Metabolomics: The Principles and Potential Applications to Transplantation
Notice bibliographique
Résumé
This review provides a summary of the applications and potential applications of metabolite profiling (i.e. metabolomics) in monitoring organ transplants. While the concept of metabolomics is relatively new to organ transplantation, the idea of measuring metabolites as a quick, noninvasive probe of organ function is not. Indeed, metabolite measurements of serum creatinine have long been used to assess pre‐ and post‐operative organ function. Over the past 10 years, a number of lesser‐known, organ‐specific metabolites have also been shown to be good diagnostic indicators of both organ function and viability. In general, metabolomics offers a complementary picture to what can be revealed via techniques based on genomics, proteomics or histology. Because metabolic changes typically happen within seconds or minutes after an ‘event’, whereas some transcript, protein abundance or tissue changes may take place over days or weeks, metabolomic measurements may offer a particularly useful and inexpensive diagnostic tool to monitor donor organ viability or to detect organ rejection. The excitement associated with metabolomics, however, must be tempered by the fact that the technology for rapid metabolite identification is still in its infancy, and that metabolites are but one part of a very complex picture pertaining to organ function. This review provides a summary of the applications and potential applications of metabolite profiling (i.e. metabolomics) in monitoring organ transplants. While the concept of metabolomics is relatively new to organ transplantation, the idea of measuring metabolites as a quick, noninvasive probe of organ function is not. Indeed, metabolite measurements of serum creatinine have long been used to assess pre‐ and post‐operative organ function. Over the past 10 years, a number of lesser‐known, organ‐specific metabolites have also been shown to be good diagnostic indicators of both organ function and viability. In general, metabolomics offers a complementary picture to what can be revealed via techniques based on genomics, proteomics or histology. Because metabolic changes typically happen within seconds or minutes after an ‘event’, whereas some transcript, protein abundance or tissue changes may take place over days or weeks, metabolomic measurements may offer a particularly useful and inexpensive diagnostic tool to monitor donor organ viability or to detect organ rejection. The excitement associated with metabolomics, however, must be tempered by the fact that the technology for rapid metabolite identification is still in its infancy, and that metabolites are but one part of a very complex picture pertaining to organ function.
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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 | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 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 ».