Aravind Medical Research Foundation
Aravind Medical Research Foundation
Yelchuri M.L.,Aravind Medical Research Foundation
Cornea | Year: 2017
PURPOSE:: To evaluate the in vitro, extended drug reservoir function of human amniotic membrane (HAM) of different thicknesses impregnated with moxifloxacin. METHODS:: HAM buttons (12 mm) were soaked with freshly prepared 0.5% wt/vol topical moxifloxacin at different soaking time intervals: 3 hours (group I), 6 hours (group II), 12 hours (group III), 24 hours (group IV), and 48 hours (group V). They were then transferred into 1 mL of fresh simulated tear fluid (pH-7.4) and incubated at 37°C. The release kinetics of moxifloxacin was studied by analyzing the amount of drug in simulated tear fluid collected at different time intervals from each pretreated HAM for 3 weeks. In another experiment, thin and thick HAMs were selected based on weight and soaked with moxifloxacin for 24 hours, and the release kinetics was studied for 7 weeks. All samples were stored at −80°C until analysis by high-performance liquid chromatography. RESULTS:: No significant difference was observed between different soaking times and the release of moxifloxacin. The cumulative amount of moxifloxacin released from thick HAM was found to be statistically significant compared with thin HAM (P < 0.05). CONCLUSIONS:: Our in vitro data showed that the sustained release of moxifloxacin from HAM was achieved up to 7 weeks. The entrapment efficiency of moxifloxacin was significantly higher in thicker HAM than in thin HAM. Moxifloxacin-impregnated HAM application can be considered in bacterial keratitis to provide sustained drug delivery through a biological bandage system for up to a period of 7 weeks. Copyright © 2017 Wolters Kluwer Health, Inc. All rights reserved.
Duvesh R.,Aravind Medical Research Foundation |
Verma A.,Aravind Medical Research Foundation |
Venkatesh R.,Aravind Eye Hospital |
Kavitha S.,Aravind Eye Hospital |
And 4 more authors.
Investigative Ophthalmology and Visual Science | Year: 2013
Purpose. Three loci defined by single nucleotide polymorphisms (SNPs) rs11024102 in PLEKHA7, rs3753841 in COL11A1, and rs1015213 between the PCMTD1 and ST18 genes, recently have been associated with primary angle closure glaucoma (PACG). We explored the genetic association of these SNPs with subtypes of primary angle closure in a South Indian population. Methods. The study included three case definitions: primary angle closure/primary angle closure glaucoma (PAC/PACG, N = 180); primary angle closure suspect (PACS, N = 171), and a combined any-angle closure group. Controls consisted of 411 individuals from South India. Genotyping for all three SNPs was performed using the TaqMan allelic discrimination assay. Genetic association was estimated using a X2 test statistics and logistic regression. Results. Among the three studied SNPs, significant genetic association was identified for rs1015213 in the PAC/PACG (P = 0.002) and any-angle closure (P = 0.003) analyses. However, no significant genetic association was seen when in PACS subjects (P = 0.052). SNPs rs3753841 and rs11024102 showed no evidence of genetic association with angleclosure phenotypes (P > 0.05) in South Indian participants. Conclusions. In our study, rs1015213 (located in the intergenic region between PCMTD1 and ST18) was associated significantly with PAC/PACG, confirming prior reports of an association between this region and angle closure glaucoma. Further work with a larger sample size is necessary to confirm the importance of COL11A1 and PLEKHA7 in the pathogenesis of glaucoma. © 2013 The Association for Research in Vision and Ophthalmology, Inc.
