Translational Vision Science & Technology

AimThe ERG b-wave is primarily attributed to ON bipolar cell activity, while the contribution of the OFF pathway and the differential role of voltage-gated sodium (NaV) channels in these pathways remain unclear. This study investigated whether pharmacological modulation of NaV channels differentially alters ON and OFF cone bipolar cell responses and ERG b-wave amplitudes. MethodsDark- and light-adapted ERGs were recorded from rats across stimulus intensities spanning rod, mixed rod-cone, and cone pathways (1-1000 lux). ON and OFF cone bipolar cell pathways were pharmacologically isolated using intravitreal cis-PDA. NaV channel activity was modulated via intravitreal administration of lidocaine and lamotrigine (blockers) and veratridine (agonist). Changes in b-wave amplitudes were analysed to assess pathway-specific effects. ResultsBoth lidocaine and lamotrigine significantly globally reduced ERG b-wave across all stimulus intensities, confirming a role for NaV channels in bipolar cell signalling. Pathway isolation revealed differential effects: lidocaine predominantly suppressed ON pathway, whereas lamotrigine preferentially reduced OFF pathway responses. In contrast, veratridine enhanced both ON and OFF pathway activity. These findings indicate that NaV channel activity in ON and OFF cone bipolar cells can be independently and differentially modulated. ConclusionThe ERG b-wave reflects integrated contributions from both ON and OFF cone bipolar cells. Differential NaV channel modulation alters these pathways distinctly, highlighting sodium channel-mediated mechanisms as potential targets for physiologically relevant retinal stimulation strategies in degenerative retinal conditions.

Purpose: To compare hypopyon detection using anterior segment optical coherence tomography (ASOCT) versus slit lamp examination (SLE) in microbial keratitis, and to evaluate intra-and inter-grader agreement for ASOCT hypopyon measurement. Methods: Two masked graders independently evaluated ASOCT images for hypopyon presence or absence in eyes with microbial keratitis, with disagreements resolved by consensus. A subset of hypopyon eyes underwent triplicate height measurement using two methods (endothelial length, vertical height). Cohen's kappa, intraclass correlation coefficients (ICC), sensitivity, and specificity were calculated comparing diagnostic performance of ASOCT versus SLE. Results: Inter-grader agreement for hypopyon detection on ASOCT was excellent (k=0.94; 95% CI 0.84-1.00) and intra-grader agreement was excellent (k=0.89-1.00). ASOCT detected hypopyon in 67.1% of eyes versus 57.0% by SLE (sensitivity 83.0%, specificity 96.2% using ASOCT as reference). Intra-grader reproducibility was excellent for both endothelial length and vertical height measurements (ICC 0.977-0.996). Inter-grader agreement was good for endothelial length (ICC 0.831) and vertical height (ICC 0.827), though a statistically significant inter-grader bias was identified for vertical height only (Wilcoxon p=0.008). Conclusions: ASOCT detected hypopyon with greater sensitivity than SLE and demonstrated excellent intra-grader and good inter-grader measurement reproducibility. Endothelial length showed slightly superior inter-grader concordance to vertical height measurement.

PurposeAtoh7 is a transiently expressed developmental transcription factor that gives rise to the seven major retinal cell types. Despite this broad lineage, Atoh7 is only required for retinal ganglion cell (RGC) genesis and survival, even though a significant portion of RGCs are Atoh7 negative based on lineage tracing in mice, suggesting a cell nonautonomous role for Atoh7 in the genesis and survival of all RGCs. Atoh7 function is conserved in zebrafish, yet the full retinal lineage, including the RGC population, has remained unidentified. Therefore, we sought to determine the atoh7 retinal lineage in wild type and atoh7 mutant zebrafish retinas. MethodsWe generated atoh7:iCre transgenic zebrafish and in combination with the established ubi:Switch lineage trace permanently labeled cells that represent the atoh7 lineage. A combination of in vivo live imaging and immunohistochemical techniques were used to validate atoh7:iCre transgene expression and the atoh7 lineage in embryonic, larval, and adult retinas as well as the adult brain. ResultsThe atoh7:iCre;ubi:Switch transgene combination successfully recapitulated the onset of endogenous atoh7 expression and transgene fluorophores persisted into adulthood labeling the atoh7 lineage. Most notably, we determined 79% of total RGCs in the wild type retina come from atoh7+ progenitor cells, a greater number than reported in the mouse retina. In atoh7 mutant retinas, we confirmed a complete loss of RGCs and observed a statistically significant increase in the proportion of atoh7+/Pax6+ amacrine cells, as well as an increase in the total number of Prox1+ bipolar cells. Interestingly, we discovered atoh7+ cells located outside the eye in other areas of the central nervous system. ConclusionsThese data demonstrate the presence of atoh7 positive and negative retinal cell types in the zebrafish retina, including RGCs, highlighting the potential to study survival mechanisms of atoh7 negative RGCs and fate switch paradigms using zebrafish retinal development models.

