Metabolomics as tool for the early detection of diagnostic biomarkers in antidoping field
Notice bibliographique
Résumé
This thesis focuses on the exploration of the metabolomics approach as a guided tool in the field of anti-doping by defining a workflow based on the synergy between High-Resolution Mass Spectrometry and chemometric tools. First, the optimization of a low-energy electron ionization source to maximize the formation of molecular ion and minimize the fragmentation degree of steroid pathways, preserving the specific fragmentation pathway of the steroids considered and increasing the m/z coverage range. To this end, the effects of electron energy, emission current and source temperature on steroid fragmentation pathways were studied by performing full factorial experimental designs, using steroid reference materials chosen to cover the entire urinary steroid profile. Second, the development and validation of systematic metabolomics workflows to reduce the time and resources required to identify direct drug metabolites for GCHRMS. To do so, the administration of 7-keto-DHEA was studied as a Proof-of-Concept to highlight the strong synergy between high-resolution mass spectrometry and chemometric tools for early detection of drug metabolites in anti-doping. A comparison of the most significant features with the spectra library validated the proposed metabolomics approach, further supported by existing data in the literature. Third, extension of the previously proposed workflow on GCHRMS data to LCHRMS data, development and validation. The primary differences between the two workflows lie in the method validation, sample analysis processes, including preparation and acquisition, as well as in the raw data preprocessing steps. This knowledge gives the opportunity to gain insight into all possible metabolic changes, regardless of whether it is the formation of new compounds or the reduction of compounds. In contrast, the metabolite-focused approach generally reduces the scope of investigation to the formation of metabolites from the parent molecule, thus losing the response that other endogenous compounds might have as a result of its intake. Fourth, application of the developed workflow for the investigation of the physiological and post training effects of ecdisteroid supplementation on the human serum metabolome. These outcomes elucidates the effectiveness of a metabolomics-based approach in detecting specific trends related to the intake of performance-enhancing substances that would otherwise remain undetected through traditional analytical methods or be masked by physiological changes. The results presented in this thesis are of relevance for a more depth understanding of the complex relationships between different steroids, which may not be apparent when examining individual steroids in isolation, and in the identification of patterns or combinations of steroids that may discover new biomarkers for disease diagnosis, prognosis, or monitoring. This is a step forward in the metabolic characterization of different physio-pathological conditions that allow for the personalization of treatment strategies and optimization of individual performance outcomes. This personalized treatment enhances the value of the proposed metabolomics approach, making it beneficial not only for improving sports performance, but also in the clinical setting, where targeted supplementation can promote better health and recovery.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 tête enseignante, 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 ».