Development of a biodegradable polymer-based implant to release dual drugs for post-operative management of cataract surgery.

Cataract surgery is followed by post-operative eye drops for a duration of 4-6 weeks. The multitude of ocular barriers, coupled with the discomfort experienced by both the patient and their relatives in frequently administering eye drops, significantly undermines patient compliance, ultimately impeding the recovery of the patient. This study aimed to design and develop an ocular drug delivery system as an effort to achieve a drop-free post-operative care after cataract surgery. An implant was prepared containing a biodegradable polymer Poly-lactic-co-glycolic acid (PLGA), Dexamethasone (DEX) as an anti-inflammatory drug, and Moxifloxacin(MOX) as an antibiotic. Implant characterization and drug loading analysis were conducted. In vitro drug release profile showed that the release of the two drugs are correlated with the clinical prescription for post operative eye drops. In vivo study was conducted on New Zealand albino rabbits where one eye underwent cataract surgery, and the drug delivery implant was inserted into the capsular bag after placement of the synthetic intraocular lens (IOL). Borderline increase in the intraocular pressure (IOP) was noted in the test sample group. Slit-lamp observations revealed no significant anterior chamber reaction in all study groups. Histopathology study of the operated eye revealed no significant pathology in the test samples. This work aims at developing the intra ocular drug delivery implant which will replace the post-operative eye drops and help the patient with the post-operative hassle of eye drops.

HPLC-LED-Induced Fluorescence Analysis of Tear Fluids: An Objective Method for Primary Open Angle Glaucoma Diagnosis.

PURPOSE: The study showcased the application of the lab-assembled HPLC-LED-IF system to analyze proteins in tear fluid samples collected from individuals diagnosed with primary open-angle glaucoma (POAG). METHODS: Clinical application of the said technique was evaluated by recording chromatograms of tear fluid samples from control and POAG subjects and by analyzing the protein profile using multivariate analysis. The data analysis methods involved are principal component analysis (PCA), Match/No-Match, and artificial neural network (ANN) based binary classification for disease diagnosis. RESULTS: Mahalanobis distance and spectral residual values calculated using a standard calibration set of clinically confirmed POAG samples for the Match/No-Match test gave 86.9% sensitivity and 81.8% specificity. ANN with leaving one out procedure has given 87.1% sensitivity and 81.8% specificity. CONCLUSION: The results of the study revealed that the utilization of a 278 nm LED excitation in the HPLC system offers good sensitivity for detecting proteins at low concentrations allowing to obtain reliable protein profiles for the diagnosis of POAG.

Protein profile pattern analysis: A multifarious, in vitro diagnosis technique for universal screening.

Universal health care is attracting increased attention nowadays, because of the large increase in population all over the world, and a similar increase in life expectancy, leading to an increase in the incidence of non-communicable (various cancers, coronary diseases, neurological and old-age-related diseases) and communicable diseases/pandemics like SARS-COVID 19. This has led to an immediate need for a healthcare technology that should be cost-effective and accessible to all. A technology being considered as a possible one at present is liquid biopsy, which looks for markers in readily available samples like body fluids which can be accessed non- or minimally- invasive manner. Two approaches are being tried now towards this objective. The first involves the identification of suitable, specific markers for each condition, using established methods like various Mass Spectroscopy techniques (Surface-Enhanced Laser Desorption/Ionization Mass Spectroscopy (SELDI-MS), Matrix-Assisted Laser Desorption/Ionization (MALDI-MS), etc., immunoassays (Enzyme-Linked Immunoassay (ELISA), Proximity Extension Assays, etc.) and separation methods like 2-Dimensional Polyacrylamide Gel Electrophoresis (2-D PAGE), Sodium Dodecyl-Sulfate Polyacrylamide Gel Electrophoresis (SDS-PAGE), Capillary Electrophoresis (CE), etc. In the second approach, no attempt is made the identification of specific markers; rather an efficient separation method like High-Performance Liquid Chromatography/ Ultra-High-Performance Liquid Chromatography (HPLC/UPLC) is used to separate the protein markers, and a profile of the protein pattern is recorded, which is analysed by Artificial Intelligence (AI)/Machine Learning (MI) methods to derive characteristic patterns and use them for identifying the disease condition. The present report gives a summary of the current status of these two approaches and compares the two in the use of their suitability for universal healthcare.

Correction: Protein profile analysis of tear fluid with hyphenated HPLC-UV LED-induced fluorescence detection for the diagnosis of dry eye syndrome.

[This corrects the article DOI: 10.1039/D3RA04389D.].

Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids.

