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Purpose: This work aimed to compare physical impairment in survivors of classic ARDS compared with COVID-19-associated ARDS (CARDS) survivors.Material and methods: This is a prospective observational cohort study on 248 patients with CARDS and compared them with a historical cohort of 48 patients with classic ARDS. Physical performance was evaluated at 6 and 12 months after ICU discharge, using the Medical Research Council Scale (MRCss), 6-min walk test (6MWT), handgrip dynamometry (HGD), and fatigue severity score (FSS). We also assessed activities of daily living (ADLs) using the Barthel index.Results: At 6 months, patients with classic ARDS had lower HGD (estimated difference [ED]: 11.71 kg, p < 0.001; ED 31.9% of predicted value, p < 0.001), 6MWT distance (ED: 89.11 m, p < 0.001; ED 12.96% of predicted value, p = 0.032), and more frequent significant fatigue (OR 0.35, p = 0.046). At 12 months, patients with classic ARDS had lower HGD (ED: 9.08 kg, p = 0.0014; ED 25.9% of predicted value, p < 0.001) and no difference in terms of 6MWT and fatigue. At 12 months, patients with classic ARDS improved their MRCss (ED 2.50, p = 0.006) and HGD (ED: 4.13 kg, p = 0.002; ED 9.45% of predicted value, p = 0.005), while those with CARDS did not. Most patients in both groups regained independence in ADLs at 6 months. COVID-19 diagnosis was a significant independent predictor of better HGD (p < 0.0001) and 6MWT performance (p = 0.001), and lower prevalence of fatigue (p = 0.018).Conclusions: Both classic ARDS and CARDS survivors experienced long-term impairments in physical functioning, confirming that post-intensive care syndrome remains a major legacy of critical illness. Surprisingly, however, persisting disability was more common in survivors of classic ARDS than in CARDS survivors. In fact, muscle strength measured with HGD was reduced in survivors of classic ARDS compared to CARDS patients at both 6 and 12 months. The 6MWT was reduced and fatigue was more co

© 2015 ARVO. The ability of the visual system to compensate for differences in blur orientation between eyes is not well understood. We measured the orientation of the internal blur code in both eyes of the same subject monocularly by presenting pairs of images blurred with real ocular point spread functions (PSFs) of similar blur magnitude but varying in orientations. Subjects assigned a level of confidence to their selection of the best perceived image in each pair. Using a classification-images–inspired paradigm and applying a reverse correlation technique, a classification map was obtained from the weighted averages of the PSFs, representing the internal blur code. Positive and negative neural PSFs were obtained from the classification map, representing the neural blur for best and worse perceived blur, respectively. The neural PSF was found to be highly correlated in both eyes, even for eyes with different ocular PSF orientations (rPos = 0.95; rNeg = 0.99; p < 0.001). We found that in subjects with similar and with different ocular PSF orientations between eyes, the orientation of the positive neural PSF was closer to the orientation of the ocular PSF of the eye with the better optical quality (average difference was ∼10°), while the orientation of the positive and negative neural PSFs tended to be orthogonal. These results suggest a single internal code for blur with orientation driven by the orientation of the optical blur of the eye with better optical quality.

The human visual system is an exquisitely engineered system that can serve as a model and inspiration for the design of many imaging systems. Optics and optical engineering play a key role in developing new techniques and approaches for both the study of human vision and the design of novel imaging systems. For example, advances in optical sensing and imaging have led to important discoveries about retinal image processing, and optical design tools are necessary for improving vision in patients. While advances in optics are improving our understanding of the human visual system, this understanding has also led to improvements in artificial vision systems, image processing algorithms, visual displays, and even modern optical elements and systems.

Background:The image formed by the eye's optics is blurred by the ocular aberrations, specific to each eye. Recent studies demonstrated that the eye is adapted to the level of blur produced by the high order aberrations (HOA). We examined whether visual coding is also adapted to the orientation of the natural HOA of the eye.Methods and Findings:Judgments of perceived blur were measured in 5 subjects in a psychophysical procedure inspired by the >Classification Images> technique. Subjects were presented 500 pairs of images, artificially blurred with HOA from 100 real eyes (i.e. different orientations), with total blur level adjusted to match the subject's natural blur. Subjects selected the image that appeared best focused in each random pair, in a 6-choice ranked response. Images were presented through Adaptive Optics correction of the subject's aberrations. The images selected as best focused were identified as positive, the other as negative responses. The highest classified positive responses correlated more with the subject's Point Spread Function, PSF, (r = 0.47 on average) than the negative (r = 0.34) and the difference was significant for all subjects (p<0.02). Using the orientation of the best fitting ellipse of angularly averaged integrated PSF intensities (weighted by the subject's responses) we found that in 4 subjects the positive PSF response was close to the subject's natural PSF orientation (within 21 degrees on average) whereas the negative PSF response was almost perpendicularly oriented to the natural PSF (at 76 degrees on average).Conclusions:The Classification-Images inspired method is very powerful in identifying the internally coded blur of subjects. The consistent bias of the Positive PSFs towards the natural PSF in most subjects indicates that the internal code of blur appears rather specific to each subject's high order aberrations and reveals that the calibration mechanisms for normalizing blur also operate using orientation cues. © 2013 Sa