The Preferred Retinal Locus in Macular Degeneration: Relating Structure and Function

Purpose: Central field loss (CFL) that ensues from macular degeneration can impact many activities of daily living, including reading, in both younger (as in Stargardt disease, STGD) and older (age-related macular degeneration, AMD) subjects. Subjects with CFL typically choose a non-central retinal location, called the preferred retinal locus (PRL) for fixation. This dissertation aims to understand and relate functional and structural changes within the PRL. Methods: Preliminary studies determined the effectiveness of the MP-1 microperimeter (a) to compensate for excessively unstable fixational eye movements (FEMs), such as occur in subjects with CFL, and (b) to accurately register the retinal test locations on baseline and subsequent automated follow-up testing. Subsequently, the following functional measures were obtained for 29 subjects with bilateral CFL: (a) reading performance using hand-held MNRead charts and LCD-displayed MP1 Read charts, (b) contrast-detection thresholds using the Freiburg acuity test, (c) fixation stability on 3-letter words, measured as bivariate contour ellipse areas (BCEAs) with the MP-1, (d) sensitivity in the central visual field determined with a standard 10-2 threshold grid, and (e) fine-grained sensitivity within the word-fixation PRL for supra-threshold 13x13 arc min spots. Spectral-domain optical coherence tomography (SD-OCT) was used to assess structural characteristics of the PRL, specifically, thickness ratios for the retinal pigment epithelium - Bruch’s membrane complex (RPE-BM), the photoreceptor and outer nuclear layer (PL), and the total retina layers (TRL) between PRL locations where test spots were and were not consistently detected. Finally, 8 younger (50 years) naive subjects with normal vision read high and low contrast sentences presented one word at a time at the fovea and 5 and 10° in the inferior field. Random 13x13 arc min blocks corresponding to 0-78% of the text area were set to the background luminance to simulate retinal micro-scotomas (MSs) and a staircase algorithm estimated the threshold reading rate. Results: The MP-1 compensated ~90% of the experimentally induced increase in FEMs and the average registration error was ~8 arc min. The maximum reading speed of subjects with CFL correlated poorly with contrast thresholds, BCEA, PRL eccentricity, median sensitivity around the PRL and all retinal thickness ratios. Twenty-two of 29 subjects with CFL (AMD: 8/10 subjects; STGD: 10/12 subjects) exhibited one or more MSs, defined as local regions of insensitivity for supra-threshold targets within the PRL. Although the average percentage of MSs was similar in the cohorts with AMD (25.4%) and STGD (20.3%), reading speed was significantly faster in STGD than AMD subjects. Average thickness ratios for RPE-BM, PL and TRL were 0.97, 0.84 and 0.86 respectively in the AMD cohort and 0.97, 0.77 and 0.89 respectively in the STGD cohort. Only TRL in subjects with AMD differed significantly from 1. In normally-sighted subjects, log reading rate decreased significantly with decreasing contrast and increasing age, eccentricity, and density of element-deletions. For a given eccentricity and contrast, a higher density of element-deletions maximally affected the older subjects. Conclusion: The compensation of the MP-1 for excessive FEMs and the registration between retinal test locations during baseline and follow-up testing are sufficient to assess functional changes within local retinal regions in subjects with CFL. MSs exist within the PRL of a high proportion of subjects with CFL, but are not strongly associated with structural changes determined using SD-OCT. Based on a simulation in normally-sighted subjects, we expect impact of MSs on reading to be greater for older than younger subjects with CFL.Optometry, College o

Root cause analysis of COVID-19 cases by enhanced text mining process

The main focus of this research is to find the reasons behind the fresh cases of COVID-19 from the public’s perception for data specific to India. The analysis is done using machine learning approaches and validating the inferences with medical professionals. The data processing and analysis is accomplished in three steps. First, the dimensionality of the vector space model (VSM) is reduced with improvised feature engineering (FE) process by using a weighted term frequency-inverse document frequency (TF-IDF) and forward scan trigrams (FST) followed by removal of weak features using feature hashing technique. In the second step, an enhanced K-means clustering algorithm is used for grouping, based on the public posts from Twitter®. In the last step, latent dirichlet allocation (LDA) is applied for discovering the trigram topics relevant to the reasons behind the increase of fresh COVID-19 cases. The enhanced K-means clustering improved Dunn index value by 18.11% when compared with the traditional K-means method. By incorporating improvised two-step FE process, LDA model improved by 14% in terms of coherence score and by 19% and 15% when compared with latent semantic analysis (LSA) and hierarchical dirichlet process (HDP) respectively thereby resulting in 14 root causes for spike in the disease