PubMed | Aravind Medical Research Foundation
Type: Journal Article | Journal: Investigative ophthalmology & visual science | Year: 2016
MicroRNAs (miRNAs) are small, stable, noncoding RNA molecules with regulatory function and marked tissue specificity that posttranscriptionally regulate gene expression. However, their role in fungal keratitis remains unknown. The purpose of this study was to identify the miRNA profile and its regulatory role in fungal keratitis.Normal donor (n = 3) and fungal keratitis (n = 5) corneas were pooled separately, and small RNA deep sequencing was performed using a sequencing platform. A bioinformatics approach was applied to identify differentially-expressed miRNAs and their targets, and select miRNAs were validated by real-time quantitative PCR (qPCR). The regulatory functions of miRNAs were predicted by combining miRNA target genes and pathway analysis. The mRNA expression levels of select target genes were further analyzed by qPCR.By deep sequencing, 75 miRNAs were identified as differentially expressed with fold change greater than 2 and probability score greater than 0.9 in fungal keratitis corneas. The highly dysregulated miRNAs (miR-511-5p, miR-142-3p, miR-155-5p, and miR-451a) may regulate wound healing as they were predicted to specifically target wound inflammatory genes. Moreover, the increased expression of miR-451a in keratitis correlated with reduced expression of its target, macrophage migration inhibitory factor, suggesting possible regulatory functions.This is, to our knowledge, the first report on comprehensive human corneal miRNA expression profile in fungal keratitis. Several miRNAs with high expression in fungal keratitis point toward their potential role in regulation of pathogenesis. Further insights in understanding their role in corneal wound inflammation may help design new therapeutic strategies.
News Article | November 29, 2016
Google researchers trained an algorithm to recognize a common form of eye disease as well as many experts can. Google researchers got an eye-scanning algorithm to figure out on its own how to detect a common form of blindness, showing the potential for artificial intelligence to transform medicine remarkably soon. The algorithm can look at retinal images and detect diabetic retinopathy—which affects almost a third of diabetes patients—as well as a highly trained ophthalmologist can. It makes use of the same machine-learning technique that Google uses to label millions of Web images. Diabetic retinopathy is caused by damage to blood vessels in the eye and results in a gradual deterioration of vision. If caught early it can be treated, but a sufferer may experience no symptoms early on, making screening vital. It is diagnosed, in part, by having an expert examine images of a patient’s retina, captured with a specialized device, for signs of bleeding and fluid leakage. Some form of automated detection could make the diagnosis more efficient and reliable, and could be especially useful in regions where the required expertise is scarce. “One of the most intriguing things about this machine-learning approach is that it has potential to improve the objectivity and ultimately the accuracy and quality of medical care,” says Michael Chiang, a professor of ophthalmology and a clinician at Oregon Health & Science University’s Casey Eye Institute. AI has had mixed success in medicine in the past. Systems that use a database of knowledge to offer advice have been shown to outperform doctors in some settings, but there has been limited uptake. Still, the power of machine learning—especially a technique known as deep learning, may make AI more common in the future (see “10 Breakthrough Technologies 2013: Deep Learning”). A team at Google DeepMind, a subsidiary of Alphabet focused entirely on AI, is doing similar work, training computers to process optical coherence tomography scans for signs of macular degeneration and other eye disease in collaboration with researchers at Moorfields Eye Hospital in London (see “DeepMind’s First Medical Research Gig Will Use AI to Diagnose Eye Disease”). This retinal-image research, published Tuesday, marked the first time a paper about deep learning has appeared in the Journal of the American Medical Association, according to the journal’s editor-in-chief, Howard Bauchner. The paper’s authors, comprised of computer scientists at Google and medical researchers from the U.S. and India, developed an algorithm to analyze retinal images. But unlike existing ophthalmology software, it was not explicitly programmed to recognize features in images that might indicate the disease. It simply looked at thousands of healthy and diseased eyes, and figured out for itself how to spot the condition. The researchers created a training set of 128,000 retinal images classified by at least three ophthalmologists. After the algorithm had been trained, the researchers tested its performance on 12,000 images and found that it matched or exceeded the performance of experts in identifying the condition and grading its severity. The Google researchers collaborated with scientists at the Aravind Medical Research Foundation in India, where a clinical trial involving real patients is ongoing. This project involves patients receiving a normal consultation, but their images are also fed into the deep-learning system for comparison. Lily Peng, a researcher at Google and a medical doctor who was involved with the project, says results from this trial are not yet ready for publication. Deep learning could be applied in many different areas of medicine that rely on image analysis, such as radiology and cardiology. But one of the biggest challenges will be to provide convincing evidence that the systems are reliable. Brendan Frey, a professor at the University of Toronto and the CEO and cofounder of a company called Deep Genomics, warns that researchers will need to develop machine-learning systems that are capable of explaining how they reached a particular conclusion (see “AI’s Language Problem”). Peng, of Google, says this is something her team is already working on. “We understand that explaining will be very important,” she says.