Diabetic Macular Edema (DME) is a severe ocular complication commonly found in patients with diabetes. The condition can precipitate a significant drop in VA and, in extreme cases, may result in irreversible vision loss. Optical Coherence Tomography (OCT), a technique that yields high-resolution retinal images, is often employed by clinicians to assess the extent of DME in patients. However, the manual interpretation of OCT B-scan images for DME identification and severity grading can be error-prone, with false negatives potentially resulting in serious repercussions. In this paper, we investigate an Artificial Intelligence (AI) driven system that offers an end-to-end automated model, designed to accurately determine DME severity using OCT B-Scan images. This model operates by extracting specific biomarkers such as Disorganization of Retinal Inner Layers (DRIL), Hyper Reflective Foci (HRF), and cystoids from the OCT image, which are then utilized to ascertain DME severity. The rules guiding the fuzzy logic engine are derived from contemporary research in the field of DME and its association with various biomarkers evident in the OCT image. The proposed model demonstrates high efficacy, identifying images with DRIL with 93.3% accuracy and successfully segmenting HRF and cystoids from OCT images with dice similarity coefficients of 91.30% and 95.07% respectively. This study presents a comprehensive system capable of accurately grading DME severity using OCT B-scan images, serving as a potentially invaluable tool in the clinical assessment and treatment of DME.

Protein profile analysis of tear fluid with hyphenated HPLC-UV LED-induced fluorescence detection for the diagnosis of dry eye syndrome.

Tear fluid contains organic and inorganic constituents, variations in their relative concentrations could provide valuable information and can be useful for the detection of several ophthalmological diseases. This report describes the application of the lab-assembled light-emitting diode (LED)-based high-performance liquid chromatography system for protein profiling of tear fluids to diagnose dry eye disease. Principal Component Analysis (PCA), match/no-match, and Artificial Neural Network (ANN) based binary classification of protein profile data were performed for disease diagnosis. Results from the match/no-match test of the protein profile data showed 94.4% sensitivity and 87.8% specificity. ANN with the leaving one out procedure has given 91.6% sensitivity and 93.9% specificity.

Assessment of prevalence of dry eye among medical students using ocular surface disease index questionnaire - Is COVID-19 to be really blamed?

Purpose: An undergraduate research conducted during the pre-covid times, to highlight the importance of screen time and its association with dry eye in medical students. The aim was to study the prevalence of dry eye among medical students using the ocular surface index (OSDI) questionnaire. Methods: This was a cross-sectional study. This study was conducted among medical students using an OSDI questionnaire in the pre-covid times. Based on the pilot study, the minimum sample size calculated was 245. A total of 310 medical students participated in the study. These medical students answered the OSDI questionnaire. The OSDI score was used to categorize students with dry eye as mild (13-22 points), moderate (23-32 points), and severe (33-100 points). In addition, the associations between the OSDI score and possible risk factors such as gender, contact lens/spectacle wear, laptop/mobile usage, and duration of exposure to air conditioners were also studied. Results: The analysis of the study revealed that out of 310 students, dry eye was seen in 143 (46.1%) and severe dry eyes were seen in 50 (16.1%). A high OSDI score (>13 points) was associated with the usage of a laptop/mobile for more than 6 h in 40 (52.6%) (P < 0.001). Conclusion: The prevalence of dry eye among medical students was 46.1% in the present study. Longer duration of usage of visual display units (laptop/mobile) was the only factor that showed a statistically significant association with dry eye in our study.

Association of Use of Statins with Progression of Diabetic Retinopathy at a Tertiary Care Hospital in Southern India.

Purpose: To study the role of statin therapy on diabetic retinopathy (DR) progression. Methods: This retrospective study was carried out at a tertiary care hospital in southern India. Data were collected from the medical records of patients admitted from January 2013 to December 2018. Out of 1673 patients of DR enrolled in the study, 171 met the inclusion criteria. Patients' demographic data, drug history, clinical characteristics, and laboratory investigations were recorded as per the pro forma. The patients were divided into statin users and nonusers. The results were analyzed to compare the DR progression between the two groups. Results: DR progressed in 67% of nonstatin users and 37% of statin users (P < 0.001). The use of statins decreased the risk of DR progression (P < 0.001). Center-involving macular edema was seen in 8 of 79 statin users (10%) and 16 of 92 statin nonusers (16%) based on optical coherence tomography findings during the follow-up period (P = 0.17). Conclusion: In patients with type 2 diabetes, lipid-lowering therapy with statins has the potential to retard DR progression.

Diabetic Retinopathy Is a Predictor of Progression of Diabetic Kidney Disease: A Systematic Review and Meta-Analysis.

