ChatGPT and dentistry: a step toward the future.

This article aims to explore the integration of ChatGPT, an advanced conversational artificial intelligence model, in the field of dentistry. The review primarily consists of information related to the capabilities and functionalities of ChatGPT and how these abilities can aid dental professionals. This study includes data from research papers, case studies, and relevant literature on language models, as well as papers on dentistry, patient communication, dental education, and clinical decision-making. A systematic approach was used to select relevant studies and literature. The selection criteria focused on papers that specifically discussed the integration of language models, ChatGPT in particular, in dentistry and their applications. The study findings revealed that ChatGPT has significant potential to revolutionize dentistry by offering various applications and benefits. It can enhance patient engagement and understanding through personalized oral health information and guidance. In dental education, ChatGPT can provide interactive learning, case studies, and virtual patient simulations. ChatGPT can also assist researchers in analyzing dental literature, identifying patterns, and generating insights. Moreover, it supports dentists with evidence-based recommendations, treatment options, and diagnostic support. Integrating ChatGPT in dentistry can be highly beneficial, but it is crucial to address ethical considerations, accuracy, and privacy concerns. Responsible implementation and continuous improvement of its functionalities are necessary to ensure that patient care and outcomes are improved.

The endophytic Aspergillus strains: A bountiful source of natural products.

Fungi that invade plant inner tissues without inducing disease symptoms are known as fungal endophytes. They represent a promising and tremendous reservoir of natural products with valuable biological potentials for application in medicine, agriculture and industry. Among the numerous existing endophytic fungi, Aspergillus strains constitute one of the most prolific sources of secondary metabolites with diverse chemical classes and interesting biological activities. This review covers the literature of the year 2020, reporting the isolation of 202 compounds obtained from more than 10 different endophytic Aspergillus species associated with different host plants. Analysis and interpretation of the collected data revealed that chemical investigation of endophytes belonging to the genus Aspergillus may greatly contribute to the discovery of potential drug leads. The isolated metabolites were chemically various and exhibited diverse biological activities such as antibacterial, anti-cancer, anti-plasmodial, anti-inflammatory, antioxidant, immunosuppressive and antifungal activities. Moreover, adoption of advanced technology in molecular biology together with modern chemical tools is anticipated to improve the discovery of new biopharmaceuticals from this valuable microbial world in the future.

Investigating Biomarkers for USH2A Retinopathy Using Multimodal Retinal Imaging.

Pathogenic mutations in USH2A are a leading cause of visual loss secondary to non-syndromic or Usher syndrome-associated retinitis pigmentosa (RP). With an increasing number of RP-targeted clinical trials in progress, we sought to evaluate the photoreceptor topography underlying patterns of loss observed on clinical retinal imaging to guide surrogate endpoint selection in USH2A retinopathy. In this prospective cross-sectional study, twenty-five patients with molecularly confirmed USH2A-RP underwent fundus autofluorescence (FAF), spectral-domain optical coherence tomography (SD-OCT) and adaptive optics scanning laser ophthalmoscopy (AOSLO) retinal imaging. Analysis comprised measurement of FAF horizontal inner (IR) and outer (OR) hyperautofluorescent ring diameter; SD-OCT ellipsoid zone (EZ) and external limiting membrane (ELM) width, normalised EZ reflectance; AOSLO foveal cone density and intact macular photoreceptor mosaic (IMPM) diameter. Thirty-two eyes from 16 patients (mean age ± SD, 36.0 ± 14.2 years) with USH2A-associated Usher syndrome type 2 (n = 14) or non-syndromic RP (n = 2) met the inclusion criteria. Spatial alignment was observed between IR-EZ and OR-ELM diameters/widths (p < 0.001). The IMPM border occurred just lateral to EZ loss (p < 0.001), although sparser intact photoreceptor inner segments were detected until ELM disruption. EZ width and IR diameter displayed a biphasic relationship with cone density whereby slow cone loss occurred until retinal degeneration reached ~1350 µm from the fovea, beyond which greater reduction in cone density followed. Normalised EZ reflectance and cone density were significantly associated (p < 0.001). As the strongest correlate of cone density (p < 0.001) and best-corrected visual acuity (p < 0.001), EZ width is the most sensitive biomarker of structural and functional decline in USH2A retinopathy, rendering it a promising trial endpoint.

