Follicular fluid alterations in endometriosis: label-free proteomics by MS<sup>E</sup>as a functional tool for endometriosis
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Notice bibliographique
Résumé
Endometriosis is a chronic gynecological condition that affects 10-32% of women of reproductive age and may lead to infertility. The study of protein profiles in follicular fluid may assist in elucidating possible biomarkers related to this disease. For this, follicular fluid samples were obtained from women with tubal factor or minimal male factor infertility who had pregnancy outcomes after in vitro fertilization (IVF) treatment (control group, n = 10), women with endometriosis (endometriosis group, n = 10), along with the endometrioma from these same patients were included (endometrioma group, n = 10). For proteomic analysis, samples were pooled according to their respective groups and normalized to protein content. Proteins were analyzed by in tandem mass spectrometry (MS(E)) Spectra processing and the ProteinLynx Global Server v.2.5. was used for database searching. Data was submitted to the biological network analysis using Cytoscape 2.8.2 with ClueGO plugin. As a result, 535 proteins were identified among all groups. The control group differentially or uniquely expressed 33 (6%) proteins and equal expression of 98 (18%) proteins was observed in the control and endometriosis groups of which 41 (7%) proteins were further identified and/or quantified. Six (1%) proteins were observed in both the endometriosis and endometrioma groups, but 212 (39%) proteins were exclusively identified and/or quantified in the endometrioma group. There were 9 (1%) proteins observed in both the control and endometrioma groups and there were 139 (25%) proteins common among all three groups. Distinct differences among the protein profiles in the follicular fluid of patients included in this study were found, identifying proteins related to the disease progression and IVF success. Thus, some pathways related to endometriosis are associated with the presence of specific proteins, as well as the absence of others. This study provides a first step to the development of more sensitive diagnostic tests and treatment.
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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 | 0,005 | 0,145 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,003 | 0,004 |
| 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 | 0,001 |
| 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 découle