This study aimed to investigate the association between ambient air pollutants and cataracts in the general population aged 50 years or older using data from the Korean National Insurance Service—National Sample Cohort. Cataract patients were defined as those diagnosed by a physician and having undergone cataract surgery. After matching the average concentrations of PM_2.5, PM₁₀, NO₂, CO, SO₂, and O₃ in residential areas, the association between quartile level of air pollutants and incidence of cataract was analyzed using a multivariate Cox-proportional hazard risk model. Among the 115,728 participants, 16,814 (14.5%) were newly diagnosed with cataract and underwent related surgery between 1 January 2004, and 31 December 2015. Exposure to PM₁₀, NO₂, and SO₂ was positively associated with cataract incidence, while O₃ was negatively associated. The adjusted hazard ratio (HR) with 95% confidence interval was 1.069 (1.025–1.115) in PM₁₀ and 1.080 (1.030–1.133) in NO₂. However, the association between cataract and the quartile of PM_2.5 measured during one year in 2015 was not clear. The HR of female participants aged 65 or older was significantly increased according to quartile of air pollutants. We identified exposure to PM₁₀, NO₂, SO₂, and O₃ associated with cataract development in Korean adults aged ≥ 50 years. This information may be helpful for policymaking to control air pollution as a risk factor for eye health.

Wonseon Ryu, Chul-Woo Park, Junghoon Kim, Hyungwoo Lee, and Hyewon Chung. Mol. Cells 2023;46:420-9. https://doi.org/10.14348/molcells.2023.2188

Processes that regulate size and patterning along an axis must be highly integrated to generate robust shapes; relative changes in these processes underlie both congenital disease and evolutionary change. Fin length mutants in zebrafish have provided considerable insight into the pathways regulating fin size, yet signals underlying patterning have remained less clear. The bony rays of the fins possess distinct patterning along the proximodistal axis, reflected in the location of ray bifurcations and the lengths of ray segments, which show progressive shortening along the axis. Here, we show that thyroid hormone (TH) regulates aspects of proximodistal patterning of the caudal fin rays, regardless of fin size. TH promotes distal gene expression patterns, coordinating ray bifurcations and segment shortening with skeletal outgrowth along the proximodistal axis. This distalizing role for TH is conserved between development and regeneration, in all fins (paired and medial), and between Danio species as well as distantly related medaka. During regenerative outgrowth, TH acutely induces Shh-mediated skeletal bifurcation. Zebrafish have multiple nuclear TH receptors, and we found that unliganded Thrab—but not Thraa or Thrb—inhibits the formation of distal features. Broadly, these results demonstrate that proximodistal morphology is regulated independently from size-instructive signals. Modulating proximodistal patterning relative to size—either through changes to TH metabolism or other hormone-independent pathways—can shift skeletal patterning in ways that recapitulate aspects of fin ray diversity found in nature.

Purpose: Predicting long-term anatomical responses in neovascular age-related macular degeneration patients is critical for patient-specific management. This study validates a generative deep learning model to predict 12-month posttreatment optical coherence tomography (OCT) images and evaluates the impact of incorporating clinical data on predictive performance. Methods: A total of 533 eyes from 513 treatment-naïve neovascular age-related macular degeneration patients were analyzed. A conditional generative adversarial network served as the baseline model, generating 12-month OCT images using pretreatment OCT, fluorescein angiography, and indocyanine green angiography. We then sequentially added OCT after three loading doses, baseline visual acuity, treatment regimen (pro re nata or treat-and-extend), drug type, and switching events. The generated and patient OCT images were compared for intraretinal fluid, subretinal fluid, pigment epithelial detachment, and subretinal hyperreflective material, both qualitatively and quantitatively. Results: The baseline model achieved acceptable accuracy for 4 macular fluid compartments (range 0.74–0.96). Incorporating OCT after loading doses and other clinical parameters improved accuracy (range 0.91–0.98). With all the clinical inputs, the model achieved 92% accuracy in distinguishing wet macular status from dry macular status. The segmented fluid compartments in the generated images correlated positively with those in the patient images. Conclusion: Integrating clinical and treatment data, particularly OCT data after loading doses, significantly enhanced the 12-month predictive performance of conditional generative adversarial networks. This approach can help clinicians anticipate anatomical outcomes and guide personalized, long-term neovascular age-related macular degeneration treatment strategies.

Purpose: We evaluated the features predicting visual acuity (VA) after one year in neovascular age-related macular degeneration (nAMD) patients. Methods: A total of 527 eyes of 506 patients were included. Machine learning (ML) models were trained to predict VA deterioration beyond a logarithm of the minimum angle of resolution of 1.0 after 1 year based on the sequential addition of multimodal data. BaseM models used clinical data (age, sex, treatment regimen, and VA), SegM models included fluid volumes from optical coherence tomography (OCT) images, and RawM models used probabilities of visual deterioration (hereafter probability) from deep learning classifiers trained on baseline OCT (OCT0) and OCT after three loading doses (OCT3), fluorescein angiography, and indocyanine green angiography. We applied SHapley Additive exPlanations (SHAP) for machine learning model interpretation. Results: The RawM model based on the probability of OCT0 outperformed the SegM model (area under the receiver operating characteristic curve of 0.95 vs. 0.91). Adding probabilities from OCT3, fluorescein angiography, and indocyanine green angiography to RawM showed minimal performance improvement, highlighting the practicality of using raw OCT0 data for predicting visual outcomes. Applied SHapley Additive exPlanations analysis identified VA after 3 months and OCT3 probability values as the most influential features over quantified fluid segments. Conclusions: Integrating multimodal data to create a visual predictive model yielded accurate, interpretable predictions. This approach allowed the identification of crucial factors for predicting VA in patients with nAMD. Translational Relevance: Interpreting a predictive model for 1-year VA in patients with nAMD from multimodal data allowed us to identify crucial factors for predicting VA.