Pub Date : 2024-02-24DOI: 10.1101/2024.02.22.24303109
Wenjing Liu, Shujin Li, Mu Yang, Jie Ma, Lu Liu, Ping Fei, Qianchun Xiang, Lulin Huang, Peiquan Zhao, Zhenglin Yang, Xianjun Zhu
Familial exudative vitreoretinopathy (FEVR) is a severe genetic disorder characterized by incomplete vascularization of the peripheral retina and associated symptoms that can lead to vision loss. However, the underlying genetic causes of approximately 50% of FEVR cases remain unknown. Here, we report two heterozygous variants, c.88C＞T (p.Arg30Ter) and c.247C＞T (p.Leu83Phe), in calcyphosine like (CAPSL), from four patients in two unrelated FEVR-affected families. Both variants exhibited compromised CAPSL protein expression. Vascular endothelial cell-specific inactivation of Capsl in postnatal mice resulted in defective sprouting, delayed radial/vertical vascular progression, compromised endothelial proliferation, and impaired cell migration, recapitulating the human FEVR phenotypes. CAPSL-depleted human retinal microvascular endothelial cells (HRECs) exhibited impaired tube formation, decreased cell proliferation, disrupted cell polarity establishment and filopodia/lamellipodia formation, as well as disrupted collective cell migration in vitro. Transcriptomic and proteomic profiling of CAPSL-depleted HRECs revealed that CAPSL abolition inhibited the MYC signaling axis, in which the expression of core MYC targeted genes were profoundly decreased. Furthermore, a combined analysis of CAPSL-depleted HRECs and c-MYC-depleted human umbilical vein endothelial cells (HUVECs) uncovered similar transcription patterns. Collectively, this study reports a novel FEVR-associated candidate gene, CAPSL, which provides invaluable information for genetic counseling and prenatal diagnosis of FEVR. This study also reveals that compromised CAPSL function causes FEVR through MYC axis, shedding light on the potential involvement of MYC signaling in the pathogenesis of FEVR.
{"title":"Defective CAPSL function causes impaired retinal angiogenesis through the MYC axis and is associated with familial exudative vitreoretinopathy","authors":"Wenjing Liu, Shujin Li, Mu Yang, Jie Ma, Lu Liu, Ping Fei, Qianchun Xiang, Lulin Huang, Peiquan Zhao, Zhenglin Yang, Xianjun Zhu","doi":"10.1101/2024.02.22.24303109","DOIUrl":"https://doi.org/10.1101/2024.02.22.24303109","url":null,"abstract":"Familial exudative vitreoretinopathy (FEVR) is a severe genetic disorder characterized by incomplete vascularization of the peripheral retina and associated symptoms that can lead to vision loss. However, the underlying genetic causes of approximately 50% of FEVR cases remain unknown. Here, we report two heterozygous variants, c.88C＞T (p.Arg30Ter) and c.247C＞T (p.Leu83Phe), in calcyphosine like (CAPSL), from four patients in two unrelated FEVR-affected families. Both variants exhibited compromised CAPSL protein expression. Vascular endothelial cell-specific inactivation of Capsl in postnatal mice resulted in defective sprouting, delayed radial/vertical vascular progression, compromised endothelial proliferation, and impaired cell migration, recapitulating the human FEVR phenotypes. CAPSL-depleted human retinal microvascular endothelial cells (HRECs) exhibited impaired tube formation, decreased cell proliferation, disrupted cell polarity establishment and filopodia/lamellipodia formation, as well as disrupted collective cell migration in vitro. Transcriptomic and proteomic profiling of CAPSL-depleted HRECs revealed that CAPSL abolition inhibited the MYC signaling axis, in which the expression of core MYC targeted genes were profoundly decreased. Furthermore, a combined analysis of CAPSL-depleted HRECs and c-MYC-depleted human umbilical vein endothelial cells (HUVECs) uncovered similar transcription patterns. Collectively, this study reports a novel FEVR-associated candidate gene, CAPSL, which provides invaluable information for genetic counseling and prenatal diagnosis of FEVR. This study also reveals that compromised CAPSL function causes FEVR through MYC axis, shedding light on the potential involvement of MYC signaling in the pathogenesis of FEVR.","PeriodicalId":501390,"journal":{"name":"medRxiv - Ophthalmology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139950335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-23DOI: 10.1101/2024.02.22.24303210
