Translational Vision Science & Technology最新文献

{"title":"Archetypal Analysis Reveals Consistent Visual Field Patterns for Stimulus Size III and Size Modulation Perimetry in Glaucoma.","authors":"David Szanto, Edward Linton, Michael Wall","doi":"10.1167/tvst.14.10.12","DOIUrl":"https://doi.org/10.1167/tvst.14.10.12","url":null,"abstract":"<p><strong>Purpose: </strong>Optic nerve disorders produce identifiable visual field (VF) patterns. Size modulation perimetry (SMP) unlike standard automated perimetry (SAP) shows little degradation of retest variability with increasing VF damage. We hypothesize that VF testing with stimulus size III (size III) and SMP would reveal analogous patterns, supporting interchangeability in clinical practice.</p><p><strong>Methods: </strong>We analyzed 274 same-day pairs of VFs from 83 eyes of patients with glaucoma. Participants underwent testing every 6 months using size III and SMP VFs. We converted raw sensitivity data to total deviation (TD) scores relative to normative datasets, censoring size III sensitivities below 20 decibels (dB). Archetypal analysis (AA) was applied separately to each modality to identify characteristic VF patterns, or archetypes (ATs). We evaluated both absolute VF patterns and longitudinal VF changes by subtracting baseline measurements. Cosine similarity measured the overlap between ATs in size III and SMP.</p><p><strong>Results: </strong>Absolute size III and SMP VFs exhibited overlapping patterns, with 95% of ATs demonstrating a cosine similarity of ≥0.50. Change ATs showed lower similarity scores (30% with cosine similarity ≥0.50). However, some ATs still revealed similar underlying defects.</p><p><strong>Conclusions: </strong>AA consistently extracted comparable VF defect patterns from size III and SMP in glaucoma. Absolute size III patterns closely aligned with SMP, whereas patterns of change showed partial agreement.</p><p><strong>Translational relevance: </strong>These findings suggest that SAP and SMP capture similar patterns of VF loss.</p>","PeriodicalId":23322,"journal":{"name":"Translational Vision Science & Technology","volume":"14 10","pages":"12"},"PeriodicalIF":2.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145275784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive Modeling of Cycloplegic Refraction Using Non-Cycloplegia Ocular Parameters With Emphasis on Lens-Related Features.","authors":"Qiang Su, Bei Du, Bingqin Li, Chen Yang, Yicheng Ge, Haochen Han, Chea-Su Kee, Wenxue Li, Ruihua Wei","doi":"10.1167/tvst.14.10.3","DOIUrl":"10.1167/tvst.14.10.3","url":null,"abstract":"<p><strong>Purpose: </strong>The study aimed to develop a predictive model for refraction after cycloplegia by leveraging non-cycloplegia ocular parameters and focusing on lens-related features.</p><p><strong>Methods: </strong>A total of 153 children 4 to 15 years old were enrolled in this study. This study randomized gender distribution. Sex, age, intraocular pressure (IOP), refraction before and after cycloplegia, and optical biometry (OB) parameters were collected. Four prediction models for spherical refraction were developed: a control group without lens-related features and three experimental groups incorporating lens-related features. Features such as lens diopter, anterior surface curvature radius, and lens thickness played significant roles. The models were evaluated using statistical measures: mean square error (MSE), Root mean square error (RSME), Mean absolute error (MAE) and r-square (r2). Least absolute shrinkage and selection operator (LASSO) regression and the L1 regularization term were used for feature screening and machine learning for extreme gradient enhancement. The extreme gradient boosting (XGBoost) method was used to develop the model.</p><p><strong>Results: </strong>The predictive model incorporating lens-related features demonstrated superior performance in estimating refraction after cycloplegia compared to the model without such features. Among the models with lens-related features, the IOL of contact lens algorithm (IOLcl) group exhibited the highest efficacy, boasting an r2 of 0.964, MSE of 0.241, RMSE of 0.472, and MAE of 0.307.</p><p><strong>Conclusions: </strong>The study provided valuable insights into developing a robust predictive model for refraction after cycloplegia, emphasizing the importance of lens-related features and the morphological changes in the crystalline lens during accommodation.