BackgroundThe visual field (VF) test results of many eyes with glaucoma progress despite treatment. This suggests that some eyes are either untreated or that the management of intraocular pressure (IOP) does not influence the outcome. In this work, we explore whether future VF parameters can be predicted from a baseline optical coherence retinal nerve fibre layer (OCT-RNFL) scan using a deep learning model. MethodsThe model was developed using 1792 eyes from 1610 patients, and externally validated on 151 eyes from a second centre using the same Zeiss Cirrus machine and 281 eyes from a third centre using scans obtained from a different (Heidelberg Spectralis) machine. The Vision Transformers (ViT)-based regression model was trained on baseline OCT-RNFL scans to predict three key VF indices (follow-up interval: 4.74 {+/-} 2.59 years). Model performance was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), with 95% confidence intervals (CI). ResultsThe model achieved an overall MAE of 2.07 (95% CI: 1.91-2.22) and RMSE of 2.87 (95% CI: 2.60-3.14) on the internal validation set. On external validation, the model showed comparable performance with an MAE of 2.07 (95% CI: 1.8-2.35) for the external validation (Zeiss OCT) cohort and 2.11 (95% CI: 1.93-2.31) for the external validation (Heidelberg OCT) cohort. Saliency maps revealed that the inner and outer RNFL layers were key structures in driving the models predictions. ConclusionsOur ViT-based regression model effectively predicts key VF indices objectively from a single OCT-RNFL scan, with strong performance across two OCT devices, offering a novel tool for predicting glaucoma progression.

Purpose: To develop and evaluate deep learning models for automated detection of corneal perforation in microbial keratitis using anterior segment optical coherence tomography (ASOCT) images. Methods: We enrolled 150 patients with microbiologically confirmed keratitis. Contralateral healthy eyes served as controls. Four convolutional neural network models using ResNet architecture were trained and evaluated using ASOCT images to classify the presence or absence of corneal perforation at the eye level. Ground truth labels for perforation were established following consensus grading by two masked ophthalmologist graders. Models differed in inclusion of healthy controls and masking of non-corneal anterior segment anatomy. Results: The best-performing model (Model 1), which included healthy controls and randomly applied masking of the inferior image portion during training, achieved an AUC of 0.965 (95% CI, 0.911-0.995), sensitivity of 84.0% (95% CI, 70.0%-97.1%), and specificity of 97.8% (95% CI, 96.1%-99.3%) for detection of corneal perforation. Models including healthy controls outperformed those without, and lens masking improved discrimination. Conclusions: Deep learning models achieved high diagnostic accuracy for detecting corneal perforation on ASOCT imaging in eyes with microbial keratitis. These findings support the potential role of automated ASOCT analysis as a clinical decision support tool for identifying this vision-threatening complication.

