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Progress in digital health and telemedicine has brought forth instruments can enhance the accessibility and efficacy of eye care services. Current research shows how technology-enabled approaches are changing the way care is provided. Traditional diagnostic methods rely on physician expertise, resulting in high misdiagnosis rates and data inefficiency. Integrating ophthalmology with artificial intelligence (AI) promises to overhaul current diagnostic approaches, potentially making a significant clinical impact. Deep learning, an emerging facet of machine learning (ML), can uncover complex data structures without explicit rule specifications. The review centers on the revolutionary potential of AI in the identification and treatment of ocular disorders, such as diabetic retinopathy, degenerative maculopathy, retinal diseases, corneal diseases, anterior ocular region issues, and glaucoma. It explores AI-driven advancements in image analysis, pattern recognition, and ML techniques for individualized treatment plans, early diagnosis, and categorization. The difficulties with data standards, interpretability, and integration are discussed in this paper into clinical practice. It also emphasizes the potential of AI to enhance screening efficiency, reduce physician workload, and improve patient outcomes in ocular pathologies. [ABSTRACT FROM AUTHOR]

A key contemporary challenge is enhancing the value of agro-industrial byproducts. Cellulose, the most abundant renewable resource, offers significant industrial potential due to its versatile properties. Produced in its pure form by various bacteria, cellulose is increasingly utilized in microscale and nanoscale fibers for composite reinforcement. In this study, cellulose microfibers were derived from palm sprout fibers through a series of treatments like pretreatment, high-pressure hydrolysis in an autoclave, and bleaching. These fibers were then characterized using techniques such as UV spectroscopy, FTIR, SEM, and XRD. The study evaluated the antibacterial properties of the treated cellulose microfibers against both gram-positive Staphylococcus aureus (07%) and gram-negative Escherichia coli (10%), finding higher effectiveness against E.coli. Additionally, the microfibers exhibited a lower EC 50 value of 0.101 mg/mL, suggesting that although the microfiber extract is effective, the standard antioxidant is somewhat more potent. Adsorption studies revealed that the cellulose microfibers followed the Langmuir isotherm model, with a high determination coefficient of 0.998, and reached maximum dye adsorption for RB 160 within 120 min. In this adsorption study, 50 mg of dye was removed. These results indicate that the treated cellulose microfibers are a promising biosorbent for improving dye removal processes., (© 2024 John Wiley & Sons Ltd.)