scholarly journals Functional evaluation in inherited retinal disease

2021 ◽  
pp. bjophthalmol-2021-319994
Author(s):  
Malena Daich Varela ◽  
Michalis Georgiou ◽  
Shaima A Hashem ◽  
Richard G Weleber ◽  
Michel Michaelides
Keyword(s):  
Clinical Trials ◽  
Primary Outcome ◽  
Retinal Disease ◽  
Functional Evaluation ◽  
Retinal Diseases ◽  
Full Field ◽  
Inherited Retinal Disease ◽  
Wide Range ◽  
Functional Assessments ◽  
Field Static

Functional assessments are a fundamental part of the clinical evaluation of patients with inherited retinal diseases (IRDs). Their importance and impact have become increasingly notable, given the significant breadth and number of clinical trials and studies investigating multiple avenues of intervention across a wide range of IRDs, including gene, pharmacological and cellular therapies. Moreover, the fact that many clinical trials are reporting improvements in vision, rather than the previously anticipated structural stability/slowing of degeneration, makes functional evaluation of primary relevance. In this review, we will describe a range of methods employed to characterise retinal function and functional vision, beginning with tests variably included in the clinic, such as visual acuity, electrophysiological assessment and colour discrimination, and then discussing assessments often reserved for clinical trials/research studies such as photoaversion testing, full-field static perimetry and microperimetry, and vision-guided mobility testing; addressing perimetry in greatest detail, given it is commonly a primary outcome metric. We will focus on how these tests can help diagnose and monitor particular genotypes, also noting their limitations/challenges and exploring analytical methodologies for better exploiting functional measurements, as well as how they facilitate patient inclusion and stratification in clinical trials and serve as outcome measures.

scholarly journals Trial Interventions in ABCA4- Retinopathy

2020 ◽  
Author(s):  
Majid Al Breiki
Keyword(s):  
Clinical Trials ◽  
Literature Review ◽  
Visual Impairment ◽  
Retinal Disease ◽  
Retinal Diseases ◽  
Research Opportunities ◽  
Treatment Trials ◽  
Inherited Retinal Disease ◽  
Intervention Trials ◽  
The Moment

Abstract: Inherited retinal diseases collectively are one of the leading causes of visual impairment worldwide. ABCA4 retinopathy is the most common inherited retinal disease. In the last few years, there are many advances in the understanding of this disease, which led to many interventional trials with promising results at the moment. This paper is going to present a brief background up to date knowledge in the context of the disease pathophysiology, the main mechanisms involved in this disease and related diseases, and different approaches of the intervention trials. I will be using a literature review to highlight the main structural, physiological, chemical, functional characteristics of ABCA4 transporter physiology. This will be followed by a brief discussion about pathophysiology. Next, analysing, extracting and comparing different interventional options and presenting the current clinical trials for each will be done. This paper expected to discusses and rationally compare between different treatment trials approaches their advantages over others and challenges. A sensible, possible application from current trials from different diseases with similar pathogenesis and possible research opportunities will be discussed.

scholarly journals Full-field stimulus testing: Role in the clinic and as an outcome measure in clinical trials of severe childhood retinal disease

2021 ◽  
pp. 101000
Author(s):  
Alejandro J. Roman ◽  
Artur V. Cideciyan ◽  
Vivian Wu ◽  
Alexandra V. Garafalo ◽  
Samuel G. Jacobson
Keyword(s):  
Clinical Trials ◽  
Outcome Measure ◽  
Retinal Disease ◽  
Full Field

Endpoints for Measuring Efficacy in Clinical Trials for Inherited Retinal Disease

2021 ◽  
Vol 61 (4) ◽  
pp. 63-78
Author(s):  
Daniel C. Chung ◽  
David G. Birch ◽  
Robert E. MacLaren
Keyword(s):  
Clinical Trials ◽  
Retinal Disease ◽  
Inherited Retinal Disease

scholarly journals Balancing the Photoreceptor Proteome: Proteostasis Network Therapeutics for Inherited Retinal Disease

Genes ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 557 ◽  
Author(s):  
Siebren Faber ◽  
Ronald Roepman
Keyword(s):  
Retinal Disease ◽  
Protein Accumulation ◽  
Retinal Diseases ◽  
High Turnover ◽  
Protein Balance ◽  
Light Sensing ◽  
Outer Segments ◽  
Inherited Retinal Disease ◽  
Tremendous Amount ◽  
Therapeutic Compounds