Lude Moekotte, Joke H. de Boer, Sanne Hiddingh, Bram Gerritsen, Jutta Lintelmann, Alexander Cecil, L. Ingeborgh van den Born, Xuan-Thanh-An Nguyen, Camiel J.F. Boon, Maria M. van Genderen
Purpose: To compare the plasma metabolic profile of patients with a CRB1-associated inherited retinal degeneration (CRB1-IRD) with healthy controls (HCs). Design: A case-control study. Methods: Plasma concentration of 619 metabolites was measured with the MxP® Quant 500 Kit in 30 patients with a CRB1-IRD and 29 HCs. We fitted a linear regression model with adjustments for age and sex based on the concentration of metabolites in μM (μmol/L), or on the sums and ratios of metabolites, to determine differences between patients and controls. Results: Over-representation of pathways among metabolites associated strongest to CRB1-IRDs (P < 0.05, n = 62) identified amino acid pathways (such as beta-alanine, histidine, and glycine/serine) and bile acid biosynthesis, driven by a decrease in deoxycholic acid derivatives produced by gut microbiota. Enrichment analysis of metabolic classes across the plasma metabolic profile further identified significant positive enrichment for lipid metabolites glycerophospholipids, cholesterol esters, and ceramides, and significant depletion for bile acid metabolites. Further investigation of the sums and ratios (i.e., metabolism indicators) ascertained a significant decrease in intestinal microbial-dependent secondary bile acid classes. Conclusions: Lipid metabolic alterations and decreased microbiota-related secondary bile acid concentrations indicate significant alterations in gut metabolism in patients with a CRB1-IRD.
{"title":"The Gut Metabolism is altered in patients with CRB1-associated inherited retinal degeneration","authors":"Lude Moekotte, Joke H. de Boer, Sanne Hiddingh, Bram Gerritsen, Jutta Lintelmann, Alexander Cecil, L. Ingeborgh van den Born, Xuan-Thanh-An Nguyen, Camiel J.F. Boon, Maria M. van Genderen","doi":"10.1101/2024.02.22.24303210","DOIUrl":"https://doi.org/10.1101/2024.02.22.24303210","url":null,"abstract":"Purpose: To compare the plasma metabolic profile of patients with a CRB1-associated inherited retinal degeneration (CRB1-IRD) with healthy controls (HCs).\u0000Design: A case-control study.\u0000Methods: Plasma concentration of 619 metabolites was measured with the MxP® Quant 500 Kit in 30 patients with a CRB1-IRD and 29 HCs. We fitted a linear regression model with adjustments for age and sex based on the concentration of metabolites in μM (μmol/L), or on the sums and ratios of metabolites, to determine differences between patients and controls.\u0000Results: Over-representation of pathways among metabolites associated strongest to CRB1-IRDs (P < 0.05, n = 62) identified amino acid pathways (such as beta-alanine, histidine, and glycine/serine) and bile acid biosynthesis, driven by a decrease in deoxycholic acid derivatives produced by gut microbiota. Enrichment analysis of metabolic classes across the plasma metabolic profile further identified significant positive enrichment for lipid metabolites glycerophospholipids, cholesterol esters, and ceramides, and significant depletion for bile acid metabolites. Further investigation of the sums and ratios (i.e., metabolism indicators) ascertained a significant decrease in intestinal microbial-dependent secondary bile acid classes.\u0000Conclusions: Lipid metabolic alterations and decreased microbiota-related secondary bile acid concentrations indicate significant alterations in gut metabolism in patients with a CRB1-IRD.","PeriodicalId":501390,"journal":{"name":"medRxiv - Ophthalmology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139950200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-14DOI: 10.1101/2024.02.13.23299445
Remi J Hernandez, Yalin Zheng, Savita Madhusudhan, Wahbi K El-Bouri
This protocol describes the A-EYE Study and provides information about procedures for entering participants. Every care was taken in its drafting, but corrections or amendments may be necessary. These will be circulated to investigators in the Study. Problems relating to this Study should be referred, in the first instance, to the Chief Investigator. This study will adhere to the principles outlined in the UK Policy Framework for Health and Social Care Research (v3.2 10th October 2017). It will be conducted in compliance with the protocol, the UK General Data Protection Regulation and Data Protection Act 2018, and other regulatory requirements as appropriate.