</p><p><strong>Translational relevance: </strong>This predictive model has potential advantages in avoiding complications associated with cycloplegia and can be widely applied for clinic vision screening in optometry.</p>","PeriodicalId":23322,"journal":{"name":"Translational Vision Science & Technology","volume":"14 10","pages":"3"},"PeriodicalIF":2.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12514981/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145201023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combined Probability Test for Sectoral Progression of the Circumpapillary Retinal Nerve Fiber Layer Thickness.","authors":"Iván Marín-Franch, Paul H Artes, Sampson L Abu, Lyne Racette","doi":"10.1167/tvst.14.9.4","DOIUrl":"10.1167/tvst.14.9.4","url":null,"abstract":"<p><strong>Purpose: </strong>Optical coherence tomography has become a widely used tool to assess structural changes at the optic nerve head and the peripapillary retina. Often, global analyses are supplemented with sectoral analyses, but it is unclear how to control specificity as trend analyses are conducted on a larger number of sectors. We introduce a random permutation analysis for a combined probability test of progression in circumpapillary retinal nerve fiber layer (cpRNFL) thickness applied to different number of sectors.</p><p><strong>Methods: </strong>A series of seven cpRNFL scans were extracted for 428 eyes of 255 patients with glaucoma from the DIGS/ADAGES dataset. The combined probability test was run for 2k sectors, where k = 0, ⋯, 8 in addition to the maximum possible number of pixels, 768. Positive rates were derived for specificity ranging from 100% to 85%.</p><p><strong>Results: </strong>At 95% specificity, the positive rate for 768 pixels was 41% [37%, 46%]. The positive rates for global thickness, and for 12 sectors, were statistically significantly smaller (28% and 35%, respectively). Positive rates remained at the observed maximum until the number of sectors fell below 128.</p><p><strong>Conclusions: </strong>The permutation of cpRNFL thickness profiles makes it possible to detect highly localized change in cpRNFL profiles from optical coherence tomography.</p><p><strong>Translational relevance: </strong>Glaucoma-related changes in the optic nerve fiber layer are often localized rather than global. Permutation analysis provides a framework to detect such changes without sacrificing specificity.</p>","PeriodicalId":23322,"journal":{"name":"Translational Vision Science & Technology","volume":"14 9","pages":"4"},"PeriodicalIF":2.6,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12410285/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144970546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stargardt's Connected Research Network Inaugural Meeting: Landscape Review and Horizon Scanning of Stargardt Disease.","authors":"Alexis Ceecee Britten-Jones, Saoud Al-Khuzaei, Matteo Rizzi, Michael D Crossland, Malgorzata B Rozanowska, Bernardo S Mendes, Thales A C de Guimaraes, Malena Daich Varela, Dayyanah Sumodhee, William A Woof, Seemi Khan, Aniz Girach, Susan M Downes, Michael E Cheetham, Davide Piccolo, Lauren N Ayton, Marina Leite Brandão, Hendrick P N Scholl, Andi Skilton, Bhavna Tailor, Michel Michaelides, Nikolas Pontikos","doi":"10.1167/tvst.14.9.26","DOIUrl":"10.1167/tvst.14.9.26","url":null,"abstract":"<p><strong>Purpose: </strong>The purpose of this study was to update the recent progress in the diagnosis, management, and treatments for Stargardt disease.</p><p><strong>Methods: </strong>On November 22, 2024, Stargardt's Connected held its inaugural meeting of their Research Network, attended by clinicians, researchers, industry partners, and patient representatives. This meeting aimed to provide an update on Stargardt disease management and research and develop a call to action for the wider community. The format was rapid-fire presentations followed by a roundtable discussion. This review presents the meeting proceeding, along with a summary of best up-to-date evidence and key calls to action.