Applications In Ophthalmology Research Articles

Molecular and cellular imaging of the eye.

The application of molecular and cellular imaging in ophthalmology has numerous benefits. It can enable the early detection and diagnosis of ocular diseases, facilitating timely intervention and improved patient outcomes. Molecular imaging techniques can help identify disease biomarkers, monitor disease progression, and evaluate treatment responses. Furthermore, these techniques allow researchers to gain insights into the pathogenesis of ocular diseases and develop novel therapeutic strategies. Molecular and cellular imaging can also allow basic research to elucidate the normal physiological processes occurring within the eye, such as cell signaling, tissue remodeling, and immune responses. By providing detailed visualization at the molecular and cellular level, these imaging techniques contribute to a comprehensive understanding of ocular biology. Current clinically available imaging often relies on confocal microscopy, multi-photon microscopy, PET (positron emission tomography) or SPECT (single-photon emission computed tomography) techniques, optical coherence tomography (OCT), and fluorescence imaging. Preclinical research focuses on the identification of novel molecular targets for various diseases. The aim is to discover specific biomarkers or molecular pathways associated with diseases, allowing for targeted imaging and precise disease characterization. In parallel, efforts are being made to develop sophisticated and multifunctional contrast agents that can selectively bind to these identified molecular targets. These contrast agents can enhance the imaging signal and improve the sensitivity and specificity of molecular imaging by carrying various imaging labels, including radionuclides for PET or SPECT, fluorescent dyes for optical imaging, or nanoparticles for multimodal imaging. Furthermore, advancements in technology and instrumentation are being pursued to enable multimodality molecular imaging. Integrating different imaging modalities, such as PET/MRI (magnetic resonance imaging) or PET/CT (computed tomography), allows for the complementary strengths of each modality to be combined, providing comprehensive molecular and anatomical information in a single examination. Recently, photoacoustic microscopy (PAM) has been explored as a novel imaging technology for visualization of different retinal diseases. PAM is a non-invasive, non-ionizing radiation, and hybrid imaging modality that combines the optical excitation of contrast agents with ultrasound detection. It offers a unique approach to imaging by providing both anatomical and functional information. Its ability to utilize molecularly targeted contrast agents holds great promise for molecular imaging applications in ophthalmology. In this review, we will summarize the application of multimodality molecular imaging for tracking chorioretinal angiogenesis along with the migration of stem cells after subretinal transplantation in vivo.

Biological and Adhesive Properties of an Autologous Protein-Based Fibrin Sealant for Ophthalmological Applications.

The aim of this study was to evaluate the biological and adhesive properties of a new autologous sealant based on plasma rich in growth factors (PRGF), named E-Sealant. Conventional PRGF and a commercial fibrin sealant (Tisseel) were included as controls. The hematological and protein content of E-Sealant was determined. Its bioactivity and biocompatibility were tested for human keratocytes (HKs). To evaluate its adhesion and regenerative capacity, E-Sealant was used on an animal model of conjunctival autograft surgery and compared to Tisseel. E-Sealant presented a high growth factor content with levels similar to those of conventional PRGF. E-Sealant induced proliferative and migratory activity on HK cells equivalent to PRGF. Although autologous membranes induced the proliferation of HKs, cells cultured over Tisseel did not adhere nor proliferate. HK cells showed increased number and flattened morphology over PRGF and E-Sealant compared to scarce and round-shape cells detected in Tisseel. Conjunctival autograft glued with E-Sealant adhered successfully, whereas Tisseel application formed irregular clots. During follow-up, both adhesives showed good integration and no dehiscence. However, Tisseel-treated samples presented slightly increased hemorrhage and inflammation. In contrast to Tisseel, E-Sealant-treated autografts presented a continuous layer of non-keratinized stratified squamous epithelium. Inflammatory infiltrates were minimal in E-Sealant-treated conjunctiva, whereas the Tisseel group showed noticeable immune reactions. Unlike Tisseel-treated grafts, E-Sealant presented low immunoreactivity for smooth muscle actin (SMA), suggesting decreased fibrotic tissue formation. E-Sealant presents optimal biological and adhesive properties suitable for use as an ophthalmic glue, with regenerative purposes superior to commercial fibrin sealants. Our study analyzed the characterization and biological activity of a new autologous fibrin sealant in ocular surface cells and in an animal model in which the adhesive and regenerative properties of the fibrin sealant were evaluated.

