Analyse von Tränenflüssigkeit als Biomarkerquelle beim idiopathischen Parkinsonsyndrom
Notice bibliographique
Résumé
Background: The idiopathic Parkinson´s syndrome is one the second most common neurodegenerative diseases in the world. The diagnosis is to this day a mainly clinical diagnosis. The accuracy of the diagnosis is depending on the expertise of the person performing the examination. Different studies already analyzed a large amount of different potential biomarker sources, such as cerebrospinal fluid, blood and brain homogenate. Another promising candidate as a potential biomarker is tear fluid. Tear fluid is considered an easily accessible body fluid which contains a vast number of proteins which could be influenced by neurodegenerative diseases and is already used as a biomarker source of several ophthalmological disorders. In this study we analyzed the proteins Apolipoprotein A1 (Apo A1), Gelsolin, Profilin 1, Glypican 4 and Apolipoprotein E (Apo E) in the tear fluid of Parkinson´s disease (PD) patients and controls. Those proteins were chosen because of their link to neurodegeneration and because of a previously done study by Boeger et al. which described the proteins Apo A1, Gelsolin and Profilin 1 as significantly regulated in the tear fluid of PD patients compared to controls. Methods: Tear fluid samples of 36 PD patients and 36 age matched and gender matched controls were collected via Schirmer tear test strips. Those samples were analyzed via western blot. Clinical data such as age, gender, ophthalmological diseases und Unified Parkinson`s disease rating scale 3, Hoehn & Yahr-stadium, Montreal cognitive assessment (MoCa) and Parkinson´s disease non-motor symptoms were correlated with the results of the western blots. Results: There was no significant regulation between the PD and control cohorts for any of the proteins examined. However, a breakdown by gender into male and female revealed a significant regulation between the Glypican 4 values of the men and women in the PD cohort. The glypican 4 values of the men were significantly higher. In line with this, there was a non-significant trend for increased Apo A1 values in the men of the PD cohort compared to the values of the women of the PD cohort. When the clinical characteristics were correlated with the proteins examined, significant and positive correlations were found between the proteins Apo A1, Gelsolin, Profilin 1, Glypican 4 and the age of the PD cohort. In addition, there were significant correlations between the proteins in the PD cohort. Profilin 1 was the only one of the proteins examined to show a significant correlation with the protein content of the samples in both the PD cohort and the control cohort. There was also a significant, negative correlation between the MoCA score and the Apo A1 values of the PD cohort. Conclusion: In summary, tear fluid presented itself as an easily accessible material for the search for a biomarker. There was a trend for reduced tear secretion in PD patients and a significantly reduced protein content in the tear fluid of the PD cohort. Gender and age could potentially influence the occurrence of individual proteins in tears and should be taken into account in future searches for biomarkers.
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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) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».