{"title":"AI and the Eye - Integrating deep learning and in silico simulations to optimise diagnosis and treatment of wet macular degeneration","authors":"Remi J Hernandez, Yalin Zheng, Savita Madhusudhan, Wahbi K El-Bouri","doi":"10.1101/2024.02.13.23299445","DOIUrl":"https://doi.org/10.1101/2024.02.13.23299445","url":null,"abstract":"This protocol describes the A-EYE Study and provides information about procedures for entering participants. Every care was taken in its drafting, but corrections or amendments may be necessary. These will be circulated to investigators in the Study. Problems relating to this Study should be referred, in the first instance, to the Chief Investigator.\u0000This study will adhere to the principles outlined in the UK Policy Framework for Health and Social Care Research (v3.2 10th October 2017). It will be conducted in compliance with the protocol, the UK General Data Protection Regulation and Data Protection Act 2018, and other regulatory requirements as appropriate.","PeriodicalId":501390,"journal":{"name":"medRxiv - Ophthalmology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139769415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-14DOI: 10.1101/2024.02.13.24302673
Michaela Joan Grimbly, Sheri-Michelle Koopowitz, Alice Ruiye Chen, Zihan Sun, Paul J Foster, Mingguang He, Dan J Stein, Jonathan C Ipser, Lisa Zhuoting Zhu
Background/Aims: The emerging concept of retinal age, a biomarker derived from retinal images, holds promise in estimating biological age. The retinal age gap (RAG) represents the difference between retinal age and chronological age which serves as an indicator of deviations from normal ageing. This scoping review aims to collate studies on retinal age to determine its potential clinical utility and to identify knowledge gaps for future research. Methods: Using the PRISMA checklist, eligible non-review, human studies were identified, selected, and appraised. Pubmed, Scopus, SciELO, PsycINFO, Google Scholar, Cochrane, CINAHL, Africa Wide EBSCO, MedRxiv, and BioRxiv databases were searched to identify literature pertaining to retinal age, the RAG, and their associations. No restrictions were imposed on publication date. Results: Thirteen articles published between 2022 and 2023 were analysed, revealing four models capable of determining biological age from retinal images. Three models, Retinal Age, EyeAge and a convolutional network-based model, achieved comparable mean absolute errors (MAE): 3.55, 3.30 and 3.97 respectively. A fourth model, RetiAGE, predicting the probability of being older than 65 years, also demonstrated strong predictive ability with respect to clinical outcomes. In the models identified, a higher predicted RAG demonstrated an association with negative occurrences, notably mortality and cardiovascular health outcomes. Conclusion: This review highlights the potential clinical application of retinal age and RAG, emphasising the need for further research to establish their generalisability for clinical use, particularly in neuropsychiatry. The identified models showcase promising accuracy in estimating biological age, suggesting its viability for evaluating health status.
{"title":"Ageing Biomarkers Derived From Retinal Imaging: A Scoping Review","authors":"Michaela Joan Grimbly, Sheri-Michelle Koopowitz, Alice Ruiye Chen, Zihan Sun, Paul J Foster, Mingguang He, Dan J Stein, Jonathan C Ipser, Lisa Zhuoting Zhu","doi":"10.1101/2024.02.13.24302673","DOIUrl":"https://doi.org/10.1101/2024.02.13.24302673","url":null,"abstract":"Background/Aims: The emerging concept of retinal age, a biomarker derived from retinal images, holds promise in estimating biological age. The retinal age gap (RAG) represents the difference between retinal age and chronological age which serves as an indicator of deviations from normal ageing. This scoping review aims to collate studies on retinal age to determine its potential clinical utility and to identify knowledge gaps for future research. Methods: Using the PRISMA checklist, eligible non-review, human studies were identified, selected, and appraised. Pubmed, Scopus, SciELO, PsycINFO, Google Scholar, Cochrane, CINAHL, Africa Wide EBSCO, MedRxiv, and BioRxiv databases were searched to identify literature pertaining to retinal age, the RAG, and their associations. No restrictions were imposed on publication date. Results: Thirteen articles published between 2022 and 2023 were analysed, revealing four models capable of determining biological age from retinal images. Three models, Retinal Age, EyeAge and a convolutional network-based model, achieved comparable mean absolute errors (MAE): 3.55, 3.30 and 3.97 respectively. A fourth model, RetiAGE, predicting the probability of being older than 65 years, also demonstrated strong predictive ability with respect to clinical outcomes. In the models identified, a higher predicted RAG demonstrated an association with negative occurrences, notably mortality and cardiovascular health outcomes. Conclusion: This review highlights the potential clinical application of retinal age and RAG, emphasising the need for further research to establish their generalisability for clinical use, particularly in neuropsychiatry. The identified models showcase promising accuracy in estimating biological age, suggesting its viability for evaluating health status.","PeriodicalId":501390,"journal":{"name":"medRxiv - Ophthalmology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139769449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-13DOI: 10.1101/2024.02.13.24302718
Teele Palumaa, Nele Taba, Maris Teder-Laving, Kadi-Liis Kivi, Kadri Reis, Urmo Võsa, Estonian Biobank Research Team, Tõnu Esko, Erik Abner
Myopia, projected to affect half of the global population by 2050, is a growing healthcare concern. Chronotype, an output of the human biological clock, and sleep parameters have been associated with several diseases, including myopia. We explored the connection between refractive errors and sleep and circadian rhythm parameters by employing a sample of 71,016 adults who completed the Munich Chronotype Questionnaire in the Estonian Biobank. After accounting for possible confounders, such as age, sex, education level, and duration of daylight exposure, we observed that individuals with late chronotype, characterised by a delayed sleep-wake pattern on free days, had higher odds for myopia. In contrast, early chronotype was associated with hyperopia. Furthermore, increased social jet lag and reduced sleep duration were associated with both myopia and hyperopia. These results emphasise the complex interplay between circadian rhythms and sleep in refractive development, with potential implications for public health and clinical practice.
