Assessing the Impact of Sodium Hyaluronate Eye Drops on the Ocular Surface Microbiome: Implications for Dry Eye Management and Ocular Health : Eye Microbiome

Abstract: Background: A powerful immunoregulatory function is provided by the ocular surface microbiome, which contributes to ocular pathogenesis, physiological integrity, and pathogenesis of ocular diseases. Using sodium hyaluronate eye drops (with or without a preservative) as a remedy for dry eye, we contrasted the bacterial communities' diversity and composition on the ocular surface before and after usage. Methods: We randomly divided 16 healthy adults into two groups. From each participant was required to provide a microbial sample at the start and after two weeks of the intervention. After sodium hyaluronate eye drops were administered, diversity and classification differences were compared between the groups. Results: Results of the present study indicated that there was a significant difference between the bacterial communities in the eyes of the two groups of healthy individuals. Although sodium hyaluronate eye drops (with or without preservatives) did alter the bacterial community, the results of alpha and beta diversity showed no significant differences between individuals or between groups. Conclusion: Eye drops containing sodium hyaluronate may affect the eye's bacterial community with or without benzalkonium chloride (BAC) levels. Depending on the individual and the eye, these changes may vary. &nbsp

Utilizing Microbiome Approaches for Antibiotic Resistance Analysis; an Ocular Case Evaluation

Background: Metaorganism or microbial communities of eukaryotic organisms provide an inclusive set of functions related to immunity, host metabolism, and stress tolerance. Ocular microbiota refers to pathogenic and commensal microorganisms in or on the eye. On the one hand, antibiotic treatment can give rise to pathogen overgrowth due to an imbalance of microbiota and cause various ophthalmic diseases. On the other, antibiotic therapy is considered the leading cause of antibiotic resistance. The present study aimed to describe the bacterial community changes following antibiotic treatment in the ocular surface microbiome. Material and Methods: In this scenario, we evaluated the composition of thirteen canine ocular microbiomes during treatment with a typical mixture of antibiotics, neomycin-polymyxin-bacitracin. Microbiome taxonomy and downstream bacterial richness and evenness were analyzed using microbiome bioinformatics platforms. Results: Accordingly, bacterial taxonomy at the level of phyla and genus was mapped, and alter of antibiotic resistance genes  werereported. An increase in the Staphylococcus genus traced during the time and one month following antibiotic treatment. Bacterial network, alpha, and beta diversity indicated a significant microbiota change at the genus level. Conclusion: This study highlights the effect of commonly used ocular antibiotics on commensal microbiota and the emergence of the antibiotic-resistant genus

A Review of the Management of Eye Diseases Using Artificial Intelligence, Machine Learning, and Deep Learning in Conjunction with Recent Research on Eye Health Problems: Eye Microbiome

In the field of computer science, Artificial Intelligence can be considered one of the branches that study the development of algorithms that mimic certain aspects of human intelligence. Over the past few years, there has been a rapid advancement in the technology of computer-aided diagnosis (CAD). This in turn has led to an increase in the use of deep learning methods in a variety of applications. For us to be able to understand how AI can be used in order to recognize eye diseases, it is crucial that we have a deep understanding of how AI works in its core concepts. This paper aims to describe the most recent and applicable uses of artificial intelligence in the various fields of ophthalmology disease

Impact of Perioperative Management on Ocular Microbiota Composition and Diversity: A Study of Intravitreal Injection Patients with 16S rRNA Sequencing

Background: The ocular microbiota, which includes both commensal and pathogenic microorganisms, is constantly exposed to the ocular surface.  Material and Methods: In this study, two groups of patients were analyzed. Group A included 19 individuals who had not received intravitreal injections or undergone perioperative management. Group B, on the other hand, consisted of 22 patients who had received one, two, or more two treatments. The microbial samples collected from the ocular surface of these patients were subjected to 16S rRNA sequencing using the HiSeq 2500 platform. Further analysis of the alpha/beta diversity and clustering of operating taxonomic units (OTUs) was carried out. Results: Our results show a significant difference in beta diversity was observed between group A (15 patients without intravitreal injections or perioperative management) and group B (patients with at least one, twice, or more than twice treatment) with a P value of 0.014. It was found that both the composition and relative abundance of cells were impacted by perioperative management in the lead-up to intravitreal injection. Additionally, a greater diversity of Gram-negative bacteria was observed and the most significant groups of microbiotas were found to be phyla and genera. Conclusion: In conclusion, our study found that perioperative management has a significant impact on the ocular microbiota, altering its composition and disrupting its balance. Therefore, it is important for clinicians to carefully consider perioperative management prior to administering intravitreal injections