Mathematical model for the novel coronavirus (2019-nCOV) with clinical data using fractional operator.

Coronavirus infection (COVID-19) is a considerably dangerous disease with a high demise rate around the world. There is no known vaccination or medicine until our time because the unknown aspects of the virus are more significant than our theoretical and experimental knowledge. One of the most effective strategies for comprehending and controlling the spread of this epidemic is to model it using a powerful mathematical model. However, mathematical modeling with a fractional operator can provide explanations for the disease's possibility and severity. Accordingly, basic information will be provided to identify the kind of measure and intrusion that will be required to control the disease's progress. In this study, we propose using a fractional-order SEIARPQ model with the Caputo sense to model the coronavirus (COVID-19) pandemic, which has never been done before in the literature. The stability analysis, existence, uniqueness theorems, and numerical solutions of such a model are displayed. All results were numerically simulated using MATLAB programming. The current study supports the applicability and influence of fractional operators on real-world problems.

RESCOVIDTCNnet: A residual neural network-based framework for COVID-19 detection using TCN and EWT with chest X-ray images.

Since the advent of COVID-19, the number of deaths has increased exponentially, boosting the requirement for various research studies that may correctly diagnose the illness at an early stage. Using chest X-rays, this study presents deep learning-based algorithms for classifying patients with COVID illness, healthy controls, and pneumonia classes. Data gathering, pre-processing, feature extraction, and classification are the four primary aspects of the approach. The pictures of chest X-rays utilized in this investigation came from various publicly available databases. The pictures were filtered to increase image quality in the pre-processing stage, and the chest X-ray images were de-noised using the empirical wavelet transform (EWT). Following that, four deep learning models were used to extract features. The first two models, Inception-V3 and Resnet-50, are based on transfer learning models. The Resnet-50 is combined with a temporal convolutional neural network (TCN) to create the third model. The fourth model is our suggested RESCOVIDTCNNet model, which integrates EWT, Resnet-50, and TCN. Finally, an artificial neural network (ANN) and a support vector machine were used to classify the data (SVM). Using five-fold cross-validation for 3-class classification, our suggested RESCOVIDTCNNet achieved a 99.5 percent accuracy. Our prototype can be utilized in developing nations where radiologists are in low supply to acquire a diagnosis quickly.

Loss of REP1 impacts choroidal melanogenesis and vasculogenesis in choroideremia.

Choroideremia (CHM) is a rare X-linked chorioretinal dystrophy affecting the photoreceptors, retinal pigment epithelium (RPE) and choroid, however, the involvement of the choroid in disease progression is not fully understood. CHM is caused by mutations in the CHM gene, encoding the ubiquitously expressed Rab escort protein 1 (REP1). REP1 plays an important role in intracellular trafficking of vesicles, including melanosomes. In this study, we examined the ultrastructure of the choroid in chmru848 fish and Chmnull/WT mouse models using transmission electron and confocal microscopy. Significant pigmentary disruptions were observed, with lack of melanosomes in the choroid of chmru848 fish from 4 days post fertilisation (4dpf), and a reduction in choroidal blood vessel diameter and interstitial pillars suggesting a defect in vasculogenesis. Total melanin and expression of melanogenesis genes tyr, tryp1a, mitf, dct and pmel were also reduced from 4dpf. In Chmnull/WT mice, choroidal melanosomes were significantly smaller at 1 month, with reduced eumelanin at 1 year. The choroid in CHM patients were also examined using spectral domain optical coherence tomography (SD-OCT) and OCT-angiography (OCT-A) and the area of preserved choriocapillaris (CC) was found to be smaller than that of overlying photoreceptors, suggesting that the choroid is degenerating at a faster rate. Histopathology of an enucleated eye from a 74-year-old CHM male patient revealed isolated areas of RPE but no associated underlying CC. Pigmentary disruptions in CHM animal models reveal an important role for REP1 in melanogenesis, and drugs that improve melanin production represent a potential novel therapeutic avenue.

Retinal vasculature of different diameters and plexuses exhibit distinct vulnerability in varying severity of diabetic retinopathy.

