Background: Globally, computer is one of the common office tools used in various institutions. Using computer for prolonged time led to the users at greater health risk of computer vision syndrome (CVS). Computer vision syndrome is the leading occupational health problem of the twenty-first century. About 70 percent of computer users are suffered from CVS. Besides the health problems, CVS causes inefficiency at workplace and deteriorate quality of work. The problem of CVS and its risk factors are not well known in Ethiopia.
Method: A cross-sectional study was conducted to assess the prevalence of CVS and associated factors among computer user government employees in Debre Tabor town from February to March, 2016. Multistage random sampling method was applied to select 607 study participants, and the data were collected by using a structured questionnaire. Computer vision syndrome was measured by self-reported method. Bivariate and multivariable binary logistic regression analyses were performed using SPSS version 20. Significance level was obtained at 95% CI and value < 0.05.
Results: The prevalence of CVS was 422 (69.5%) with 95% CI of 65.60, 73.0%. Blurred vision, eyestrain, and eye irritation were the commonest reported symptoms of CVS with proportion of 62.60%, 47.63%, and 47.40%, respectively. Occupation: officer (adjusted odds ratio (AOR) = 4.74) and secretary (AOR = 9.17), daily computer usage (AOR: 2.29), and preexisting eye disease (AOR = 3.19) were risk factors for CVS. However, computer users with high payment, who took regular health break, and with good knowledge on computer safety measures were less impacted by CVS.
Conclusion: The prevalence of computer vision syndrome was found to be higher in Debre Tabor town. Monthly income, occupation, daily computer usage, regular health break, knowledge, and preexisting eye disease were predictor variables for CVS. Optimizing exposure time, improving awareness on safety measures, and management support are important to tackle CVS.
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http://dx.doi.org/10.1155/2018/4107590 | DOI Listing |
Vet Res
January 2025
Veterinary Diagnostic Laboratory, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, USA.
Cranioventral pulmonary consolidation (CVPC) is a common lesion observed in the lungs of slaughtered pigs, often associated with Mycoplasma (M.) hyopneumoniae infection. There is a need to implement simple, fast, and valid CVPC scoring methods.
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January 2025
Department of Optometry, College of Medicine and Health Sciences, Comprehensive Specialized Hospital, University of Gondar, Gondar, Ethiopia.
Baground: Cataract is a major public health concern and the leading cause of blindness and low vision in Ethiopia. However, no studies have been conducted to assess the prevalence of cataract and associated factors among adult diabetic patients in the study area. Therefore, this study aimed to assess the prevalence of cataract and associated factors among adult diabetic patients in Northwest Ethiopia.
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January 2025
From the Orthopedic Data Innovation Lab (ODIL), Hospital for Special Surgery (A.M.L.S., M.A.F.), Department of Radiology and Imaging, Hospital for Special Surgery Centre (E.E.X, Z.I, E.T.T, D.B.S, J.L.C)and Department of Population Health Sciences, Weill Cornell Medicine (M.A.F), New York, New York, USA.
Background And Purpose: To train and evaluate an open-source generative adversarial networks (GANs) to create synthetic lumbar spine MRI STIR volumes from T1 and T2 sequences, providing a proof-of-concept that could allow for faster MRI examinations.
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Neuroscience
January 2025
Department of Computer Engineering, Faculty of Engineering, Igdir University, 76000, Igdir, Turkey. Electronic address:
Neurological disorders, including cerebral vascular occlusions and strokes, present a major global health challenge due to their high mortality rates and long-term disabilities. Early diagnosis, particularly within the first hours, is crucial for preventing irreversible damage and improving patient outcomes. Although neuroimaging techniques like magnetic resonance imaging (MRI) have advanced significantly, traditional methods often fail to fully capture the complexity of brain lesions.
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January 2025
Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin 14195, Germany.
In catalysis research, the amount of microscopy data acquired when imaging dynamic processes is often too much for nonautomated quantitative analysis. Developing machine learned segmentation models is challenged by the requirement of high-quality annotated training data. We thus substitute expert-annotated data with a physics-based sequential synthetic data model.
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