Development and Application of Solid-Phase Microextraction Probe Electrospray Ionization
Notice bibliographique
Résumé
Ambient ionization mass spectrometry (AIMS) is a category of mass spectrometry (MS) techniques originally characterized as using ambient ionization sources to analyze samples with little to no sample preparation and no chromatography step. This set of techniques have quickly gained popularity due to fast workflows and the ability to perform high throughput analysis. However, AIMS is prone to high matrix effects and reduced sensitivities. Solid-phase microextraction (SPME) is commonly used to mitigate these effects due to easy integration into pre-existing AIMS workflows, enabling preconcentration and extraction. Probe electrospray ionization (PESI) is a technique developed by Hiraoka and colleagues in 2007, then commercialized by Shimadzu Corporation years later. In PESI a small metal probe is dipped into the sample and immediately moved upwards, close to the inlet of a mass spectrometer to facilitate electrospray ionization (ESI). In recent years there has been a shift from using PESI for qualitative studies towards quantitative studies. With this shift in intentions, sample preparation has been incorporated into PESI workflows. The objective of this work is to incorporate SPME as a sample preparation method for PESI and develop applications for this technique. The first objective was to see if the PESI probes could be coated and to see if SPME-PESI-MS/MS could give reliable MS data. To ensure reproducibility of such small probes, intra- and inter-probe reproducibility tests by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) were conducted using drugs of abuse. These results show reproducibility of the probes with almost all relative standard deviations being ≤ 15%. Afterward, the optimal desorption solution for SPME-PESI-MS/MS was determined. It was also found that a coated PESI probe used for SPME-PESI-MS/MS could not be used for a subsequent LC-MS/MS run without extracting the sample again due to significant desorption by SPME-PESI-MS/MS. Furthermore, an application of SPME-PESI-MS/MS to quantitate drugs of abuse from 30µL of plasma was developed. The intra-day precision of said method was under 15% for all compounds. The inter-day precision of all compounds was under 15% except for lorazepam at the 30 ng mL-1 validation point and oxazepam at the 90 ng mL-1 validation point. The accuracy of all compounds for this method was within 80-120% except for lorazepam at the 30 ng mL-1 validation point. The small dimensions of the coated PESI probes were then leveraged to determine the free concentration and plasma protein binding of diazepam from human plasma by SPME-PESI-MS/MS. The plasma protein binding determined by SPME-PESI-MS/MS was 99.3% which falls within literature values of 97-99% from human plasma samples spiked with 25 ng mL-1 of diazepam. Finally, the development of a screening method for aminoglycosides was explored with SPME-PESI-MS/MS. This was to explore the use of AIMS technologies as an alternative screening method for compounds that require conditions that are highly detrimental to MS systems (i.e. high salt concentrations or ion-pairing reagents).
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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,003 |
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.
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 ».