ObjectiveTo determine whether non-axon optic nerve morphometric features correlate with clinical visual function as strongly as the traditional axon count gold standard. DesignCross-sectional histological analysis with longitudinal clinical correlation. SubjectsEighteen mice from three strains: C57BL/6J (n=6), BXD51 (n=6), and DBA/2J (n=6). MethodsLeft eye (OS) optic nerves from mice euthanized at 12 months of age were resin-embedded and stained with p-phenylenediamine. Bright-field cross-sectional images were segmented using an AxonDeepSeg-based workflow to generate axon, myelin, whole nerve, and glial coverage masks for morphometric quantification. Seven morphometrics were extracted: axon count (nAx), axon density (AxDen), glial coverage area ratio (GliaR), mean solidity (Sol), mean axon diameter (AxDiam), mean myelin area (MyArea), and mean axon-myelin area (AxMyArea). Morphometrics were correlated with longitudinal clinical data collected at 1, 3, 6, 9, and 12 months, including visual acuity (VA), contrast threshold, intraocular pressure (IOP), and pattern electroretinography P50 and N95 amplitudes (PERG P50 and N95). Main Outcome MeasuresPearson correlation coefficients were used to assess associations between morphometric features and clinical measures, and Fisher z-transformed meta-analytic correlations were used to aggregate these associations across ages. ResultsVA and contrast threshold demonstrated strong correlations with GliaR that matched or exceeded nAx. Meta-analysis across ages revealed GliaR correlated with VA (r = -0.84, p = 4.49 x 10-21) and contrast threshold (r = 0.86, p=7.55 x 10-23), comparable to nAx correlations with VA (r = 0.80, p=8.13x10-17) and contrast threshold (r = -0.80, p= 1.74x10-16). Structure-function relationships shifted with age: at 6 months, GliaR had the strongest correlation with contrast threshold (r = 0.96), while at 12 months, AxDiam became the dominant correlate of both VA (r = 0.77) and contrast threshhold (r = -0.74). IOP, PERG P50, and PERG N95 exhibited weak correlations with all morphometrics (|r| < 0.27). ConclusionsNon-axon morphometrics, particularly glial coverage area ratio, correlate with visual function as strongly as traditional axon count. Automated optic nerve assessment should incorporate glial and other non-axon features. Further, stage-aware biomarker selection may better capture structure-function relationships in glaucoma.

PurposeTo evaluate the intra- and inter-grader concordance of anterior segment optical coherence tomography (ASOCT) grading for detection of endothelial plaque in microbial keratitis, and to compare endothelial plaque detection via ASOCT grading versus in-person slit lamp examination. MethodsDiagnostic concordance study of 150 consecutive patients with microbiologically confirmed bacterial or fungal keratitis at a high-volume tertiary eye hospital in India. Two masked ophthalmologist graders independently evaluated ASOCT images for presence of two morphologically distinct endothelial plaque subtypes noted during image review (round and flat plaques). We assessed intra-grader and inter-grader concordance for each endothelial plaque morphology and for presence of either morphology. Diagnostic agreement between ASOCT and in-person slit lamp examination was evaluated using percent agreement, Cohens kappa, sensitivity, and specificity. Univariable and multivariable logistic regression was used to assess odds of disagreement between ASOCT and slit lamp examination for endothelial plaque detection. ResultsASOCT detection showed near perfect inter-grader agreement for round endothelial plaques (kappa 0.88, 94.7% agreement), flat endothelial plaques (kappa 0.84, 92.0% agreement), and either plaque (kappa 0.88, 94.0% agreement). Intra-grader agreement was substantial to near perfect for both graders across all plaque types (kappa 0.70-0.86). Ophthalmologist slit lamp examination identified endothelial plaque in 6.0% eyes, while ASOCT detected round plaques in 32.7%, flat plaques in 43.3%, and either plaque in 55.3% of eyes. Using ASOCT as reference, slit lamp examination demonstrated sensitivity of 16.3% for round plaques, 6.2% for flat plaques, and 9.6% for either plaque, with specificity exceeding 94% for all. Poor visual acuity (logMAR [&ge;]1.0) was associated with increased disagreement for round plaques (adjusted OR 5.04), flat plaques (adjusted OR 3.63), and either plaque (adjusted OR 3.98). Bacterial infection was associated with increased disagreement for any endothelial plaque (adjusted OR 4.56). ConclusionSlit lamp examination substantially under-detects endothelial plaque compared to ASOCT, while ASOCT enables reproducible detection with excellent intra- and inter-grader agreement. These findings support incorporation of ASOCT imaging into microbial keratitis evaluation protocols. Differences in round and flat endothelial plaque morphologies warrant further investigation.