Hornbeak D.M.,University of Pennsylvania |
Payal A.,University of Pennsylvania |
Payal A.,Johns Hopkins University |
Payal A.,Massachusetts Eye Research and Surgery Institution |
And 9 more authors.
Ophthalmology | Year: 2014
Purpose To evaluate the reliability of clinical grading of vitreous haze using a new 9-step ordinal scale versus the existing 6-step ordinal scale. Design Evaluation of diagnostic test (interobserver agreement study). Participants A total of 119 consecutive patients (204 uveitic eyes) presenting for uveitis subspecialty care on the study day at 1 of 3 large uveitis centers. Methods Five pairs of uveitis specialists clinically graded vitreous haze in the same eyes, one after the other using the same equipment, using the 6- and 9-step scales. Main Outcome Measures Agreement in vitreous haze grade between each pair of specialists was evaluated by the κ statistic (exact agreement and agreement within 1 or 2 grades). Results The scales correlated well (Spearman's ρ = 0.84). Exact agreement was modest using both the 6-step and 9-step scales: average κ = 0.46 (range, 0.28-0.81) and κ = 0.40 (range, 0.15-0.63), respectively. Within 1-grade agreement was slightly more favorable for the scale with fewer steps, but values were excellent for both scales: κ = 0.75 (range, 0.66-0.96) and κ = 0.62 (range, 0.38-0.87), respectively. Within 2-grade agreement for the 9-step scale also was excellent (κ = 0.85; range, 0.79-0.92). Two-fold more cases were potentially clinical trial eligible on the basis of the 9-step than the 6-step scale (P<0.001). Conclusions Both scales are sufficiently reproducible using clinical grading for clinical and research use with the appropriate threshold (≥2- and ≥3-step differences for the 6- and 9-step scales, respectively). The results suggest that more eyes are likely to meet eligibility criteria for trials using the 9-step scale. The 9-step scale appears to have higher reproducibility with Reading Center grading than clinical grading, suggesting that Reading Center grading may be preferable for clinical trials. © 2014 by the American Academy of Ophthalmology.
Devarajan B.,Aravind Medical Research Foundation |
Prakash L.,Aravind Medical Research Foundation |
Kannan T.R.,Aravind Medical Research Foundation |
Abraham A.A.,Aravind Medical Research Foundation |
And 3 more authors.
BMC Cancer | Year: 2015
Background: The spectrum of RB1gene mutations in Retinoblastoma (RB) patients and the necessity of multiple traditional methods for complete variant analysis make the molecular diagnosis a cumbersome, labor-intensive and time-consuming process. Here, we have used targeted next generation sequencing (NGS) approach with in-house analysis pipeline to explore its potential for the molecular diagnosis of RB. Methods: Thirty-three patients with RB and their family members were selected randomly. DNA from patient blood and/or tumor was used for RB1 gene targeted sequencing. The raw reads were obtained from Illumina Miseq. An in-house bioinformatics pipeline was developed to detect both single nucleotide variants (SNVs) and small insertions/deletions (InDels) and to distinguish between somatic and germline mutations. In addition, ExomeCNV and Cn. MOPS were used to detect copy number variations (CNVs). The pathogenic variants were identified with stringent criteria, and were further confirmed by conventional methods and cosegregation in families. Results: Using our approach, an array of pathogenic variants including SNVs, InDels and CNVs were detected in 85% of patients. Among the variants detected, 63% were germline and 37% were somatic. Interestingly, nine novel pathogenic variants (33%) were also detected in our study. Conclusions: We demonstrated for the first time that targeted NGS is an efficient approach for the identification of wide spectrum of pathogenic variants in RB patients. This study is helpful for the molecular diagnosis of RB in a comprehensive and time-efficient manner. © 2015 Devarajan et al.; licensee BioMed Central.
Sivakumar R.R.,Aravind Eye Hospital |
Prajna L.,Aravind Medical Research Foundation |
Arya L.K.,Aravind Medical Research Foundation |
Muraly P.,Aravind Eye Hospital |
And 3 more authors.
