Context: The pathogenic basis for Graves’ disease (GD) continues to elude our understanding. Specifically why activating antibodies are generated against self-antigens remains uncertain as does the identity of the antigen(s) that provokes orbital involvement in GD, a process known as thyroid-associated ophthalmopathy (TAO). Objective: The aim of the study was to determine whether CD34⁺ fibrocytes are generated more frequently in GD, whether they infiltrate orbital connective tissues in TAO, and whether they express the thyrotropin receptor (TSHR). Design/Setting/Participants: Generation of fibrocytes from peripheral blood mononuclear cells was examined in samples from 70 patients with GD and 25 healthy control subjects. Fibrocytes were characterized by flow cytometry. Orbital tissues and fibroblast culture strains were examined for their presence. Main Outcome Measures: The frequency of CD34⁺ fibrocyte generation from peripheral blood cells, characterization of their phenotype, cytokine production, and their presence in affected orbital tissues were analyzed. Results: CD34⁺CXCR4⁺Col I⁺ fibrocytes expressing IGF-I receptor are far more frequently generated from cultured peripheral blood mononuclear cells of donors with GD compared with healthy subjects. They express TSHR at high levels and TSH induces fibrocytes to produce IL-6 and TNF-α. Numerous CD34⁺ fibrocytes were detected in orbital tissues in TAO but were absent in healthy orbits. Tissue-infiltrating fibrocytes express TSHR in situ and comprise a subpopulation of TAO-derived orbital fibroblasts. Conclusions: Our findings suggest that fibrocytes may participate in the pathogenesis of TAO because they express relevant autoantigens such as IGF-I receptor and functional TSHR and differentially accumulate in orbital tissue in TAO.

Background The impacts of social restrictions for COVID-19 on children’s vision and lifestyle remain unknown. Aims To investigate myopia incidence, spherical equivalent refraction (SER) and lifestyle changes among schoolchildren during the COVID-19 pandemic. Methods Two separate longitudinal cohorts of children aged 6–8 years in Hong Kong were included. The COVID-19 cohort was recruited at the beginning of the COVID-19 outbreak, whereas the pre-COVID-19 cohort was recruited before the COVID-19 pandemic. All children received ocular examinations, and answered a standardised questionnaire relating to their lifestyle, including time spent on outdoor activities and near work, both at baseline and at follow-up visits. Results A total of 1793 subjects were recruited, of whom 709 children comprised the COVID-19 cohort with 7.89±2.30 months of follow-up, and 1084 children comprised the pre-COVID-19 cohort with 37.54±3.12 months of follow-up. The overall incidence was 19.44% in the COVID-19 cohort, and 36.57% in pre-COVID-19 cohort. During the COVID-19 pandemic, the change in SER and axial length was –0.50±0.51 D and 0.29±0.35 mm, respectively; the time spent on outdoor activities decreased from 1.27±1.12 to 0.41±0.90 hours/day (p<0.001), while screen time increased from 2.45±2.32 to 6.89±4.42 hours/day (p<0.001). Conclusions We showed a potential increase in myopia incidence, significant decrease in outdoor time and increase in screen time among schoolchildren in Hong Kong during the COVID-19 pandemic. Our results serve to warn eye care professionals, and also policy makers, educators and parents, that collective efforts are needed to prevent childhood myopia—a potential public health crisis as a result of COVID-19.

Purpose. To evaluate the relationships between optic disc measurements, obtained by an optical coherence tomograph and a confocal scanning laser ophthalmoscope, and myopia.

Purpose. To evaluate the structure/function relationship between visual field sensitivity and retinal nerve fiber layer (RNFL) thickness measured by StratusOCT (Carl Zeiss Meditec, Inc., Dublin, CA) and GDx VCC (Laser Diagnostic Technologies, Inc., San Diego, CA).

This study explores the potential of Artificial Intelligence (AI) in early screening and prognosis of Dry Eye Disease (DED), aiming to enhance the accuracy of therapeutic approaches for eye-care practitioners. Despite the promising opportunities, challenges such as diverse diagnostic evidence, complex etiology, and interdisciplinary knowledge integration impede the interpretability, reliability, and applicability of AI-based DED detection methods. The research conducts a comprehensive review of datasets, diagnostic evidence, and standards, as well as advanced algorithms in AI-based DED detection over the past five years. The DED diagnostic methods are categorized into three groups based on their relationship with AI techniques: (1) those with ground truth and/or comparable standards, (2) potential AI-based methods with significant advantages, and (3) supplementary methods for AI-based DED detection. The study proposes suggested DED detection standards, the combination of multiple diagnostic evidence, and future research directions to guide further investigations. Ultimately, the research contributes to the advancement of ophthalmic disease detection by providing insights into knowledge foundations, advanced methods, challenges, and potential future perspectives, emphasizing the significant role of AI in both academic and practical aspects of ophthalmology.