Methods: A systematic search was conducted on PubMed, Embase, and the Google scholar for eligible studies through September 2021. The quality of selected articles was assessed using JBI checklist. Higgins and Thompson's I 2 statistic was used to see the degree of heterogeneity. Based on degree of heterogeneity, fixed or random effects model was used to estimate pooled effect using inverse variance method. Results were expressed as hazard ratios and odds ratios with 95% CIs. Results: After scrutinizing 18017 articles, data from ten relevant studies (seven prospective and three retrospective) was extracted. DR was significantly associated with DKD progression with a pooled HR of 2.42 (95% CI: 1.70-3.45) and a pooled OR of 2.62 (95% CI: 1.76-3.89). There was also a significant association between the severity of DR and risk of progression of DKD with a pooled OR of 2.13 (95% CI: 1.82-2.50) for nonproliferative DR and 2.56 (95% CI: 2.93-.33) for proliferative DR. Conclusion: Our study suggests that presence of DR is a strong predictor of risk of kidney disease progression in DKD patients. Furthermore, the risk of DKD progression increases with DR severity. Screening for retinal vascular changes could potentially help in prognostication and risk-stratification of patients with DKD.

Assessment of Role of Platelet Indices in the Occurrence of Retinopathy of Prematurity.

PURPOSE: Platelets have a major role in the regulation of angiogenesis. Platelets have proangiogenic factors like vascular endothelial growth factor, which causes neovascularization of immature retina. However, there is no conclusive evidence to show that platelet indices have a role in retinopathy of prematurity (ROP). This study is aimed at assessing the role of platelet indices in the occurrence and need for treatment of ROP. METHODS: This prospective cohort study included the screening of preterm babies (<37 weeks of gestation with birth weight <2000 g). The samples of platelet indices (mean platelet volume [MPV], platelet count [PLT], plateletcrit [PCT], and platelet distribution width [PDW]) collected within 1st week of life were obtained from the electronic medical records and correlated to ROP status. Statistical analysis was done using SPSS version 22, and the Chi-square test and odds ratio were used for analysis. RESULTS: A total of 300 preterm babies were screened, of whom, 55 (18.3%) babies had ROP changes. The association of the presence of ROP changes and platelet indices was not statistically significant (P value being MPV [0.22], PLT [0.58], PCT [0.98], and PDW [0.17]). Similarly, the requirement of treatment for ROP (Type I ROP) could not be correlated with abnormal platelet indices (odds ratio at 95% confidence interval - MPV [6 (0.44-81.44)], PLT [1.7 (0.25-11.37)], PCT [3 (0.44-20.90)], and PDW [0.32 (0.33-3.05)]). CONCLUSION: Abnormal platelet indices did not show any significant risk with the occurrence or need for treatment of ROP.

Small interfering RNAs (siRNAs) based gene silencing strategies for the treatment of glaucoma: Recent advancements and future perspectives.

RNA-interference-based mechanisms, especially the use of small interfering RNAs (siRNAs), have been under investigation for the treatment of several ailments and have shown promising results for ocular diseases including glaucoma. The eye, being a confined compartment, serves as a good target for the delivery of siRNAs. This review focuses on siRNA-based strategies for gene silencing to treat glaucoma. We have discussed the ocular structures and barriers to gene therapy (tear film, corneal, conjunctival, vitreous, and blood ocular barriers), methods of administration for ocular gene delivery (topical instillation, periocular, intracameral, intravitreal, subretinal, and suprachoroidal routes) and various viral and non-viral vectors in siRNA-based therapy for glaucoma. The components and mechanism of siRNA-based gene silencing have been mentioned briefly followed by the basic strategies and challenges faced during siRNA therapeutics development. We have emphasized different therapeutic targets for glaucoma which have been under research by scientists and the current siRNA-based drugs used in glaucoma treatment. We also mention briefly strategies for siRNA-based treatment after glaucoma surgery.

The association between diabetic retinopathy and periodontal disease.

PURPOSE: The purpose of the study was to study the association between diabetic retinopathy (DR) and periodontal disease (PD) in a South Indian cohort. METHODS: This was a cross-sectional, observational, interdisciplinary hospital-based study wherein patients with diabetes mellitus visiting the ophthalmology department of a university teaching hospital in coastal Karnataka, south India, during the study period, were screened independently for retinopathy by an ophthalmologist and PD by a periodontal surgeon. All the patients were above 18 years of age and did not have juvenile or gestational diabetes. A total of 213 patients consented to participate in the study. The data were analyzed for association using the Chi-square test. RESULTS: There was a statistically significant association between the presence of DR and PD (P = 0.02). The increasing severity of DR was associated with an increase in the components of PD including plaque index (P < 0.001) and gingival index (P < 0.001). CONCLUSION: There is a significant association between DR and PD. The awareness of this association can aid in the screening of potentially sight-threatening retinopathy in diabetics presenting to the dental clinic with PD.