Prediction of T2DM Using Conjunctival Sac Microbiota, a Machine Learning Approach: Eye Microbiome

Background: Association of T2DM and OS disorders addresses a human eye metagenome drift. Despite the clarity of diabetic retinopathy, process of involvement of conjunctival sac microbiota is still ambiguous. We seek predictive value of OS microbiota using ML-based methods. Material and Methods: 16S rRNA characterization of human eye metagenome for samples of 192 patients (with mean age of 66 years and 56 % females) with different onsets of T2DM is analyzed using various metrics including abundance and diversity indices and LDA at phyla, families, and genera levels. We took advantage of variance threshold, Chi-squared significance, and LDA Effect Size (LEfSe) feature selection strategies for inclusion of predictive families and genera in the T2DM prediction model. ML models with different algorithms including RF, GB, SVM, and ANN are implemented. Generalizability and robust performance of the models are also ensured using a 5-fold cross-validation process. DeLong’s test is also used to investigate different performance of the methods. Results: Microbiome analyses revealed that eye metagenome profiles of the patients with <15 years of T2DM history show significantly higher richness and diversity. ML model performance shows ROC-AUC of ~0.8. ML model with the superior performance exhibit sensitivity and accuracy of 0.86 and 0.68, respectively, in the prediction of T2DM occurrence. Conclusion: significant correlation and co-occurrence of T2DM and eye microbiome dysbiosis is trackable and well-optimized ML-strategies can predict T2DM onsets based on the microbiome of conjunctival sac

Exploring the Microbial Changes in Meibomian Gland Dysfunction through 16S rDNA Sequencing: Eye Microbiome

Background:  The study examines bacterial community diversity in patients with Meibomian Gland Dysfunction (MGD) using 16S rDNA sequencing, compared to healthy individuals. The goal is to understand microbial changes in MGD and provide insights into potential treatments. Material and Methods: 27 16S rRNA gene sequences were obtained from the (EMBL-EBI) website, consisting of 3 sequences from healthy individuals, 7 sequences from individuals with mild Meibomian gland dysfunction, 6 sequences from individuals with moderate Meibomian gland dysfunction, and 11 sequences from individuals with severe Meibomian gland dysfunction. An algorithm utilizing machine learning was applied to identify the association with each sequence. A trained classifier was then used to create an OTU table. Results: Our results found that there were significant differences in alpha diversity among individuals with severe (MGD) and healthy individuals. Furthermore, the microbial composition was found to be similar across all groups, regardless of their MGD status. Conclusions: This study highlights the correlation between meibomian gland dysfunction (MGD) and imbalances in the bacterial microbiota on the ocular surface. The results suggest a role for Staphylococcus, Corynebacterium, and Sphingomonas in the development of MGD, with a positive correlation between MGD severity and bacterial abundance. The findings provide a basis for considering antibiotics in MGD treatment, with insights into the microbiome's role in the pathogenesis of the condition

Ocular Surface Microbiome Analysis: Exploring Dry Eye Disease: Eye microbiome

Background: The human body's microbiome having a powerful impact on many diseases, it was necessary to study the relationship between DED and the ocular microbiome, and the purpose of this study is to examine the existence of this connection. Material and Methods: Two datasets of the ocular surface microbiome in dry eye patients were used for this research, one with data of patients before and after treatment with intense pulsed light (IPL), while the other only holds information on cases suffering from a dry eye condition.  Results: The first dataset of both eyes of 20 patients with dry eye symptoms before and after IPL therapy was analyzed entirely. Bacteroidales (in 61 percent of the patients), Actinomycetales (in 60 percent of patients), Lactobacillales (in 61 percent of patients), and Erysipelotrichales (in 61 percent of patients) declined after the treatment. Still, the total difference between patient populations and treatment was not statistically proven (P value > 0.05). The second dataset contained data from 87 patients with dry eye disease, and it demonstrated that Burkholderiales, Actinomycetales, Pseudomonadales, and Clostridiales are among the most abundant bacteria in this group, in contrast to the first dataset, which was occupied by Clostridiales, Burkholderiales, Actinomycetales, Bacteroidales, Bacillales, and Lactobacillales. Conclusion: This study showed there are multiple bacterial orders that have increased or decreased after the patients received their treatment with IPL, stating a potential connection between the mentioned orders and DED. More research is necessary to indicate a solid relationship between these two