BackgroundAchieving precise postoperative refractive outcomes remains a significant challenge in cataract surgery. While advanced intraocular lens (IOL) power calculation formulas exist, they are constrained by their singular algorithmic structures. This study investigated whether a stacking ensemble machine learning approach could overcome these limitations. MethodsA dataset of 1,710 eyes from patients who underwent cataract surgery with monofocal IOL implantation (Vivinex or SA60AT) was utilized. Following rigorous preprocessing and feature engineering, a stacking ensemble architecture was developed comprising three diverse base learners (Multi-Layer Perceptron, Support Vector Regressor with RBF kernel, and SplineTransformer with Linear Regression) and a Ridge Regressor meta-learner. The model was trained on 80% of the data using 5-fold cross-validation and evaluated on an independent 20% test set (n=341). Performance was compared against six standard IOL formulas. ResultsThe stacking ensemble model demonstrated excellent predictive accuracy, achieving a Mean Absolute Error (MAE) of 0.272 D on the independent test set (n=341). The model achieved lower MAE compared to all six standard IOL formulas, including Kane (MAE 0.295 D) and Barrett Universal II (MAE 0.318 D). Clinically, 85.1% of eyes achieved predictions within {+/-}0.50 D, compared to 82.5% for Kane formula and 81.8% for Barrett Universal II. ConclusionThe stacking ensemble machine learning model significantly enhances postoperative refraction prediction accuracy compared to established IOL calculation formulas. By leveraging algorithmic diversity and data-driven learning, this approach represents a promising advancement toward reducing refractive surprises and improving patient satisfaction in cataract surgery. External validation on independent datasets is required to confirm generalizability.

Conventional assays (questionnaires, acuity, visual fields) may be insufficient to assess the real-world impact of new glaucoma treatments. Here, we report a novel virtual reality shopping task, and assess its sensitivity to differences in mild visual field loss. Eight glaucoma patients were asked to freely navigate a virtual grocery store and place items from a shopping list into a trolley. Across a range of metrics (time, head/body movements), performance was associated with variations in binocular visual field loss [r238=0.35, P<.001]. This indicates promise for using virtual reality tasks to evaluate the benefits of new treatments. Improvements and use-cases are discussed. ONLINE ONLY SUPPLEMENTARY MATERIALThe article contains supplementary material which will be provided as a single stand-alone PDF document.

PurposeCompare the effect of MEK inhibition on iPSC-derived retinal pigmental epithelial (RPE) cells generated from a patient who developed MEK inhibitor-Associated Retinopathy (MEKAR) versus a patient who did not develop retinopathy. DesignCase-control SubjectsTwo female patients with Neurofibromatosis Type 1 who were treated with MEK inhibitors. One patient developed MEKAR, the other did not. MethodsRPE were generated from human induced pluripotent stem cells (hiPSCs) from these two patients. These hiPSC-derived RPE were treated with selumetinib for 10 days. Main Outcome MeasuresPhagocytic activity and changes in gene expression ResultsAs previously reported, there was a significant increase in internalized rhodopsin in phagocytosis assays, yet this was only found in hiPSC-derived RPE from the patient who developed MEKAR. Selumetinib decreased expression of genes related to fluid transport and cell volume, including aquaporins and solute transporters. At baseline, cells from the patients without MEKAR had higher expression of these genes. Interestingly, selumetinib-induced changes in gene expression only reached statistical significance in cells from the patient who did not develop MEKAR, suggesting these changes may be a compensatory protective mechanism. Patients susceptible to forming MEKAR may have increased phagocytosis without a compensatory change in expression of genes related to fluid flux, thereby inhibiting their ability to transport fluid out of the subretinal space. ConclusionsMEK inhibitor-Associated Retinopathy may only affect susceptible patients whose retinal pigment epithelium cannot sufficiently regulate expression of genes related to fluid transport and cell volume, altering the ability of these cells to properly function.

