YAP in development and disease: Navigating the regulatory landscape from retina to brain

Biomedicine & Pharmacotherapy, Journal Year: 2024, Volume and Issue: 175, P. 116703 - 116703

Published: May 6, 2024

The distinctive role of Yes-associated protein (YAP) in the nervous system has attracted widespread attention. This comprehensive review strategically uses retina as a vantage point, embarking on an extensive exploration YAP's multifaceted impact from to brain development and pathology. Initially, we explore crucial roles YAP embryonic cerebral development. Our focus then shifts retinal development, examining detail regulatory influence pigment epithelium (RPE) progenitor cells (RPCs), its significant effects hierarchical structure functionality retina. We also investigate essential contributions maintaining homeostasis, highlighting precise regulation cell proliferation survival. In terms retinal-related diseases, epigenetic connections pathophysiological diabetic retinopathy (DR), glaucoma, proliferative vitreoretinopathy (PVR). Lastly, broaden our brain, emphasizing research paradigm "retina: window brain." Special is given emerging studies disorders such Alzheimer's disease (AD) Parkinson's (PD), underlining potential therapeutic value neurodegenerative neuroinflammation.

Language: Английский

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Electroretinogram Analysis Using a Short-Time Fourier Transform and Machine Learning Techniques

Bioengineering, Journal Year: 2024, Volume and Issue: 11(9), P. 866 - 866

Published: Aug. 26, 2024

Electroretinography (ERG) is a non-invasive method of assessing retinal function by recording the retina's response to brief flash light. This study focused on optimizing ERG waveform signal classification utilizing Short-Time Fourier Transform (STFT) spectrogram preprocessing with machine learning (ML) decision system. Several window functions different sizes and overlaps were compared enhance feature extraction concerning specific ML algorithms. The obtained spectrograms employed train deep models alongside manual for more classical models. Our findings demonstrated superiority Visual Transformer architecture Hamming function, showcasing its advantage in classification. Also, as result, we recommend RF algorithm scenarios necessitating extraction, particularly Boxcar (rectangular) or Bartlett functions. By elucidating optimal methodologies classification, this contributes advancing diagnostic capabilities analysis clinical settings.

Language: Английский

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Attention to the Electroretinogram: Gated Multilayer Perceptron for ASD Classification

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 52352 - 52362

Published: Jan. 1, 2024

The electroretinogram (ERG) is a clinical test that records the retina's electrical response to brief flash of light as waveform signal. Analysis ERG signal offers promising non-invasive method for studying different neurodevelopmental and neurodegenerative disorders. Autism Spectrum Disorder (ASD) condition characterized by poor communication, reduced reciprocal social interaction, restricted and/or repetitive stereotyped behaviors should be detected early possible ensure timely appropriate intervention support individual their family. In this study, we applied gated Multilayer Perceptron (gMLP) light-adapted classification an effective alternative Transformers. first reported application model ASD which consisted basic multilayer perceptrons, with fewer parameters than We compared performance time-series models on ASD-Control dataset found superiority gMLP in accuracy was best at 89.7% supports use based recordings involving case-control comparisons.

Language: Английский

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Reduced contrast sensitivity, pattern electroretinogram ratio, and diminished a-wave amplitude in patients with major depressive disorder

European Archives of Psychiatry and Clinical Neuroscience, Journal Year: 2024, Volume and Issue: unknown

Published: May 28, 2024

Abstract The electroretinogram (ERG), a non-invasive electrophysiological tool used in ophthalmology, is increasingly applied to investigate neural correlates of depression. present study aimed reconsider previous findings major depressive disorder (MDD) reporting (1) diminished contrast sensitivity and (2) reduced patten ERG (PERG) amplitude ratio, additionally, assess (3) the photopic negative response (PhNR) from flash (fERG), with RETeval® device, more practical option for clinical routine use. We examined 30 patients MDD 42 healthy controls (HC), assessing individual thresholds an optotype-based test. Moreover, we compared PERG established method early glaucoma detection, between both groups. handheld device was measure amplitudes peak times fERG components including a-wave, b-wave PhNR HCs. exhibited together HC. With found a-wave MDD, whereas no significant differences were observed or controls. ratio supports hypothesis that depression associated altered visual processing. underscore PERG’s potential as possible objective marker recorded system might open new avenues using devices simplified approaches advancing research PERG.