The light sensing outer segments of photoreceptors (PRs) are renewed every ten days due to their high photoactivity, especially of the cones during daytime vision. This demands a tremendous amount of energy, as well as a high turnover of their main biosynthetic compounds, membranes, and proteins. Therefore, a refined proteostasis network (PN), regulating the protein balance, is crucial for PR viability. In many inherited retinal diseases (IRDs) this balance is disrupted leading to protein accumulation in the inner segment and eventually the death of PRs. Various studies have been focusing on therapeutically targeting the different branches of the PR PN to restore the protein balance and ultimately to treat inherited blindness. This review first describes the different branches of the PN in detail. Subsequently, insights are provided on how therapeutic compounds directed against the different PN branches might slow down or even arrest the appalling, progressive blinding conditions. These insights are supported by findings of PN modulators in other research disciplines.

A nationwide genetic analysis of inherited retinal diseases in Israel as assessed by the Israeli inherited retinal disease consortium (IIRDC)

2019 ◽  
Vol 41 (1) ◽  
pp. 140-149 ◽  
Author(s):  
Dror Sharon ◽  
Tamar Ben‐Yosef ◽  
Nitza Goldenberg‐Cohen ◽  
Eran Pras ◽  
Libe Gradstein ◽  
...  
Keyword(s):  
Genetic Analysis ◽  
Retinal Disease ◽  
Retinal Diseases ◽  
Inherited Retinal Disease

scholarly journals Deep Learning-Based Classification of Inherited Retinal Diseases Using Fundus Autofluorescence

2020 ◽  
Vol 9 (10) ◽  
pp. 3303
Author(s):  
Alexandra Miere ◽  
Thomas Le Meur ◽  
Karen Bitton ◽  
Carlotta Pallone ◽  
Oudy Semoun ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Fundus Autofluorescence ◽  
Area Under The Curve ◽  
Retinal Disease ◽  
Relevant Information ◽  
Retinal Diseases ◽  
Autofluorescence Imaging ◽  
Comparable Group ◽  
Inherited Retinal Disease

Background. In recent years, deep learning has been increasingly applied to a vast array of ophthalmological diseases. Inherited retinal diseases (IRD) are rare genetic conditions with a distinctive phenotype on fundus autofluorescence imaging (FAF). Our purpose was to automatically classify different IRDs by means of FAF images using a deep learning algorithm. Methods. In this study, FAF images of patients with retinitis pigmentosa (RP), Best disease (BD), Stargardt disease (STGD), as well as a healthy comparable group were used to train a multilayer deep convolutional neural network (CNN) to differentiate FAF images between each type of IRD and normal FAF. The CNN was trained and validated with 389 FAF images. Established augmentation techniques were used. An Adam optimizer was used for training. For subsequent testing, the built classifiers were then tested with 94 untrained FAF images. Results. For the inherited retinal disease classifiers, global accuracy was 0.95. The precision-recall area under the curve (PRC-AUC) averaged 0.988 for BD, 0.999 for RP, 0.996 for STGD, and 0.989 for healthy controls. Conclusions. This study describes the use of a deep learning-based algorithm to automatically detect and classify inherited retinal disease in FAF. Hereby, the created classifiers showed excellent results. With further developments, this model may be a diagnostic tool and may give relevant information for future therapeutic approaches.

scholarly journals Clinical polymorphism and genetic heterogeneity of inherited retinal diseases

2020 ◽  
pp. 42-43
Author(s):  
И.В. Зольникова ◽  
В.В. Кадышев ◽  
А.В. Марахонов ◽  
Р.А. Зинченко
Keyword(s):  
Molecular Genetics ◽  
Genetic Heterogeneity ◽  
Retinal Disease ◽  
Retinal Diseases ◽  
Inherited Retinal Disease ◽  
Rod System ◽  
Cone System ◽  
Clinical Molecular Genetics

Наследственные заболевания сетчатки (НЗС) представляют собой класс офтальмологических болезней, в котором выделяют заболевания с преимущественным поражением палочковой системы и заболевания с преимущественным поражением колбочковой системы, включающие макулярные дистрофии. В представленном сообщении описана структура спектра НЗС в клинически полиморфной выборке на основании данных клинических, инструментальных (оптической когерентной томографии, аутофлюоресценции, электроретинографии) и молекулярно-генетических методов диагностики (NGS, секвенирование по Сэнгеру). Inherited retinal disease (IRD) is a class of ophthalmic disorders in which can be classified into diseases of primarily of rod system and with primarily of cone system, which include macular dystrophies. In the presented report the structure of spectrum of IRD in clinically polymorphic is presented on the base of clinical, molecular-genetics and instrumental (OCТ, autofluorescencе, eletroretinography).