Purpose: To assess the repeatability of a prototype super acuity test chart for measuring visual acuity at 12.5 cm, and its ability to detect hyperopia in adolescents and young adults. Methods: Repeatability was estimated as within-subject standard deviation of three repeated super acuity measurements performed in 41 university students (19-26 years). Associations between super acuity and cycloplegic refractive errors, ocular biometry, distance visual acuity, accommodation, age, and sex were assessed in 119 high school students (16-18 years) using linear mixed-effects models. ROC curves and Youden index were used to estimate the best super acuity thresholds to detect rest hyperopia. Results: Mean super acuities in the university and high school cohorts were 0.14 {+/-} 0.13 and 0.12 {+/-} 0.11 logMAR, respectively. Repeatability was 0.031. Super acuity was poorer in those with uncorrected hyperopia [spherical equivalent refractive error (SER) [&ge;] 1.00 D] than the others [SER < 1.00 D; P = 0.039]. There were significant associations between poorer super acuity and more positive ametropia (SER; P = 0.026), poorer accommodation amplitude (P < 0.001), shorter axial length (P = 0.013), male sex (P < 0.001), and age (P = 0.037). Sensitivity and specificity for detecting hyperopia (SER [&ge;] 1.00 D) were 63.2% and 64.2%, respectively, at a super acuity threshold of 0.09 logMAR. Discussion: The super acuity prototype shows promise as a screening indicator for hyperopia. Further studies are needed to optimize the test and testing protocol, and to assess its ability to detect uncorrected hyperopia in children.

PurposeTo evaluate the performance of machine learning models for automated glaucoma detection using multimodal clinical, structural, and functional data from a West African clinical cohort. MethodsIn this retrospective observational study, we analyzed clinical records from two major eye care centers in Ghana. A total of 605 eyes from 417 patients who underwent comprehensive glaucoma evaluation were included. Extracted features included demographic data, intraocular pressure, optical coherence tomography (OCT) structural parameters, and Humphrey visual field indices. We assessed the diagnostic performance of individual parameters using receiver operating characteristic (ROC) analysis. Supervised machine learning classifiers, including support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), and a multi-layer perceptron (MLP), were trained using a forward feature selection approach and evaluated using five-fold cross-validation. We assessed model performance by computing performance metrics like sensitivity, specificity, and area under the ROC curve (AUC). ResultsAmong the 605 eyes analyzed, 361 (59.7%) were glaucomatous, and 244 (40.3%) were healthy. Individual structural and functional parameters demonstrated moderate discriminative ability, while age showed no significant diagnostic value (AUC = 0.49, p = 0.841). Among machine learning models, the MLP achieved the highest diagnostic performance (AUC = 0.90 [95% CI: 0.86-0.92], sensitivity = 0.88, specificity = 0.86), outperforming SVM (AUC = 0.82), RF (AUC = 0.78), and GBM (AUC = 0.77). Multimodal integration of clinical, structural, and functional features improved discrimination compared with individual parameters. ConclusionsMultimodal machine learning models can effectively automate glaucoma detection using routinely collected clinical data. In this West African cohort, an MLP-based approach demonstrated superior diagnostic performance compared with traditional machine learning models and individual clinical metrics. These findings highlight the potential of clinically grounded artificial intelligence tools to support glaucoma diagnosis and triage in resource-constrained eye care settings.

Purpose:To improve determination of the treatment area for the personalization of subliminal transscleral cyclophotocoagulation (SL-TSCPC) procedures in glaucoma treatment, we designed a biometry based model of the human eye to find the estimated cilary body (CB) arc length (ECBAL) and the calculated CB distance (CCBD). Methods: We developed a rotationally symmetric modified two-sphere eye model based on axial length (AL), mean keratometry (mean K), anterior chamber depth (ACD), lens thickness (LT), and white-to-white (WTW). ECBAL and CCBD were calculated for each eye. Fluence was calculated with standardized parameters. Results: Publicly accessible biometric measurements for 24,001 eyes were pooled for analysis. The mean ECBAL was 23.99+-1.8 mm. The correlations of ECBAL with AL and ACD were 0.723 and 0.754 respectively (p < 0.01). The number of eyes with an ECBAL 21.7-22.0 mm was 131 of 24,001 (0.55%). The mean CCBD was 4.21+-0.8 mm. The number of eyes with a CCBD of 3.8 mm was 1,445 of 24,001 (6.02%). Mean fluence was 120.33+-9.0 J/cm2. A mean difference of -8.18+-6.9%, ranging from -22.66% to +29.07% in fluence was observed with treating only the recommended 22 mm versus the ECBAL. Conclusions: This study demonstrated that use of 22.0 mm as the standard treatment arc length may under or overdose laser treatment in many eyes. Precise estimation or exact localization of the CB treatment area is required to accurately calculate fluence. Translational Relevance:The model proves that CB arc length is a variable while current guidelines consider it a constant