A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images.

Glaucoma is a type of eye condition which may result in partial or consummate vision loss. Higher intraocular pressure is the leading cause for this condition. Screening for glaucoma and early detection can avert vision loss. Computer aided diagnosis (CAD) is an automated process with the potential to identify glaucoma early through quantitative analysis of digital fundus images. Preparing an effective model for CAD requires a large database. This study presents a CAD tool for the precise detection of glaucoma using a machine learning approach. An autoencoder is trained to determine effective and important features from fundus images. These features are used to develop classes of glaucoma for testing. The method achieved an F - measure value of 0.95 utilizing 1426 digital fundus images (589 control and 837 glaucoma). The efficacy of the system is evident, and is suggestive of its possible utility as an additional tool for verification of clinical decisions.

Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques.

BACKGROUND AND OBJECTIVE: Glaucoma is a ocular disorder which causes irreversible damage to the retinal nerve fibers. The diagnosis of glaucoma is important as it may help to slow down the progression. The available clinical methods and imaging techniques are manual and require skilled supervision. For the purpose of mass screening, an automated system is needed for glaucoma diagnosis which is fast, accurate, and helps in reducing the burden on experts. METHODS: In this work, we present a bit-plane slicing (BPS) and local binary pattern (LBP) based novel approach for glaucoma diagnosis. Firstly, our approach separates the red (R), green (G), and blue (B) channels from the input color fundus image and splits the channels into bit planes. Secondly, we extract LBP based statistical features from each of the bit planes of the individual channels. Thirdly, these features from the individual channels are fed separately to three different support vector machines (SVMs) for classification. Finally, the decisions from the individual SVMs are fused at the decision level to classify the input fundus image into normal or glaucoma class. RESULTS: Our experimental results suggest that the proposed approach is effective in discriminating normal and glaucoma cases with an accuracy of 99.30% using 10-fold cross validation. CONCLUSIONS: The developed system is ready to be tested on large and diverse databases and can assist the ophthalmologists in their daily screening to confirm their diagnosis, thereby increasing accuracy of diagnosis.

Computer-aided diagnosis of glaucoma using fundus images: A review.

BACKGROUND AND OBJECTIVES: Glaucoma is an eye condition which leads to permanent blindness when the disease progresses to an advanced stage. It occurs due to inappropriate intraocular pressure within the eye, resulting in damage to the optic nerve. Glaucoma does not exhibit any symptoms in its nascent stage and thus, it is important to diagnose early to prevent blindness. Fundus photography is widely used by ophthalmologists to assist in diagnosis of glaucoma and is cost-effective. METHODS: The morphological features of the disc that is characteristic of glaucoma are clearly seen in the fundus images. However, manual inspection of the acquired fundus images may be prone to inter-observer variation. Therefore, a computer-aided detection (CAD) system is proposed to make an accurate, reliable and fast diagnosis of glaucoma based on the optic nerve features of fundus imaging. In this paper, we reviewed existing techniques to automatically diagnose glaucoma. RESULTS: The use of CAD is very effective in the diagnosis of glaucoma and can assist the clinicians to alleviate their workload significantly. We have also discussed the advantages of employing state-of-art techniques, including deep learning (DL), when developing the automated system. The DL methods are effective in glaucoma diagnosis. CONCLUSIONS: Novel DL algorithms with big data availability are required to develop a reliable CAD system. Such techniques can be employed to diagnose other eye diseases accurately.

Diabetic retinopathy in patients with diabetic foot syndrome in South India.

Purpose: The purpose was to study the retinopathy status in diabetic patients with a risk of diabetic foot (DF) syndrome visiting a tertiary care hospital in South India. Methods: In this cross sectional study all patients with diabetes mellitus (DM) with a risk of DF syndrome, visiting a tertiary care hospital during the study period, underwent an ophthalmological evaluation for documentation of their retinopathy status. Results: One hundred and eighty-two patients diagnosed to have a risk profile for DF syndrome were included in the study. Their mean age was 59.28 years and 75.27% were males. The mean duration of Type 1 and Type 2 variants of DM was 14.9 years and 10.9 years, respectively. Of the 182 patients, 67.58% had retinopathy changes. Proliferative diabetic retinopathy (DR) constituted 17.88% of the total patients with retinopathy. An increased presence of retinopathy in patients with an increased risk grade of DF was found significant by the Chi-square test (P < 0.001). Conclusion: Our study found an increased presence of DR in a South Indian cohort with DF syndrome. The severity of retinopathy was greater in patients with higher grades of risk for DF. The establishment of an association between DR and DF syndrome will help in developing an integrated management strategy for these two debilitating consequences of diabetes.