</p><p><strong>Results: </strong>Topics included: (1) advances in the understanding of Stargardt disease: natural history, genetic basis, animal models, and emerging therapies; (2) supporting individuals with Stargardt disease: disease impact, technological advancements, and lifestyle modifications; and (3) advancing research through stakeholder engagement, research registries, and patient input. The network acknowledges the importance of collaboration among patients, clinicians, researchers, and industry to address critical gaps in the diagnosis, management, and treatment of Stargardt disease. Patient engagement was emphasized as being crucial for driving progress in the field.</p><p><strong>Conclusions: </strong>Recent years have seen significant progress in understanding and managing Stargardt disease. Key calls to action included the areas of improving genetic testing and counseling, advancing research registries, supporting research and clinical trials, growing multidisciplinary care, addressing lifestyle and dietary modifications, and enhancing technological support.</p><p><strong>Translational relevance: </strong>This Stargardt's Connected Research Network initiative outlines a multistakeholder engagement model to discuss ongoing research and emerging treatments for Stargardt disease, a condition with promising therapeutic developments.</p>","PeriodicalId":23322,"journal":{"name":"Translational Vision Science & Technology","volume":"14 9","pages":"26"},"PeriodicalIF":2.6,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12449819/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145076192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Code-Free Machine Learning for the Detection of Common Ophthalmic Diseases.","authors":"Trevor Lin, Theodore Leng","doi":"10.1167/tvst.14.9.16","DOIUrl":"10.1167/tvst.14.9.16","url":null,"abstract":"<p><strong>Purpose: </strong>We explore a code-free method enabling physicians without programming experience to develop machine learning (ML) models for detecting diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma from fundus photographs.</p><p><strong>Methods: </strong>Two classification models were developed using Google Vertex AI's no-code AutoML Vision platform: a binary model detecting any pathology and a multi-class model classifying specific diseases. The development dataset consisted of 800 fundus photography images (200 each of DR, AMD, glaucoma, and normal) from the publicly available Fundus Image dataset for Vessel Segmentation. Ten percent of the dataset was saved for testing and 10% for internal validation. External validation was performed using the Eye Disease Diagnosis and Fundus Synthesis dataset, from which 100 single-diagnosis images per class were randomly selected (total N = 400). Model performances were evaluated using area under the precision-recall curve (AUPRC), precision, recall, accuracy, F1 score, and confidence score analysis.</p><p><strong>Results: </strong>Internally, the binary model yielded an AUPRC of 0.967, with 95.0% precision and recall. The multi-class model had an AUPRC of 0.906, with 91.0% precision and 90.0% recall. On external validation, the binary model reached 92.3% accuracy, whereas the multi-class model achieved 90% overall accuracy.</p><p><strong>Conclusions: </strong>Code-free ML approaches can enable physicians to create ML models for retinal disease detection without requiring programming expertise, supporting early detection of eye diseases.</p><p><strong>Translational relevance: </strong>This work bridges the gap between AI research and clinical deployment by demonstrating that physicians can independently build ML models using accessible, no-code tools.</p>","PeriodicalId":23322,"journal":{"name":"Translational Vision Science & Technology","volume":"14 9","pages":"16"},"PeriodicalIF":2.6,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12439502/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145034139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of a Deep Learning-Assisted Evaluation of Total Corneal Endothelial Cells Viability.","authors":"Matteo Airaldi, Filippo Airaldi, Zhuangzhi Gao, Alessandro Ruzza, Mohit Parekh, Diego Ponzin, Stephen Kaye, Francesco Semeraro, Stefano Ferrari, Yalin Zheng, Vito Romano","doi":"10.1167/tvst.14.9.20","DOIUrl":"10.1167/tvst.14.9.20","url":null,"abstract":"<p><strong>Purpose: </strong>To describe the validation of a novel automated analysis of preoperative pan-corneal endothelial cell viability.