Lesion Detection in Optical Coherence Tomography with Transformer-Enhanced Detector.

Optical coherence tomography (OCT) is an emerging imaging tool in healthcare with common applications in ophthalmology for the detection of retinal diseases and in dentistry for the early detection of tooth decay. Speckle noise is ubiquitous in OCT images, which can hinder diagnosis by clinicians. In this paper, a region-based, deep learning framework for the detection of anomalies is proposed for OCT-acquired images. The core of the framework is Transformer-Enhanced Detection (TED), which includes attention gates (AGs) to ensure focus is placed on the foreground while identifying and removing noise artifacts as anomalies. TED was designed to detect the different types of anomalies commonly present in OCT images for diagnostic purposes and thus aid clinical interpretation. Extensive quantitative evaluations were performed to measure the performance of TED against current, widely known, deep learning detection algorithms. Three different datasets were tested: two dental and one CT (hosting scans of lung nodules, livers, etc.). The results showed that the approach verifiably detected tooth decay and numerous lesions across two modalities, achieving superior performance compared to several well-known algorithms. The proposed method improved the accuracy of detection by 16-22% and the Intersection over Union (IOU) by 10% for both dentistry datasets. For the CT dataset, the performance metrics were similarly improved by 9% and 20%, respectively.

Novel nanomicelle butenafine formulation for ocular drug delivery against fungal keratitis: In Vitro and In Vivo study

Fungal keratitis (FK) is a serious infectious corneal disease that leads to blindness. Butenafine (BTF) is an allylamine drug with high antifungal activity, but its poor water solubility and low bioavailability limit its clinical application in ophthalmology. To increase its aqueous solubility and corneal permeability, butenafine was encapsulated in d-ɑ-tocopheryl polyethylene glycol succinate (TPGS) polymeric nanomicelles to improve the bioavailability of the drug for the treatment of FK. Butenafine was successfully fabricated into nanomicelles with a high EE of 96.34 ± 1.65 % and DL of 6.71 ± 0.099 %. The BTF-NM showed an average particle size of 13.12 ± 0.24 nm, a zeta potential of -0.56 ± 0.44 mV and a narrow PDI of 0.12 ± 0.02 with a nearly spherical shape. The characterization results of FTIR, XRD and DSC indicated that BTF was encapsulated in the TPGS nanomicelles. The BTF-NM formulation also showed high storage stability, and the in vitro drug release study showed typical biphasic-release characteristics. In addition, the BTF-NM formulation displayed good cellular tolerance and excellent ocular tolerance in rabbits. Significantly elevated in vitro antifungal activity was also observed in the BTF-NM formulation, and remarkable improvements regarding in vivo corneal permeation were observed compared with the BTF suspension formulation. Finally, the in vivo antifungal activity studies indicated that the BTF-NM formulation had a good therapeutic effect on FK and had similar efficacy to that of commercial natamycin suspension eye drops. These results suggest that the BTF-NM ophthalmic formulation could be a promising ocular drug delivery system for the treatment of FK.

Artificial Intelligence in Ophthalmic Surgery: Current Applications and Expectations.

Artificial Intelligence (AI) has found rapidly growing applications in ophthalmology, achieving robust recognition and classification in most kind of ocular diseases. Ophthalmic surgery is one of the most delicate microsurgery, requiring high fineness and stability of surgeons. The massive demand of the AI assist ophthalmic surgery will constitute an important factor in boosting accelerate precision medicine. In clinical practice, it is instrumental to update and review the considerable evidence of the current AI technologies utilized in the investigation of ophthalmic surgery involved in both the progression and innovation of precision medicine. Bibliographic databases including PubMed and Google Scholar were searched using keywords such as "ophthalmic surgery", "surgical selection", "candidate screening", and "robot-assisted surgery" to find articles about AI technology published from 2018 to 2023. In addition to the Editorials and letters to the editor, all types of approaches are considered. In this paper, we will provide an up-to-date review of artificial intelligence in eye surgery, with a specific focus on its application to candidate screening, surgery selection, postoperative prediction, and real-time intraoperative guidance.

Exploring the Potential of Nanoporous Materials for Advancing Ophthalmic Treatments.