{"title":"Myopia and hyperopia are associated with opposite chronotypes in a sample of 71,016 individuals","authors":"Teele Palumaa, Nele Taba, Maris Teder-Laving, Kadi-Liis Kivi, Kadri Reis, Urmo Võsa, Estonian Biobank Research Team, Tõnu Esko, Erik Abner","doi":"10.1101/2024.02.13.24302718","DOIUrl":"https://doi.org/10.1101/2024.02.13.24302718","url":null,"abstract":"Myopia, projected to affect half of the global population by 2050, is a growing healthcare concern. Chronotype, an output of the human biological clock, and sleep parameters have been associated with several diseases, including myopia. We explored the connection between refractive errors and sleep and circadian rhythm parameters by employing a sample of 71,016 adults who completed the Munich Chronotype Questionnaire in the Estonian Biobank. After accounting for possible confounders, such as age, sex, education level, and duration of daylight exposure, we observed that individuals with late chronotype, characterised by a delayed sleep-wake pattern on free days, had higher odds for myopia. In contrast, early chronotype was associated with hyperopia. Furthermore, increased social jet lag and reduced sleep duration were associated with both myopia and hyperopia. These results emphasise the complex interplay between circadian rhythms and sleep in refractive development, with potential implications for public health and clinical practice.","PeriodicalId":501390,"journal":{"name":"medRxiv - Ophthalmology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139769418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-13DOI: 10.1101/2024.02.11.24302670
Zubin Mishra, Ziyuan Chris Wang, Emily Xu, Sophia Xu, Iyad Majid, SriniVas Reddy Sadda, Zhihong Jewel Hu
Stargardt disease and age-related macular degeneration are the leading causes of blindness in the juvenile and geriatric populations, respectively. The formation of atrophic regions of the macula is a hallmark of the end-stages of both diseases. The progression of these diseases is tracked using various imaging modalities, two of the most common being fundus autofluorescence (FAF) imaging and spectral-domain optical coherence tomography (SD-OCT). This study seeks to investigate the use of longitudinal FAF and SD-OCT imaging (month 0, month 6, month 12, and month 18) data for the predictive modelling of future atrophy in Stargardt and geographic atrophy. To achieve such an objective, we develop a set of novel deep convolutional neural networks enhanced with recurrent network units for longitudinal prediction and concurrent learning of ensemble network units (termed ReConNet) which take advantage of improved retinal layer features beyond the mean intensity features. Using FAF images, the neural network presented in this paper achieved mean (standard deviation, SD) and median Dice coefficients of 0.895 (0.086) and 0.922 for Stargardt atrophy, and 0.864 (0.113) and 0.893 for geographic atrophy. Using SD-OCT images for Stargardt atrophy, the neural network achieved mean and median Dice coefficients of 0.882 (0.101) and 0.906, respectively. When predicting only the interval growth of the atrophic lesions with FAF images, mean (SD) and median Dice coefficients of 0.557 (0.094) and 0.559 were achieved for Stargardt atrophy, and 0.612 (0.089) and 0.601 for geographic atrophy. The prediction performance in OCT images is comparably good to that using FAF which opens a new, more efficient, and practical door in the assessment of atrophy progression for clinical trials and retina clinics, beyond widely used FAF. These results are highly encouraging for a high-performance interval growth prediction when more frequent or longer-term longitudinal data are available in our clinics. This is a pressing task for our next step in ongoing research.
