Explore the vast range of titles available.

Purpose Automated variant filtering is an essential part of diagnostic genome-wide sequencing but may generate false negative results. We sought to investigate whether some previously identified pathogenic variants may be being routinely excluded by standard variant filtering pipelines. Methods We evaluated variants that were previously classified as pathogenic or likely pathogenic in ClinVar in known developmental disorder genes using exome sequence data from the Deciphering Developmental Disorders (DDD) study. Results Of these ClinVar pathogenic variants, 3.6% were identified among 13,462 DDD probands, and 1134/1352 (83.9%) had already been independently communicated to clinicians using DDD variant filtering pipelines as plausibly pathogenic. The remaining 218 variants failed consequence, inheritance, or other automated variant filters. Following clinical review of these additional variants, we were able to identify 112 variants in 107 (0.8%) DDD probands as potential diagnoses. Conclusion Lower minor allele frequency (<0.0005%) and higher gold star review status in ClinVar (>1 star) are good predictors of a previously identified variant being plausibly diagnostic for developmental disorders. However, around half of previously identified pathogenic variants excluded by automated variant filtering did not appear to be disease-causing, underlining the continued need for clinical evaluation of candidate variants as part of the diagnostic process.

Background The dystrophin glycoprotein complex is disrupted in Duchenne muscular dystrophy and many other neuromuscular diseases. The principal heterodimeric partner of dystrophin at the heart of the dystrophin glycoprotein complex in the main clinically affected tissues (skeletal muscle, heart and brain) is its distant relative, α-dystrobrevin. The α-dystrobrevin gene is subject to complex transcriptional and post-transcriptional regulation, generating a substantial range of isoforms by alternative promoter use, alternative polyadenylation and alternative splicing. The choice of isoform is understood, amongst other things, to determine the stoichiometry of syntrophins (and their ligands) in the dystrophin glycoprotein complex. Results We show here that, contrary to the literature, most α-dystrobrevin genes, including that of humans, encode three distinct syntrophin-binding sites, rather than two, resulting in a greatly enhanced isoform repertoire. We compare in detail the quantitative tissue-specific expression pattern of human and mouse α-dystrobrevin isoforms, and show that two major gene features (the novel syntrophin-binding site-encoding exon and the internal promoter and first exon of brain-specific isoforms α-dystrobrevin-4 and -5) are present in most mammals but specifically ablated in mouse and rat. Conclusion Lineage-specific mutations in the murids mean that the mouse brain has fewer than half of the α-dystrobrevin isoforms found in the human brain. Our finding that there are likely to be fundamental functional differences between the α-dystrobrevins (and therefore the dystrophin glycoprotein complexes) of mice and humans raises questions about the current use of the mouse as the principal model animal for studying Duchenne muscular dystrophy and other related disorders, especially the neurological aspects thereof.

Purpose and scopeThe aim of this position statement is to provide recommendations regarding the delivery of genomic testing to patients with rare disease in the UK and Ireland. The statement has been developed to facilitate timely and equitable access to genomic testing with reporting of results within commissioned turnaround times.Methods of statement developmentA 1-day workshop was convened by the UK Association for Clinical Genomic Science and attended by key stakeholders within the NHS Genomic Medicine Service, including clinical scientists, clinical geneticists and patient support group representatives. The aim was to identify best practice and innovations for streamlined, geographically consistent services delivering timely results. Attendees and senior responsible officers for genomic testing services in the UK nations and Ireland were invited to contribute.Results and conclusionsWe identified eight fundamental requirements and describe these together with key enablers in the form of specific recommendations. These relate to laboratory practice (proportionate variant analysis, bioinformatics pipelines, multidisciplinary team working model and test request monitoring), compliance with national guidance (variant classification, incidental findings, reporting and reanalysis), service development and improvement (multimodal testing and innovation through research, informed by patient experience), service demand, capacity management, workforce (recruitment, retention and development), and education and training for service users. This position statement was developed to provide best practice guidance for the specialist genomics workforce within the UK and Ireland but is relevant to any publicly funded healthcare system seeking to deliver timely rare disease genomic testing in the context of high demand and limited resources.

