Drusen classification and quantification in eye bank eyes of cataract patients previously implanted with intraocular lenses with or without blue-light filtration
Notice bibliographique
Résumé
Purpose: Age-related macular degeneration (AMD) is a disease that causes central vision loss and is the leading cause of blindness in the western world. A hallmark of AMD is the presence of large, soft drusen in the macular region of the retina. Drusen are lipoproteinaceous deposits found between the basal lamina of the retinal pigment epithelium (RPE) and Bruch’s membrane. Although the pathogenesis of AMD is unclear, growing evidence suggests a link between drusen formation and photo-oxidative stress caused by short-wavelength blue light (BL). As such, this project aims to investigate the effect of BL-filtering intraocular lenses (IOL) on drusen formation in post-mortem eyes via histopathological analysis.Methods: 193 post-mortem pseudophakic human eyes (100 with a UV-filtering, clear IOL [cIOL] and 93 with a UV+BL-filtering, yellow IOL [yIOL]) were obtained from the Lions Gift of Sight eye bank and examined at the MUHC-McGill Ocular Pathology and Translational Research Laboratory. Clinical data was collected for each eye and included sex, age at the time of cataract surgery (age-at-surgery), age-at-death, time between cataract surgery and death (surgery-to-death time), implanted IOL model, and clinical history (smoking, diabetes, hypertension, glaucoma, cancer, AMD, and cardiovascular disease). Eyes were sectioned on their coronal and sagittal axes, and formalin-fixed, paraffin-embedded macular cross sections were obtained. The sections were then stained with hematoxylin and eosin, and scanned with the Zeiss Axio Scan.Z1 scanner. Drusen were classified by type, size or subtype, and quantity. Statistical analyses were performed using Microsoft Excel (Microsoft Corporation) and OriginPro® 9 (OriginLab Corporation).Results: Large, soft drusen were present in 49% (n=95) of eyes, while 9% (n=17) had cuticular drusen, 16% (n=30) had hard drusen, and 26% (n=51) had no drusen. There were significantly more cIOL eyes (n=62) with large, soft drusen than yIOL eyes (n=33, p<0.001), and significantly more yIOL eyes (n=38) with no drusen than cIOL eyes (n=13, p<0.0001). No significant differences in the presence of hard or cuticular drusen were found. yIOL eyes had significantly higher mean age-at-surgery (76.5 vs. 72.2 years, p<0.001) and mean age-at-death (82.4 vs. 79.6 years, p<0.05) than cIOL eyes, and cIOL eyes had a significantly higher mean surgery-to-death time (6.96 vs. 5.41 years, p<0.05) than yIOL eyes. There were also significantly more yIOL eyes with a history of smoking (p<0.01) and hypertension (p<0.05), and significantly more cIOL eyes with a history of glaucoma (p<0.05).Conclusion: Large, soft drusen were significantly less prevalent in yIOL eyes than in cIOL eyes and significantly more yIOL eyes had an absence of drusen. These findings suggest that yIOLs may prevent the incidence and development of AMD post cataract surgery
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| 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) | 0,002 | 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 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 ».