scholarly journals Prior National Drug Abuse Treatment Clinical Trials Network (CTN) opioid use disorder trials as background and rationale for NIDA CTN-0100 “optimizing retention, duration and discontinuation strategies for opioid use disorder pharmacotherapy (RDD)”

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Matisyahu Shulman ◽  
Roger Weiss ◽  
John Rotrosen ◽  
Patricia Novo ◽  
Elizabeth Costello ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Extended Release ◽  
Primary Outcome ◽  
Opioid Use Disorder ◽  
Drug Abuse Treatment ◽  
Opioid Use ◽  
National Drug ◽  
Retention In Treatment ◽  
Abuse Treatment

AbstractOpioid use disorder continues to be a significant problem in the United States and worldwide. Three medications—methadone, buprenorphine, and extended-release injectable naltrexone,— are efficacious for treating opioid use disorder (OUD). However, the utility of these medications is limited, in part due to poor rates of retention in treatment. In addition, minimum recovery milestones and other factors that influence when and whether individuals can safely discontinue medications are unknown. The National Drug Abuse Treatment Clinical Trials Network (CTN) study “Optimizing Retention, Duration, and Discontinuation Strategies for Opioid Use Disorder Pharmacotherapy” (RDD; CTN-0100) will be among the largest clinical trials on treatment of OUD yet conducted, consisting of two phases, the Retention phase, and the Duration-Discontinuation phase. The Retention phase, open to patients initiating treatment, will test different doses and formulations of buprenorphine (standard dose sublingual, high dose sublingual, or extended-release injection), and a digital therapeutic app delivering contingency management and cognitive behavioral counseling on the primary outcome of retention in treatment. The Discontinuation phase, open to patients in stable remission from OUD and choosing to discontinue medication (including participants from the Retention phase or from the population of patients treated at the clinical site, referred by an outside prescriber or self-referred) will study different tapering strategies for buprenorphine (sublingual taper vs taper with injection buprenorphine), and a digital therapeutic app which provides resources to promote recovery, on the primary outcome of relapse-free discontinuation of medication. This paper describes how the RDD trial derives from two decades of research in the CTN. Initial trials (CTN-0001; CTN-0002; CTN-0003) focused on opioid detoxification, showing buprenorphine-naloxone was effective for detoxification, but that acute detoxification did not appear to be an effective treatment strategy. Trials on comparative effectiveness of medications for opioid use disorder (MOUD) (CTN-0027; CTN-0030; and CTN-0051) highlighted the problem of dropout from treatment and few trials defined retention on MOUD as the primary outcome. Long-term follow-up studies on those patient samples demonstrated the importance of long-term continuation of medication for many patients to sustain remission. Overall, these trials highlight the potential of a stable research infrastructure such as CTN to advance treatment effectiveness through a programmatic succession of large clinical trials.

scholarly journals Development of a Fundus Image-Based Deep Learning Diagnostic Tool for Various Retinal Diseases

2021 ◽  
Vol 11 (5) ◽  
pp. 321
Author(s):  
Kyoung Min Kim ◽  
Tae-Young Heo ◽  
Aesul Kim ◽  
Joohee Kim ◽  
Kyu Jin Han ◽  
...  
Keyword(s):  
Network Models ◽  
Retinal Disease ◽  
Age Related Macular Degeneration ◽  
Diagnostic Tools ◽  
Retinal Images ◽  
Dense Layer ◽  
Retinal Diseases ◽  
Fundus Image ◽  
Neural Network Models ◽  
Age Related

Artificial intelligence (AI)-based diagnostic tools have been accepted in ophthalmology. The use of retinal images, such as fundus photographs, is a promising approach for the development of AI-based diagnostic platforms. Retinal pathologies usually occur in a broad spectrum of eye diseases, including neovascular or dry age-related macular degeneration, epiretinal membrane, rhegmatogenous retinal detachment, retinitis pigmentosa, macular hole, retinal vein occlusions, and diabetic retinopathy. Here, we report a fundus image-based AI model for differential diagnosis of retinal diseases. We classified retinal images with three convolutional neural network models: ResNet50, VGG19, and Inception v3. Furthermore, the performance of several dense (fully connected) layers was compared. The prediction accuracy for diagnosis of nine classes of eight retinal diseases and normal control was 87.42% in the ResNet50 model, which added a dense layer with 128 nodes. Furthermore, our AI tool augments ophthalmologist’s performance in the diagnosis of retinal disease. These results suggested that the fundus image-based AI tool is applicable for the medical diagnosis process of retinal diseases.

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