Designing Cost-Effective Telemedicine Camps for Underprivileged Individuals in Less Developed Countries: A Decomposed Affordance-Effectivity Framework

Free telemedicine camps (telecamps) are emergent joint initiatives of healthcare organizations, national and local governments, and not-for-profit nongovernmental organizations (NGOs) with the goal of alleviating the health divide for underprivileged individuals in rural areas of less developed countries. Our study seeks to understand the effectiveness of physician-patient communication at telecamps with several salient characteristics: rural underprivileged patients, physicians in remote cities, and frugal telemedicine technology—specifically, videoconferencing—deployed in Hospitals on Wheels and appropriated by operators. We adopt a multiple-actor perspective, propose a decomposed affordance-effectivity framework, and combine variance and process perspectives to examine the phenomenon of interest. We collaborated with Apollo Hospitals, a leading hospital system in India, and collected multisource data from two major telecamps in rural South India. Based on an analysis of survey data from 216 telecamp participants through a variance perspective, we found support for the fit of patient-perceived media richness with two contingency factors—(1) disease diagnostic complexity and (2) patient healthcare needs fulfillment—in influencing patient satisfaction with teleconsultation. Based on an analysis of 46 sessions of teleconsultation video archives through a process perspective, we found that technology appropriation is realized through verbal and nonverbal communication events between patients and physicians, with on-site operators playing multiple roles that serve as “compensatory user effectivity.” Our findings yield theoretical and practical implications for how effective telemedicine encounters using frugal technologies can be designed in combination with other cost-effective support personnel resources to broaden healthcare access for underprivileged individuals in less developed countries and, more broadly, to actualize technology affordances in use situations involving multiple actors

Understanding Economic Attitude Elasticity toMHealth Services among the Underprivileged in Rural India: A Piecewise Latent Growth Modeling Approach

We examine the changes in economic attitudes toward mHealth services at four different levels of pricing, i.e., economic attitude elasticity (EAE), among 2129 Indian villagers who participated in two free health camps. We employed an innovative contingent valuation survey instrument for data collection and a piecewise latent growth modeling method for data analysis. Three key findings emerged from the study. First, we found that when mHealth services are free, three groups of villagers who (a) did not have mobile phones, (b) who shared mobile phones, or (c) who owned dedicated personal mobile phones showed different economic attitudes at one health camp, while the latter two groups showed similar attitudes at the other camp. Second, when the price for mHealth services changed from free to less than 100 Indian rupees (INR), the two groups who shared and who owned mobile phones had an identical EAE, while the group who had no mobile phones displayed a different EAE from the other two groups who had access to phones at both camps. Third, when the price changed from less than 100 INR to between 100 and 200 INR and to greater than 200 INR, similar cross-group EAE patterns were found at both camps. Our findings provide insights for policy makers in developing countries to promote the use of mHealth services among the socioeconomically disadvantaged

GeNESiS: gene network evolution simulation software

<p>Abstract</p> <p>Background</p> <p>There has been a lot of interest in recent years focusing on the modeling and simulation of Gene Regulatory Networks (GRNs). However, the evolutionary mechanisms that give rise to GRNs in the first place are still largely unknown. In an earlier work, we developed a framework to analyze the effect of objective functions, input types and starting populations on the evolution of GRNs with a specific emphasis on the robustness of evolved GRNs.</p> <p>Results</p> <p>In this work, we present a parallel software package, GeNESiS for the modeling and simulation of the evolution of gene regulatory networks (GRNs). The software models the process of gene regulation through a combination of finite-state and stochastic models. The evolution of GRNs is then simulated by means of a genetic algorithm with the network connections represented as binary strings. The software allows users to simulate the evolution under varying selective pressures and starting conditions. We believe that the software provides a way for researchers to understand the evolutionary behavior of populations of GRNs.</p> <p>Conclusion</p> <p>We believe that GeNESiS will serve as a useful tool for scientists interested in understanding the evolution of gene regulatory networks under a range of different conditions and selective pressures. Such modeling efforts can lead to a greater understanding of the network characteristics of GRNs.</p

