Peripapillary Nerve Fiber Layer Thickness and Optic Nerve Head Parameters in Patients Recovered from COVID-19: A Longitudinal Study

Abstract

Purpose. To quantify the longitudinal changes of the optic nerve head (ONH) parameters and peripapillary retinal nerve fiber layer (pRNFL) thickness in patients recovered from coronavirus disease 2019 (COVID-19) using spectral-domain optical coherence tomography (OCT) analysis. Materials and Methods. In an observational longitudinal study, in patients recovered from COVID-19, ONH OCT images were recorded at least two weeks after recovery from the systemic disease as the baseline and after one and three-month follow-up. Ganglion cell complex (GCC) analysis, ONH parameters, and pRNFL thickness changes were measured. Results. A total of 36 eyes from 18 recovered COVID-19 patients including eleven (61.2%) females were studied. The average age was 35.5 ± 7.5 years. The pRNFL thickness in the nasal lower sector significantly decreased from 77 ± 18 μm in the first post-COVID-19 month to 74 ± 10 μm in the third month ( P = 0.8 ). The average, superior, and inferior pRNFL thickness remained unchanged. The average cup volume significantly decreased from 0.27 ± 0.15 mm3 at baseline to 0.19 ± 0.15 mm3 in the third post-COVID-19 visit ( P = 0.028 ). In terms of ONH morphologies including rim, disc and cup to disc area, and the vertical and horizontal ratio, the changes were not significant over the 3-month study period. Focal loss volume and global loss volume values were not changed significantly. Conclusion. Localized defect in the nasal lower sector of pRNFL is observed in 3-month post-recovery from COVID-19. Larger studies with longer follow-ups are required to reveal the exact changes in ONH parameters.

The prone position in COVID-19 impacts the thickness of peripapillary retinal nerve fiber layers and macular ganglion cell layers

“…Our research showed that the average and segmental p-RNFL thicknesses tended to decrease over time in the Prone and the Non-Prone Groups but remained thicker than those in the controls at all time points that was supported by previous report [ 33 , 34 ]. ( Fig 1 ) One exception to this was that the superior and inferior segments for the Prone Group were not as thick as those for the Control Group, although the difference was not significant.…”

Role of different types of RNA molecules in the severity prediction of SARS-CoV-2 patients

“…Next set of features are related to the prediction of treatment stage of the COVID-19 patients. Same analysis on those genes shows that they are closely related to the biological processes such as neutral amino acid transport [95] , regulation of cellular protein metabolic process [84] , negative regulation of cellular macromolecule biosynthetic process [93] , endosome to lysosome transport [50] , chondrocyte morphogenesis involved in endochondral bone morphogenesis, growth plate cartilage chondrocyte morphogenesis, ferrochelatase activity, chondrocyte morphogenesis, phosphatase activator activity [116] , regulation of protein kinase activity [52] , [79] , negative regulation of cellular metabolic process [11] , [8] , response to inorganic substance [111] , [22] , toll-like receptor 9 signaling pathway [1] , [25] , [49] , [70] , [90] , positive regulation of NIK/NF-kappaB signaling [54] , myeloid dendritic cell activation [6] , [85] , regulation of transcription from RNA polymerase II promoter, protein localization to cilium, positive regulation of cell morphogenesis involved in differentiation, mRNA/RNA polyadenylation [102] , [5] , protein localization to cilium, phosphatidylinositol binding, optic cup structural organization [2] , endosome to plasma membrane transport vesicle, SET domain binding, negative regulation of phosphorus metabolic process [110] , regulation of fatty acid transport/ long-chain fatty acid transport [101] , [108] , [32] , [94] , positive regulation of mRNA catabolic process [87] , positive regulation of mRNA metabolic process [54] , transcription from RNA polymerase II promoter, positive regulation of cellular biosynthetic process, negative regulation of RNA metabolic process, negative regulation of nitrogen compound metabolic process [16] , [76] and negative regulation of cellular macromolecule biosynthetic process [30] .…”