{"title":"Recurrent and Concurrent Prediction of Longitudinal Progression of Stargardt Atrophy and Geographic Atrophy","authors":"Zubin Mishra, Ziyuan Chris Wang, Emily Xu, Sophia Xu, Iyad Majid, SriniVas Reddy Sadda, Zhihong Jewel Hu","doi":"10.1101/2024.02.11.24302670","DOIUrl":"https://doi.org/10.1101/2024.02.11.24302670","url":null,"abstract":"Stargardt disease and age-related macular degeneration are the leading causes of blindness in the juvenile and geriatric populations, respectively. The formation of atrophic regions of the macula is a hallmark of the end-stages of both diseases. The progression of these diseases is tracked using various imaging modalities, two of the most common being fundus autofluorescence (FAF) imaging and spectral-domain optical coherence tomography (SD-OCT). This study seeks to investigate the use of longitudinal FAF and SD-OCT imaging (month 0, month 6, month 12, and month 18) data for the predictive modelling of future atrophy in Stargardt and geographic atrophy. To achieve such an objective, we develop a set of novel deep convolutional neural networks enhanced with recurrent network units for longitudinal prediction and concurrent learning of ensemble network units (termed ReConNet) which take advantage of improved retinal layer features beyond the mean intensity features. Using FAF images, the neural network presented in this paper achieved mean (standard deviation, SD) and median Dice coefficients of 0.895 (0.086) and 0.922 for Stargardt atrophy, and 0.864 (0.113) and 0.893 for geographic atrophy. Using SD-OCT images for Stargardt atrophy, the neural network achieved mean and median Dice coefficients of 0.882 (0.101) and 0.906, respectively. When predicting only the interval growth of the atrophic lesions with FAF images, mean (SD) and median Dice coefficients of 0.557 (0.094) and 0.559 were achieved for Stargardt atrophy, and 0.612 (0.089) and 0.601 for geographic atrophy. The prediction performance in OCT images is comparably good to that using FAF which opens a new, more efficient, and practical door in the assessment of atrophy progression for clinical trials and retina clinics, beyond widely used FAF. These results are highly encouraging for a high-performance interval growth prediction when more frequent or longer-term longitudinal data are available in our clinics. This is a pressing task for our next step in ongoing research.","PeriodicalId":501390,"journal":{"name":"medRxiv - Ophthalmology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139769450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-13DOI: 10.1101/2024.02.12.24302700
Dom CP Marticorena, Quinn Wai Wong, Jake Browning, Ken Wilbur, Pinakin Gunvant Davey, Aaron R Seitz, Jacob R Gardner, Dennis Barbour
Recent advances in nonparametric Contrast Sensitivity Function (CSF) estimation have yielded a new tradeoff between accuracy and efficiency not available to classical parametric estimators. An additional advantage of this new framework is the ability to independently tune multiple aspects of the estimator to seek further improvements. Machine Learning CSF (MLCSF) estimation with Gaussian processes allows for design optimization in the kernel, acquisition function and underlying task representation, to name a few. This paper describes a novel kernel for psychometric function estimation that is more flexible than a kernel based on signal detection theory. Despite being more flexible, it can result in a more efficient estimator. Further, trial selection for data acquisition that is generalized beyond pure information gain can also improve estimator quality. Finally, introducing latent variable representations underlying general CSF shapes can enable simultaneous estimation of multiple CSFs, such as from different eyes, eccentricities or luminances. The conditions under which the new procedures perform better than previous nonparametric estimation procedures are presented and quantified.