OBJECTIVES: To study the changes in vessel densities (VD) stratified by vessel diameter in the retinal superficial and deep vascular complexes (SVC/DVC) using optical coherence tomography angiography (OCTA) images obtained from people with diabetes and age-matched healthy controls. METHODS: We quantified the VD based on vessel diameter categorized as <10, 10-20 and >20 µm in the SVC/DVC obtained on 3 × 3 mm2 OCTA scans using a deep learning-based segmentation and vascular graph extraction tool in people with diabetes and age-matched healthy controls. RESULTS: OCTA images obtained from 854 eyes of 854 subjects were divided into 5 groups: healthy controls (n = 555); people with diabetes with no diabetic retinopathy (DR, n = 90), mild and moderate non-proliferative DR (NPDR) (n = 96), severe NPDR (n = 42) and proliferative DR (PDR) (n = 71). Both SVC and DVC showed significant decrease in VD with increasing DR severity (p < 0.001). The largest difference was observed in the <10 µm vessels of the SVC between healthy controls and no DR (13.9% lower in no DR, p < 0.001). Progressive decrease in <10 µm vessels of the SVC and DVC was seen with increasing DR severity (p < 0.001). However, 10-20 µm vessels only showed decline in the DVC, but not the SVC (p < 0.001) and there was no change observed in the >20 µm vessels in either plexus. CONCLUSIONS: Our findings suggest that OCTA is able to demonstrate a distinct vulnerability of the smallest retinal vessels in both plexuses that worsens with increasing severity of DR.

Systematic review of prognostic factors associated with progression to late age-related macular degeneration: Pinnacle study report 2.

There is a need to identify accurately prognostic factors that determine the progression of intermediate to late-stage age-related macular degeneration (AMD). Currently, clinicians cannot provide individualised prognoses of disease progression. Moreover, enriching clinical trials with rapid progressors may facilitate delivery of shorter intervention trials aimed at delaying or preventing progression to late AMD. Thus, we performed a systematic review to outline and assess the accuracy of reporting prognostic factors for the progression of intermediate to late AMD. A meta-analysis was originally planned. Synonyms of AMD and disease progression were used to search Medline and EMBASE for articles investigating AMD progression published between 1991 and 2021. Initial search results included 3229 articles. Predetermined eligibility criteria were employed to systematically screen papers by two reviewers working independently and in duplicate. Quality appraisal and data extraction were performed by a team of reviewers. Only 6 studies met the eligibility criteria. Based on these articles, exploratory prognostic factors for progression of intermediate to late AMD included phenotypic features (e.g. location and size of drusen), age, smoking status, ocular and systemic co-morbidities, race, and genotype. Overall, study heterogeneity precluded reporting by forest plots and meta-analysis. The most commonly reported prognostic factors were baseline drusen volume/size, which was associated with progression to neovascular AMD, and outer retinal thinning linked to progression to geographic atrophy. In conclusion, poor methodological quality of included studies warrants cautious interpretation of our findings. Rigorous studies are warranted to provide robust evidence in the future.

Deep Learning-Based Clustering of OCT Images for Biomarker Discovery in Age-Related Macular Degeneration (PINNACLE Study Report 4).