Background and Objective As optical coherence tomography (OCT) has enabled the identification of an expanding set of age related macular degeneration (AMD) risk biomarkers and become central to routine clinical practice, there remains a need for a simplified grading scheme that allows physicians to communicate and synchronize AMD grading directly from standard OCT imaging rather than relying on traditional color fundus imaging. This study aims to establish a standardized OCT based AMD classification that balances diagnostic accuracy with practicality for use across clinical and research settings. Patients and Methods Spectral domain optical coherence tomography scans were independently graded by two retinal specialists following the newly proposed Stanford OCT Based AMD Classification (SOAC). Discrepancies were adjudicated by a third independent retinal specialist. Intergrader agreement was assessed using weighted kappa coefficients. Results Among the 109 eyes from 108 patients, AMD staging based on SOAC was distributed as follows: normal aging in 9 patients (8.3%), early AMD in 16 (14.7%), intermediate AMD in 32 (29.4%), neovascular AMD (nAMD) in 18 (16.5%), geographic atrophy (GA) in 20 (18.3%), and combined nAMD and GA in 14 (12.8%). The overall intergrader agreement demonstrated robust consistency, with a weighted kappa value of 0.95 (95% CI: 0.92 to 0.98), signifying excellent intergrader reliability and reinforcing the validity of SOAC. Conclusion SOAC provides a standardized, OCT based framework for AMD grading that demonstrates high intergrader agreement. By enabling consistent classification from commonly acquired OCT scans, SOAC supports reliable disease staging and facilitates integration across clinical studies and translational research. As imaging and molecular data continue to expand, SOAC can serve as a common OCT based reference for phenotype refinement and longitudinal AMD studies.

Purpose To evaluate associations between optical coherence tomography angiography (OCT-A) metrics and diabetic retinopathy (DR) and compare their discrimination against conventional clinical risk factors. Methods In this cross-sectional study, 108 adult eyes (right eye if both eligible) with diabetes were recruited from tertiary ophthalmology/optometry clinics. DR was clinically graded using ETDRS categories and dichotomised as no DR vs >= mild NPDR (primary outcome). Macular 6x6 mm OCT-A (Zeiss AngioPlex) was acquired; scans with signal strength >7 and without major artefact were included. Quantitative metrics from the superficial capillary plexus included vessel density (VD) and perfusion density (PD) (central/inner/outer/full regions); structural OCT measures and FAZ parameters were secondary. Associations with >= mild NPDR were assessed using multivariable logistic regression adjusted for age, sex, HbA1c, and diabetes duration. Discrimination was evaluated with ROC curves/AUC (95% CI) and DeLong comparisons of AUCs. Results DR was present in 63% of eyes. DR was associated with lower VD (central, inner, outer, full) and lower PD (central, inner, full) (all p<=0.04). After adjustment, central VD (OR 0.82, 95% CI 0.68-0.98) and central PD (OR 0.92, 95% CI 0.86-0.99) remained independently associated with DR. The OCT-A model outperformed the clinical model (AUC 0.73 vs 0.60); the combined model yielded AUC 0.76. Conclusion VD and PD from the superficial plexus are independently associated with DR and show superior discrimination versus conventional clinical factors alone, supporting OCT-A as an adjunct for earlier DR detection.

