Results 191 to 200 of about 110,459 (310)

Upregulation of sphingomyelin and ABCA8 in response to TDP‐43 pathology in amyotrophic lateral sclerosis brain

Brain Pathology, EarlyView.
Increases in sphingomyelin in response to TDP‐43 pathology in the disease‐affected motor cortex of amyotrophic lateral sclerosis (ALS) brain. Abstract Amyotrophic lateral sclerosis (ALS) is a rapidly progressing neurodegenerative disease characterized by the degeneration of motor neurons and the presence of TAR DNA‐binding protein 43 (TDP‐43 ...
Finula I. Isik, Russell Pickford, Nicolas Dzamko, Kerry‐Anne Rye, Woojin Scott Kim   +4 more
wiley   +1 more source

Retinal degeneration driven by brain-derived neurotrophic factor deficiency in microglia and T-lymphocytes. [PDF]

Sci Rep
Reinehr S, Zehge JP, Klöster K, Mueller M, Hendek HH, Sendtner M, Dick HB, Gold R, Joachim SC, Faissner S.   +9 more
europepmc   +1 more source

MMP-9 inhibitors attenuate retinal neurovascular degeneration in a neonatal rat model of NMDA-induced retinal neurotoxicity

, 2019
Daiki Asano, Akane Morita, Asami Mori, Kenji Sakamoto, Tsutomu Nakahara   +4 more
openalex   +2 more sources

Deep Learning‐Based Detection of Reticular Pseudodrusen in Age‐Related Macular Degeneration

Clinical &Experimental Ophthalmology, EarlyView.
ABSTRACT Background Reticular pseudodrusen (RPD) signify a critical phenotype driving vision loss in age‐related macular degeneration (AMD). This study sought to develop and externally test a deep learning (DL) model to detect RPD on optical coherence tomography (OCT) scans with expert‐level performance.
Himeesh Kumar, Yelena Bagdasarova, Scott Song, Doron G. Hickey, Amy C. Cohn, Mali Okada, Robert P. Finger, Jan H. Terheyden, Ruth E. Hogg, Pierre‐Henry Gabrielle, Louis Arnould, Maxime Jannaud, Xavier Hadoux, Peter van Wijngaarden, Carla J. Abbott, Lauren A. B. Hodgson, Roy Schwartz, Adnan Tufail, Emily Y. Chew, Cecilia S. Lee, Erica L. Fletcher, Melanie Bahlo, Brendan R. E. Ansell, Alice Pébay, Robyn H. Guymer, Aaron Y. Lee, Zhichao Wu   +26 more
wiley   +1 more source

Exon Skipping Therapy Restores Ciliary Function in USH2A-Related Retinal Degeneration. [PDF]

Invest Ophthalmol Vis Sci
Li W, Li Y, Zhou Y, Liu Y, Liu H, Wei X, Sun Z, Han X, Zou X, Li H, Sui R.   +10 more
europepmc   +1 more source

Retinal Degeneration in a Goat

Journal of the American Veterinary Medical Association, 1980
openaire   +3 more sources

Ten‐Year Incidence, Risk Factors and Progression Rate of Macular Atrophy in Neovascular Age‐Related Macular Degeneration

Clinical &Experimental Ophthalmology, EarlyView.
ABSTRACT Background To evaluate the 10‐year cumulative incidence, progression rates, and risk factors for macular atrophy (MA) in neovascular age‐related macular degeneration (nAMD) patients receiving long‐term anti‐vascular endothelial growth factor (VEGF) therapy.
Francesco Romano, Marco Casaluci, Antonio Valastro, Matteo Airaldi, Paolo Milella, Francesco Pozzo Giuffrida, Elisa Cozzi, Andrea Aretti, Kelvin YC Teo, Chui Ming Gemmy Cheung, Marco Nassisi, Francesco Viola, Giovanni Staurenghi, Alessandro Invernizzi   +13 more
wiley   +1 more source

Ubap1l Knockout Mice Model Recapitulates Retinal Degeneration Phenotype Observed in Patients and Exhibits Irregular Photoreceptor Morphology. [PDF]

Invest Ophthalmol Vis Sci
Wang Y, Zhang S, Zheng Y, Guo D, Jiang Y, Ouyang J, Sun W, Li S, Xiao X, Liang L, Yi Z, Zhang Q.   +11 more
europepmc   +1 more source

The Michigan Retinal Degeneration Questionnaire: A Patient-Reported Outcome Instrument for Inherited Retinal Degenerations

, 2020
Gabrielle D. Lacy, Maria Fernanda Abalem, Chris Andrews, Lilia Popova, Erin P. Santos, Gina Yu, Hanan Rakine, Natasha Baig, Joshua R. Ehrlich, Abigail T. Fahim, Kari Branham, Joan A. Stelmack, Bonnielin K. Swenor, Gislin Dagnelie, David C. Musch, Thiran Jayasundera   +15 more
openalex   +2 more sources

Health Economic Considerations for the Implementation of Artificial Intelligence‐Enabled Diabetic Retinopathy Screening: A Review

Clinical &Experimental Ophthalmology, EarlyView.
ABSTRACT Artificial intelligence (AI) has comparable accuracy to ophthalmologists for diabetic retinopathy (DR) screening, yet its cost‐effectiveness is crucial for implementation. Our review of 18 health economic analyses of AI versus manual grading for DR found significant methodological variation, with cost‐utility analysis and Markov modelling ...
James Leigh, Jocelyn Drinkwater, Angus Turner, Elizabeth‐Ann Schroeder   +3 more
wiley   +1 more source