Purpose: We introduce a deep learning-based biomarker proposal system for the purpose of accelerating biomarker discovery in age-related macular degeneration (AMD). Design: Retrospective analysis of a large data set of retinal OCT images. Participants: A total of 3456 adults aged between 51 and 102 years whose OCT images were collected under the PINNACLE project. Methods: Our system proposes candidates for novel AMD imaging biomarkers in OCT. It works by first training a neural network using self-supervised contrastive learning to discover, without any clinical annotations, features relating to both known and unknown AMD biomarkers present in 46 496 retinal OCT images. To interpret the learned biomarkers, we partition the images into 30 subsets, termed clusters, that contain similar features. We conduct 2 parallel 1.5-hour semistructured interviews with 2 independent teams of retinal specialists to assign descriptions in clinical language to each cluster. Descriptions of clusters achieving consensus can potentially inform new biomarker candidates. Main Outcome Measures: We checked if each cluster showed clear features comprehensible to retinal specialists, if they related to AMD, and how many described established biomarkers used in grading systems as opposed to recently proposed or potentially new biomarkers. We also compared their prognostic value for late-stage wet and dry AMD against an established clinical grading system and a demographic baseline model. Results: Overall, both teams independently identified clearly distinct characteristics in 27 of 30 clusters, of which 23 were related to AMD. Seven were recognized as known biomarkers used in established grading systems, and 16 depicted biomarker combinations or subtypes that are either not yet used in grading systems, were only recently proposed, or were unknown. Clusters separated incomplete from complete retinal atrophy, intraretinal from subretinal fluid, and thick from thin choroids, and, in simulation, outperformed clinically used grading systems in prognostic value. Conclusions: Using self-supervised deep learning, we were able to automatically propose AMD biomarkers going beyond the set used in clinically established grading systems. Without any clinical annotations, contrastive learning discovered subtle differences between fine-grained biomarkers. Ultimately, we envision that equipping clinicians with discovery-oriented deep learning tools can accelerate the discovery of novel prognostic biomarkers. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Synthetic Optical Coherence Tomography Angiographs for Detailed Retinal Vessel Segmentation Without Human Annotations.

Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that can acquire high-resolution volumes of the retinal vasculature and aid the diagnosis of ocular, neurological and cardiac diseases. Segmenting the visible blood vessels is a common first step when extracting quantitative biomarkers from these images. Classical segmentation algorithms based on thresholding are strongly affected by image artifacts and limited signal-to-noise ratio. The use of modern, deep learning-based segmentation methods has been inhibited by a lack of large datasets with detailed annotations of the blood vessels. To address this issue, recent work has employed transfer learning, where a segmentation network is trained on synthetic OCTA images and is then applied to real data. However, the previously proposed simulations fail to faithfully model the retinal vasculature and do not provide effective domain adaptation. Because of this, current methods are unable to fully segment the retinal vasculature, in particular the smallest capillaries. In this work, we present a lightweight simulation of the retinal vascular network based on space colonization for faster and more realistic OCTA synthesis. We then introduce three contrast adaptation pipelines to decrease the domain gap between real and artificial images. We demonstrate the superior segmentation performance of our approach in extensive quantitative and qualitative experiments on three public datasets that compare our method to traditional computer vision algorithms and supervised training using human annotations. Finally, we make our entire pipeline publicly available, including the source code, pretrained models, and a large dataset of synthetic OCTA images.

Quantitative response of macular neovascularisation to loading phase of aflibercept in neovascular age-related macular degeneration.

PURPOSE: To evaluate quantitative morphological changes in macular neovascularisation (MNV) network after aflibercept therapy in neovascular age-related macular degeneration (nAMD) patients. METHODS: Consecutive treatment-naïve patients with optical coherence tomography (OCT) angiography confirmed MNV due to nAMD who completed a loading phase of intravitreal aflibercept injections. A quantitative analysis of the vascular network remodelling was performed using a computational software (Angiotool). RESULTS: A total of 53 eyes of 52 patients were included in the analysis. The total MNV area decreased significantly after three aflibercept injections (p = 0.003). Total vessel area and vessel density decreased respectively of 20% and 12% at V3 (p < 0.001 in both cases). Other parameters that reduced significantly were total vessel length, average vessel length and density of vascular junctions (p = 0.018, p = 0.002, and p = 0.044, respectively). The number of vascular endpoints (p = 0.001) and lacunarity (p = 0.011) increased significantly, whilst the number of vascular junctions did not vary significantly (p = 0.068). Changes in vascular metrics were predominantly driven by MNV type 1 and 2. No clear relationship was observed between any of the vascular metrics and the macular fluid status. CONCLUSION: Although objective quantification of vascular parameters showed a significant remodelling of the MNV post-loading phase of aflibercept in type 1 and 2 MNV subtypes, none of the quantified vascular metrics correlated to the macular fluid response. These findings highlight a dissociation of anti-angiogenic and anti-permeability properties of aflibercept therapy during the loading phase.

Comparison of Novel Volumetric Microperimetry Metrics in Intermediate Age-Related Macular Degeneration: PINNACLE Study Report 3.