</p><p><strong>Methods: </strong>Preclinical experimental study. Dead endothelial cells and denuded areas of Descemet membrane of corneoscleral rims were stained with trypan blue (TB) 0.05%. Endothelial mortality was estimated by an experienced eye bank technician (\"gold standard\") and by deep learning-aided automated segmentation of TB-positive areas (TBPAs) on images of the stained corneas (\"V-CHECK method\"). V-CHECK mortality was calculated for the whole cornea and for concentric 2-mm rings. The agreement in the estimation of endothelial mortality between the two methods was assessed with Bland-Altman analysis and correlation tests.</p><p><strong>Results: </strong>Nineteen corneas deemed unsuitable for transplantation were used for the experiment. The automated V-CHECK method was able to accurately segment the corneal endothelium and the TBPAs. The gold standard and the V-CHECK method showed a strong positive correlation for all rings (Pearson's ρ, range 0.76-0.81, all P < 0.001). The V-CHECK method resulted in a higher average estimated endothelial mortality (mean difference range +6.5% to +9.5%).</p><p><strong>Conclusions: </strong>The V-CHECK method enables reproducible estimation of endothelial cell viability in donor corneas. Incorporating this technique into the preoperative assessment of donor corneal tissues (in the eye bank and in the operating theater) can provide a reliable evaluation of endothelial health, thereby improving the consistency of tissue quality and further supporting optimal surgical results.</p><p><strong>Translational relevance: </strong>The V-CHECK deep learning-assisted computer vision protocol will allow surgeons and eye bank technicians to perform an independent, preoperative assessment of global corneal endothelial viability.</p>","PeriodicalId":23322,"journal":{"name":"Translational Vision Science & Technology","volume":"14 9","pages":"20"},"PeriodicalIF":2.6,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12442935/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145065715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ophthalmic Segmentation and Analysis Software (OASIS): A Comprehensive Tool for Quantitative Evaluation of Meibography Images.","authors":"Naomi Joseph, Ved Shivade, Jiawei Chen, Ian Marshall, Emma Avery, Dominique Jennings, Harry Menegay, Ramkumar Ramamirtham, David Wilson, Beth Ann Benetz, Thomas Stokkermans","doi":"10.1167/tvst.14.9.22","DOIUrl":"10.1167/tvst.14.9.22","url":null,"abstract":"<p><strong>Purpose: </strong>To develop an interactive image editor, Ophthalmic Segmentation and Analysis Software (OASIS) for automating and improving the analysis of meibography images to gain insight into the progression of meibomian gland dysfunction.</p><p><strong>Methods: </strong>A natural history study was conducted, collecting 2,439 meibography images from 325 patients. Clinicians used OASIS for image analysis, which involved both manual and deep-learning assisted processes. In the manual process, clinicians annotated three distinct masks per image: the eyelid, glands, and gland loss. In the assisted process, OASIS incorporated deep-learning models to infer gland masks, reducing the time required for gland-by-gland annotation. The software's interface provided additional tools for image enhancement and calculation of currently used clinical metrics including the Pult scale.</p><p><strong>Results: </strong>OASIS enabled clinicians to quantitatively analyze MGD in under 3 minutes, representing an 87% reduction in time compared to traditional manual analysis methods. The software accurately calculated Pult meiboscale grades with fair agreement between the clinician and software (kappa = 0.79), demonstrating a high level of consistency.</p><p><strong>Conclusions: </strong>OASIS significantly streamlines the analysis of meibography images and allows for a more objective and efficient evaluation of MGD. By implementing deep learning models for gland inference and providing a suite of custom annotation tools, OASIS may reduce the time burden on clinicians while maintaining accuracy.</p><p><strong>Translational relevance: </strong>OASIS paves the way for developing quantitative biomarkers for MGD and may have applications in both clinical practices and research. OASIS also further demonstrates the potential for AI-driven tools in improving ophthalmic image analysis.