The landscape of ophthalmology is undergoing significant transformations, driven by technological advancements and innovations in materials science. One of the advancements in this evolution is the application of nanoporous materials, endowed with unique physicochemical properties ideal for a variety of ophthalmological applications. Characterized by their high surface area, tunable porosity, and functional versatility, these materials have the potential to improve drug delivery systems and ocular devices. This review, anchored by a comprehensive literature focusing on studies published within the last five years, examines the applications of nanoporous materials in ocular drug delivery systems (DDS), contact lenses, and intraocular lenses. By consolidating the most current research, this review aims to serve as a resource for clinicians, researchers, and material scientists engaged in the rapidly evolving field of ophthalmology.

Three-dimensional bioprinting in ophthalmic care.

Three-dimensional (3D) bioprinting is widely used in ophthalmic clinic, including in diagnosis, surgery, prosthetics, medications, drug development and delivery, and medical education. Articles published in 2011-2022 into bioinks, printing technologies, and bioprinting applications in ophthalmology were reviewed and the strengths and limitations of bioprinting in ophthalmology highlighted. The review highlighted the trade-offs of printing technologies and bioinks in respect to, among others, material type cost, throughput, gelation technique, cell density, cell viability, resolution, and printing speed. There is already widespread ophthalmological application of bioprinting outside clinical settings, including in educational modelling, retinal imaging/visualization techniques and drug design/testing. In clinical settings, bioprinting has already found application in pre-operatory planning. Even so, the findings showed that even with its immense promise, actual translation to clinical applications remains distant, but relatively closer for the corneal (except stromal) tissues, epithelium, endothelium, and conjunctiva, than it was for the retina. This review similarly reflected on the critical on the technical, practical, ethical, and cost barrier to rapid progress of bioprinting in ophthalmology, including accessibility to the most sophisticated bioprinting technologies, choice, and suitability of bioinks, tissue viability and storage conditions. The extant research is encouraging, but more work is clearly required for the push towards clinical translation of research.

Роботизированные инструменты и обучаемые алгоритмы в офтальмологии

In recent years, robotic tools and machine learning algorithms have become increasingly popular in ophthalmology. These technologies reduce the risk of errors during surgical procedures and improve treatment outcomes, as well as enable automation. one of the most common robotic systems in ophthalmology is the Femto-laSIK system, which is used to correct vision by changing the shape of the cornea. Machine learning algorithms have also found wide application in ophthalmology. they are used to analyze medical images, such as photographs of the eye or retina scans. These algorithms automatically detect and classify various eye diseases, such as glaucoma, diabetic retinopathy, and macular degeneration. This helps doctors diagnose diseases more quickly and accurately, and prescribe appropriate treatment. One of the most interesting examples of using machine learning algorithms in ophthalmology is the Eyeart system, which is used for automatic diagnosis of diabetic retinopathy. This article describes approaches and developments that allow for the diagnosis and treatment of glaucoma, tools for automating cataract treatment using machine learning algorithms and a robotic manipulator. It also describes a digital microscope that enables the use of these technologies in the operating process. Overall, robotic tools and machine learning algorithms have become an integral part of ophthalmology. they improve treatment outcomes and reduce the risk of errors. the future of ophthalmology is linked to the development of these technologies, which will become increasingly accurate and intelligent. Keywords: robotic tools, machine learning, ophthalmology

Correlation Analysis of Surface and Physical Properties of Ophthalmic Lenses Containing Nanoparticles.

Since contact lenses directly contact the cornea, the surface roughness of the lens may cause various side effects. In addition, gold nanoparticles can realize a variety of colors and characteristics depending on their shape and size. In this study, the surface roughness of tinted lenses containing gold nanoparticles of various sizes was analyzed using atomic force microscopy (AFM) at aspect ratio(surface to volume ratio) ranging from 1:1 to 1:10. The characteristics of the lenses were then confirmed. As a result, tinted lenses with different colors depending on the size of the gold nanoparticles were manufactured. The surface roughness of the lens decreased with increasing size of the gold nanoparticles. However, at aspect ratio of 1:10, increase in surface roughness was observed. In addition, it was confirmed that the wettability and antibacterial properties of the lens had the same effect according to the average surface roughness value. Therefore, it was confirmed that the addition of gold nanoparticles reduced the surface roughness of the lens, which had a great effect on properties such as wettability and antimicrobial properties of the lens. The produced copolymer controls the surface roughness of the lens, and thus it is judged that it can be used as a material for various ophthalmology applications.

Guidelines on clinical research evaluation of artificial intelligence in ophthalmology (2023).

