Purpose. To identify systemic factors that may influence the response to anti-VEGF therapy in patients with diabetic macular edema (DME). Methods. 35 patients undergoing anti-VEGF injections for centre-involving DME were studied in this prospective observational study. The primary outcome was change in macular thickness one month after treatment, measured using spectral-domain optical coherence tomography (OCT). At baseline, information on various systemic factors was collected including glycosylated hemoglobin (HbA1c), serum VEGF levels, lipid profile and markers of renal function, and blood pressure. Thirty-three of the 35 patients were included in this study. Nonparametric statistical tests were used for the analysis of the data in view of the nonnormal distribution of the outcome variables. Multivariate analysis was performed using logistic regression. Stata 12.1 software was used for the analysis. Main Outcome Measures. Reduction in macular central subfield thickness (on spectral-domain OCT) and change in logMAR visual acuity at one month after injection. Results. Lower HbA1c levels (7% or less) were significantly associated with greater reduction in central macular subfield thickness at one month after injection of bevacizumab or ranibizumab on both univariate analysis (p=0.012) and multivariate analysis (p=0.042). Conclusions. Better glycemic control is associated with a greater reduction in central macular thickness after the first injection of bevacizumab or ranibizumab in diabetic macular edema. Patients with high levels of HbA1c and poor response to anti-VEGF may benefit from strict control of their blood glucose.

(1) Background: Intravitreal anti-vascular endothelial growth factor (anti-VEGF) is an established treatment for center-involving diabetic macular edema (ci-DME). However, the clinical response is heterogeneous. This study investigated miRNAs as a biomarker to predict treatment response to anti-VEGF in DME. (2) Methods: Tear fluid, aqueous, and blood were collected from patients with treatment-naïve DME for miRNA expression profiling with quantitative polymerase chain reaction. Differentially expressed miRNAs between good and poor responders were identified from tear fluid. Bioinformatics analysis with the miEAA tool, miRTarBase Annotations, Gene Ontology categories, KEGG, and miRWalk pathways identified interactions between enriched miRNAs and biological pathways. (3) Results: Of 24 participants, 28 eyes received bevacizumab (15 eyes) or aflibercept (13 eyes). Tear fluid had the most detectable miRNA species (N = 315), followed by serum (N = 309), then aqueous humor (N = 134). MiRNAs that correlated with change in macular thickness were miR-214-3p, miR-320d, and hsa-miR-874-3p in good responders; and miR-98-5p, miR-196b-5p, and miR-454-3p in poor responders. VEGF-related pathways and the angiogenin-PRI complex were enriched in good responders, while transforming growth factor-β and insulin-like growth factor pathways were enriched in poor responders. (4) Conclusions: We reported a panel of novel miRNAs that provide insight into biological pathways in DME. Validation in larger independent cohorts is needed to determine the predictive performance of these miRNA candidate biomarkers.

This article desribes the ophthalmic involvement in patients with nasopharyngeal carcinoma (NPC) pre- and post-treatment. We retrospectively reviewed data for 354 consecutive patients diagnosed with or treated for NPC at a single tertiary centre between April 2007 and July 2015. We identified 27 (7.6%) patients with ophthalmic involvement due to NPC or its treatment. Symptomatic orbital invasion by tumor occurred in 13 of 27 patients (48.1%). The mean age of diagnosis in these patients was 54 years, and 8 (61.5%) had no prior diagnosis of NPC. Ocular signs, but no orbital invasion, was present in 8 patients (29.6%). Incidental orbital involvement was detected on imaging in 2 patients (7.4%). Radiotherapy-related ocular complications affected 4 patients (14.8%). Ophthalmic symptoms from orbital invasion can be the initial presentation of NPC. This disease tends to affect a younger group of patients and early recognition is important to minimize morbidity and mortality.

Background: The advent of generative artificial intelligence has led to the emergence of multiple vision large language models (VLLMs). This study aimed to evaluate the capabilities of commonly available VLLMs, such as OpenAI’s GPT-4V and Google’s Gemini, in detecting and diagnosing ocular diseases from retinal images. Methods and analysis: From the Singapore Epidemiology of Eye Diseases (SEED) study, we selected 44 representative retinal photographs, including 10 healthy and 34 representing six eye diseases (age-related macular degeneration, diabetic retinopathy, glaucoma, visually significant cataract, myopic macular degeneration and retinal vein occlusion). OpenAI’s GPT-4V (both default and data analyst modes) and Google Gemini were prompted with each image to determine if the retina was normal or abnormal and to provide diagnostic descriptions if deemed abnormal. The outputs from the VLLMs were evaluated for accuracy by three attending-level ophthalmologists using a three-point scale (poor, borderline, good). Results: GPT-4V default mode demonstrated the highest detection rate, correctly identifying 33 out of 34 detected correctly (97.1%), outperforming its data analyst mode (61.8%) and Google Gemini (41.2%). Despite the relatively high detection rates, the quality of diagnostic descriptions was generally suboptimal—with only 21.2% of GPT-4V’s (default) responses, 4.8% of GPT-4V’s (data analyst) responses and 28.6% for Google Gemini’s responses rated as good. Conclusions: Although GPT-4V default mode showed generally high sensitivity in abnormality detection, all evaluated VLLMs were inadequate in providing accurate diagnoses for ocular diseases. These findings emphasise the need for domain-customised VLLMs and suggest the continued need for human oversight in clinical ophthalmology.