Lutein and zeaxanthin are highly concentrated at the central region of the human retina, forming a distinct yellow spot known as the macula lutea. The delivery and retention of the macular pigment carotenoids in the macula lutea involves many proteins, but their exact roles remain incompletely understood. In our study, we examined the distribution of the twelve known macular carotenoid-related proteins within the human macula and the underlying retinal pigment epithelium (RPE) using both fluorescence and Raman modes on our confocal resonance Raman microscope. Additionally, we assessed protein and gene expression through Western blot analysis and a single-cell RNA sequencing database. Our findings revealed that GSTP1, BCO2, and Aster-B exhibited distribution patterns similar to the macular carotenoids, with higher expression levels within the macular region compared to the periphery, while SR-BI and ABCA1 did not exhibit specific distribution patterns within the macula or RPE. Interestingly, LIPC, SR-BI's partner, accumulated specifically in the sub-foveal RPE. All three of these carotenoid transport proteins were found to be highly expressed in the RPE. These results offer valuable insights into the roles these proteins play in the formation of the macula lutea.
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http://dx.doi.org/10.1016/j.exer.2024.110043 | DOI Listing |
Sci Rep
December 2024
Neurology, Icahn School of Medicine at Mount Sinai, New York, USA.
We used machine learning to investigate the residual visual field (VF) deficits and macula retinal ganglion cell (RGC) thickness loss patterns in recovered optic neuritis (ON). We applied archetypal analysis (AA) to 377 same-day pairings of 10-2 VF and optical coherence tomography (OCT) macula images from 93 ON eyes and 70 normal fellow eyes ≥ 90 days after acute ON. We correlated archetype (AT) weights (total weight = 100%) of VFs and total retinal thickness (TRT), inner retinal thickness (IRT), and macular ganglion cell-inner plexiform layer (GCIPL) thickness.
View Article and Find Full Text PDFBMC Ophthalmol
December 2024
Department of Ophthalmology, Faculty of Medicine, Ankara University, Ankara, Turkey.
Background: To evaluate the change in time in visual acuity, central macular thickness (CMT), and microperimetry (MP) findings in Behçet uveitis (BU) patients who were in remission with maintenance therapy.
Methods: This single center, retrospective, observational cohort study included twenty-five eyes of 25 patients with BU who were in remission during maintenance therapy. Best corrected visual acuity (BCVA), CMT, macular integrity index, average threshold, and fixation stabilities (P1 and P2) evaluated at six-month intervals were recorded.
Retin Cases Brief Rep
January 2025
Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio.
Purpose: Best vitelliform macular dystrophy is an inherited macular dystrophy associated with over 250 pathogenic variants of the Bestrophin-1 ( BEST1 ) gene. Although several types of lesions of best vitelliform macular dystrophy are well-described, reports of phenotypic variations associated with rare genetic variants are limited.
Methods: This was a retrospective case series performed in 2021 at a tertiary eye care center.
Int Ophthalmol
December 2024
Eye Clinic, University of Health Sciences Umraniye Training and Research Hospital, 34766, Umraniye, Istanbul, Turkey.
Background: To execute comprehensive study about optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) findings in fibromyalgia (FM) to elaborate macula, optic disk changes.
Methods: A total of 84 participants comprising 44 FM patients and 40 healthy controls were included. Macular full thickness, retinal nerve fiber layer (RNFL), ganglion cell layer (GCL)+, GCL++, superficial vessel density (SVD), deep vessel density (DVD), foveal avascular zone (FAZ), circumpapillary vessel density (cpVD), RNFL measurements were evaluated using OCT/OCTA.
PLoS One
December 2024
Department of Ophthalmology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Tokyo, Japan.
Purpose: The objective of this study is to estimate the area of the Foveal Avascular Zone (FAZ) from B-scan OCT images using machine learning algorithms.
Methods: We developed machine learning models to predict the FAZ area from OCT B-scan images of eyes without retinal vascular diseases. The study involved three models: Model 1 predicted the FAZ length from B-scan images; Model 2 estimated the FAZ area from the predicted length using 1, 3, or 5 horizontal measurements; and Model 3 converted the FAZ area from pixels to mm2.
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