An Entropy-based gene selection method for cancer classification using microarray data

BACKGROUND: Accurate diagnosis of cancer subtypes remains a challenging problem. Building classifiers based on gene expression data is a promising approach; yet the selection of non-redundant but relevant genes is difficult. The selected gene set should be small enough to allow diagnosis even in regular clinical laboratories and ideally identify genes involved in cancer-specific regulatory pathways. Here an entropy-based method is proposed that selects genes related to the different cancer classes while at the same time reducing the redundancy among the genes. RESULTS: The present study identifies a subset of features by maximizing the relevance and minimizing the redundancy of the selected genes. A merit called normalized mutual information is employed to measure the relevance and the redundancy of the genes. In order to find a more representative subset of features, an iterative procedure is adopted that incorporates an initial clustering followed by data partitioning and the application of the algorithm to each of the partitions. A leave-one-out approach then selects the most commonly selected genes across all the different runs and the gene selection algorithm is applied again to pare down the list of selected genes until a minimal subset is obtained that gives a satisfactory accuracy of classification. The algorithm was applied to three different data sets and the results obtained were compared to work done by others using the same data sets CONCLUSION: This study presents an entropy-based iterative algorithm for selecting genes from microarray data that are able to classify various cancer sub-types with high accuracy. In addition, the feature set obtained is very compact, that is, the redundancy between genes is reduced to a large extent. This implies that classifiers can be built with a smaller subset of genes

Effect of Non-Coding RNA on Post-Transcriptional Gene Silencing of Alzheimer Disease

A large amount of hidden biological information is contained in the human genome, which is not expressed or revealed in the form of proteins; the usual end product form of gene expression. Instead, most of such information is in the form of non-coding RNAs (ncRNAs). ncRNAs correspond to genes that are transcribed, but do not get translated into proteins. This part of the genome was, till recently, considered as &#x2018;junk&#x2019;. The term &#x2018;junk&#x2019; implied lack of any discernible function of these RNA. More than 98% of the human genomic size encompasses these non-coding RNAs. But, recent research has evidently brought out the indispensible contribution of non-coding RNA in controlling and regulating gene expression. ncRNA such as siRNAs and microRNAs have been reported to greatly help in causing post-transcriptional gene silencing (PTGS) in cells through RNA interference (RNAi) pathway. In this work, we have investigated the possibility of using siRNAs and microRNAs to aid in gene silencing of early onset Alzheimer&#x2019;s disease genes. &#xd;&#xa;Alzheimer&#x2019;s disease specific mutations and their corresponding positions in mRNA have been identified for six genes; Presenilin-1, Presenilin-2, APP (amyloid beta precursor protein), APBB3, BACE-1 and PSENEN. &#xd;&#xa;&#xd;&#xa;Small interfering RNAs (siRNAs) that can cause PTGS through RNA interference pathway have been designed. RNA analysis has been done to verify complementarity of antisense siRNA sequence with target mRNA sequence. Interaction studies have been done computationally between these antisense siRNA strands and seven Argonaute proteins. From the interaction studies, only one of the seven Argonaute proteins; 1Q8K, was found to have interaction with the siRNAs indicating the importance and uniqueness of this particular protein in RISC (RNA induced silencing complex). &#xd;&#xa;&#xd;&#xa;The interaction studies have been carried out for the microRNAs also. Out of the 700 mature human microRNAs collected, 394 microRNAs have been identified to show partial complementarity with their target sequence on PSEN-1 mRNA. Of these 394, five microRNAs have shown partial complementarity to early onset Alzheimer&#x2019;s disease specific mutations in PSEN-1 mRNA. Interaction studies have been done between these microRNAs and Argonaute proteins. Thus, design, characterization and analysis of ncRNAs that contribute to post transcriptional gene silencing of Alzheimer&#x2019;s disease have been achieved.&#xd;&#xa