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To demonstrate viral proteins/inflammatory cytokines in a patient with unilateral keratouveitis. Retrospective case report. A 70-year-old Asian-Indian male presented with acute onset of blurring of vision in the left eye (OS) of 2 days duration. He had was coronavirus disease 2019 (COVID-19)-positive 3 months earlier. He had undergone cataract surgery/retinal laser photocoagulation in both the eyes. The corrected distance visual acuity (CDVA) (Snellen) in the right eye (RE) (OD) and left eye (LE) (OS) was 20/20 and 20/80, respectively. OS showed decreased corneal sensation, Descemet’s folds, mild stromal edema, and fine and pigmented keratic precipitates with anterior chamber 1+ flare and 1+ cells. Fundus evaluation showed scattered laser marks in the OD and temporal sectoral laser marks in OS. He was diagnosed with viral keratouveitis in OS. Tear samples were collected on Schirmer’s strips and tear wash for mass spectrometry and cytokines, which had 368 and 451 viral proteins in the RE and LE, respectively, using nano liquid chromatography–mass spectrometry, which were more than controls. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and varicella zoster virus proteins were detected. Cytokine analysis using flow cytometer analysis showed higher inflammation in OS as compared to OD. The patient was treated with oral acyclovir and topical steroids and resulted in resolution of his keratouveitis. SARS-CoV-2 proteins were present in the tear sample 3 months after COVID-19. The presence of viral proteins does not indicate causality
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Purpose: The purpose of this study was to identify and analyze the clinical and ocular surface risk factors influencing the progression of keratoconus (KC) using an artificial intelligence (AI) model. Methods: This was a prospective analysis in which 450 KC patients were included. We used the random forest (RF) classifier model from our previous study (which evaluated longitudinal changes in tomographic parameters to predict “progression” and “no progression”) to classify these patients. Clinical and ocular surface risk factors were determined through a questionnaire, which included presence of eye rubbing, duration of indoor activity, usage of lubricants and immunomodulator topical medications, duration of computer use, hormonal disturbances, use of hand sanitizers, immunoglobulin E (IgE), and vitamins D and B12 from blood investigations. An AI model was then built to assess whether these risk factors were linked to the future progression versus no progression of KC. The area under the curve (AUC) and other metrics were evaluated. Results: The tomographic AI model classified 322 eyes as progression and 128 eyes as no progression. Also, 76% of the cases that were classified as progression (from tomographic changes) were correctly predicted as progression and 67% of cases that were classified as no progression were predicted as no progression based on clinical risk factors at the first visit. IgE had the highest information gain, followed by presence of systemic allergies, vitamin D, and eye rubbing. The clinical risk factors AI model achieved an AUC of 0.812. Conclusion: This study demonstrated the importance of using AI for risk stratification and profiling of patients based on clinical risk factors, which could impact the progression in KC eyes and help manage them better
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Purpose: This study aims to investigate the effects of maqui?berry extract (MBE) in improving signs and symptoms of dry eye disease (DED) along with ocular surface inflammation in patients with DED. Methods: Twenty patients were randomly assigned to a MBE or a placebo group (PLC). DED parameters including Schirmer’s test 1 (ST1), tear film break?up time (TBUT), ocular surface disease index (OSDI), and corneal staining were assessed before treatment and 2 months post?treatment. Tear fluid samples before and after treatment from a subset of these patients were collected from the study subjects using sterile Schirmer’s strips, and the levels of interleukin (IL)?1?, IL?10, IL?6, IL?17A, tumor necrosis factor?? (TNF?), matrix metalloproteinase?9 (MMP9), soluble intercellular adhesion molecule?1 (sICAM1), and vascular endothelial growth factor?A (VEGF?A) were measured using a microfluidic cartridge?based multiplex ELISA. Results: The MBE group demonstrated a significant (p < 0.05) decrease in OSDI scores along with a significant increase in Schirmer’s test 1 compared to the PLC group. No significant change in TBUT and corneal staining was observed between the study groups. Levels of proinflammatory factors such as IL?1?, IL?6, IL?17A, TNF?, and MMP9 were observed to be significantly reduced, along with a significant increase in IL?10 levels following treatment in the MBE group compared with the PLC group. Conclusion: Consumption of MBE resulted in the resolution of DED signs and symptoms, along with a reduction in ocular surface inflammation.