Automated retinal health diagnosis using pyramid histogram of visual words and Fisher vector techniques.

Untreated age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma may lead to irreversible vision loss. Hence, it is essential to have regular eye screening to detect these eye diseases at an early stage and to offer treatment where appropriate. One of the simplest, non-invasive and cost-effective techniques to screen the eyes is by using fundus photo imaging. But, the manual evaluation of fundus images is tedious and challenging. Further, the diagnosis made by ophthalmologists may be subjective. Therefore, an objective and novel algorithm using the pyramid histogram of visual words (PHOW) and Fisher vectors is proposed for the classification of fundus images into their respective eye conditions (normal, AMD, DR, and glaucoma). The proposed algorithm extracts features which are represented as words. These features are built and encoded into a Fisher vector for classification using random forest classifier. This proposed algorithm is validated with both blindfold and ten-fold cross-validation techniques. An accuracy of 90.06% is achieved with the blindfold method, and highest accuracy of 96.79% is obtained with ten-fold cross-validation. The highest classification performance of our system shows the potential of deploying it in polyclinics to assist healthcare professionals in their initial diagnosis of the eye. Our developed system can reduce the workload of ophthalmologists significantly.

Iterative variational mode decomposition based automated detection of glaucoma using fundus images.

Glaucoma is one of the leading causes of permanent vision loss. It is an ocular disorder caused by increased fluid pressure within the eye. The clinical methods available for the diagnosis of glaucoma require skilled supervision. They are manual, time consuming, and out of reach of common people. Hence, there is a need for an automated glaucoma diagnosis system for mass screening. In this paper, we present a novel method for an automated diagnosis of glaucoma using digital fundus images. Variational mode decomposition (VMD) method is used in an iterative manner for image decomposition. Various features namely, Kapoor entropy, Renyi entropy, Yager entropy, and fractal dimensions are extracted from VMD components. ReliefF algorithm is used to select the discriminatory features and these features are then fed to the least squares support vector machine (LS-SVM) for classification. Our proposed method achieved classification accuracies of 95.19% and 94.79% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the ophthalmologists in confirming their manual reading of classes (glaucoma or normal) using fundus images.

A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images.

Glaucoma is an optic neuropathy defined by characteristic damage to the optic nerve and accompanying visual field deficits. Early diagnosis and treatment are critical to prevent irreversible vision loss and ultimate blindness. Current techniques for computer-aided analysis of the optic nerve and retinal nerve fiber layer (RNFL) are expensive and require keen interpretation by trained specialists. Hence, an automated system is highly desirable for a cost-effective and accurate screening for the diagnosis of glaucoma. This paper presents a new methodology and a computerized diagnostic system. Adaptive histogram equalization is used to convert color images to grayscale images followed by convolution of these images with Leung-Malik (LM), Schmid (S), and maximum response (MR4 and MR8) filter banks. The basic microstructures in typical images are called textons. The convolution process produces textons. Local configuration pattern (LCP) features are extracted from these textons. The significant features are selected using a sequential floating forward search (SFFS) method and ranked using the statistical t-test. Finally, various classifiers are used for classification of images into normal and glaucomatous classes. A high classification accuracy of 95.8% is achieved using six features obtained from the LM filter bank and the k-nearest neighbor (kNN) classifier. A glaucoma integrative index (GRI) is also formulated to obtain a reliable and effective system.

Recurrent Optic Nerve Pilocytic Astrocytoma: A Rare Case.

Pilocytic astrocytoma is a low grade glioma that affects mostly children and young adults and can occur anywhere in the central nervous system. Pilocytic astrocytoma of the optic nerve is an equally indolent subtype that is often associated with Neurofibromatosis Type I (NFI). A 40-year-old male presented with left sided axial proptosis and exposure keratopathy. MRI revealed a mass in left proximal orbit, extending posteriorly abutting the chiasma and the right optic nerve on MRI. Enucleation of the left eye along with near total excision of intracranial part of the mass was performed. Histopathology report was suggestive of pilocytic astrocytoma (WHO Grade I). Interestingly, his records showed evidence of surgery for removal of the optic nerve pilocytic astrocytoma twice (27 years and six years ago). We hereby, present an unusual case of recurrent pilocytic astrocytoma of the optic nerve in absence of NFI.