Results GV of RGCL thickness differed significantly between pre-OAG (p 0.05) and controls, respectively. Qualitative ROC analysis of QTV showed AUCs of 0.908 (NTG) 0.914 (POAG), 0.930 (SOAG), 0.734 (pre-POAG), and 0.519 (OHT). Implementation of eQTV yielded even higher AUCs for NTG (0.919), POAG (0.969), and SOAG (0.973) compared to GV. Similar AUCs of eQTV and GV were observed for OHT (0.514) and pre-OAG (0.770). Conclusion The results of the present study showed that quantitative and qualitative analysis of RGCL thickness yielded similar diagnostic impacts compared to RNFL. Qualitative analysis might be a quick and easy useable tool for clinical all-day life. The present data suggest that analysis of an extended macula region might improve its diagnostic impact.Background Novel approaches to photoprotection must go beyond classical MED measurements, as discoveries on the effect of UV radiation on skin paints a more complex and multi-pronged scenario with multitude of skin cell types involved. Of these, photoimmunoprotection emerges as a crucial factor that protects against skin cancer and photoaging. A novel immune parameter is enabled by the precise knowledge of the wavelength and dose of solar radiation that induces photoimmunosupression. Natural substances, that can play different roles in photoprotection as antioxidant, immune regulation, and DNA protection as well as its possible ability as sunscreen are the new goals in cosmetic industry. Objective To analyze the effect of a specific natural extract from Polypodium leucotomos (PLE, Fernblock®), as part of topical sunscreen formulations to protect from photoimmunosuppression, as well as other deleterious biological effects of UV radiation. this website Methods The possible sunscreen effect of PLE was analyzed by including 1darkening but also against two photoimmunoprotection factors.Objectives We previously reported that SARS-CoV-2 infects the gastrointestinal (GI) epithelium. In this study, we aimed to explore the impact of SARS-CoV-2 GI infection on clinical outcomes of COVID-19. Materials and Methods For this retrospective cohort study, 104 patients with COVID-19 were classified into a SARS-CoV-2 GI infection group and a non-infection group. The primary endpoint was the time of negative conversion of SARS-CoV-2 RNA in respiratory tract samples. The secondary outcome was the time of hospitalization for COVID-19. Results Patients with SARS-CoV-2 GI infection had a longer duration of positive SARS-CoV-2 RNA in respiratory tract samples (median 12.0 days [95% CI 10.0-13.2] vs. 9.0 days [95% CI 7.5-10.5]; HR 0.575 [95% CI 0.386-0.857]; P = 0.003) and hospitalization (median 28.0 days [95% CI 23.2-32.8] vs. 15.0 days [95% CI 13.6-16.4]; HR 0.149 [95% CI 0.087-0.252]; P less then 0.001) than patients without SARS-CoV-2 GI infection. Subgroup analyses for sex, age, epidemiological history, clinical classification and antiviral treatment showed consistent results. Conclusion Our study indicates that SARS-CoV-2 GI infection prolongs the duration of SARS-CoV-2 shedding and hospitalization in the patients with COVID-19. More attention should be paid to SARS-CoV-2 GI infection of COVID-19 and fecal SARS-CoV-2 RNA test should be completed in time.Background 3D Pseudocontinuous Arterial Spin Labeling (3D-PCASL) MRI and optical coherence tomography angiography (OCTA) have been applied to detect ocular blood flow (BF). We aim to characterize the ocular BF in diabetic retinopathy (DR) using 3D-PCASL and OCTA, to discuss the relationship between ocular and cerebral BF, and to evaluate their potential utility to assess the severity of DR. Methods A total of 66 participants (132 eyes) were included. Seventy-two eyes were classified in the proliferative diabetic retinopathy (PDR) group, and 60 were in the non-proliferative diabetic retinopathy NPDR group. Ocular and cerebral BF values were detected by 3D-PCASL using a 3.0T MRI scanner with two post-labeling delays (PLDs). Vessel density (VD)/perfusion density (PD) of the macular or peripapillary area were detected by OCTA. Parameters and clinical characteristics were compared between the PDR and NPDR eyes utilizing two-sample t-tests and chi-square tests. Spearman's rank correlation analysis, logistic regressL and OCTA may be effective non-invasive methods to measure ocular blood flow in DR patients and assess the severity of DR.Macrophage Activation Syndrome (MAS) is a very severe complication of different rheumatic diseases, including pediatric Systemic Lupus Erythematosus (pSLE). MAS is not considered as a frequent complication of pSLE; however, its occurrence could be under-estimated and the diagnosis can be challenging. In order to address this issue, we performed a systematic review of the available medical literature, aiming to retrieve all those papers providing diagnostic (clinical/laboratory) data on patients with pSLE-related MAS, in individual or aggregated form. The selected case reports and series provided a pool of 46 patients, accounting for 48 episodes of MAS in total. We re-analyzed these patients in light of the diagnostic criteria for MAS validated in systemic Juvenile Idiopathic Arthritis (sJIA) patients and the preliminary diagnostic criteria for MAS in pSLE, respectively. Five clinical studies were also selected and used to support this analysis. This systematic review confirms that MAS diagnosis in pSLE patients is characterized by several diagnostic challenges, which could lead to delayed diagnosis and/or under-estimation of this complication. Specific criteria should be considered to diagnose MAS in different rheumatic diseases; as regards pSLE, the aforementioned preliminary criteria for MAS in pSLE seem to perform better than the sJIA-related MAS criteria, because of a lower ferritin cut-off.Background Phenotypes have been identified within heterogeneous disease, such as acute respiratory distress syndrome and sepsis, which are associated with important prognostic and therapeutic implications. The present study sought to assess whether phenotypes can be derived from intensive care patients with coronavirus disease 2019 (COVID-19), to assess the correlation with prognosis, and to develop a parsimonious model for phenotype identification. Methods Adult patients with COVID-19 from Tongji hospital between January 2020 and March 2020 were included. The consensus k means clustering and latent class analysis (LCA) were applied to identify phenotypes using 26 clinical variables. We then employed machine learning algorithms to select a maximum of five important classifier variables, which were further used to establish a nested logistic regression model for phenotype identification. Results Both consensus k means clustering and LCA showed that a two-phenotype model was the best fit for the present cohort (N = 504).