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Enregistrement W7020821333

Multi-Class Liquid Chromatography-High Resolution Mass Spectrometry Methods for Monitoring of Mycotoxins and Metabolites in Human Plasma for Exposure Studies

2020· dissertation· en· W7020821333 sur OpenAlexaboutno aff

Notice bibliographique

RevueSpectrum Research Repository (Concordia University) · 2020
Typedissertation
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueMycotoxins in Agriculture and Food
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMycotoxinOchratoxin AFumonisinAflatoxinFumonisin B1OchratoxinZeranolMass spectrometryProtein precipitationBioanalysis
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Mycotoxins are secondary metabolites produced by fungi that can pose a serious threat to human and animal health due to their toxicity. The assessment of human chronic exposure to mycotoxins requires reliable and highly sensitive multi-analyte assay(s) enabling simultaneous measurements of common toxicologically important mycotoxins and their metabolites in human plasma. \nThe first goal of the thesis was to develop sensitive liquid chromatography – high-resolution mass spectrometry (LC-HRMS) multi-mycotoxin method(s) for the detection and quantification of common toxicologically important mycotoxins frequently occurring in Canada and emerging mycotoxins of interest. Based on the results of extraction recoveries and chromatographic separation, two LC-HRMS methods were required to cover the full mycotoxin panel of interest. The first method combined liquid-liquid extraction with pentafluorophenyl reversed-phase LC-HRMS for the quantification of 17 mycotoxins, aflatoxins B1, B2, G1 and G2, zearalenone, 7-α-hydroxy-zearalenol (α-ZOL), 7-β-hydroxy-zearalenol, zearalanone, 7-α-hydroxy-zearalanol, 7-β-hydroxy-zearalanol, T-2 toxin, HT-2 toxin, deoxynivalenol, nivalenol, 15-acetyldeoxynivalenol, 3-acetyldeoxynivalenol and fusarenon X. The method was validated using procedures described in the Food and Drug Administration (FDA) guidance for Industry Bioanalytical Method Validation. Lower limits of quantification (LLOQs) ranged from 0.1 to 0.5 ng/ml, except for nivalenol (3 ng/ml). The method (intra-day and inter-day) accuracy and precision ranged from 85.6% to 116.4% and from 1.6% to 15.6% RSD, respectively, excluding α-ZOL for which an accuracy of 72.9 % to 97.2% was observed. The second method covered ten mycotoxins, fumonisin B1, fumonisin B2, ochratoxin α (OTα), citrinin, ochratoxin A, beauvericin, enniatin A, enniatin A1 (ENNA1), enniatin B (ENNB) and enniatin B1, and combined methanol protein precipitation with C18 reversed-phase chromatography and polarity-switching LC-HRMS. LLOQs ranged from 1.25 to 4 ng/ml. Absolute recovery ranged from 86.6% to 127.7% in individual plasma samples. Significant matrix effects were observed for OTα (77.5%) in one out of ten individual plasma samples and fumonisins (134.8% to 167.8%), ENNB (69.7% to 79.4%) and ENNA (69.3% to 79.2%) in all individual plasma samples. The rest of the mycotoxins showed negligible matrix effects ranging from 87.2% to 112.2% in all lots of plasma tested. \nExcellent LLOQs, negligible matrix effects and accurate quantitation capability of the first method coupled with the lower cost of analysis per sample make the method suitable for large-scale analysis of human plasma samples. The second method is also simple and low cost but requires additional modification to further improve LLOQs and reduce the matrix effect before full validation and implementation. Both methods are versatile and can be applied for retrospective analysis and other applications such as metabolism studies due to the use of HRMS and superior chromatographic separation. To show this capability, the first method was successfully applied for the in-depth metabolism studies of 17 mycotoxins. The method showed excellent suitability and advantages for the detection of various mycotoxin metabolites from Phase I metabolism and glucuronidation obtained from human microsomal incubations. Two ppm mass accuracy with internal mass calibration reduced the number of possible elemental formulas for a measured m/z value. Data-dependent acquisition in combination with collision-induced dissociation or higher energy collisional dissociation was used to ensure adequate fragmentation and to study the structure of the mycotoxin metabolites. The Compound Discoverer 2.1 software, which contains extensive libraries of common metabolic pathways and mass spectral libraries, was used to streamline the identification and the characterization of the metabolites. In total, 188 mycotoxin metabolites were generated, characterized and used to build an extensive in-house library of human mycotoxin metabolites. One hundred metabolites were reported for the first time, showing the power and sensitivity of the approach. For these 17 mycotoxins, 92 metabolites were previously described in literature, and among these known metabolites only four could not be generated using our approach. Currently, this is the most comprehensive LC-MS library of human mycotoxin metabolites. \nIn conclusion, both LC-MS methods and the in-house mycotoxin metabolite library will allow the monitoring of 27 mycotoxins and their 188 metabolites in large-scale biomonitoring studies. In the long-term, this will help to prioritize metabolites that should be routinely included during exposure monitoring studies and will provide important new data on mycotoxin exposure of the Canadian population.

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)0,002
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Méthodes · Signal consensuel: Méthodes
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,015

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,002
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,003

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 Opus0,056
Tête enseignante GPT0,333
Écart entre enseignants0,276 · 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'étudeExpérimental (laboratoire)
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

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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