Purpose: To investigate and compare novel volumetric microperimetry (MP)-derived metrics in intermediate age-related macular degeneration (iAMD), as current MP metrics show high variability and low sensitivity. Methods: This is a cross-sectional analysis of microperimetry baseline data from the multicenter, prospective PINNACLE study (ClinicalTrials.gov NCT04269304). The Visual Field Modeling and Analysis (VFMA) software and an open-source implementation (OSI) were applied to calculate MP-derived hill-of-vison (HOV) surface plots and the total volume (VTOT) beneath the plots. Bland-Altman plots were used for methodologic comparison, and the association of retinal sensitivity metrics with explanatory variables was tested with mixed-effects models. Results: In total, 247 eyes of 189 participants (75 ± 7.3 years) were included in the analysis. The VTOT output of VFMA and OSI exhibited a significant difference (P < 0.0001). VFMA yielded slightly higher coefficients of determination than OSI and mean sensitivity (MS) in univariable and multivariable modeling, for example, in association with low-luminance visual acuity (LLVA) (marginal R2/conditional R2: VFMA 0.171/0.771, OSI 0.162/0.765, MS 0.133/0.755). In the multivariable analysis, LLVA was the only demonstrable predictor of VFMA VTOT (t-value, P-value: -7.5, <0.001) and MS (-6.5, <0.001). Conclusions: The HOV-derived metric of VTOT exhibits favorable characteristics compared to MS in evaluating retinal sensitivity. The output of VFMA and OSI is not exactly interchangeable in this cross-sectional analysis. Longitudinal analysis is necessary to assess their performance in ability-to-detect change. Translational Relevance: This study explores new volumetric MP endpoints for future application in therapeutic trials in iAMD and reports specific characteristics of the available HOV software applications.

Developing and validating a multivariable prediction model which predicts progression of intermediate to late age-related macular degeneration-the PINNACLE trial protocol.

AIMS: Age-related macular degeneration (AMD) is characterised by a progressive loss of central vision. Intermediate AMD is a risk factor for progression to advanced stages categorised as geographic atrophy (GA) and neovascular AMD. However, rates of progression to advanced stages vary between individuals. Recent advances in imaging and computing technologies have enabled deep phenotyping of intermediate AMD. The aim of this project is to utilise machine learning (ML) and advanced statistical modelling as an innovative approach to discover novel features and accurately quantify markers of pathological retinal ageing that can individualise progression to advanced AMD. METHODS: The PINNACLE study consists of both retrospective and prospective parts. In the retrospective part, more than 400,000 optical coherent tomography (OCT) images collected from four University Teaching Hospitals and the UK Biobank Population Study are being pooled, centrally stored and pre-processed. With this large dataset featuring eyes with AMD at various stages and healthy controls, we aim to identify imaging biomarkers for disease progression for intermediate AMD via supervised and unsupervised ML. The prospective study part will firstly characterise the progression of intermediate AMD in patients followed between one and three years; secondly, it will validate the utility of biomarkers identified in the retrospective cohort as predictors of progression towards late AMD. Patients aged 55-90 years old with intermediate AMD in at least one eye will be recruited across multiple sites in UK, Austria and Switzerland for visual function tests, multimodal retinal imaging and genotyping. Imaging will be repeated every four months to identify early focal signs of deterioration on spectral-domain optical coherence tomography (OCT) by human graders. A focal event triggers more frequent follow-up with visual function and imaging tests. The primary outcome is the sensitivity and specificity of the OCT imaging biomarkers. Secondary outcomes include sensitivity and specificity of novel multimodal imaging characteristics at predicting disease progression, ROC curves, time from development of imaging change to development of these endpoints, structure-function correlations, structure-genotype correlation and predictive risk models. CONCLUSIONS: This is one of the first studies in intermediate AMD to combine both ML, retrospective and prospective AMD patient data with the goal of identifying biomarkers of progression and to report the natural history of progression of intermediate AMD with multimodal retinal imaging.

Exploring Healthy Retinal Aging with Deep Learning.