This article summarizes the results of the validation studies of a multi-residue method for the determination of pesticide residues in fruits and vegetables. The results of the monitoring control programs carried out in Cyprus during 2016 are also presented. In total, 314 samples of fruits and vegetables were analyzed for residues of, in total, 243 different pesticides. Gas chromatography and liquid chromatography coupled with mass spectrometry (GC–MS/MS and LC–MS/MS) were used to assess the levels of pesticide residues. For the extraction of the compounds the ethyl acetate extraction method with Ultra Turrax (Christodoulou et al. in J Wine Res 26:81–98, 2015a , Int J Environ Anal Chem 95:894–910, 2015b ) was modified so that a quick and efficient extraction method could be applied. For the validation of the method blank samples were spiked with a solution of 203 and 101 pesticides for the LC–MS/MS and GC–MS/MS analyses, respectively, at two levels. The validation study was in accordance with DG SANTE guidelines (European Commission, Safety of the Food Chain Pesticides and biocides SANTE/11813/2017 (21–22 November 2017 Rev.0), 2017 ). The scope of validation included recovery, linearity, limit of quantification and precision. The measurement uncertainty was calculated using the results of proficiency tests. Out of a total of 314 analyzed samples 196 (62.4 %) were found contaminated with pesticide residues, whereas 38.5 % of the samples contained more than one pesticide. 6.7 % of the samples exceeded the MRLs of the regulation EU 396/2005. The most frequently found pesticides were cypermethrin, boscalid, imidacloprid and tebuconazole.

The assumption of separability is used heavily in spatiotemporal statistics. Separability means that the spatiotemporal covariance structure factors into the product of two functions, one depending only on space and the other only on time. Separability is a property which can dramatically improve computational efficiency by substantially reducing model complexity. It is especially useful for functional data as it implies that the functional principal components are the same for each spatial location. In Chapter 1, we give a brief introduction to functional data, separability and introduce the data sets that motivate this dissertation. In Chapter 2, we present a new methodology to test for separability of spatiotemporal functional data. We present three tests, one being a functional extension of the Monte Carlo likelihood method of Mitchell et al. (2005), while the other two are based on quadratic forms. Our tests are based on asymptotic distributions of maximum likelihood estimators, and do not require Monte Carlo or bootstrap replications. The specification of the joint asymptotic distribution of these estimators is the main theoretical contribution in this chapter. It can be used to derive many other tests. The main methodological finding is that one of the quadratic form methods, which we call a norm approach, emerges as a clear winner in terms of finite sample performance in nearly every setting we considered. The norm approach focuses directly on the Frobenius distance between the spatiotemporal covariance function and its separable approximation. We demonstrate the efficacy of our methods via simulations, and applications to Irish wind data and Nitrogen Dioxide levels on the east coast of the United States. In Chapter 3, we derive and study a significance test for determining if a panel of functional time series is separable. In this context, separability means that the covariance structure factors into the product of two functions, one depending only on time and the other depending only on the coordinates of the panel. In this case, under the assumption of separability, the functional principal components are the same for each member of the panel. However such an assumption must be verified before proceeding with further inference. Our approach is based on functional norm differences and provides a test with well controlled size and high power. In addition to an asymptotic justification, our methodology is validated by a simulation study. It is applied to functional panels of particulate pollution and stock market data.