Ophthalmology | Year: 2013
Purpose: To describe the ocular features of West Nile virus (WNV) infection proven by serology and molecular diagnostic techniques. Design: Prospective case series. Participants: Fifty-two patients who presented to the uveitis clinic with ocular inflammatory signs and history of fever preceding ocular symptoms between January 2010 and January 2012 were enrolled for laboratory diagnosis. Serum samples were collected from 30 healthy controls from the same geographic area. Methods: Patients were tested for all endemic infectious diseases that can cause ocular inflammation by serology or molecular diagnostics. When patients had positive antibodies for WNV, serum/plasma samples were tested by real-time reverse transcription (RT) polymerase chain reaction (PCR) and RT loop-mediated isothermal gene amplification assays. The PCR product was subjected to nucleotide sequencing. Fundus fluorescence angiography (FFA), optical coherence tomography (OCT), and indocyanine green angiography were performed. Visual prognosis was analyzed. Main Outcome Measures: Clinical signs (retinitis, neuroretinitis, and choroiditis) and ocular complications (decrease in vision). Results: A total of 37 of 52 patients (71%) showed positive results for at least 2 laboratory tests for WNV. Fundus examination revealed discrete, superficial, white retinitis; arteritis; phlebitis; and retinal hemorrhages with or without macular star. The FFA revealed areas of retinal inflammation with indistinct borders, vascular and optic disc leakage, vessel wall staining, or capillary nonperfusion. Indocyanine green angiography confirmed choroidal inflammation in 1 of the patients who was diabetic. The OCT scan of the macula revealed inner retinal layer edema in active inflammation and retinal atrophy in late stage. At the final visit, 43% of patients had visual acuity better than 6/12. Conclusions: In addition to previously reported clinical signs, retinitis, neuroretinitis, and retinal vasculitis were seen in this population. Atrophy of the inner retinal layer was seen on OCT after resolution of inflammation. Visual prognosis was good in patients with focal retinitis and poor in patients with occlusive vasculitis. Financial Disclosure(s): The author(s) have no proprietary or commercial interest in any materials discussed in this article. © 2013 American Academy of Ophthalmology.
PubMed | Aravind Medical Research Foundation, Google, University of California at Berkeley, Harvard University and Shri Bhagwan Mahavir Vitreoretinal Services
Type: | Journal: JAMA | Year: 2016
Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation.To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs.A specific type of neural network optimized for image classification called a deep convolutional neural network was trained using a retrospective development data set of 128175 retinal images, which were graded 3 to 7 times for diabetic retinopathy, diabetic macular edema, and image gradability by a panel of 54 US licensed ophthalmologists and ophthalmology senior residents between May and December 2015. The resultant algorithm was validated in January and February 2016 using 2 separate data sets, both graded by at least 7 US board-certified ophthalmologists with high intragrader consistency.Deep learning-trained algorithm.The sensitivity and specificity of the algorithm for detecting referable diabetic retinopathy (RDR), defined as moderate and worse diabetic retinopathy, referable diabetic macular edema, or both, were generated based on the reference standard of the majority decision of the ophthalmologist panel. The algorithm was evaluated at 2 operating points selected from the development set, one selected for high specificity and another for high sensitivity.The EyePACS-1 data set consisted of 9963 images from 4997 patients (mean age, 54.4 years; 62.2% women; prevalence of RDR, 683/8878 fully gradable images [7.8%]); the Messidor-2 data set had 1748 images from 874 patients (mean age, 57.6 years; 42.6% women; prevalence of RDR, 254/1745 fully gradable images [14.6%]). For detecting RDR, the algorithm had an area under the receiver operating curve of 0.991 (95% CI, 0.988-0.993) for EyePACS-1 and 0.990 (95% CI, 0.986-0.995) for Messidor-2. Using the first operating cut point with high specificity, for EyePACS-1, the sensitivity was 90.3% (95% CI, 87.5%-92.7%) and the specificity was 98.1% (95% CI, 97.8%-98.5%). For Messidor-2, the sensitivity was 87.0% (95% CI, 81.1%-91.0%) and the specificity was 98.5% (95% CI, 97.7%-99.1%). Using a second operating point with high sensitivity in the development set, for EyePACS-1 the sensitivity was 97.5% and specificity was 93.4% and for Messidor-2 the sensitivity was 96.1% and specificity was 93.9%.In this evaluation of retinal fundus photographs from adults with diabetes, an algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy. Further research is necessary to determine the feasibility of applying this algorithm in the clinical setting and to determine whether use of the algorithm could lead to improved care and outcomes compared with current ophthalmologic assessment.