Purpose: To study the individual course of retinal changes caused by healthy aging using deep learning. Design: Retrospective analysis of a large data set of retinal OCT images. Participants: A total of 85 709 adults between the age of 40 and 75 years of whom OCT images were acquired in the scope of the UK Biobank population study. Methods: We created a counterfactual generative adversarial network (GAN), a type of neural network that learns from cross-sectional, retrospective data. It then synthesizes high-resolution counterfactual OCT images and longitudinal time series. These counterfactuals allow visualization and analysis of hypothetical scenarios in which certain characteristics of the imaged subject, such as age or sex, are altered, whereas other attributes, crucially the subject's identity and image acquisition settings, remain fixed. Main Outcome Measures: Using our counterfactual GAN, we investigated subject-specific changes in the retinal layer structure as a function of age and sex. In particular, we measured changes in the retinal nerve fiber layer (RNFL), combined ganglion cell layer plus inner plexiform layer (GCIPL), inner nuclear layer to the inner boundary of the retinal pigment epithelium (INL-RPE), and retinal pigment epithelium (RPE). Results: Our counterfactual GAN is able to smoothly visualize the individual course of retinal aging. Across all counterfactual images, the RNFL, GCIPL, INL-RPE, and RPE changed by -0.1 µm ± 0.1 µm, -0.5 µm ± 0.2 µm, -0.2 µm ± 0.1 µm, and 0.1 µm ± 0.1 µm, respectively, per decade of age. These results agree well with previous studies based on the same cohort from the UK Biobank population study. Beyond population-wide average measures, our counterfactual GAN allows us to explore whether the retinal layers of a given eye will increase in thickness, decrease in thickness, or stagnate as a subject ages. Conclusion: This study demonstrates how counterfactual GANs can aid research into retinal aging by generating high-resolution, high-fidelity OCT images, and longitudinal time series. Ultimately, we envision that they will enable clinical experts to derive and explore hypotheses for potential imaging biomarkers for healthy and pathologic aging that can be refined and tested in prospective clinical trials. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

The Diagnostic Accuracy of Double-Layer Sign in Detection of Macular Neovascularization Secondary to Central Serous Chorioretinopathy.

PURPOSE: To investigate the diagnostic value of elevated retinal pigment epithelium (RPE) and double-layer sign (DLS) in identifying macular neovascularization (MNV) secondary to central serous chorioretinopathy (CSCR). DESIGN: Retrospective, cross-sectional study. METHODS: Patients with CSCR underwent optical coherence tomography (OCT) and OCT angiography (OCT-A) scanning at Moorfields Eye Hospital. OCT scans were reviewed to identify the presence/absence of an RPE elevation. The maximum length and maximum height of the elevated RPE were measured. A minimum length of 1000 µm and a maximum height of 150 µm were used to define the "double-layer sign." Other qualitative anatomical features were also graded from OCT scans. OCT-A was examined to confirm the presence/absence of MNV. Binary logistic regression analyses were used to assess the association between OCT features and the detection of MNV on OCT-A. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the diagnostic accuracy. RESULTS: One hundred sixty-three eyes from 132 patients were included. Elevated RPE was detected in 148 eyes (91%). OCT-A-confirmed MNV was detected in 54 eyes (33%). The sensitivity and specificity of RPE elevation were 100% and 13.8%, respectively. DLS was identified in 95 eyes (58%). The sensitivity and specificity of DLS for detecting MNV were 87% and 56%, respectively. Hyperreflectivity and nonhomogeneity of the sub-RPE space were independently associated with MNV within the DLS (odds ratio, 17.7 and 14.8, P < .001 and P = .02, respectively). None of the other demographic or anatomical features were associated with MNV. The presence of nonhomogeneous hyperreflective RPE elevation had a sensitivity and specificity of 98% and 67%, with PPV and NPV of 60% and 99%, respectively. CONCLUSIONS: Nonhomogeneous and hyperreflective space under an elevated RPE of any length or height indicates an eye with higher risk of MNV than DLS. OCT-A should at least be performed for these eyes to confirm the presence of MNV and treat accordingly.

Adjunct neutralization plating in patella fracture fixation: a technical trick.

Patella fracture outcomes are positive overall; however, in some cases, traditional fixation methods result in complications, including loss of fixation and irritable hardware requiring removal. We present a technique of plate fixation that we believe has the potential to improve stability and is less offensive in more comminuted fracture patterns. Improved stability should allow unfettered advancement of rehabilitation without concern for loss of fixation. Lower profile fixation offers a potential for diminishing the presence of irritating hardware requiring removal. We present our technique for using plate fixation to augment more complex patella fracture patterns.