{"title":"Active Mutual Conjoint Estimation of Multiple Contrast Sensitivity Functions","authors":"Dom CP Marticorena, Quinn Wai Wong, Jake Browning, Ken Wilbur, Pinakin Gunvant Davey, Aaron R Seitz, Jacob R Gardner, Dennis Barbour","doi":"10.1101/2024.02.12.24302700","DOIUrl":"https://doi.org/10.1101/2024.02.12.24302700","url":null,"abstract":"Recent advances in nonparametric Contrast Sensitivity Function (CSF) estimation have yielded a new tradeoff between accuracy and efficiency not available to classical parametric estimators. An additional advantage of this new framework is the ability to independently tune multiple aspects of the estimator to seek further improvements. Machine Learning CSF (MLCSF) estimation with Gaussian processes allows for design optimization in the kernel, acquisition function and underlying task representation, to name a few. This paper describes a novel kernel for psychometric function estimation that is more flexible than a kernel based on signal detection theory. Despite being more flexible, it can result in a more efficient estimator. Further, trial selection for data acquisition that is generalized beyond pure information gain can also improve estimator quality. Finally, introducing latent variable representations underlying general CSF shapes can enable simultaneous estimation of multiple CSFs, such as from different eyes, eccentricities or luminances. The conditions under which the new procedures perform better than previous nonparametric estimation procedures are presented and quantified.","PeriodicalId":501390,"journal":{"name":"medRxiv - Ophthalmology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139769740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-10DOI: 10.1101/2024.02.09.24302604
R. J. Nell, M. Versluis, N. V. Menger, M. C. Gelmi, T. H.K. Vu, R. M. Verdijk, G. P.M. Luyten, M. J. Jager, P. A. van der Velden
Background Uveal melanoma is an aggressive ocular malignancy. Early molecular characterisation of primary tumours is crucial to identify those at risk of metastatic dissemination. Although tumour biopsies are being taken, liquid biopsies of ocular fluids may form a less invasive but relatively unexplored alternative. In this study, we aim to evaluate the DNA content of vitreous fluid from eyes with a uveal melanoma to obtain molecular information from the tumour. Methods DNA was isolated from 65 vitreous fluid samples from enucleated eyes with a uveal melanoma and studied using digital PCR. Primary and additional driver mutations (in GNAQ, GNA11, PLCB4, CYSLTR2, BAP1, SF3B1 and EIF1AX) were investigated using accustomed targeted and drop-off assays. The copy numbers of chromosome 3p and 8q were measured using multiplex and single-nucleotide polymorphism-based assays. Our findings were compared to the molecular profile of matched primary tumours and to the clinicopathological tumour characteristics. Results Almost all (63/65) vitreous fluids had measurable levels of DNA, but melanoma-cell derived DNA (containing the primary driver mutation) was detected in 39/65 samples (median proportion 18%, range 0.2%-94%) and was associated with a larger tumour prominence, but not with any of the molecular tumour subtypes. Among the vitreous fluids with melanoma-cell derived DNA, not all samples harboured (analysable) other mutations or had sufficient statistical power to measure copy numbers. Still, additional mutations in BAP1, SF3B1 and EIF1AX were detected in 13/15 samples and chromosome 3p and 8q copy numbers matched the primary tumour in 19/21 and 18/20 samples, respectively. Collectively, a clinically-relevant molecular classification of the primary tumour could be inferred from 27/65 vitreous fluids. Discussion This proof-of-concept study shows that substantial amounts of DNA could be detected in vitreous fluids from uveal melanoma patients, including melanoma-cell derived DNA in 60% of the samples. Prognostically-relevant genetic alterations of the primary tumour could be identified in 42% of the patients. A follow-up study is needed to evaluate our approach in a prospective clinical context.
{"title":"Vitreous fluid-isolated DNA for the genetic analysis of primary uveal melanoma: a proof-of-concept study","authors":"R. J. Nell, M. Versluis, N. V. Menger, M. C. Gelmi, T. H.K. Vu, R. M. Verdijk, G. P.M. Luyten, M. J. Jager, P. A. van der Velden","doi":"10.1101/2024.02.09.24302604","DOIUrl":"https://doi.org/10.1101/2024.02.09.24302604","url":null,"abstract":"Background\u0000Uveal melanoma is an aggressive ocular malignancy. Early molecular characterisation of primary tumours is crucial to identify those at risk of metastatic dissemination. Although tumour biopsies are being taken, liquid biopsies of ocular fluids may form a less invasive but relatively unexplored alternative. In this study, we aim to evaluate the DNA content of vitreous fluid from eyes with a uveal melanoma to obtain molecular information from the tumour. Methods\u0000DNA was isolated from 65 vitreous fluid samples from enucleated eyes with a uveal melanoma and studied using digital PCR. Primary and additional driver mutations (in GNAQ, GNA11, PLCB4, CYSLTR2, BAP1, SF3B1 and EIF1AX) were investigated using accustomed targeted and drop-off assays. The copy numbers of chromosome 3p and 8q were measured using multiplex and single-nucleotide polymorphism-based assays. Our findings were compared to the molecular profile of matched primary tumours and to the clinicopathological tumour characteristics. Results\u0000Almost