</p>","PeriodicalId":23322,"journal":{"name":"Translational Vision Science & Technology","volume":"14 9","pages":"22"},"PeriodicalIF":2.6,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12442936/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145065749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Near-Real Surgical Specimens (NRSS): A Novel Platform for Standardized Assessment of Suprachoroidal Drug Delivery.","authors":"Vinit B Mahajan, Yoreh Barak, David R P Almeida","doi":"10.1167/tvst.14.9.6","DOIUrl":"10.1167/tvst.14.9.6","url":null,"abstract":"<p><strong>Purpose: </strong>Suprachoroidal (SC) drug delivery is a promising avenue for treating posterior segment ocular diseases. Current ex vivo models, primarily human cadaveric eyes, are limited by tissue variability and altered post-mortem fluid dynamics. We introduce near-real surgical specimens (NRSS), an engineered ocular tissue platform, to overcome these limitations and provide standardized, reproducible evaluation of SC drug delivery.</p><p><strong>Methods: </strong>NRSS ocular models were engineered with biomimetic properties calibrated to fresh human tissue. Suprachoroidal injections of a contrast-enhanced balanced salt solution (100 µL) were performed in NRSS specimens (n = 8) using the Everads Suprachoroidal Injector, which features a 30-gauge needle and a non-sharp nitinol tissue separator for tangential blunt dissection into the suprachoroidal space (SCS). An integrated contrast imaging system enabled real-time visualization. Key parameters, including SC coverage patterns, interspecimen variability (coefficient of variation [CV]), and injection dynamics, were quantified and compared to published human cadaveric eye data.</p><p><strong>Results: </strong>The NRSS platform demonstrated consistent SC coverage patterns. With the Everads Suprachoroidal Injector, injectate exhibited an initial posterior diffusion from the injection site, followed by postero-circumferential spread. NRSS specimens showed significantly reduced inter-specimen variability in SC coverage compared to reported cadaveric studies (CV: 6.8% for NRSS vs. 17.7% for cadaveric tissue; P < 0.01). Real-time visualization provided detailed insights into the dynamic characteristics of SC injection, including fluid flow patterns and tissue interactions not readily observable in static cadaveric models.</p><p><strong>Conclusions: </strong>The NRSS platform offers a reproducible and standardized method for evaluating SC drug delivery, closely mimicking clinically relevant biomechanical properties. It enables direct visualization of injection dynamics and holds potential for customization to model pathologic conditions, providing distinct advantages over traditional cadaveric models for device testing, therapeutic assessment, and surgical training. The Everads Suprachoroidal Injector demonstrated effective and reliable access to the SCS in NRSS models with a characteristic posterior-first spread pattern.</p><p><strong>Translational relevance: </strong>NRSS technology addresses critical limitations in current SC injection assessment models. It provides a robust, standardized platform for optimizing delivery devices, quantitatively evaluating novel therapeutics, and enhancing surgical training for SC procedures, thereby potentially accelerating the clinical translation of SC therapies.</p>","PeriodicalId":23322,"journal":{"name":"Translational Vision Science & Technology","volume":"14 9","pages":"6"},"PeriodicalIF":2.6,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12416518/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144993618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wildfire Smoke Induces Eye Surface Inflammation and Tear Film Changes in a Human Experimental Model.","authors":"Sukanya Jaiswal, Blanka Golebiowski, Meagan Porter, Ha T Duong, Michele C Madigan, Fay H Johnston, Isabelle Jalbert","doi":"10.1167/tvst.14.9.13","DOIUrl":"10.1167/tvst.14.9.13","url":null,"abstract":"<p><strong>Purpose: </strong>To investigate the short-term impact of exposure to smoke from vegetation burns on ocular surface symptoms and signs.</p><p><strong>Methods: </strong>Woody bushfuels were burnt in an enclosed room (Flammability Laboratory, University of Tasmania, Australia) to generate particulate matter and monitored in real time (Dust Trak II). Eighteen participants (aged 20-63 years, 8 males and 10 females) fitted with respirators were seated 1.5 m from the burn for 15 minutes. Clinical ocular surface measurements were conducted in the right eye. Tears were collected from the left eye and analyzed for the cytokine interleukin-1β (enzyme-linked immunosorbent assay). Pre- and postexposure differences were analyzed using paired t tests or Wilcoxon signed rank tests. Associations between symptoms and signs were analyzed using Spearman's correlation.