Background And Objective: To analyze the examination practices and referral of patients with diabetic retinopathy (DR) by optometrists in routine clinical care.
Patients And Methods: Diabetic patient records from 2012 to 2018 were retrospectively reviewed for documentation of dilated fundus exam (DFE), imaging, follow-up appointments, and referrals. Concordance between clinical exam and coding was also analyzed.
Results: For 97.8% of encounters, DFE was performed, the patient was referred for DFE, or DFE was scheduled for follow-up. When DFE was performed at the initial visit, this resulted in referral of 19.8% of patients to an ophthalmologist. Imaging was obtained occasionally, with fundus photos in 2.6% and optical coherence tomography in 14.5% of encounters. Concordance of DR grading between exam and coding was 78.8%. Recommended follow-up times were incorrect based on DR severity level in 13.8% of encounters.
Conclusion: Although DFE was performed reliably by optometrists, utilization of imaging, DR grading and coding, and appropriate follow-up periods could be improved. [Ophthalmic Surg Lasers Imaging Retina. 2019;50:608-612.].
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http://dx.doi.org/10.3928/23258160-20191009-02 | DOI Listing |
Genetics
December 2024
Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.
For many problems in population genetics, it is useful to characterize the distribution of fitness effects (DFE) of de novo mutations among a certain class of sites. A DFE is typically estimated by fitting an observed site frequency spectrum (SFS) to an expected SFS given a hypothesized distribution of selection coefficients and demographic history. The development of tools to infer gene trees from haplotype alignments, along with ancient DNA resources, provides us with additional information about the frequency trajectories of segregating mutations.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia.
This work aims to develop a novel convolutional neural network (CNN) named ResNet50* to detect various gastrointestinal diseases using a new ResNet50*-based deep feature engineering model with endoscopy images. The novelty of this work is the development of ResNet50*, a new variant of the ResNet model, featuring convolution-based residual blocks and a pooling-based attention mechanism similar to PoolFormer. Using ResNet50*, a gastrointestinal image dataset was trained, and an explainable deep feature engineering (DFE) model was developed.
View Article and Find Full Text PDFbioRxiv
November 2024
School of Life Sciences, Center for Evolution & Medicine, Arizona State University, Tempe, AZ, US.
The demographic history of a population, and the distribution of fitness effects (DFE) of newly arising mutations in functional genomic regions, are fundamental factors dictating both genetic variation and evolutionary trajectories. Although both demographic and DFE inference has been performed extensively in humans, these approaches have generally either been limited to simple demographic models involving a single population, or, where a complex population history has been inferred, without accounting for the potentially confounding effects of selection at linked sites. Taking advantage of the coding-sparse nature of the genome, we propose a 2-step approach in which coalescent simulations are first used to infer a complex multi-population demographic model, utilizing large non-functional regions that are likely free from the effects of background selection.
View Article and Find Full Text PDFThis study investigates the potential of long-wave infrared (LWIR) free-space optical (FSO) transmission using multilevel signals to achieve high spectral efficiency. The FSO transmission system includes a directly modulated-quantum cascade laser (DM-QCL) operating at 9.1 µm and a mercury cadmium telluride (MCT) detector.
View Article and Find Full Text PDFChemosphere
October 2024
Department of Ecology, Environment and Plant Sciences (DEEP), Stockholm University, Svante Arrhenius väg 20A, 106 91, Stockholm, Sweden. Electronic address:
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