Technical Note: A comparison of central and peripheral intraocular pressure using rebound tonometry

Purpose: To compare central and peripheral intraocular pressure (IOP) readings obtained with rebound tonometry.Methods: Intraocular pressure was measured on the right eye of 153 patients (65 males, 88 females), aged from 21 to 85 years (mean +/- S.D., 55.5 +/- 15.2 years) with the ICare rebound tonometer at centre, and 2 mm from the limbus (in the nasal and temporal regions along the 0-180O corneal meridian).Results: Intraocular pressure values obtained with the ICare were 14.9 +/- 2.8; 14.1 +/- 2.5 and 14.5 +/- 2.7 mmHg at centre, nasal and temporal corneal locations, respectively. On average, nasal and temporal IOP readings were 0.75 and 0.37 mmHg lower than the central reading (p 0.05, respectively). A highly significant correlation was found between central and peripheral measurements in nasal (r(2) = 0.905; p < 0.001) and temporal (r(2) = 0.879; p < 0.001) regions along the horizontal meridian. Almost 80% of patients presented nasal IOP values within +/- 1 mmHg of the central value.Conclusions: Intraocular pressure values measured with the ICare (R) rebound tonometer on the nasal corneal region is slightly lower on average and highly correlated with IOP values recorded at corneal centre. Both nasal and temporal readings are in good agreement with central IOP, and could be used to obtain a reliable estimate of rebound IOP in corneas where central readings cannot be taken.- (undefined

Cell Pattern in Adult Human Corneal Endothelium

A review of the current data on the cell density of normal adult human endothelial cells was carried out in order to establish some common parameters appearing in the different considered populations. From the analysis of cell growth patterns, it is inferred that the cell aging rate is similar for each of the different considered populations. Also, the morphology, the cell distribution and the tendency to hexagonallity are studied. The results are consistent with the hypothesis that this phenomenon is analogous with cell behavior in other structures such as dry foams and grains in polycrystalline materials. Therefore, its driving force may be controlled by the surface tension and the mobility of the boundaries

Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, individuals with clonal mosaic events (clonal mosaicism for chromosome alterations and/or loss of chromosome Y) showed an increased risk of COVID-19 lethality

Determination of central corneal clearance in scleral lenses with an optical biometer and agreement with subjective evaluation

Purpose: To compare three methods to measure central corneal clearance (CCC) during scleral lens wear: subjective (slit lamp), image processed (ImageJ) and with an optic biometer. The optic biometer technique was validated in comparison to an OCT in the first part of the study.Methods: Twenty-two eyes (11 subjects) with healthy corneas were recruited. Three measures of OCT with scleral lens and ten measurements of axial length (AL) with IOLMaster with and without lens were performed. For the second part, 61 eyes (35 subjects) enrolled in a clinical study were selected. Measurements of CCC were done with IOLMaster, SlitLamp and ImageJ.Results: The measurements of CCC indirectly obtained with IOLMaster had a strong correlation with AS-OCT measurements (r = 0.981), showing a mean difference of 122.18 +/- 46.05 mu m (higher with IOLMaster). Regarding the second part, measurements of CCC were 238.66 +/- 95.94 mu m, 250.16 +/- 124.31 mu m and 263.15 +/- 90.60 mu m, for the IOLMaster, SlitLamp and ImageJ, respectively. The correlations were higher for ImageJ vs Subjective measure (r = 0.891) than for IOL vs Subjective (r = 0.748) and IOL vs ImageJ (r = 0.745). Analysis of differences and correlations between SlitLamp and ImageJ through time showed a mean difference of -32.28 +/- 89.95 mu m (r = 0.683) at V1month, 12.53 +/- 59.46 mu m (r = 0.850) at V6months and 11.57 +/- 32.95 mu m (r = 0.940) at V12months.Conclusions: It is possible to measure CCC with IOLMaster, considering AL measured with and without lens and lens thickness. The three methods tested have good correspondence, showing that IOLMaster and ImageJ could be objective techniques to measure CCC. Also, it is possible to improve the agreement of subjective measures when compared to objective measures trough time.This project was supported in part by an unrestricted grant from Bausch & Lomb (Rochester, USA) and Portuguese Science and Technology Foundation projects PTDC/SAU-BEB/098391/2008, PTDC/FIS-OPT/0677/2014 and the Strategic Funding UID/FIS/04650/2013. Authors declare that they do not have any proprietary or financial interest in any of the materials mentioned in this article. The authors want to acknowledge Procornea and Ron Beerten for their support of this project and for providing all of the lenses used in this study

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19

Data availability: Downloadable summary data are available through the GenOMICC data site (https://genomicc.org/data). Summary statistics are available, but without the 23andMe summary statistics, except for the 10,000 most significant hits, for which full summary statistics are available. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For further information and to apply for access to the data, see the 23andMe website (https://research.23andMe.com/dataset-access/). All individual-level genotype and whole-genome sequencing data (for both academic and commercial uses) can be accessed through the UKRI/HDR UK Outbreak Data Analysis Platform (https://odap.ac.uk). A restricted dataset for a subset of GenOMICC participants is also available through the Genomics England data service. Monocyte RNA-seq data are available under the title ‘Monocyte gene expression data’ within the Oxford University Research Archives (https://doi.org/10.5287/ora-ko7q2nq66). Sequencing data will be made freely available to organizations and researchers to conduct research in accordance with the UK Policy Framework for Health and Social Care Research through a data access agreement. Sequencing data have been deposited at the European Genome–Phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001007111.Extended data figures and tables are available online at https://www.nature.com/articles/s41586-023-06034-3#Sec21 .Supplementary information is available online at https://www.nature.com/articles/s41586-023-06034-3#Sec22 .Code availability: Code to calculate the imputation of P values on the basis of SNPs in linkage disequilibrium is available at GitHub (https://github.com/baillielab/GenOMICC_GWAS).Acknowledgements: We thank the members of the Banco Nacional de ADN and the GRA@CE cohort group; and the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available online (https://www.covid19hg.org/acknowledgements).Change history: 11 July 2023: A Correction to this paper has been published at: https://doi.org/10.1038/s41586-023-06383-z. -- In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.Copyright © The Author(s) 2023, Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).GenOMICC was funded by Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (to J.K.B., 223164/Z/21/Z), the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council, UKRI, a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. A.D.B. acknowledges funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z), the Edinburgh Clinical Academic Track (ECAT) programme. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant MC_PC_20029). Laboratory work was funded by a Wellcome Intermediate Clinical Fellowship to B.F. (201488/Z/16/Z). We acknowledge the staff at NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre who provided clinical data on the participants; and the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. GenOMICC genotype controls were obtained using UK Biobank Resource under project 788 funded by Roslin Institute Strategic Programme Grants from the BBSRC (BBS/E/D/10002070 and BBS/E/D/30002275) and Health Data Research UK (HDR-9004 and HDR-9003). UK Biobank data were used in the GSMR analyses presented here under project 66982. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The work of L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). J.Y. is supported by the Westlake Education Foundation. SCOURGE is funded by the Instituto de Salud Carlos III (COV20_00622 to A.C., PI20/00876 to C.F.), European Union (ERDF) ‘A way of making Europe’, Fundación Amancio Ortega, Banco de Santander (to A.C.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.). We also acknowledge the contribution of the Centro National de Genotipado (CEGEN) and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures. A.D.L. is a recipient of fellowships from the National Council for Scientific and Technological Development (CNPq)-Brazil (309173/2019-1 and 201527/2020-0)