PurposeTo develop an immunodeficient retinal degenerate (RD) rat model with fluorescent label for studying retinal degeneration and transplant-host connectivity. MethodsGene constructs for CAG-LSL-TdTomato and Pcp2-Cre were developed and injected into rat embryos at Envigo. The LSL TdTomato reporter strain, created on immunodeficient RhoS334ter-3 rats (RRRC#539), was bred to homozygosity at UCI (strain SD-Foxn1rnuTg((Rho-S334X)3,CAG-TdTomato)1010Mjsuc, "RNT"). The second gene construct Pcp2-Cre was injected into Long-Evans (LE) rat embryos, resulting in two Pcp2-cre founders (strain PCP2 Cre-1105 RKI, "Pcp2"), with targeted and targeted/random insertion. Founders were mated with an LE male and a foxn1+/- NIH nude female. F1 offspring was bred to homozygosity and immunodeficiency. Homozygous rats of both strains were crossbred to generate TdTomato-Pcp2 RD ("RTP") rats. Retinas were processed for immunohistochemistry for various retinal markers. GFP-expressing rat retinas were transplanted to 6 week old "RTP" rats and analyzed 37 and 77 days post-surgery (pilot experiments). ResultsTdTomato-Pcp2 RD rats exhibit RD similar to the original Rho S334ter-3 rat strain, with < 1 row photoreceptors remaining at 1 month. Retinas with targeted Pcp2 insertion showed TdTomato in retinal interneurons and cones. Retinas with random Pcp2 insertion exhibited additional TdTomato in RPE, glial, and endothelial cells. Pcp2-TdTomato expression was useful to define transplant-host boundaries. ConclusionsWe created an unique RD rat model for studying retinal transplant connectivity. The RD LSL-TdTomato reporter rat can also be used to generate RD rats with other cell-specific labels using the cre/lox system. Translational relevanceThis newly created rat model is useful for cell therapy studies.

Purpose: To identify the ocular biometric parameters associated with refractive outcomes in Chinese Primary angle closure glaucoma (PACG) patients receiving phacoemulsification and intraocular lens (IOL) implantation (PEI) surgery. Methods: 165 Chinese PACG patients receiving PEI and goniosynechialysis (GSL) and 53 cataract patients as controls only receiving PEI surgery were recruited. The prediction accuracy of IOL power calculation was assessed by the prediction error (PE), mean absolute error (MAE), median absolute error (MedAE), and proportions of eyes with a PE within {+/-} 0.25 diopters (D), {+/-} 0.50 D, {+/-} 0.75 D, and {+/-} 1.00 D. The association of different ocular biometric parameters with the PE of IOL calculation were evaluated. Results: The PACG patients had significantly higher absolute of PE as compared to the control subjects, especially the acute PACG patients. The axial length (AL), changes in aqueous depth pre- and post-surgery ({bigtriangleup}AD), and the ratio of {bigtriangleup}AD/AL were significantly associated with the PE in acute PACG patients. The association of {bigtriangleup}AD with the PE of IOL power calculation was found in PACG patients with AL [&ge;] 22 mm. Conclusions: This study revealed the association of AL and {bigtriangleup}AD with the PE of IOL calculation in Chinese PACG patients. Precisely predict the {bigtriangleup}AD is necessary for acute PACG patients, especially for those with AL [&ge;] 22 mm, to improve the refractive outcomes.

Immunohistochemistry (IHC) is widely used to assess protein expression in corneal tissue, yet staining outcomes are strongly influenced by tissue preparation methods and regional differences within the cornea. This study aimed to systematically compare three preparation techniques including paraffin (wax) embedding, wax embedding with antigen retrieval (wax AR), and cryosectioning for IHC analysis in embryonic day 18 chicken corneal tissue. Markers representing key biological functions were evaluated, including progenitor activity (PAX6, P40), tissue architecture (actin), and immune surveillance (TAP1, CD68), across central and limbal regions. Cryosectioning consistently produced the most specific staining for nuclear and antigen-sensitive markers. PAX6 and P40 exhibited strong, nuclear-localized expression in the corneal epithelium only under cryo conditions, whereas wax-based methods resulted in reduced specificity and irregular signal distribution. TAP1-positive immune cells were detectable in the limbal stroma exclusively in cryosections, highlighting improved antigen preservation. In contrast, actin staining, was best preserved with wax AR, and provided superior structural clarity and expected expression patterns across corneal layers. CD68 showed minimal or inconsistent staining in corneal tissue across all methods despite positive control validation. These findings demonstrate that optimal IHC outcomes in corneal tissue are marker-dependent and influenced by preparation methods and regional tissue context. Cryosectioning is recommended for detecting nuclear and immune-related antigens, while wax AR is preferable for preserving tissue architecture. This study provides a practical framework for improving reproducibility and interpretation of corneal immunostaining in avian models.