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Purpose: This randomized, controlled, blinded study evaluates the efficacy of intense pulsed light (IPL) therapy with low?level light therapy (LLLT) in the treatment of meibomian gland dysfunction (MGD) and evaporative dry eye (EDE) compared to a control group. Methods: Hundred patients with MGD and EDE were randomized into control (50 subjects, 100 eyes) and study group (50 subjects, 100 eyes). The study group underwent three sittings of IPL with LLLT 15 days apart and were followed up 1 month and 2 months after the last treatment sitting. The control group underwent sham treatment and was followed up at the same intervals. The patients were evaluated at baseline and 1 month and 3 months (post 1st treatment) for dry eye. Schirmer's test and tear breakup time (TBUT), OSDI, meibomian gland expression, and meibography. Results: The study group showed significant improvement in OSDI scores (P < 0.0001) compared to the control group and a significant improvement in TBUT (P < 0.005) compared to the control group. There was no change in schirmer's test and an improvement in the meibomian gland expression but not significant. Conclusion: The results show that a combined therapy of IPL with LLT is effective in treating MGD with EDE compared to controls, and repeated treatment sessions have a cumulative effect on the disease outcomes
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Purpose: To create a predictive model using artificial intelligence (AI) and assess if available data from patients’ registration records can help in predicting definitive endpoints such as the probability of patients signing up for refractive surgery. Methods: This was a retrospective analysis. Electronic health records data of 423 patients presenting to the refractive surgery department were incorporated into models using multivariable logistic regression, decision trees classifier, and random forest (RF). Mean area under the receiver operating characteristic curve (ROC?AUC), sensitivity (Se), specificity (Sp), classification accuracy, precision, recall, and F1?score were calculated for each model to evaluate performance. Results: The RF classifier provided the best output among the various models, and the top variables identified in this study by the RF classifier excluding income were insurance, time spent in the clinic, age, occupation, residence, source of referral, and so on. About 93% of the cases that did undergo refractive surgery were correctly predicted as having undergone refractive surgery. The AI model achieved an ROC?AUC of 0.945 with an Se of 88% and Sp of 92.5%. Conclusion: This study demonstrated the importance of stratification and identifying various factors using an AI model which could impact patients’ decisions while selecting a refractive surgery. Eye centers can build specialized prediction profiles across disease categories and may allow for the identification of prospective obstacles in the patient’s decision?making process, as well as strategies for dealing with them.
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Purpose: To assess impact of tear optics on repeatability of a Scheimpflug device with a Hartmann Shack aberrometer and a ray tracing aberrometer. Methods: One hundred healthy and 100 postrefractive surgery eyes underwent dry eye evaluation including Schirmer抯 test and tear film break?up time (TBUT). They underwent optical quality analyzer (OQAS, Visio metrics S.L, Terrassa, Spain) to assess objective scatter index (OSI), three scans each on Pentacam AXL wave (OCULUS Optikgerate Gmbh, Wetzlar, Germany), iTrace (Tracey� Technologies, Texas, USA) for flat, steep keratometry, thinnest corneal thickness, root mean square higher?order aberrations (RMS HOA), RMS lower?order aberrations (LOA), spherical aberrations, RMS COMA. Repeatability of Pentacam AXL wave and iTrace in healthy and postrefractive eyes (OSI >1 vs OSI <1) was studied using within?subject standard deviation (Sw) test杛etest repeatability (TRT), coefficient of variation (COV). Results: OSI showed an inverse association with TBUT (P < 0.001). All measurements with Pentacam AXL wave with OSI < 1 had excellent repeatability, intraclass correlation coefficient (ICC) ranging from 0.88 for HOA, to 0.92 for LOA. The Sw, TRT, and COV of all aberration measurements were significantly lower (better) than those of iTrace. In eyes with OSI ?1, the repeatability with Pentacam AXL wave dropped with ICC ranging from 0.77 for HOA, to 0.84 for LOA with lower Sw, TRT, and COV of all aberration measurements as compared to iTrace. Maximum variation was seen with HOA and minimum with LOA. Conclusion: Tear optics affected repeatability of Pentacam wave and iTrace. Pentacam wave had better repeatability in eyes with a poor tear film as compared to iTrace. Thus, the tear film can impact repeatability of an instrument and it is important to assess the tear film prior to imaging patients, which can change the way we interpret and image these patients.