all (63/65) vitreous fluids had measurable levels of DNA, but melanoma-cell derived DNA (containing the primary driver mutation) was detected in 39/65 samples (median proportion 18%, range 0.2%-94%) and was associated with a larger tumour prominence, but not with any of the molecular tumour subtypes. Among the vitreous fluids with melanoma-cell derived DNA, not all samples harboured (analysable) other mutations or had sufficient statistical power to measure copy numbers. Still, additional mutations in BAP1, SF3B1 and EIF1AX were detected in 13/15 samples and chromosome 3p and 8q copy numbers matched the primary tumour in 19/21 and 18/20 samples, respectively. Collectively, a clinically-relevant molecular classification of the primary tumour could be inferred from 27/65 vitreous fluids. Discussion\u0000This proof-of-concept study shows that substantial amounts of DNA could be detected in vitreous fluids from uveal melanoma patients, including melanoma-cell derived DNA in 60% of the samples. Prognostically-relevant genetic alterations of the primary tumour could be identified in 42% of the patients. A follow-up study is needed to evaluate our approach in a prospective clinical context.","PeriodicalId":501390,"journal":{"name":"medRxiv - Ophthalmology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139770821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-07DOI: 10.1101/2024.02.03.24302291
Faisal Ahmed, Baris Coskunuzer
The analysis of fundus images for the early screening of eye diseases is of great clinical importance. Traditional methods for such analysis are time-consuming and expensive as they require a trained clinician. Therefore, the need for a comprehensive and automated clinical decision support system to diagnose and grade retinal diseases has long been recognized. In the past decade, with the substantial developments in computer vision and deep learning, machine learning methods have become highly effective in this field to address this need. However, most of these algorithms face challenges like computational feasibility, reliability, and interpretability. In this paper, our contributions are two-fold. First, we introduce a very powerful feature extraction method for fundus images by employing the latest topological data analysis methods. Through our experiments, we observe that our topological feature vectors are highly effective in distinguishing normal and abnormal classes for the most common retinal diseases, i.e., Diabetic Retinopathy (DR), Glaucoma, and Age-related Macular Degeneration (AMD). Furthermore, these topological features are interpretable, computationally feasible, and can be seamlessly integrated into any forthcoming ML model in the domain. Secondly, we move forward in this direction, constructing a topological deep learning model by integrating our topological features with several deep learning models. Empirical analysis shows a notable enhancement in performance aided by the use of topological features. Remarkably, our model surpasses all existing models, demonstrating superior performance across several benchmark datasets pertaining to two of these three retinal diseases.
分析眼底图像以早期筛查眼部疾病具有重要的临床意义。传统的眼底图像分析方法需要训练有素的临床医师，既耗时又昂贵。因此，人们很早就认识到需要一个全面、自动化的临床决策支持系统来诊断和分级视网膜疾病。在过去十年中，随着计算机视觉和深度学习的长足发展，机器学习方法在这一领域变得非常有效，以满足这一需求。然而，这些算法大多面临着计算可行性、可靠性和可解释性等挑战。在本文中，我们有两方面的贡献。首先，我们采用最新的拓扑数据分析方法，为眼底图像引入了一种非常强大的特征提取方法。通过实验，我们发现我们的拓扑特征向量在区分最常见视网膜疾病（即糖尿病视网膜病变、青光眼和老年性黄斑变性）的正常和异常类别方面非常有效。此外，这些拓扑特征是可解释的，在计算上也是可行的，并且可以无缝集成到该领域即将推出的任何 ML 模型中。其次，我们将拓扑特征与多个深度学习模型相结合，构建了拓扑深度学习模型。实证分析表明，拓扑特征的使用显著提高了性能。值得注意的是，我们的模型超越了所有现有模型，在与这三种视网膜疾病中的两种疾病相关的几个基准数据集上表现出卓越的性能。
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Pub Date : 2024-02-06DOI: 10.1101/2024.02.05.24302327
Maximilian Pfau, Jasleen K Jolly, Jason Charng, Leon von der Emde, Philipp L. Mueller, Georg Ansari, Kristina Pfau, Fred K Chen, Zhichao Wu
Purpose: To provide a large, multi-center normative dataset for the Macular Integrity Assessment (MAIA) microperimeter and compare the goodness-of-fit and prediction interval calibration-error for a panel of hill-of-vision models. Methods: Microperimetry examinations from five independent study groups and one previously available dataset were included. Linear mixed models (LMMs) were fitted to the data to obtain interpretable hill-of-vision models. For predicting age-adjusted normative values, an array of regression models were compared using cross-validation with site-wise splits. The mean absolute error (MAE) and miscalibration area (area between the calibration curve and the ideal diagonal) were evaluated as the performance measures. Results: 1,052 tests from 531 eyes of 432 participants were included. Based on the parameters 'participant age', 'eccentricity from the fovea', 'overlap with the central fixation target' and 'eccentricity along the four principal meridians', a Bayesian mixed model had the lowest MAE (2.13 dB; 95% confidence interval [CI] = 1.86, 2.40 dB) and miscalibration area (0.14; 95% CI = 0.07, 0.20). However, a parsimonious linear model provided a comparable MAE (2.16 dB; 95% CI = 1.89, 2.43 dB) and a similar miscalibration area (0.14; 95% CI = 0.08, 0.20). Conclusions: Normal variations in visual sensitivity on mesopic microperimetry can be effectively explained by a linear model that includes age and eccentricity. The dataset and a code vignette are provided for estimating normative values across a large range of retinal locations, applicable to customized testing patterns.
