Fabio Daniel Padilla-Pantoja

Purpose: To evaluate the impact of eyelid hygiene on conjunctival microbial species and ocular discharge in patients with ocular prostheses(OP). Methods: A prospective study included patients with unilateral anophthalmic sockets or phthisis bulbi using OP. Two visits were scheduled: baseline and 7 days later. At each visit, three samples were collected: lower tarsal conjunctiva of the affected eye, bulbar conjunctiva of the affected eye, and lower tarsal conjunctiva of the healthy eye. Patients followed one of two hygiene regimens: Ozonest ^® group (wipes and liposomal-ozonated-oil drops) or Lephanet ^® group (wipes and saline solution). Demographic data, clinically significant discharge(CSD), and eyelid cleaning habits were recorded. Statistical significance was set at P < 0.05. Results: Sixty-one patients (mean age 63.7 ± 15.5 years, 54.1% male) contributed 366 samples. 31 (50.8%) were in Ozonest and 30(49.2%) in Lephanet. Staphylococcus epidermidis (39%) and Corynebacterium macginleyi (24.6%) were most common. Positive cultures were higher in affected eyes (78.7%) than in healthy eyes (32.8%, P < 0.05). Tarsal conjunctiva cultures decreased with both Ozonest(64.5% to 51.6%) and Lephanet (56.7% to 46.7%). Overall CSD decreased from 47.5% to 16.4%, particularly in Lephanet (60% to 10%, P < 0.05). Conclusions: Positive cultures were more frequent in prosthetic eyes than in healthy eyes being with S. epidermidis and C. macginleyi being the most frequent microorganisms. Both hygiene regimens reduced positive tarsal conjunctival cultures and improved CSD.

Purpose: To develop a deep learning (DL) model for the automatic detection of retinal vein occlusion (RVO) biomarkers in optical coherence tomography angiography (OCT‐A) images using retrospective data. Methods: OCT‐A images from 254 patients were used to train and test an artificial intelligence (AI) model. Scans of the superficial, deep, en face, choriocapillaris, and outer retina to choriocapillaris (ORCC) layers were manually annotated with four biomarkers: perifoveal capillary plexus disruption, nonperfusion areas (NPAs), vascular tortuosity, and cystoid spaces. Deep convolutional neural networks (CNNs) were trained to segment and identify these biomarkers. The detection rate and Jaccard index were the primary outcome measures. Results: The DL model achieved detection rates of 93% for perifoveal capillary plexus disruption, 92% for NPAs, 91% for vascular tortuosity, and 84% for cystoid spaces. The corresponding Jaccard index values were 0.85, 0.77, 0.72, and 0.73, respectively. Conclusion: The proposed DL model achieves outstanding performance in identifying key biomarkers of RVO in OCT‐A images, supporting its potential role in automated disease assessment.

Background: The re-emergence of Monkeypox (MPX) and its related ophthalmic disease represent a clinical challenge in the initial stages because of the presence of lesions like those caused by varicella zoster, syphilis, and other infections due to other poxviruses. Human Immunodeficiency Virus (HIV) infection and secondary immunodepression raise the risk of severe and prolonged disease. Purpose: We present the case of a young immunosuppressed male patient with MPX, who presented with multiple skin lesions, also including risky ophthalmological manifestations due to extensive eyelid involvement. Conclusions: We describe a novel form of late-onset conjunctivitis and eyelid lesions, without active extraocular disease, highlighting the heterogeneous behavior of the new clinical form of MPX, that exhibits a wide spectrum of lesions in different stages of evolution.

Purpose: To develop an automated method based on deep learning (DL) to classify macular edema (ME) from the evaluation of optical coherence tomography (OCT) scans. Methods: A total of 4230 images were obtained from data repositories of patients attended in an ophthalmology clinic in Colombia and two free open-access databases. They were annotated with four biomarkers (BMs) as intraretinal fluid, subretinal fluid, hyperreflective foci/tissue, and drusen. Then the scans were labeled as control or ocular disease among diabetic macular edema (DME), neovascular age-related macular degeneration (nAMD), and retinal vein occlusion (RVO) by two expert ophthalmologists. Our method was developed by following four consecutive phases: segmentation of BMs, the combination of BMs, feature extraction with convolutional neural networks to achieve binary classification for each disease, and, finally, multiclass classification of diseases and control images. Results: The accuracy of our model for nAMD was 97%, and for DME, RVO, and control were 94%, 93%, and 93%, respectively. Area under curve values were 0.99, 0.98, 0.96, and 0.97, respectively. The mean Cohen's kappa coefficient for the multiclass classification task was 0.84. Conclusions: The proposed DL model may identify OCT scans as normal and ME. In addition, it may classify its cause among three major exudative retinal diseases with high accuracy and reliability. Translational Relevance: Our DL approach can optimize the efficiency and timeliness of appropriate etiological diagnosis of ME, thus improving patient access and clinical decision making. It could be useful in places with a shortage of specialists and for readers that evaluate OCT scans remotely.

Ocular involvement in Zika virus (ZIKV) infection can be present both in adults and infants as acquired and congenital diseases respectively. Through experimental studies, there has been clarified important mechanisms of ocular pathogenesis that allow the establishment of potential objectives for antiviral drugs development. The spread of the virus at the ocular level could be hematogenous or axonal, however the hematogenous route through the choroid is suggested as the most important initial mechanism for infection. Ocular manifestations vary according to the age of presentation, being mild and self-limited in adults and potentially devastating in children, related to congenital Zika syndrome (CZS). Ocular diagnosis is made based in clinical features and contact/travel history to countries of epidemiological importance; fundoscopy, optical coherence tomography, fluoresceinic/green indocianine angiography, cultures, serological and molecular tests are useful diagnostic tools. Ocular management is focused according to the clinical context of each patient. Prevention is carried out in a comprehensive manner and further research is directed to vaccine development and specific antiviral treatment. Proper attention requires a multidisciplinary team in order to reach complete visual evaluation and early rehabilitation.