</p><p><strong>Results: </strong>Mean particulate matter with a diameter of 2.5 µm or smaller exposure was 1903 µg/m3. After smoke exposure, an increase in symptoms (median change, 2; interquartile range [IQR], 1-6; P = 0.001), ocular surface staining (median change, 1; IQR, 0-1; P = 0.007), limbal redness (mean change, 0.28 ± 0.36; P = 0.02), palpebral conjunctival redness (mean change, 0.35 ± 0.36; P = 0.009), palpebral conjunctival roughness (mean change, 0.3 ± 0.4; P = 0.046), and decrease in tear breakup time (mean change. 1.4 ± 2.6 seconds; P = 0.03) occurred. The change in bulbar conjunctival redness correlated with the change in dryness symptoms (r = 0.70; P = 0.001). The interleukin-1β concentration increased in the majority of participants post exposure (median change, 6.6 pg/mL; IQR, 2.2-21.1 pg/mL; P = 0.01).</p><p><strong>Conclusions: </strong>This study demonstrated that short-term wildfire smoke directly and adversely affects the ocular surface and induces symptoms.</p><p><strong>Translational relevance: </strong>This study used a unique enclosed experimental laboratory to simulate ocular exposure to wildfire smoke and demonstrates the need to elucidate the role of anti-inflammatory therapies in mitigating the impact of smoke on the ocular surface.</p>","PeriodicalId":23322,"journal":{"name":"Translational Vision Science & Technology","volume":"14 9","pages":"13"},"PeriodicalIF":2.6,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12425145/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145024309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of Diagnostic Nomograms Using Corneal Nerve Parameters for Diabetic Peripheral Neuropathy in Type 2 Diabetes Mellitus.","authors":"Qincheng Qiao, Juan Cao, Xinguo Hou","doi":"10.1167/tvst.14.9.29","DOIUrl":"10.1167/tvst.14.9.29","url":null,"abstract":"<p><strong>Purpose: </strong>Diabetic peripheral neuropathy (DPN), a common complication of type 2 diabetes mellitus (T2DM), lacks effective diagnostic tools. This study aimed to develop a nomogram that integrates corneal nerve parameters for individualized DPN risk prediction.</p><p><strong>Methods: </strong>A total of 111 patients with T2DM and 110 healthy controls were enrolled. All participants underwent bilateral corneal confocal microscopy (CCM). High-quality images were selected by four blinded investigators. Corneal nerve fiber length (CNFL), corneal nerve branch density (CNBD), and corneal nerve fiber density (CNFD) were quantified using ACCMetrics and AiCCMetrics software. Diagnostic models-including single- and multi-parameter models-and a nomogram incorporating CNFL, CNBD, CNFD, and age were developed. Model performance was evaluated using receiver operating characteristic analysis with 500 bootstrap resamples, calibration curves, decision curve analysis, and clinical impact curves. Sensitivity analyses assessed robustness.</p><p><strong>Results: </strong>Patients with DPN were significantly older (P = 0.005). CNFL and CNFD were higher in the DPN- group (P < 0.05), whereas CNBD showed no group difference. Single-parameter models yielded area under the curve (AUC) values ranging from 0.495 to 0.727, whereas multivariate models demonstrated improved performance with AUCs between 0.737 and 0.782. In the nomogram, CNFL and CNFD were protective factors, whereas CNBD paradoxically increased DPN risk. The model demonstrated good discrimination, calibration, clinical utility, and robustness.</p><p><strong>Conclusions: </strong>A nomogram combining multiple corneal nerve parameters may outperform single-parameter models, thereby representing a potential tool for DPN risk stratification in T2DM.</p><p><strong>Translational relevance: </strong>The corneal nerve-based nomogram may assist in personalized DPN risk prediction and holds potential translational value for individuals with T2DM.</p>","PeriodicalId":23322,"journal":{"name":"Translational Vision Science & Technology","volume":"14 9","pages":"29"},"PeriodicalIF":2.6,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12462533/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145114066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}