Purpose: To study whether there is a difference in central macular thickness (CMT) and peripapillary retinal nerve fiber layer (RNFL) thickness between the two eyes of individuals having anisometropia >1 diopter (D) using spectral domain optical coherence tomography (OCT).
Material And Methods: One hundred and one subjects, 31 with myopic anisometropia, 28 with astigmatic anisometropia, and 42 with hypermetropic anisometropia, were enrolled in the study. After informed consent, detailed ophthalmological examination was performed for every patient including cycloplegic refraction, best corrected visual acuity, slit lamp, and fundus examination. After routine ophthalmic examination peripapillary RNFL and CMT were measured using spectral domain OCT and the values of the two eyes were compared in the three types of anisometropia. Axial length was measured using an A Scan ultrasound biometer (Appa Scan-2000).
Results: The average age of subjects was 21.7±9.3 years. The mean anisometropia was 3.11±1.7 D in myopia; 2±0.99 D in astigmatism; and 3.68±1.85 D in hypermetropia. There was a statistically significant difference in axial length of the worse and better eye in both myopic and hypermetropic anisometropia (=0.00). There was no significant difference between CMT of better and worse eyes in anisomyopia (=0.79), anisohypermetropia (=0.09), or anisoastigmatism (=0.16). In anisohypermetropia only inferior quadrant RNFL was found to be significantly thicker (=0.011) in eyes with greater refractive error.
Conclusion: There does not appear to be a significant difference in CMT and peripapillary RNFL thickness in anisomyopia and anisoastigmatism. However, in anisohypermetropia inferior quadrant RNFL was found to be significantly thicker.
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http://dx.doi.org/10.2147/OPTH.S123273 | DOI Listing |
Sci Rep
January 2025
College of Computer and Information Engineering, Nanjing Tech University, Nanjing, Jiangsu, China.
Intelligent transportation systems heavily rely on forecasting urban traffic flow, and a variety of approaches have been developed for this purpose. However, most current methods focus on exploring spatial and temporal dependencies in historical traffic data, while often overlooking the inherent spectral characteristics hidden in traffic time series. In this paper, we introduce an approach to analyzing traffic flow in the frequency domain.
View Article and Find Full Text PDFPhys Med Biol
January 2025
Department of Electrical and Electronic Engineering, The University of Hong Kong, Chow Yei Ching 506, Hong Kong, 999077, HONG KONG.
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View Article and Find Full Text PDFComput Methods Biomech Biomed Engin
January 2025
The School of Computer Science, Hangzhou Dianzi University, Hangzhou, China.
Convolutional neural networks (CNNs) have been widely utilized for decoding motor imagery (MI) from electroencephalogram (EEG) signals. However, extracting discriminative spatial-temporal-spectral features from low signal-to-noise ratio EEG signals remains challenging. This paper proposes MBMSNet , a multi-branch, multi-scale, and multi-view CNN with a lightweight temporal attention mechanism for EEG-Based MI decoding.
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October 2024
Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida.
Purpose: Spectral-domain OCT angiography (SD-OCTA) scans were used in an algorithm developed for swept-source OCT angiography (SS-OCTA) scans to determine if SD-OCTA scans yielded similar results for the measurement of hyperreflective foci (HRF) in intermediate age-related macular degeneration (iAMD).
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NPJ Parkinsons Dis
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Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland.
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