The outbreak of recently identified 2019 novel coronavirus (2019-nCOV) infection has become a world-wide health threat. Currently, more information is needed for further understanding the transmission, clinical characteristics, and infection control procedures of 2019-nCOV. Recently, the role of the eye in transmitting 2019-nCOV has been intensively discussed. Previous investigations about other high infectious human COVs, that is, severe acute respiratory syndrome coronavirus (SARS-CoV) and the Middle East respiratory syndrome coronavirus (MERS-CoV), may provide helpful information. In this review, we describe the genomics and morphology of human CoVs, the epidemiology, systemic and ophthalmic manifestations, mechanisms of human CoVs infection, and infection control procedures. The role of the eye in the transmission of SARS-CoV and 2019-nCOV is discussed. Although the conjunctiva is directly exposed to extraocular pathogens, and the mucosa of ocular surface and upper respiratory tract is connected by nasolacrimal duct and share same entry receptors for some respiratory viruses. The eye is rarely involved in human CoVs infection, conjunctivitis is quite rare in patients with SARS-CoV and 2019-nCoV infection, and COV RNA positive rate by RT-PCR test in tears and conjunctival secretions from patients with SARS-CoV and 2019-nCoV infection is also very low, which imply that the eye is neither a preferred organ of human COVs infection, nor is a preferred gateway of entry for human COVs to infect respiratory tract. However, pathogens exposed to the ocular surface might be transported to nasal and nasopharyngeal mucosa by constant tear rinsing through lacrimal duct, and then cause respiratory tract infection. Considering close doctor-patient contact is quite common in ophthalmic practice which are apt to transmit human COVs by droplets and fomites, hand hygiene and personal protection are still highly recommended for health care workers to avoid hospital-related viral transmission during ophthalmic practice.
Introduction retinal age derived from fundus images using deep learning has been verified as a novel biomarker of ageing. We aim to investigate the association between retinal age gap (retinal age–chronological age) and incident Parkinson’s disease (PD). Methods a deep learning (DL) model trained on 19,200 fundus images of 11,052 chronic disease-free participants was used to predict retinal age. Retinal age gap was generated by the trained DL model for the remaining 35,834 participants free of PD at the baseline assessment. Cox proportional hazards regression models were utilised to investigate the association between retinal age gap and incident PD. Multivariable logistic model was applied for prediction of 5-year PD risk and area under the receiver operator characteristic curves (AUC) was used to estimate the predictive value. Results a total of 35,834 participants (56.7 ± 8.04 years, 55.7% female) free of PD at baseline were included in the present analysis. After adjustment of confounding factors, 1-year increase in retinal age gap was associated with a 10% increase in risk of PD (hazard ratio [HR] = 1.10, 95% confidence interval [CI]: 1.01–1.20, P = 0.023). Compared with the lowest quartile of the retinal age gap, the risk of PD was significantly increased in the third and fourth quartiles (HR = 2.66, 95% CI: 1.13–6.22, P = 0.024; HR = 4.86, 95% CI: 1.59–14.8, P = 0.005, respectively). The predictive value of retinal age and established risk factors for 5-year PD risk were comparable (AUC = 0.708 and 0.717, P = 0.821). Conclusion retinal age gap demonstrated a potential for identifying individuals at a high risk of developing future PD.
Background: Epithelial-mesenchymal transition (EMT) of the retinal pigment epithelial (RPE) cells is a critical step in the pathogenesis of proliferative vitreoretinopathy (PVR). Some microRNAs (miRNAs) participate in regulating RPE cell EMT as post-transcriptional regulators. However, the function of miR-194 in RPE cell EMT remains elusive. Here, the role of miR-194 in PVR was investigated.Methods: Retinal layers were obtained using laser capture microdissection (LCM). Gene expression at the mRNA and protein level in the tissues and cells was examined using quantitative reverse transcription (RT)polymerase chain reaction and Western blotting, respectively. The related protein expression was observed by immunostaining. The effect of miR-194 on RPE cell EMT was examined by gel contraction, wound healing, and cell migration assays. RNAseq was performed in ARPE-19 with transfection of pSuper-scramble and pSuper-miR-194. The target gene of miR-194 was identified and confirmed via bioinformatics analysis and dual-luciferase reporter assay. ARPE-19 (adult retinal pigment epithelium-19) cells were treated with transforming growth factor (TGF)-β1 in the same fashion as the in vitro RPE cell EMT model. A PVR rat model was prepared by intravitreous injection of ARPE-19 cells with plasma-rich platelets.Results: miR-194 was preferentially expressed in the RPE cell layer compared with the outer nuclear layer (ONL), inner nuclear layer (INL), and ganglion cell layer in rat retina. RNAseq analysis indicated that miR-194 overexpression was involved in RPE cell processes, including phagocytosis, ECM-receptor interaction, cell adhesion molecules, and focal adhesion. miR-194 overexpression significantly inhibited the TGF-β1-induced EMT phenotype of RPE cells in vitro. Zinc finger E-box binding homeobox 1 (ZEB1), a key transcription factor in EMT, was confirmed as the direct functional target of miR-194. Knockdown of ZEB1 attenuated TGF-β1-induced α-smooth muscle actin expression in ARPE-19 cells, and overexpression of miR-194 could significantly reduce the expression of some genes which were up-regulated by ZEB1. Exogenous miR-194 administration in vivo effectively suppressed PVR in the rat model, both functionally and structurally. Cui et al. miR-194 targeting ZEB1 in PVR
Background Plasma metabolomic profile is disturbed in dementia patients, but previous studies have discordant conclusions. Methods Circulating metabolomic data of 110,655 people in the UK Biobank study were measured with nuclear magnetic resonance technique, and incident dementia records were obtained from national health registers. The associations between plasma metabolites and dementia were estimated using Cox proportional hazard models. The 10-fold cross-validation elastic net regression models selected metabolites that predicted incident dementia, and a 10-year prediction model for dementia was constructed by multivariable logistic regression. The predictive values of the conventional risk model, the metabolites model, and the combined model were discriminated by comparison of area under the receiver operating characteristic curves (AUCs). Net reclassification improvement (NRI) was used to estimate the change of reclassification ability when adding metabolites into the conventional prediction model. Results Amongst 110,655 participants, the mean (standard deviation) age was 56.5 (8.1) years, and 51 186 (46.3%) were male. A total of 1439 (13.0%) developed dementia during a median follow-up of 12.2 years (interquartile range: 11.5–12.9 years). A total of 38 metabolites, including lipids and lipoproteins, ketone bodies, glycolysis-related metabolites, and amino acids, were found to be significantly associated with incident dementia. Adding selected metabolites (n=24) to the conventional dementia risk prediction model significantly improved the prediction for incident dementia (AUC: 0.824 versus 0.817, p =0.042) and reclassification ability (NRI = 4.97%, P = 0.009) for identifying high risk groups. Conclusions Our analysis identified various metabolomic biomarkers which were significantly associated with incident dementia. Metabolomic profiles also provided opportunities for dementia risk reclassification. These findings may help explain the biological mechanisms underlying dementia and improve dementia prediction.
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