Zika Virus Infection of Human Iris Pigment Epithelial Cells

During recent Zika epidemics, adults infected with Zika virus (ZIKV) have developed organ-specific inflammatory complications. The most serious Zika-associated inflammatory eye disease is uveitis, which is commonly anterior in type, affecting both eyes and responding to corticosteroid eye drops. Mechanisms of Zika-associated anterior uveitis are unknown, but ZIKV has been identified in the aqueous humor of affected individuals. The iris pigment epithelium is a target cell population in viral anterior uveitis, and it acts to maintain immune privilege within the anterior eye. Interactions between ZIKV and human iris pigment epithelial cells were investigated with infectivity assays and RNA-sequencing. Primary cell isolates were prepared from eyes of 20 cadaveric donors, and infected for 24 hours with PRVABC59 strain ZIKV or incubated uninfected as control. Cytoimmunofluorescence, RT-qPCR on total cellular RNA, and focus-forming assays of culture supernatant showed cell isolates were permissive to infection, and supported replication and release of infectious ZIKV. To explore molecular responses of cell isolates to ZIKV infection at the whole transcriptome level, RNA was sequenced on the Illumina NextSeq 500 platform, and results were aligned to the human GRCh38 genome. Multidimensional scaling showed clear separation between transcriptomes of infected and uninfected cell isolates. Differential expression analysis indicated a vigorous molecular response of the cell to ZIKV: 7,935 genes were differentially expressed between ZIKV-infected and uninfected cells (FDR < 0.05), and 99% of 613 genes that changed at least two-fold were up-regulated. Reactome and KEGG pathway and Gene Ontology enrichment analyses indicated strong activation of viral recognition and defense, in addition to biosynthesis processes. A CHAT network included 6275 molecular nodes and 24 contextual hubs in the cell response to ZIKV infection. Receptor-interacting serine/threonine kinase 1 (RIPK1) was the most significantly connected contextual hub. Correlation of gene expression with read counts assigned to the ZIKV genome identified a negative correlation between interferon signaling and viral load across isolates. This work represents the first investigation of mechanisms of Zika-associated anterior uveitis using an in vitro human cell model. The results suggest the iris pigment epithelium mounts a molecular response that limits intraocular pathology in most individuals.

Novel Technique for Retinal Nerve Cell Regeneration with Electrophysiological Functions Using Human Iris-Derived iPS Cells

Regenerative medicine in ophthalmology that uses induced pluripotent stem cells (iPS) cells has been described, but those studies used iPS cells derived from fibroblasts. Here, we generated iPS cells derived from iris cells that develop from the same inner layer of the optic cup as the retina, to regenerate retinal nerves. We first identified cells positive for p75NTR, a marker of retinal tissue stem and progenitor cells, in human iris tissue. We then reprogrammed the cultured p75NTR-positive iris tissue stem/progenitor (H-iris stem/progenitor) cells to create iris-derived iPS (H-iris iPS) cells for the first time. These cells were positive for iPS cell markers and showed pluripotency to differentiate into three germ layers. When H-iris iPS cells were pre-differentiated into neural stem/progenitor cells, not all cells became positive for neural stem/progenitor and nerve cell markers. When these cells were pre-differentiated into neural stem/progenitor cells, sorted with p75NTR, and used as a medium for differentiating into retinal nerve cells, the cells differentiated into Recoverin-positive cells with electrophysiological functions. In a different medium, H-iris iPS cells differentiated into retinal ganglion cell marker-positive cells with electrophysiological functions. This is the first demonstration of H-iris iPS cells differentiating into retinal neurons that function physiologically as neurons.

A Novel Approach for Diagnosis of Diabetes Using Iris Image Processing Technique and Evaluation Parameters

This paper presented here deals with study of identification and verification approach of Diabetes based on human iris pattern. In the pre-processing of this work, region of interest according to color (ROI) concept is used for iris localization, Dougman's rubber sheet model is used for normalization and Circular Hough Transform can be used for pupil and boundary detection. To extract features, Gabor Filter, Histogram of Oriented Gradients, five level decomposition of wavelet transforms likeHaar, db2, db4, bior 2.2, bior6.8 waveletscan be used. Binary coding scheme binaries’ the feature vector coefficients and classifier like hamming distance, Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), Neural Networks (NN), Random Forest (RF) and Linear Discriminative Analysis (LDA) with shrinkage parametercan be used for template matching. Performance parameters such as Computational time, Hamming distance variation, False Acceptance Rate (FAR), False Rejection Rate (FRR), Accuracy, and Match ratio can be calculated for the comparison purpose.

A Review of the Iris Recognition Methods Used for the Individual Authentication

The automatic iris recognition has become one of the most important techniques for authenticating the identity of individuals. The analysis of human iris is a reliable tool for the individual authentication due to the iris structure. Iris patterns constitute one of the uniqueness, permanence, and performance biometric traits. Moreover, the iris is considered as not easily tampered biometric traits. Therefore, this paper considers investigating the common automated methods of iris recognition. It surveys the development of utilizing iris images as an authentication means through the explanation of the historical improvement of the processes of the iris analysis. The contribution of this paper is to provide readers with huge information collected and discussed from more than 40 papers of iris recognition studies which have been published in a period of more than 20 years.

An Intelligent Iris Based Chronic Kidney Identification System

In recent years, the demand for alternative medical diagnostics of the human kidney or renal is growing, and some of the reasons behind this relate to its non-invasive, early, real-time, and pain-free mechanism. The chronic kidney problem is one of the major kidney problems, which require an early-stage diagnosis. Therefore, in this work, we have proposed and developed an Intelligent Iris-based Chronic Kidney Identification System (ICKIS). The ICKIS takes an image of human iris as input and on the basis of iridology a deep neural network model on a GPU-based supercomputing machine is applied. The deep neural network models are trained while using 2000 subjects that have healthy and chronic kidney problems. While testing the proposed ICKIS on 2000 separate subjects (1000 healthy and 1000 chronic kidney problems), the system achieves iris-based chronic kidney assessment with an accuracy of 96.8%. In the future, we will work to improve our AI algorithm and try data-set cleaning, so that accuracy can be increased by more efficiently learning the features.

A fast specular reflection removal based on pixels properties method

Iris recognition has been around for many years due to an extensive research on the uniqueness of human iris. It is well known that the iris is not similar to each other which means every human in the planet has their own iris pattern and cannot be shared. One of the main issues in iris recognition is iris segmentation. One element that can reduce the accuracy of iris segmentation is the presence of specular reflection. Another issue is the speed of specular reflection removal since the iris recognition system needs to process a lot of irises. In this paper, a specular reflection removal method was proposed to achieve a fast and accurate specular reflection removal. Some modifications were implemented on the existing pixels properties method. Based on the results, the proposed method achieved the fastest execution time, the highest segmentation accuracy and the highest SSIM compared to the other methods. This proves that the proposed method is fast and accurate to be implemented in the iris recognition systems.

An optimized rubber sheet model for normalization phase of IRIS recognition

Iris recognition is a promising biometric authentication approach and it is a very active topic in both research and realistic applications because the pattern of the human iris differs from person to person, even between twins. In this paper, an optimized iris normalization method for the conversion of segmented image into normalized form has been proposed. The existing methods are converting the Cartesian coordinates of the segmented image into polar coordinates. To get more accuracy, the proposed method is using an optimized rubber sheet model which converts the polar coordinates into spherical coordinates followed by localized histogram equalization. The experimental result shows the proposed method scores an encouraging performance with respect to accuracy.