Balaji K.,Alagappa University |
Thenmozhi R.,Alagappa University |
Prajna L.,Aravind Medical Research Foundation |
Dhananjeyan G.,Alagappa University |
Pandian S.K.,Alagappa University
Infection, Genetics and Evolution | Year: 2013
Group A Streptococcus (Streptococcus pyogenes) is responsible for a wide array of infections and incidence is high in developing countries like India. Although distribution of emm types of S. pyogenes in India has been described, its association with the virulence genes and ocular isolates is less concentrated. In the present study emm type surveillance as well as its association with toxin gene profile was analyzed. Ocular infected cases such as lacrimal abscess, corneal ulcers, mucocoele showed the presence of 20 S. pyogenes isolates. For noninvasive isolates, we screened 370 pharyngitis cases and 400 asymptomatic school children and recovered 33 pharyngitis and 14 carrier isolates respectively. 14 Emm type distributions were observed in ocular isolates, 11 emm types each in pharyngitis and asymptomatic carrier isolates. The two dominant emm types, emm49 and emm63 were accounted for 33% of the total S. pyogenes isolates. Among ocular isolates, slo, smeZ, speB and speG were found in >50% of isolates, in pharyngitis smeZ (48%), speB (45%) and speG (42%) genes were found to be prevalent. Alarmingly, carrier isolates showed more prevalence to virulence genes than the ocular and pharyngitis isolates with speF (79%), speB, speG (64%), slo and sil (64%). Among the three groups, pharyngitis isolates harbored more prtF1 (33%) and prtF2 (94%) than the asymptomatic carriers (28% and 71%) and the ocular isolates (45% and 40%). 450 bp Size band in prtF1 and 350 bp size band in prtF2 showed dominance. Among the three groups tested, the distribution of ermB and mefA was high in pharyngitis isolates (30%) where 10 isolates showed the presence of both genes. None of the isolates showed the presence of ermA and tetO genes. Dendrogram generated based on the virulence and antibiotic resistance gene profiles revealed that except one cluster, all other clusters showed some correlation with ocular, pharyngitis and asymptomatic carrier isolates, irrespective of their emm types. © 2013 Elsevier B.V.
Boomiraj H.,Aravind Medical Research Foundation |
Mohankumar V.,Aravind Medical Research Foundation |
Lalitha P.,Aravind Medical Research Foundation |
Devarajan B.,Aravind Medical Research Foundation
Investigative Ophthalmology and Visual Science | Year: 2015
PURPOSE. MicroRNAs (miRNAs) are small, stable, noncoding RNA molecules with regulatory function and marked tissue specificity that posttranscriptionally regulate gene expression. However, their role in fungal keratitis remains unknown. The purpose of this study was to identify the miRNA profile and its regulatory role in fungal keratitis. METHODS. Normal donor (n = 3) and fungal keratitis (n = 5) corneas were pooled separately, and small RNA deep sequencing was performed using a sequencing platform. A bioinformatics approach was applied to identify differentially-expressed miRNAs and their targets, and select miRNAs were validated by real-time quantitative PCR (qPCR). The regulatory functions of miRNAs were predicted by combining miRNA target genes and pathway analysis. The mRNA expression levels of select target genes were further analyzed by qPCR. RESULTS. By deep sequencing, 75 miRNAs were identified as differentially expressed with fold change greater than 2 and probability score greater than 0.9 in fungal keratitis corneas. The highly dysregulated miRNAs (miR-511-5p, miR-142-3p, miR-155-5p, and miR-451a) may regulate wound healing as they were predicted to specifically target wound inflammatory genes. Moreover, the increased expression of miR-451a in keratitis correlated with reduced expression of its target, macrophage migration inhibitory factor, suggesting possible regulatory functions. CONCLUSIONS. This is, to our knowledge, the first report on comprehensive human corneal miRNA expression profile in fungal keratitis. Several miRNAs with high expression in fungal keratitis point toward their potential role in regulation of pathogenesis. Further insights in understanding their role in corneal wound inflammation may help design new therapeutic strategies. © 2015 The Association for Research in Vision and Ophthalmology, Inc.