19 research outputs found

    Health, education, and social care provision after diagnosis of childhood visual disability

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    Aim: To investigate the health, education, and social care provision for children newly diagnosed with visual disability.Method: This was a national prospective study, the British Childhood Visual Impairment and Blindness Study 2 (BCVIS2), ascertaining new diagnoses of visual impairment or severe visual impairment and blindness (SVIBL), or equivalent vi-sion. Data collection was performed by managing clinicians up to 1-year follow-up, and included health and developmental needs, and health, education, and social care provision.Results: BCVIS2 identified 784 children newly diagnosed with visual impairment/SVIBL (313 with visual impairment, 471 with SVIBL). Most children had associated systemic disorders (559 [71%], 167 [54%] with visual impairment, and 392 [84%] with SVIBL). Care from multidisciplinary teams was provided for 549 children (70%). Two-thirds (515) had not received an Education, Health, and Care Plan (EHCP). Fewer children with visual impairment had seen a specialist teacher (SVIBL 35%, visual impairment 28%, χ2p < 0.001), or had an EHCP (11% vs 7%, χ2p < 0 . 01).Interpretation: Families need additional support from managing clinicians to access recommended complex interventions such as the use of multidisciplinary teams and educational support. This need is pressing, as the population of children with visual impairment/SVIBL is expected to grow in size and complexity.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited

    The FANCM:p.Arg658* truncating variant is associated with risk of triple-negative breast cancer

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    Abstract: Breast cancer is a common disease partially caused by genetic risk factors. Germline pathogenic variants in DNA repair genes BRCA1, BRCA2, PALB2, ATM, and CHEK2 are associated with breast cancer risk. FANCM, which encodes for a DNA translocase, has been proposed as a breast cancer predisposition gene, with greater effects for the ER-negative and triple-negative breast cancer (TNBC) subtypes. We tested the three recurrent protein-truncating variants FANCM:p.Arg658*, p.Gln1701*, and p.Arg1931* for association with breast cancer risk in 67,112 cases, 53,766 controls, and 26,662 carriers of pathogenic variants of BRCA1 or BRCA2. These three variants were also studied functionally by measuring survival and chromosome fragility in FANCM−/− patient-derived immortalized fibroblasts treated with diepoxybutane or olaparib. We observed that FANCM:p.Arg658* was associated with increased risk of ER-negative disease and TNBC (OR = 2.44, P = 0.034 and OR = 3.79; P = 0.009, respectively). In a country-restricted analysis, we confirmed the associations detected for FANCM:p.Arg658* and found that also FANCM:p.Arg1931* was associated with ER-negative breast cancer risk (OR = 1.96; P = 0.006). The functional results indicated that all three variants were deleterious affecting cell survival and chromosome stability with FANCM:p.Arg658* causing more severe phenotypes. In conclusion, we confirmed that the two rare FANCM deleterious variants p.Arg658* and p.Arg1931* are risk factors for ER-negative and TNBC subtypes. Overall our data suggest that the effect of truncating variants on breast cancer risk may depend on their position in the gene. Cell sensitivity to olaparib exposure, identifies a possible therapeutic option to treat FANCM-associated tumors

    SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

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    Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

    A first update on mapping the human genetic architecture of COVID-19

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    Laboratory and home comparison of wrist-activity monitors and polysomnography in middle-aged adults

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    © 2017, Japanese Society of Sleep Research. Accurate measurement of time at lights out is essential for calculation of several measures of sleep in wrist-activity monitors. While some devices use subjective reporting of time of lights out from a sleep diary, others utilise an automated proprietary scoring algorithm to calculate time at lights out, thereby negating the need for a sleep diary. This study aimed to compare sleep measures from two such devices to polysomnography (PSG) measures (In laboratory) and against each other when worn at home (At home). Fifty middle-aged adults from the Raine Study underwent overnight PSG during which they wore an ActiGraph™ and a Readiband™. They also wore both devices at home for 7 nights. The Readiband uses an automated proprietary algorithm to determine time at lights out whereas the ActiGraph requires completion of a sleep diary noting this time. In laboratory, compared to PSG: Readiband underestimated time at lights out, sleep onset, and wake after sleep onset, overestimated sleep latency and duration (p &lt; 0.001 for all); while ActiGraph underestimated sleep latency and wake after sleep onset and overestimated sleep efficiency and duration (p &lt; 0.001 for all). Similar differences between devices were observed on the laboratory night and when at home. In conclusion, an automated algorithm such as the Readiband may be used in the same capacity as the ActiGraph for the collection of sleep measures including time at sleep onset, sleep duration and time at wake. However, Readiband and ActiGraph measures of sleep latency, efficiency and wake after sleep onset should be interpreted with caution
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