With the upsurge of artificial intelligence (AI) technology in the medical field, its application in ophthalmology has become a cutting-edge research field. Notably, machine learning techniques have shown remarkable achievements in diagnosing, intervening, and predicting ophthalmic diseases. To meet the requirements of clinical research and fit the actual progress of clinical diagnosis and treatment of ophthalmic AI, the Ophthalmic Imaging and Intelligent Medicine Branch and the Intelligent Medicine Committee of Chinese Medicine Education Association organized experts to integrate recent evaluation reports of clinical AI research at home and abroad and formed a guideline on clinical research evaluation of AI in ophthalmology after several rounds of discussion and modification. The main content includes the background and method of developing this guideline, an introduction to international guidelines on the clinical research evaluation of AI, and the evaluation methods of clinical ophthalmic AI models. This guideline introduces general evaluation methods of clinical ophthalmic AI research, evaluation methods of clinical ophthalmic AI models, and commonly-used indices and formulae for clinical ophthalmic AI model evaluation in detail, and amply elaborates the evaluation methods of clinical ophthalmic AI trials. This guideline aims to provide guidance and norms for clinical researchers of ophthalmic AI, promote the development of regularization and standardization, and further improve the overall level of clinical ophthalmic AI research evaluations.

Bessel Beams in Ophthalmology: A Review.

The achievable resolution of a conventional imaging system is inevitably limited due to diffraction. Dealing with precise imaging in scattering media, such as in the case of biomedical imaging, is even more difficult owing to the weak signal-to-noise ratios. Recent developments in non-diffractive beams such as Bessel beams, Airy beams, vortex beams, and Mathieu beams have paved the way to tackle some of these challenges. This review specifically focuses on non-diffractive Bessel beams for ophthalmological applications. The theoretical foundation of the non-diffractive Bessel beam is discussed first followed by a review of various ophthalmological applications utilizing Bessel beams. The advantages and disadvantages of these techniques in comparison to those of existing state-of-the-art ophthalmological systems are discussed. The review concludes with an overview of the current developments and the future perspectives of non-diffractive beams in ophthalmology.

MBT: Model-Based Transformer for retinal optical coherence tomography image and video multi-classification

Background and objectiveThe detection of retinal diseases using optical coherence tomography (OCT) images and videos is a concrete example of a data classification problem. In recent years, Transformer architectures have been successfully applied to solve a variety of real-world classification problems. Although they have shown impressive discriminative abilities compared to other state-of-the-art models, improving their performance is essential, especially in healthcare-related problems. MethodsThis paper presents an effective technique named model-based transformer (MBT). It is based on popular pre-trained transformer models, particularly, vision transformer, swin transformer for OCT image classification, and multiscale vision transformer for OCT video classification. The proposed approach is designed to represent OCT data by taking advantage of an approximate sparse representation technique. Then, it estimates the optimal features, and performs data classification. ResultsThe experiments are carried out using three real-world retinal datasets. The experimental results on OCT image and OCT video datasets show that the proposed method outperforms existing state-of-the-art deep learning approaches in terms of classification accuracy, precision, recall, and f1-score, kappa, AUC-ROC, and AUC-PR. It can also boost the performance of existing transformer models, including Vision transformer and Swin transformer for OCT image classification, and Multiscale Vision Transformers for OCT video classification. ConclusionsThis work presents an approach for the automated detection of retinal diseases. Although deep neural networks have proven great potential in ophthalmology applications, our findings demonstrate for the first time a new way to identify retinal pathologies using OCT videos instead of images. Moreover, our proposal can help researchers enhance the discriminative capacity of a variety of powerful deep learning models presented in published papers. This can be valuable for future directions in medical research and clinical practice.

Collagen-Based Hydrogels for the Eye: A Comprehensive Review.

Collagen-based hydrogels have emerged as a highly promising platform for diverse applications in ophthalmology, spanning from drug delivery systems to biomedical interventions. This review explores the diverse sources of collagen, which give rise to different types of collagen protein. The critical isolation and purification steps are discussed, emphasizing their pivotal role in preparing collagen for biomedical use. To ensure collagen quality and purity, and the suitability of collagen for targeted applications, a comprehensive characterization and quality control are essential, encompassing assessments of its physical, chemical, and biological properties. Also, various cross-linking collagen methods have been examined for providing insight into this crucial process. This comprehensive review delves into every facet of collagen and explores the wide-ranging applications of collagen-based hydrogels, with a particular emphasis on their use in drug delivery systems and their potential in diverse biomedical interventions. By consolidating current knowledge and advancements in the field, this review aims to provide a detailed overview of the utilization of engineered collagen-based hydrogels in ocular therapeutics.