Longitudinal genotype-phenotype analysis in 86 patients with PAX6-related aniridia.

Aniridia is most commonly caused by haploinsufficiency of the PAX6 gene, characterized by variable iris and foveal hypoplasia, nystagmus, cataracts, glaucoma, and aniridia-related keratopathy (ARK). Genotype-phenotype correlations have previously been described; however, detailed longitudinal studies of aniridia are less commonly reported. We identified 86 patients from 62 unrelated families with molecularly confirmed heterozygous PAX6 variants from a UK-based single-center ocular genetics service. They were categorized into mutation groups, and a retrospective review of clinical characteristics (ocular and systemic) from baseline to most recent was recorded. One hundred and seventy-two eyes were evaluated, with a mean follow-up period of 16.3 ± 12.7 years. Nystagmus was recorded in 87.2% of the eyes, and foveal hypoplasia was found in 75%. Cataracts were diagnosed in 70.3%, glaucoma in 20.6%, and ARK in 68.6% of eyes. Prevalence, age of diagnosis and surgical intervention, and need for surgical intervention varied among mutation groups. Overall, the missense mutation subgroup had the mildest phenotype, and surgically naive eyes maintained better visual acuity. Systemic evaluation identified type 2 diabetes in 12.8% of the study group, which is twice the UK prevalence. This is the largest longitudinal study of aniridia in the UK, and as such, it can provide insights into prognostic indicators for patients and guiding clinical management of both ocular and systemic features.

Adaptive optics: principles and applications in ophthalmology.

This is a comprehensive review of the principles and applications of adaptive optics (AO) in ophthalmology. It has been combined with flood illumination ophthalmoscopy, scanning laser ophthalmoscopy, as well as optical coherence tomography to image photoreceptors, retinal pigment epithelium (RPE), retinal ganglion cells, lamina cribrosa and the retinal vasculature. In this review, we highlight the clinical studies that have utilised AO to understand disease mechanisms. However, there are some limitations to using AO in a clinical setting including the cost of running an AO imaging service, the time needed to scan patients, the lack of normative databases and the very small size of area imaged. However, it is undoubtedly an exceptional research tool that enables visualisation of the retina at a cellular level.

Prospective deep phenotyping of choroideremia patients using multimodal structure-function approaches.

OBJECTIVE: To investigate the retinal changes in choroideremia (CHM) patients to determine correlations between age, structure and function. SUBJECTS/METHODS: Twenty-six eyes from 13 male CHM patients were included in this prospective longitudinal study. Participants were divided into <50-year (n = 8) and ≥50-year (n = 5) old groups. Patients were seen at baseline, 6-month, and 1-year visits. Optical coherence tomography (OCT), OCT angiography, and fundus autofluorescence were performed to measure central foveal (CFT) and subfoveal choroidal thickness (SCT), as well as areas of preserved choriocapillaris (CC), ellipsoid zone (EZ), and autofluorescence (PAF). Patients also underwent functional investigations including visual acuity (VA), contrast sensitivity (CS), colour testing, microperimetry, dark adaptometry, and handheld electroretinogram (ERG). Vision-related quality-of-life was assessed by using the NEI-VFQ-25 questionnaire. RESULTS: Over the 1-year follow-up period, progressive loss was detected in SCT, EZ, CC, PAF, and CFT. Those ≥50-years exhibited more structural and functional defects with SCT, EZ, CC, and PAF showing strong correlation with patient age (rho ≤ -0.47, p ≤ 0.02). CS and VA did not change over the year, but CS was significantly correlated with age (rho = -0.63, p = 0.001). Delayed to unmeasurable dark adaptation, decreased colour discrimination and no detectable ERG activity were observed in all patients. Minimal functional deterioration was observed over one year with a general trend of slower progression in the ≥50-years group. CONCLUSIONS: Quantitative structural parameters including SCT, CC, EZ, and PAF are most useful for disease monitoring in CHM. Extended follow-up studies are required to determine longitudinal functional changes.