vesiclesUrinary systemBiomarkerUrinalysisBioinformaticsIntensive care medicineInternal medicineBiology

Résumé

récupéré en direct d'OpenAlex

Dear Editor, The high diagnostic potential of urinary extracellular vesicles (uEVs) for urogenital disease has been recognized for more than a decade. This is emphasized by the identification of different molecular biomarkers (i.e. protein, mRNA, miRNA, lipids and metabolites) in uEV preparations that may assist the clinical management of prostate, bladder, and renal cancer (Junker et al., 2016). uEV biomarkers for other pathologies like acute and chronic kidney disease of various etiologies, cystic and tubule-interstitial disease, or for kidney transplantation are also under active investigation (Grange & Bussolati, 2022). Apart from the growing need for validation studies, the translational potential of uEV biomarkers is hampered by several biological factors. Such factors include the diverse cellular origins of uEVs throughout the renal and urogenital tract, but also the dynamic molecular composition of urine due to hydration status, diet, salt regulation, exercise, and circadian rhythm. In addition to these inherent factors, the reproducibility of uEV analysis is also strongly influenced by logistic variables like the differences in the time of sampling or the preanalytical procedures for handling of urine samples (Erdbrügger et al., 2021). The general reporting recommendations for EV sample processing and analysis are covered in detail in the Minimal Information for Studies of Extracellular Vesicles (MISEV 2018) position paper (Thery et al., 2018). However, a community consensus on best methodological practices that is tailored to the biofluid-specific characteristics and requirements is of particular importance for the success of preclinical and clinical studies on biomarker discovery, validation and future use in clinical decision making. To address this need in uEVs research, the Urine Task Force of the Rigor and Standardization Subcommittee of the International Society for Extracellular Vesicles (ISEV) published a position paper summarizing the current state of the art and listing detailed recommendations for improved rigor, reproducibility and inter-operability in uEV research (Erdbrügger et al., 2021). To support the implementation of the published recommendations, and enhance their application in daily research practices, here we provide a Quick Reference Card on STORAGE of urinary EVs (Figures 1 and 2, Supplementary File 1). The Quick Reference Card does not substitute a uEVs protocol for storage, isolation or processing but it summarizes the expert community consensus recommendations on the most critical factors affecting storage of fresh or biobank urine and uEVs samples as discussed in the uEV position paper (Erdbrügger et al., 2021). The Card is organized according to six critical stages: Biobanking, Storage of urine prior to processing, Preprocessing, Storage of urinary supernatant and uEVs, Defrosting, and Transportation. Evidence level and reporting priority for each stage are color-coded in accordance to the findings as described in the ISEV uEVs position paper (Erdbrügger et al., 2021) and according to the MISEV 2018 guidelines (Thery et al., 2018). The Card is intended as an easily accessible guideline tool that can be used during study planning and manuscript preparation, but also as a “bench top” reference during everyday laboratory work. To conclude, we present a novel format of communication for EV study guidelines and recommendations that can also be applied to other topics within, but importantly also outside the field of urinary EVs. Ultimately, by using this format, we endeavor to enhance adherence to pre-analytical best practice guidelines in order to promote reproducibility and, above all, the translational potential of uEV studies. This work was supported by the Alpe d'HuZes grant “IMMPROVE” of the Dutch Cancer Society (grant #EMCR2015-8022), by the Norges Forskningsråd, Kreftforeningen and Helse Sør-Øst RHF (NO), by the NIH, National Heart, Lung, and Blood Institute, Award number K23-HL-126101 and by the Dutch Kidney Foundation (Nierstichting), Award number: CP18.05. The authors report no conflict of interest. Conceptualization: M.v.R., C.S., C.G., J.W., T.T., M.D., A.B., B.G., A.L., C.B., D.B., U.E., E.M.U. Writing, original draft preparation: M.v.R., E.M.U.; Writing, review and editing: M.v.R., C.S., C.G., J.W., T.T., M.D., A.B., B.G., A.L., C.B., D.B., U.E., E.M.U. All authors have read and agreed to the published version of the manuscript. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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)0,020
score de la tête « metaresearch » (Gemma)0,138
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: Méthodes · Signal consensuel: aucune
Score de désaccord entre enseignants0,065
Score d'incertitude au seuil0,216

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0200,138
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0030,001
Bibliométrie0,0040,002
Études des sciences et des technologies0,0010,003
Communication savante0,0070,008
Science ouverte0,0040,004
Intégrité de la recherche0,0100,010
Charge utile insuffisante (le modèle a refusé de juger)0,0650,071

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 Opus0,109
Tête enseignante GPT0,429
Écart entre enseignants0,321 · 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
GenreMéthodes

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

En bref

Citations26
Publié2023
Routes d'admission1
Résumé présentoui

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