Durable 3D murine ex vivo retina glaucoma models for optical coherence tomography.

Durable and standardized phantoms with optical properties similar to native healthy and disease-like biological tissues are essential tools for the development, performance testing, calibration and comparison of label-free high-resolution optical coherence tomography (HR-OCT) systems. Available phantoms are based on artificial materials and reflect thus only partially ocular properties. To address this limitation, we have performed investigations on the establishment of durable tissue phantoms from ex vivo mouse retina for enhanced reproduction of in vivo structure and complexity. In a proof-of-concept study, we explored the establishment of durable 3D models from dissected mouse eyes that reproduce the properties of normal retina structures and tissue with glaucoma-like layer thickness alterations. We explored different sectioning and preparation procedures for embedding normal and N-methyl-D-aspartate (NMDA)-treated mouse retina in transparent gel matrices and epoxy resins, to generate durable three-dimensional tissue models. Sample quality and reproducibility were quantified by thickness determination of the generated layered structures utilizing computer-assisted segmentation of OCT B-scans that were acquired with a commercial HR-OCT system at a central wavelength of 905 nm and analyzed with custom build software. Our results show that the generated 3D models feature thin biological layers close to current OCT resolution limits and glaucoma-like tissue alterations that are suitable for reliable HR-OCT performance characterization. The comparison of data from resin-embedded tissue with native murine retina in gels demonstrates that by utilization of appropriate preparation protocols, highly stable samples with layered structures equivalent to native tissues can be fabricated. The experimental data demonstrate our concept as a promising approach toward the fabrication of durable biological 3D models suitable for high-resolution OCT system performance characterization supporting the development of optimized instruments for ophthalmology applications.

Adherence of randomised controlled trials using artificial intelligence in ophthalmology to CONSORT-AI guidelines: a systematic review and critical appraisal

PurposeMany efforts have been made to explore the potential of deep learning and artificial intelligence (AI) in disciplines such as medicine, including ophthalmology. This systematic review aims to evaluate the...

Dexamethasone-Loaded Pseudo-Protein Nanoparticles for Ocular Drug Delivery: Evaluation of Drug Encapsulation Efficiency and Drug Release

Ophthalmic drug delivery for treating various eye diseases still remains a challenge in ophthalmology. One perspective way of overcoming this problem is to use nanoscale biodegradable drug carriers that are able to safely deliver pharmaceuticals directly to the locus of disease and maintain a therapeutic concentration of drug for a long time. The goal of the present study was the preparation of drug- (dexamethasone-, DEX-) loaded pseudo-protein nanoparticles (NPs) and investigation of drug encapsulation efficiency and drug release kinetics. DEX-loaded pseudo-protein NPs (DEX-NPs) were successfully prepared by the nanoprecipitation method. DEX-NPs were characterized by size (average diameter, AD), size distribution (polydispersity index, PDI), and surface charge (zeta-potential, ZP) using the dynamic light scattering technique. DEX encapsulation characteristics were determined using the UV-spectrophotometric method, and kinetics of DEX release from DEX-NPs was studied according to the dialysis method in PBS at 37°C. The obtained results showed that size of DEX-NPs varies within 143.6–164.1 nm depending on DEX content during the preparation. DEX incorporation characteristics were determined—encapsulation efficiency (EE) and actual drug loading (DL) were high enough and reached 55.1 and 10.2%, respectively. The kinetics of DEX release from DEX-NPs showed a typical biphasic release pattern—an initial rapid (burst) release and further much more continuous slow release. Based on the obtained data, we can conclude that the elaborated DEX-NPs have potential for the application in ophthalmology as ocular drug delivery nanocarriers.

Advances in artificial intelligence models and algorithms in the field of optometry.

The rapid development of computer science over the past few decades has led to unprecedented progress in the field of artificial intelligence (AI). Its wide application in ophthalmology, especially image processing and data analysis, is particularly extensive and its performance excellent. In recent years, AI has been increasingly applied in optometry with remarkable results. This review is a summary of the application progress of different AI models and algorithms used in optometry (for problems such as myopia, strabismus, amblyopia, keratoconus, and intraocular lens) and includes a discussion of the limitations and challenges associated with its application in this field.

