Significance: One-year follow-up is recommended for patients with macular diseases to assess functional changes associated with disease progression and to modify low-vision (LV) treatment plans, if indicated.
Purpose: The purpose of this study was to observe 255 patients with macular diseases who received LV rehabilitation (rehabilitation with a therapist) or basic LV services (LV devices dispensed without therapy) during Veterans Affairs Low-vision Intervention Trial II after the trial ended at 4 months until 1-year follow-up.
Methods: The primary outcome measure was visual ability measured with the 48-item Veterans Affairs Low-vision Visual Functioning Questionnaire. Mean visual ability scores for the treatment groups were compared from baseline to 4 months, 4 months to 1 year, and baseline to 1 year. Changes from baseline to 1 year were compared between the two groups. Predictors of changes in visual ability from 4 months to 1 year were assessed using linear regression.
Results: Both groups experienced significant improvement in all measures of visual ability from baseline to 1 year but lost visual reading ability during the observation period (LV rehabilitation group, -0.64 [1.2] logit; 95% confidence interval [CI], -0.84 to -0.44 logit; basic LV group, -0.63 [1.4] logit; 95% CI, -0.88 to -0.38 logit), and overall visual ability was lost in the LV rehabilitation group (-0.20 [0.8] logit; 95% CI, -0.34 to -0.06 logit). Loss of visual reading ability in both groups from 4 months to 1 year was predicted by reading ability scores at 4 months, loss of near visual acuity from 4 months to 1 year, and lower EuroQol-5D utility index scores; loss of overall visual ability in the LV rehabilitation group during the same time period was predicted by lower overall ability scores at 4 months.
Conclusions: Visual ability significantly improved in all groups from baseline to 1 year. However, the loss of visual reading ability experienced by both groups from 4 months to 1 year reduced the benefit of the services provided.
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http://dx.doi.org/10.1097/OPX.0000000000001428 | DOI Listing |
Sci Rep
January 2025
Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, 08193, Spain.
In this study, we explore an enhancement to the U-Net architecture by integrating SK-ResNeXt as the encoder for Land Cover Classification (LCC) tasks using Multispectral Imaging (MSI). SK-ResNeXt introduces cardinality and adaptive kernel sizes, allowing U-Net to better capture multi-scale features and adjust more effectively to variations in spatial resolution, thereby enhancing the model's ability to segment complex land cover types. We evaluate this approach using the Five-Billion-Pixels dataset, composed of 150 large-scale RGB-NIR images and over 5 billion labeled pixels across 24 categories.
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January 2025
Institute of Brain Diseases and Cognition, School of Medicine, Xiamen University, Xiamen, 361102, Fujian, China.
Altitude training has been widely adopted. This study aimed to establish a mice model to determine the time point for achieving the best endurance at the lowland. C57BL/6 and BALB/c male mice were used to establish a mice model of hypoxic training with normoxic training mice, hypoxic mice, and normoxic mice as controls.
View Article and Find Full Text PDFLipids Health Dis
January 2025
Department of Obstetrics and Gynecology, Center for Reproductive Medicine, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, Guangdong-Hong Kong-Macao Greater Bay Area Higher Education Joint Laboratory of Maternal-Fetal Medicine, The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, China.
Background: Prior research indicates a potential link between dyslipidemia and endometriosis (EMs). However, the relationship between remnant cholesterol (RC) and EMs has not been thoroughly investigated. Consequently, looking into and clarifying the connection between RC and EMs was the primary goal of this study.
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January 2025
OSIS, Silver Spring, Maryland, U.S.A.
Travel restrictions during the novel coronavirus, SARS-CoV-2 (COVID-19) public health emergency affected the U.S. Food and Drug Administration's (FDA) ability to conduct on-site bioavailability/bioequivalence (BA/BE) and Good Laboratory Practice (GLP) nonclinical inspections.
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January 2025
College of Computer Sciences, Anhui University, Hefei, 230039, China.
Decoding the semantic categories of complex sceneries is fundamental to numerous artificial intelligence (AI) infrastructures. This work presents an advanced selection of multi-channel perceptual visual features for recognizing scenic images with elaborate spatial structures, focusing on developing a deep hierarchical model dedicated to learning human gaze behavior. Utilizing the BING objectness measure, we efficiently localize objects or their details across varying scales within scenes.
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