Fecal Metabolites_original.XLSX
Notice bibliographique
Résumé
Fecal and serum samples were maintained at -80◦C until metabolic profiling was performed. Global non-targeted mass spectrometry metabolomics analysis was performed at Metabolon, Inc (Metabolon, Inc, Durham, NC), a commercial supplier of metabolic analysis, which has developed a platform that integrates chemical analysis (including identification and relative quantification), data-reduction, and quality assurance. To maximize compound detection and accuracy, 3 separate analytical methods were utilized including ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-LC-MS) in both positive and negative ion modes and gas chromatography/mass spectrometry (GC-MS) (40, 41). Following receipt, samples were inventoried, accessioned into the Metabolon Laboratory Information Management System (LIMS), assigned a unique identifier, and stored at -80◦C until analyzed. Sample preparation was performed by the automated Mircolab STAR system (Hamilton Company, Salt Lake City, UT, USA). Extraction solvents (i.e., methanol containing recovery standards) were added to each sample. Extraction was carried out by shaking using a Geno/Grinder 2000 (Glen Mills, Clifton, NJ) followed by centrifugation. The resulting extract was divided into 5 fractions: 2 for analysis by 2 separate reverse phase (RP) LC/MS methods with positive ion mode electrospray ionization (ESI), 1 for analysis by RP LC/MS with negative ion mode ESI, 1 for GC/MC analysis, and 1 reserve aliquot. Samples were placed on a TurboVap® (Zymark, Midland, Ontario, Canada) to remove organic solvent and were stored overnight under nitrogen before preparation for analysis. All UPLC-MS/MS methods utilized a Waters Acquity UPLC (Walters, Milford, MA, USA) and a Q-Exactive high resolution/accurate mass spectrometer interfaced with heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution (Thermo Scientific, Walthram, MA, USA). Sample extracts were reconstituted in solvents compatible to each method employed. The MS analysis alternated between MS and data-dependent MSn scans using dynamic exclusion. The scan range varied slighted between methods but covered mass to charge ratios of 70 to1000 m/z. Raw data were archived and extracted. For GC/MS, derivatized samples were separated on a 5% phenyldimethyl silicone column with helium carrier gas. Data were analyzed on a Thermo-Finnigan Trace DSQ MS (Thermo Fisher) operated at a unit mass resolving power with electron impact ionization. Detection, identification, integration, and clustering of all ion features into individual compounds was performed using a library of conserved chemical standards using a software developed by Metabolon (42). Authenticated standards within the library contain retention time/index (RI), mass to charge ratio (m/z), and chromatographic data (including MS/MS spectral data). At present, 3,000 commercially available purified standards are registered into the LIMS for distribution to both the LC and GC platforms for determination of their analytical characteristics. Compound abundance was quantified by calculation of area under the curve for the quantification ion of the compound. Quality control measurements were used to individually check all compounds.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,784 | 0,454 |
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 ».