目的：为黄斑完整性评估（MAIA）微压计提供一个大型、多中心的标准数据集，并比较一组视丘模型的拟合优度和预测间隔校准误差。方法：纳入了五个独立研究小组的微透视检查结果和一个以前可用的数据集。对数据进行线性混合模型（LMM）拟合，以获得可解释的视丘模型。为了预测年龄调整后的常模值，使用按部位分割的交叉验证对一系列回归模型进行了比较。平均绝对误差（MAE）和误校正面积（校正曲线与理想对角线之间的面积）作为性能指标进行评估。结果如下共纳入 432 名参与者 531 只眼睛的 1052 次测试。根据 "参与者年龄"、"距眼窝偏心率"、"与中心固定目标重叠 "和 "沿四条主要经线的偏心率 "等参数，贝叶斯混合模型的 MAE（2.13 dB；95% 置信区间 [CI] = 1.86，2.40 dB）和误校正面积（0.14；95% CI = 0.07，0.20）最低。然而，解析线性模型提供了相似的 MAE（2.16 dB；95% CI = 1.89，2.43 dB）和相似的误判面积（0.14；95% CI = 0.08，0.20）。结论包括年龄和偏心率在内的线性模型可以有效解释中视显微测距法视觉灵敏度的正常变化。该数据集和代码小节可用于估算大范围视网膜位置的正常值，适用于定制的测试模式。
{"title":"Multicenter normative data for mesopic microperimetry","authors":"Maximilian Pfau, Jasleen K Jolly, Jason Charng, Leon von der Emde, Philipp L. Mueller, Georg Ansari, Kristina Pfau, Fred K Chen, Zhichao Wu","doi":"10.1101/2024.02.05.24302327","DOIUrl":"https://doi.org/10.1101/2024.02.05.24302327","url":null,"abstract":"Purpose: To provide a large, multi-center normative dataset for the Macular Integrity Assessment (MAIA) microperimeter and compare the goodness-of-fit and prediction interval calibration-error for a panel of hill-of-vision models. Methods: Microperimetry examinations from five independent study groups and one previously available dataset were included. Linear mixed models (LMMs) were fitted to the data to obtain interpretable hill-of-vision models. For predicting age-adjusted normative values, an array of regression models were compared using cross-validation with site-wise splits. The mean absolute error (MAE) and miscalibration area (area between the calibration curve and the ideal diagonal) were evaluated as the performance measures. Results: 1,052 tests from 531 eyes of 432 participants were included. Based on the parameters 'participant age', 'eccentricity from the fovea', 'overlap with the central fixation target' and 'eccentricity along the four principal meridians', a Bayesian mixed model had the lowest MAE (2.13 dB; 95% confidence interval [CI] = 1.86, 2.40 dB) and miscalibration area (0.14; 95% CI = 0.07, 0.20). However, a parsimonious linear model provided a comparable MAE (2.16 dB; 95% CI = 1.89, 2.43 dB) and a similar miscalibration area (0.14; 95% CI = 0.08, 0.20). Conclusions: Normal variations in visual sensitivity on mesopic microperimetry can be effectively explained by a linear model that includes age and eccentricity. The dataset and a code vignette are provided for estimating normative values across a large range of retinal locations, applicable to customized testing patterns.","PeriodicalId":501390,"journal":{"name":"medRxiv - Ophthalmology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139770822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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