Language: Английский

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Generating Synthetic Light‐Adapted Electroretinogram Waveforms Using Artificial Intelligence to Improve Classification of Retinal Conditions in Under‐Represented Populations

Journal of Ophthalmology, Journal Year: 2024, Volume and Issue: 2024(1)

Published: Jan. 1, 2024

Visual electrophysiology is often used clinically to determine the functional changes associated with retinal or neurological conditions. The full‐field flash electroretinogram (ERG) assesses global contribution of outer and inner layers initiated by rods cone pathways depending on state adaptation. Within clinical centers, reference normative data are compare cases that may be rare underpowered within a specific demographic. To bolster either dataset case dataset, application synthetic ERG waveforms offer benefits disease classification case‐control studies. In this study as proof concept, artificial intelligence (AI) generate signals using generative adversarial networks deployed upscale male participants an ISCEV containing 68 participants, from right left eye. Random forest classifiers further improved for sex group balanced accuracy 0.72–0.83 added waveforms. This first demonstrate generation improve machine learning modelling

Language: Английский

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Remodeling the light-adapted electroretinogram using a bayesian statistical approach

BMC Research Notes, Journal Year: 2025, Volume and Issue: 18(1)

Published: Jan. 23, 2025

Language: Английский

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Sex-Dependent Variations of Retinal Function and Architecture in a Neurofibromatosis Type I Mouse Model with Normal Vision

Experimental Eye Research, Journal Year: 2025, Volume and Issue: unknown, P. 110279 - 110279

Published: Feb. 1, 2025

We aimed to characterize the structure and function of early visual system neurofibromatosis type 1 (NF1) mouse model, a syndromic model autism spectrum disorders (ASD). used Nf1+/- mice WT littermates performed retinal structural analysis by optical coherence tomography (OCT), functional assessment electrophysiological recordings. then behavioral tests using optomotor response (OMR) sensitivity stimulus familiarity. From analysis, we found increased thickness for ganglion cell layer-inner plexiform layer (GCL-IPL) outer nuclear (ONL) in male compared with littermates. Regarding electrophysiology, female exhibited amplitudes second oscillatory potential (OP2) Nevertheless, both presented normal acuity as measured OMR were able exhibit regular familiarity responses. While sex-dependent changes are line previous results brain anatomic measures, subtle activity may relate GABAergic neurotransmission NF1. Overall, these do not seem translate into alterations.

Language: Английский

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Retinal fundus imaging as biomarker for ADHD using machine learning for screening and visual attention stratification

npj Digital Medicine, Journal Year: 2025, Volume and Issue: 8(1)

Published: March 17, 2025

Attention-deficit/hyperactivity disorder (ADHD), characterized by diagnostic complexity and symptom heterogeneity, is a prevalent neurodevelopmental disorder. Here, we explored the machine learning (ML) analysis of retinal fundus photographs as noninvasive biomarker for ADHD screening stratification executive function (EF) deficits. From April to October 2022, 323 children adolescents with were recruited from two tertiary South Korean hospitals, age- sex-matched individuals typical development retrospectively collected. We used AutoMorph pipeline extract features four types ML models EF subdomain prediction, adopted Shapely additive explanation method. achieved 95.5%-96.9% AUROC. For stratification, visual auditory subdomains showed strong (AUROC > 85%) poor performances, respectively. Our demonstrated potential deficit in attention domain.

Language: Английский

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Effects of medications on the human electroretinogram: A comprehensive review.

Survey of Ophthalmology, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

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Synthetic electroretinogram signal generation using a conditional generative adversarial network

Documenta Ophthalmologica, Journal Year: 2025, Volume and Issue: unknown

Published: April 16, 2025

Abstract Purpose The electroretinogram (ERG) records the functional response of retina. In some neurological conditions, ERG waveform may be altered and could support biomarker discovery. heterogeneous or rare populations, where either large data sets availability a challenge, synthetic signals with Artificial Intelligence (AI) help to mitigate against these factors classification models. Methods This approach was tested using publicly available dataset real ERGs, n = 560 (ASD) 498 (Control) recorded at 9 different flash strengths from 18 ASD (mean age 12.2 ± 2.7 years) 31 Controls 11.8 3.3 that were augmented waveforms, generated through Conditional Generative Adversarial Network. Two deep learning models used classify groups only combined ERGs. One Time Series Transformer (with waveforms in their original form) second Visual model utilizing images wavelets derived Continuous Wavelet Transform Model performance classifying evaluated Balanced Accuracy (BA) as main outcome measure. Results BA improved 0.756 0.879 when ERGs included across all recordings for training Transformer. also achieved best 0.89 single strength 0.95 log cd s m −2 . Conclusions supports application AI improve group recordings.

Language: Английский

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