Purpose: Offer a personal perspective on the scientific advances on macular telangiectasia type 2 (MacTel2) since the launch of the MacTel Project in 2005.
Design: Literature review and personal perspective.
Methods: Critical review of the peer-reviewed literature and personal perspective.
Results: Generous financial support from the Lowy Medical Research Institute laid the foundations of the MacTel Project. MacTel Project investigators used state of the art multimodal retinal imaging and advanced modern biological methods to unravel many of the mysteries surrounding MacTel2. Major accomplishments includes elucidation of the pathogenic role that low serine levels, elevated 1-deoxysphingolipids and other mechanisms induce mitochondrial dysfunction which lead to Müller cell and photoreceptor degeneration; the use of objective measures of retinal structures such as the area of ellipsoid zone disruption as an outcome measure in clinical trials; the demonstration that the ciliary neurotrophic factor slows down retinal degeneration and the development of a new severity scale classification based on multimodal imaging findings.
Conclusions: MacTel2 is a predominantly metabolic disease characterized by defects in energy metabolism. Despite relatively good visual acuities, MacTel2 patients experience significant visual disability. The Mac Tel Project has been instrumental in advancing MacTel2 knowledge in the past two decades.
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http://dx.doi.org/10.1186/s40942-023-00505-5 | DOI Listing |
Invest Ophthalmol Vis Sci
September 2024
Ophthalmology Department, AP-HP, Hôpital Lariboisière, Université Paris Cité, Paris, France and Centre de Référence des Maladies Vasculaires Rares du Cerveau et de l'Œil (CERVCO), Hôpital Lariboisière, APHP, Paris, France.
Purpose: To assess the correspondence between interdigitation zone (IZ) reflectivity, ellipsoid zone (EZ) loss, inner retinal layer reflectivity, patterns of capillary dilation, and telangiectasia in eyes with early macular telangiectasia type 2 (MacTel).
Patients And Methods: Twenty-eight eyes of 22 patients with grade 0-2 MacTel (according to the MacTel project classification) and 28 healthy control eyes were included in this study. Multimodal imaging, including optical coherence tomography (OCT) angiography, adaptive optics flood illumination ophthalmoscopy (AO-FIO) and blue light reflectance (BLR), was performed.
JAMA Ophthalmol
March 2024
Department of Ophthalmology, University of Washington, Seattle.
Importance: Deep learning image analysis often depends on large, labeled datasets, which are difficult to obtain for rare diseases.
Objective: To develop a self-supervised approach for automated classification of macular telangiectasia type 2 (MacTel) on optical coherence tomography (OCT) with limited labeled data.
Design, Setting, And Participants: This was a retrospective comparative study.
Int J Retina Vitreous
November 2023
Asociados de Macula, Vitreo y Retina de Costa Rica, Primer Piso Torre Mercedes Paseo Colon, San Jose, Costa Rica.
Purpose: Offer a personal perspective on the scientific advances on macular telangiectasia type 2 (MacTel2) since the launch of the MacTel Project in 2005.
Design: Literature review and personal perspective.
Methods: Critical review of the peer-reviewed literature and personal perspective.
Ophthalmology
February 2024
Department of Ophthalmology, University of Washington, Seattle, Washington; The Roger and Angie Karalis Johnson Retina Center, Seattle, Washington. Electronic address:
Purpose: Deep learning (DL) models have achieved state-of-the-art medical diagnosis classification accuracy. Current models are limited by discrete diagnosis labels, but could yield more information with diagnosis in a continuous scale. We developed a novel continuous severity scaling system for macular telangiectasia (MacTel) type 2 by combining a DL classification model with uniform manifold approximation and projection (UMAP).
View Article and Find Full Text PDFOphthalmol Sci
June 2023
The Lowy Medical Research Institute and the Department of Molecular Medicine, The Scripps Research Institute, La Jolla, California.
Purpose: To develop a severity classification for macular telangiectasia type 2 (MacTel) disease using multimodal imaging.
Design: An algorithm was used on data from a prospective natural history study of MacTel for classification development.
Subjects: A total of 1733 participants enrolled in an international natural history study of MacTel.
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