A new convolution neural network model “KR-NET” for retinal fundus glaucoma classification

In recent years, deep learning is an emerging trend with potential applications in ophthalmology. Glaucoma is one ophthalmic disease where early detection is required to avoid vision loss. In the context of deep learning, convolution neural networks (CNN) are preferred for glaucoma classification because they can extract the highly discriminate features effectively from raw pixel intensities. In our approach, the region-of-interest (ROI) can be segmented from the fundus images using variable mode decomposition (VMD) by splitting the coefficients into 5 recurrence decays and upgrading the normal recurrence ranges. This approach will improve the possibility of identifying even poorly differentiated exudates. Later, a 26-layer CNN which involves six convolution layers, four pooling layers, and one fully connected layer that is designed and trained to extract distinctive features from the selective ROI and classified using the softmax layer. Data augmentation technique is employed in our model to enhance the size of dataset and the model is validated using 10-fold cross validation (CV) and 70:30 split ratio approach, and evaluated the performance metrics for different values of batch size, and initial learning rate. The proposed model is verified on a set of balanced and unbalanced public datasets (Drishti-GS1, RIM-ONE and ACRIMA) and the significant accuracy values of the results can be obtained as 90.32% for Drishti-GS1, 90.51% for RIM-ONE, and 96.70% for ACRIMA datasets. To verify the robustness of the model, the proposed model is also validated on two other datasets: (i) a private dataset, and (ii) a huge challenge dataset Refuge. The proposed model given satisfactory results when implemented on these two datasets and the significant accuracy values of the results can be obtained as 88.89% for the private dataset, and 89.17% for Refuge datasetThe obtained results and evaluated performance metrics show that our proposed model classifies the retinal fundus images effectively.

Artificial Intelligence for Anterior Segment Diseases: A Review of Potential Developments and Clinical Applications.

Artificial intelligence (AI) technology is promising in the field of healthcare. With the developments of big data and image-based analysis, AI shows potential value in ophthalmology applications. Recently, machine learning and deep learning algorithms have made significant progress. Emerging evidence has demonstrated the capability of AI in the diagnosis and management of anterior segment diseases. In this review, we provide an overview of AI applications and potential future applications in anterior segment diseases, focusing on cornea, refractive surgery, cataract, anterior chamber angle detection, and refractive error prediction.

Functional Peptide-Loaded Gelatin Nanoparticles as Eyedrops for Cornea Neovascularization Treatment.

Corneal neovascularization (NV) is a process of abnormal vessel growth into the transparent cornea from the limbus and can disturb the light passing through the cornea, resulting in vision loss or even blindness. The use of nanomedicine as an effective therapeutic formulation in ophthalmology has led to higher drug bioavailability and a slow drug release rate. In this research, we designed and explored the feasibility of a new nanomedicine, gp91 ds-tat (gp91) peptide-encapsulated gelatin nanoparticles (GNP-gp91), for inhibiting corneal angiogenesis. GNP-gp91 were prepared by a two-step desolvation method. The characterization and cytocompatibility of GNP-gp91 were analyzed. The inhibition effect of GNP-gp91 on HUVEC cell migration and tube formation was observed by an inverted microscope. The drug retention test in mouse cornea was observed by in vivo imaging system, fluorescence microscope, and DAPI/TAMRA staining. Finally, the therapeutic efficacy and evaluation of neovascularization-related factors were conducted through the in vivo corneal NV mice model via topical delivery. The prepared GNP-gp91 had a nano-scale diameter (550.6 nm) with positive charge (21.7 mV) slow-release behavior (25%, 240hr). In vitro test revealed that GNP-gp91 enhanced the inhibition of cell migration and tube formation capacity via higher internalization of HUVEC. Topical administration (eyedrops) of the GNP-gp91 significantly prolongs the retention time (46%, 20 min) in the mouse cornea. In chemically burned corneal neovascularization models, corneal vessel area with a significant reduction in GNP-gp91 group (7.89%) was revealed when compared with PBS (33.99%) and gp91 (19.67%) treated groups via every two days dosing. Moreover, GNP-gp91 significantly reduced the concentration of Nox2, VEGF and MMP9 in NV's cornea. The nanomedicine, GNP-gp91, was successfully synthesized for ophthalmological application. These data suggest that GNP-gp91 contained eyedrops that not only have a longer retention time on the cornea but also can treat mice corneal NV effectively delivered in a low dosing frequency, GNP-gp91 eyedrops provides an alternative strategy for clinical ocular disease treatment in the culture.