Primary open angle glaucoma (POAG), a major cause of blindness worldwide, is a complex disease with a significant genetic contribution. We performed Exome Array (Illumina) analysis on 3504 POAG cases and 9746 controls with replication of the most significant findings in 9173 POAG cases and 26 780 controls across 18 collections of Asian, African and European descent. Apart from confirming strong evidence of association at CDKN2B-AS1 (rs2157719 [G], odds ratio [OR] = 0.71, P = 2.81 × 10⁻³³), we observed one SNP showing significant association to POAG (CDC7–TGFBR3 rs1192415, OR_G-allele = 1.13, P_meta = 1.60 × 10⁻⁸). This particular SNP has previously been shown to be strongly associated with optic disc area and vertical cup-to-disc ratio, which are regarded as glaucoma-related quantitative traits. Our study now extends this by directly implicating it in POAG disease pathogenesis.

Anterior chamber depth (ACD) is a key anatomical risk factor for primary angle closure glaucoma (PACG). We conducted a genome-wide association study (GWAS) on ACD to discover novel genes for PACG on a total of 5,308 population-based individuals of Asian descent. Genome-wide significant association was observed at a sequence variant within ABCC5 (rs1401999; per-allele effect size = −0.045 mm, P = 8.17×10⁻⁹). This locus was associated with an increase in risk of PACG in a separate case-control study of 4,276 PACG cases and 18,801 controls (per-allele OR = 1.13 [95% CI: 1.06–1.22], P = 0.00046). The association was strengthened when a sub-group of controls with open angles were included in the analysis (per-allele OR = 1.30, P = 7.45×10⁻⁹; 3,458 cases vs. 3,831 controls). Our findings suggest that the increase in PACG risk could in part be mediated by genetic sequence variants influencing anterior chamber dimensions. The anterior chamber is the space within the eye which is bound by the cornea, and the anterior surfaces of the iris and lens. Anterior chamber depth (ACD) is the distance measured along the eye's optical axis, from the cornea to the lens surface. ACD is an important risk factor for primary angle closure glaucoma (PACG), a major cause of irreversible blindness worldwide, and in particular, individuals of Asian ethnicity. In order to identify the genes that underlie PACG susceptibility, we conducted a two-staged study. We first conducted a large scale genetic study on a total of 5,308 population-based individuals of Asian descent to identify the genetic variants that influence ACD. This was followed by testing for associations between the identified genetic variant and PACG in another independent collection of 4,276 PACG cases and 18,801 controls. We found that a genetic variant within ABCC5 was associated with an increased risk of having PACG. Our findings suggest that the increase in PACG risk could in part be mediated by genetic sequence variants that influence the anterior chamber dimensions of the eye.

A 24 year-old Malay lady presented with high grade fever, myalgia, generalized rashes, severe headache and was positive for dengue serology test. Her lowest platelet count was 45 × 10(9) cells/L. She complained of sudden onset of painlessness, profound loss of vision bilaterally 7 days after the onset of fever. On examination, her right eye best corrected vision was 6/30 and left eye was 6/120. Her anterior segment examination was unremarkable. Funduscopy revealed there were multiple retinal haemorrhages found at posterior pole of both fundi and elevation at fovea area with subretinal fluid. Systemic examination revealed normal findings except for residual petechial rashes. She was managed conservatively. Her vision improved tremendously after 2 months. The retinal hemorrhages and foveal elevation showed sign of resolving. Ocular manifestations following dengue fever is rare. However, bilateral visual loss can occur if both fovea are involved.

Introduction: Purtscher's retinopathy is a rare condition that is noted in cases related to various types of trauma. The characteristic finding in the fundus is the presence of multiple Purtscher flecken. Purtscher-like retinopathy has a similar presentation in the fundus, but without an association with trauma. Case presentation: A 43-year old Malay man presented with a sudden onset of central foggy vision in the left eye after holding his breath for two minutes while catching a falling object. It was not associated with floaters, flashes of light, or head trauma. His vision in the right eye was 6/6, and in his left eye it was finger counting. He had bilateral temporal sub-conjunctival hemorrhages. An examination of his left fundus revealed multiple white cotton wool spots and dot-blot retinal hemorrhages with diffuse retinal edema at the posterior pole. His right fundus was noted to have only mild temporal peri-papillary edema associated with a few dot-blot hemorrhages. Fundus fluorescein angiography showed good arterial perfusion and no evidence of leaking or neo-vascularization. A diagnosis of Purtscher-like retinopathy was made, and the patient was treated with indomethacin tablets for six weeks. At his six-week follow-up examination, his left eye visual acuity had improved to 6/12. His bilateral sub-conjunctival hemorrhage had resolved. His left fundus showed residual multiple cotton wool spots and reduced retinal edema. Conclusions: Treatment with non-steroidal anti-inflammatory drugs seems to be effective in reducing edema in patients with Purtscher-like retinopathy.

Numerous research have demonstrated that Convolutional Neural Network (CNN) models are capable of classifying visual field (VF) defects with great accuracy. In this study, we evaluated the performance of different pre-trained models (VGG-Net, MobileNet, ResNet, and DenseNet) in classifying VF defects and produced a comprehensive comparative analysis to compare the performance of different CNN models before and after hyperparameter tuning and fine-tuning. Using 32 batch sizes, 50 epochs, and ADAM as the optimizer to optimize weight, bias, and learning rate, VGG-16 obtained the highest accuracy of 97.63 percent, according to experimental findings. Subsequently, Bayesian optimization was utilized to execute automated hyperparameter tuning and automated fine-tuning layers of the pre-trained models to determine the optimal hyperparameter and fine-tuning layer for classifying many VF defect with the highest accuracy. We found that the combination of different hyperparameters and fine-tuning of the pre-trained models significantly impact the performance of deep learning models for this classification task. In addition, we also discovered that the automated selection of optimal hyperparameters and fine-tuning by Bayesian has significantly enhanced the performance of the pre-trained models. The results observed the best performance for the DenseNet-121 model with a validation accuracy of 98.46% and a test accuracy of 99.57% for the tested datasets.