PurposeThickness changes of corneal sub-layers after phacoemulsification were investigated by spectral domain ultra-high resolution optical coherence tomography (UHR-OCT). MethodsThe corneas (n = 26) of 26 age-related cataract surgery patients were studied. UHR-OCT was used to evaluate the thickness of Descemet’s Endothelium Complex (DEC), stroma, Bowman’s layer, epithelium, and full cornea at the center (CCT) before, one day after, and one week after surgery. Non-contact specular microscopy measured CCT, endothelial cell density, and morphology.ResultsThe DEC, stroma, Bowman’s layer, and epithelium were visualized by UHR-OCT. Before surgery, the DEC in all cases appeared as a translucent space between two smooth opaque lines. One day after surgery, the posterior corneal surfaces in half of the eyes were wavy and irregular. Compared to the baseline, one day after surgery the thickness increases of the DEC, stroma, and CCT were 4.3 ± 2.6 µm, 25.5 ± 24.9 µm, and 32.1 ± 26.6 µm, respectively (P < 0.001). The morphology of the DEC and the CCT recovered to baseline one week after surgery (P > 0.05), but endothelial cell density was 8.7% less than at baseline. There were no significant changes in Bowman’s layer and epithelium after the operation. The pre-operative DEC thickness was positively correlated with the decreased endothelial cell density at 1 day after surgery (r = 0.55, P = 0.003).ConclusionsThe DEC showed edematous thickening and different degrees of morphological changes after phacoemulsification. The DEC deformation and corneal edema recovered by one week after surgery, which indicated recovery of endothelial function. UHR-OCT is a useful tool to evaluate function of the DEC after phacoemulsification. Pre-operative DEC thickness may indicate the integrity of the endothelium and could be used for predicting endothelial cell loss after phacoemulsification.
Punctal occlusion transiently increased tear menisci in symptomatic and asymptomatic lens wearers, with a longer duration in the symptomatic group. For both symptomatic and asymptomatic lens wearers, the increased meniscus volume was associated with improved ocular comfort.
Dynamic changes in the tear menisci, NITBUT, and comfort level were evident in both inexperienced and experienced CL wearers during and after overnight wear. Decreased tear meniscus volumes and tear film stability may contribute to lower eye comfort for extended CL wearers. The tearing on eye opening may facilitate improvement of ocular comfort and tear refreshment for a reconditioning of the tear system and the CL.
Surveillance studies for Covid-19 prevalence estimation are subject to sampling bias due to oversampling of symptomatic individuals and error-prone tests, particularly rapid antigen tests which are known to have high false negative rates for asymptomatic individuals. This results in naïve estimators which can be very far from the truth.In this work, we present a method that removes these two sources of error directly. Moreover, our procedure can be easily extended to the stratified error situation in which a test has very different error rate profiles for symptomatic and asymptomatic individuals as is the case for rapid antigen testing. The result is an easily understandable four-step algorithm that produces much more reliable prevalence estimates as demonstrated on data from the Israeli Ministry of Health. Thus it may re-open the debate about whether we are under-valuing rapid testing as a surveillance tool and may have policy implications in Third-World countries or disadvantaged communities where access to PCR testing may be less accessible.
Krill herd algorithm (KHA) is an emerging nature-inspired approach that has been successfully applied to optimization. However, KHA may get stuck into local optima owing to its poor exploitation. In this paper, the orthogonal learning (OL) mechanism is incorporated to enhance the performance of KHA for the first time, then an improved method named orthogonal krill herd algorithm (OKHA) is obtained. Compared with the existing hybridizations of KHA, OKHA could discover more useful information from historical data and construct a more promising solution. The proposed algorithm is applied to solve CEC2017 numerical problems, and its robustness is verified based on the simulation results. Moreover, OKHA is applied to tackle data clustering problems selected from the UCI Machine Learning Repository. The experimental results illustrate that OKHA is superior to or at least competitive with other representative clustering techniques.
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