Artificial Intelligence–Assisted Early Detection of Retinitis Pigmentosa — the Most Common Inherited Retinal Degeneration

AbstractMutations in the photoreceptor protein rhodopsin are known as one of the leading causes of retinal degeneration in humans. Two rhodopsin mutations, Y102H and I307N, obtained in chemically mutagenized mice, are currently the subject of increased interest as relevant models for studying the process of retinal degeneration in humans. Here, we report on the biochemical and functional characterization of the structural and functional alterations of these two rhodopsin mutants and we compare them with the G90V mutant previously analyzed, as a basis for a better understanding of in vivo studies. This mechanistic knowledge is fundamental to use it for developing novel therapeutic approaches for the treatment of inherited retinal degeneration in retinitis pigmentosa. We find that Y102H and I307N mutations affect the inactive–active equilibrium of the receptor. In this regard, the mutations reduce the stability of the inactive conformation but increase the stability of the active conformation. Furthermore, the initial rate of the functional activation of transducin, by the I307N mutant is reduced, but its kinetic profile shows an unusual increase with time suggesting a profound effect on the signal transduction process. This latter effect can be associated with a change in the flexibility of helix 7 and an indirect effect of the mutation on helix 8 and the C-terminal tail of rhodopsin, whose potential role in the functional activation of the receptor has been usually underestimated. In the case of the Y102H mutant, the observed changes can be associated with conformational alterations affecting the folding of the rhodopsin intradiscal domain, and its presumed involvement in the retinal binding process by the receptor.

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Amplitude and Latency of Electroretinographical Peaks as a tool to Predict the Extent of Retinal Degeneration in Retinitis Pigmentosa Patients

http://www.fortunejournals.com/journal-of-ophthalmology-and-research-home-fjor.php ◽

10.26502/fjor.2644-00240024 ◽

2020 ◽

Vol 03 (03) ◽

Author(s):

Maryam Naser ◽

Seyed Mohammad Masoud Shushtarian

Keyword(s):

Retinitis Pigmentosa ◽

Retinal Degeneration

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Assessment of inner retinal oxygen metrics and thickness in a mouse model of inherited retinal degeneration

Experimental Eye Research ◽

10.1016/j.exer.2021.108480 ◽

2021 ◽

Vol 205 ◽

pp. 108480

Author(s):

Mansour Rahimi ◽

Sophie Leahy ◽

Nathanael Matei ◽

Norman P. Blair ◽

Shinwu Jeong ◽

...

Keyword(s):

Mouse Model ◽

Retinal Degeneration ◽

Inherited Retinal Degeneration

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Neurochemical studies on the inherited retinal degeneration of the rat

Vision Research ◽

10.1016/0042-6989(63)90001-4 ◽

1963 ◽

Vol 3 (7-8) ◽

pp. 271-280 ◽

Cited By ~ 31

Author(s):

Vincenzo Bonavita ◽

Francesco Ponte ◽

Giuseppe Amore

Keyword(s):

Retinal Degeneration ◽

Inherited Retinal Degeneration

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Short-term high-fat feeding exacerbates degeneration in retinitis pigmentosa by promoting retinal oxidative stress and inflammation

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2100566118 ◽

2021 ◽

Vol 118 (43) ◽

pp. e2100566118

Author(s):

Oksana Kutsyr ◽

Agustina Noailles ◽

Natalia Martínez-Gil ◽

Lucía Maestre-Carballa ◽

Manuel Martinez-Garcia ◽

...

Keyword(s):

Oxidative Stress ◽

Retinitis Pigmentosa ◽

Retinal Degeneration ◽

Gut Microbiome ◽

Cell Activation ◽

High Fat ◽

Short Term ◽

Oxidative Markers ◽

Degenerative Disorders ◽

Inflammatory State

A high-fat diet (HFD) can induce hyperglycemia and metabolic syndromes that, in turn, can trigger visual impairment. To evaluate the acute effects of HFD feeding on retinal degeneration, we assessed retinal function and morphology, inflammatory state, oxidative stress, and gut microbiome in dystrophic retinal degeneration 10 (rd10) mice, a model of retinitis pigmentosa, fed an HFD for 2 to 3 wk. Short-term HFD feeding impaired retinal responsiveness and visual acuity and enhanced photoreceptor degeneration, microglial cell activation, and Müller cell gliosis. HFD consumption also triggered the expression of inflammatory and oxidative markers in rd10 retinas. Finally, an HFD caused gut microbiome dysbiosis, increasing the abundance of potentially proinflammatory bacteria. Thus, HFD feeding drives the pathological processes of retinal degeneration by promoting oxidative stress and activating inflammatory-related pathways. Our findings suggest that consumption of an HFD could accelerate the progression of the disease in patients with retinal degenerative disorders.

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Metabolism of the developing retina. 2. The effect of an inherited retinal degeneration on the development of glycolysis in the rat retina*

Biochemical Journal ◽

10.1042/bj0720459 ◽

1959 ◽

Vol 72 (3) ◽

pp. 459-461 ◽

Cited By ~ 14

Author(s):

C. N. Graymore ◽

Katharine Tansley ◽

Margaret Kerly

Keyword(s):

Retinal Degeneration ◽

Rat Retina ◽

Inherited Retinal Degeneration

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Cloud based mobile solution for early detection of Skin Cancer using Artificial Intelligence

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit217327 ◽

2021 ◽

pp. 312-324

Author(s):

Pawan Sonawane ◽

Sahel Shardhul ◽

Raju Mendhe

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Skin Cancer ◽

Early Detection ◽

Transfer Learning ◽

Network Model ◽

Neural Network Model ◽

Mobile Application ◽

Low Cost ◽

Skin Tumors

The vast majority of skin cancer deaths are from melanoma, with about 1.04 million cases annually. Early detection of the same can be immensely helpful in order to try to cure it. But most of the diagnosis procedures are either extremely expensive or not available to a vast majority, as these centers are concentrated in urban regions only. Thus, there is a need for an application that can perform a quick, efficient, and low-cost diagnosis. Our solution proposes to build a server less mobile application on the AWS cloud that takes the images of potential skin tumors and classifies it as either Malignant or Benign. The classification would be carried out using a trained Convolution Neural Network model and Transfer learning (Inception v3). Several experiments will be performed based on Morphology and Color of the tumor to identify ideal parameters.

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