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Enregistrement W4362523181 · doi:10.3724/sp.j.1123.2022.05016

Determination of seven monoaromatic hydrocarbon metabolites by ultra performance liquid chromatography-tandem mass spectrometry

2023· article· en· W4362523181 sur OpenAlexaboutno aff
Tian Qiu, Xu Zhang, Yang Yan-wei, Xiaojian Hu, Song Luo, Ying Zhu

Notice bibliographique

RevueChinese Journal of Chromatography · 2023
Typearticle
Langueen
DomaineChemistry
ThématiqueAnalytical Chemistry and Chromatography
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésChemistryChromatographyFormic acidMass spectrometrySolid phase extractionElutionTandem mass spectrometryTriple quadrupole mass spectrometerSelected reaction monitoringDetection limitMethanolElectrospray ionizationExtraction (chemistry)TolueneHigh-performance liquid chromatography

Résumé

récupéré en direct d'OpenAlex

<p id="p00005">Monoaromatic hydrocarbons (MAHs) such as benzene, toluene, and xylene are important anthropogenic pollutants in the urban atmosphere. The detection of urinary MAH metabolites are included in human biomonitoring programs in several countries, including Canada, the United States, Italy, and Germany, because their evaluation is vital to monitor the exposure of humans to MAHs. To this end, herein, a method was developed for the determination of seven MAH metabolites through ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). An aliquot of 0.5 mL urine was fortified with an isotopic labeled internal standard solution before being hydrolyzed by 40 μL of 6 mol/L HCl solution, followed by extraction using a 96-well EVOLUTE<sup>®</sup>EXPRESS ABN solid-phase extraction plate. The samples were washed with 1.0 mL of methanol-water (10∶90, v/v) and eluted with 1.0 mL methanol. The eluate was diluted four times with water prior to use in instrumental analysis. Chromatographic separation was achieved using an ACQUITY UPLC HSS T3 column (100 mm×2.1 mm, 1.8 μm), with gradient elution using 0.1% formic acid as mobile phase A and methanol as mobile phase B. The detection of seven analytes was performed using a triple-quadrupole mass spectrometer equipped with a negative electrospray ionization source in the multiple reaction monitoring mode. The linear ranges of the seven analytes varied from 0.1-20 μg/L to 2.5-500 mg/L, with correlation coefficients greater than 0.995. The method detection limits were 1.5, 0.02, 0.1, 900, 0.6, and 4 μg/L for <italic>trans</italic>,<italic>trans</italic>-muconic acid (MU), <italic>S</italic>-phenylmercapturic acid (PMA), <italic>S</italic>-benzylmercapturic acid (BMA), hippuric acid (HA), 2-methyl hippuric acid (2MHA), and 3-methyl hippuric acid (3MHA)+4-methyl hippuric acid (4MHA), respectively. The limits of quantification were 5, 0.05, 0.4, 3000, 2, and 12 μg/L for MU, PMA, BMA, HA, 2MHA, and 3MHA+4MHA, respectively. The method was verified by spiking urine samples at three different concentration levels, with recovery rates ranging from 84% to 123%. The intra- and inter-day precisions were 1.8%-8.6% and 1.9%-21.4%, respectively. The extraction efficiencies were 68%-99%, and the matrix effects ranged from -11% to -87%. The urine samples obtained from the German external quality assessment scheme (round 65) were used to assess the accuracy of this method. Both high and low concentrations of MU, PMA, HA, and methyl hippuric acid were within the tolerance range. All analytes in the urine samples were found to be stable for up to seven days at room temperature (20 ℃, absence of light), with less than 15% change in concentration. Analytes in urine samples were found to be stable for at least 42 d at 4 ℃ and -20 ℃, or for six freeze-thaw cycles and up to 72 h in an autosampler (8 ℃). The method was applied to the analysis of 16 non-smokers’ and 16 smokers’ urine samples. The detection rates of MU, BMA, HA, and 2MHA were 100% in both non-smokers’ and smokers’ urine samples. PMA was detected in 75% non-smokers’ and 100% smokers’ urine samples. 3MHA+4MHA was detected in 81% non-smokers’ urine and in all smokers’ urine samples. Statistical differences were found for MU, PMA, 2MHA, and 3MHA+4MHA between the two groups (<italic>p</italic>&lt;0.001). The established method has good robustness and can provide reliable results. The experiments were carried out in a high-throughput manner with large sample sizes, owing to the small sample volume, and allowed the successful detection of the seven MAH metabolites in human urine.

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 candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,076
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0010,005
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
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 Opus0,006
Tête enseignante GPT0,230
Écart entre enseignants0,224 · 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.

Devis d'étudeExpérimental (laboratoire)
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

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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