Aim: To evaluate the potential of a three-dimensional microscope combining Laser scanning confocal imaging and white-light interferometry for quantitative topographic characterisation of Descemet's membrane (DM) in Fuchs endothelial corneal dystrophy (FECD). Methods: Descemet's membranes were collected from 38 FECD patients undergoing endothelial keratoplasty and 4 healthy donors. After flat-mounting on glass slide and drying, specimens were analysed using the VK-X3000 system (KEYENCE). Entire samples were reconstructed by image stitching at low magnification (x10) in white-light interferometry mode (0.01nm axial resolution). Higher magnifications (x20-x150) in confocal mode (12nm axial resolution) enabled detailed structural analysis. Three-dimensional height maps were generated to calculate standardised surface roughness parameters. Guttae and other DM features were classified according to spatial organisation and elevation profiles. Results: White-light interferometry enabled full-field mapping of whole 8mm diameter DMs with nanometric vertical resolution (~2 hours/sample). Surface roughness (Sa) was higher in FECD than in controls (median{+/-}IQR: 0.571{+/-}0.259 m vs 0.239{+/-}0.161 m ; p = 0.0018). In FECD, three zones were identified: central (guttae buried in the posterior fibrillar layer; Sa 0.442 {+/-} 0.112 m), paracentral (large uncovered guttae; Sa 0.562{+/-}0.170 m ; p = 0.0423), and outer zone (no confluent guttae; Sa 0.261{+/-}0.143 m ; p < 0.0001). Confocal 3D imaging revealed radial striae, embossments and furrows in the DM, confluent central guttae, and fused or buried structures. Conclusions: Combining white-light interferometry and confocal microscopy enables label-free, high-resolution surface characterisation of DM in FECD, providing quantitative metrics to compare histological subtypes and supporting the predominance of radial structural organisation.

PurposeTo evaluate the diagnostic performance of anterior segment optical coherence tomography (ASOCT) compared to slit lamp examination for identification of corneal perforation in microbial keratitis, and to assess ASOCT grading reproducibility. MethodsWe conducted a diagnostic concordance study of 150 eyes with microbial keratitis at a tertiary eye hospital in India. Two masked graders independently evaluated ASOCT scans for perforation, with disagreements resolved by consensus. We calculated Cohens kappa for inter-grader concordance and intra-grader repeatability. Sensitivity and specificity of slit lamp examination were calculated using ASOCT as the reference standard. Logistic regression identified factors associated with disagreement between modalities. ResultsInter-grader agreement for ASOCT was near-perfect ({kappa}=0.98; 95% CI, 0.92-1.00). ASOCT identified perforation in 24 eyes (16.0%) compared to 12 eyes (8.0%) by slit lamp examination. Using ASOCT as reference, slit lamp examination demonstrated 33.3% sensitivity (95% CI, 14.9-52.2%) and 96.8% specificity (95% CI, 93.4-99.2%). Odds of disagreement were significantly higher for eyes with stromal thinning (OR=8.19; 95% CI, 2.27-29.54), mid-stromal involvement (OR=4.44; 95% CI, 1.08-18.30), and infection within 2mm of the limbus (OR=8.81; 95% CI, 1.77-43.80). ConclusionsASOCT enables highly reproducible perforation grading and detects substantially more perforations than slit lamp examination, particularly in severe disease. These findings support ASOCT as an objective tool for clinical assessment and outcome ascertainment in microbial keratitis.