Pathogenic ELN gene mutations (ELN, MIM#130160) cause AD Supravalvular Aortic Stenosis (SVAS) a congenital narrowing of the ascending aorta, and Cutis Laxa (CL) characterised by inelastic, loose-hanging skin. Variable phenotype and penetrance is apparent. Pathogenic ELN variants result in loss of function and include frameshift (most common), nonsense, splice site and missense variants. The well characterised contiguous gene deletion syndrome, Williams-Beuren syndrome includes SVAS and encompasses at least 114kb on 7q11.23 including the ELN gene; however, there are only 5 case reports of CNVs within ELN (single or multiple exons).Bristol Genetics Laboratory provides a UKGTN approved service for ELN gene sequencing (33 coding exons). In three years, 52 UK and foreign patients with SVAS, CL or features such as pulmonary artery stenosis and aortic dilation have been tested. 18/52 (34%) patients were heterozygous for a likely pathogenic variant including frameshift (6), nonsense (4), splice (4), and missense (4). 12 of these cases were novel variants, 5 are supported by segregation analysis and 1 is sporadic. The remaining novel variants are classed as possibly pathogenic as they are phenotypically compatible.12/35 patients negative on sequencing have so far been screened for CNVs by MLPA (MRC Holland) covering the Williams-Beuren syndrome region, including 10 exons of the ELN gene (1, 3, 4, 6, 9, 16, 20, 26, 27 and 33) and in addition a bespoke MLPA assay including probes for exons 28 to 30, 32 and 3’UTR.4/12 (33%) patients have a heterozygous deletion within the ELNgene. A mother and daughter with pulmonary stenosis and an extended family history have a deletion spanning exons 30 to 33. This deletion was also identified in another patient with SVAS and arteriopathy. A deletion of the 5’ end of the gene, involving at least exon 1 (but not exon 3) was identified in an infant with SVAS and pulmonary branch stenosis, and a deletion involving the entire coding region of the ELN gene and at least the first two exons of the adjacent 3’ gene LIMK1 was detected in a neonate who died at 2 months with SVAS, pulmonary stenosis and mild hypoplasia with PDA. The deletion was detected in this patient’s father who consequentially was found to have an aortic regurgitation and in a subsequent pregnancy of this family which was lost at 31 weeks with pulmonary stenosis and significant aortic stenosisMLPA analysis has enhanced the clinical utility of this service giving an increased diagnostic yield in patients with SVAS and CL and related presentations.

We derive and study a significance test for determining if a panel of functional time series is separable. In the context of this paper, separability means that the covariance structure factors into the product of two functions, one depending only on time and the other depending only on the coordinates of the panel. Separability is a property which can dramatically improve computational efficiency by substantially reducing model complexity. It is especially useful for functional data as it implies that the functional principal components are the same for each member of the panel. However such an assumption must be verified before proceeding with further inference. Our approach is based on functional norm differences and provides a test with well controlled size and high power. We establish our procedure quite generally, allowing one to test separability of autocovariances as well. In addition to an asymptotic justification, our methodology is validated by a simulation study. It is applied to functional panels of particulate pollution and stock market data.

We present a new methodology and accompanying theory to test for separability of spatio-temporal functional data. In spatio-temporal statistics, separability is a common simplifying assumption concerning the covariance structure which, if true, can greatly increase estimation accuracy and inferential power. While our focus is on testing for the separation of space and time in spatio-temporal data, our methods can be applied to any area where separability is useful, including biomedical imaging. We present three tests, one being a functional extension of the Monte Carlo likelihood method of Mitchell et. al. (2005), while the other two are based on quadratic forms. Our tests are based on asymptotic distributions of maximum likelihood estimators, and do not require Monte Carlo or bootstrap replications. The specification of the joint asymptotic distribution of these estimators is the main theoretical contribution of this paper. It can be used to derive many other tests. The main methodological finding is that one of the quadratic form methods, which we call a norm approach, emerges as a clear winner in terms of finite sample performance in nearly every setting we considered. The norm approach focuses directly on the Frobenius distance between the spatio-temporal covariance function and its separable approximation. We demonstrate the efficacy of our methods via simulations and an application to Irish wind data.

Complex transmission patterns are not immediately obvious to epidemiologists, hindering the development of effective intervention strategies. The aim is to develop network-based tools to identify transmission patterns across age-groups, occupations, and locations. Infection networks were constructed using COVID-19 contact tracing data, provided by the Cyprus Ministry of Health, for March 2020 to May 2021. Transmission patterns within/across age-groups, districts, and economic activities, as well as the presence of super-spreaders and the vulnerability of different groups, were assessed using the constructed networks for the first four pandemic waves. The constructed networks for all waves were sparse. Network analysis, showed that the first wave primarily involved older individuals and healthcare settings. During the second wave, a higher infection rate among young adults was observed. The dominant transmission patterns during the third and fourth wave existed between (i) individuals of similar ages, who more commonly interact, and (ii) individuals with an age difference of ~ 30 years (i.e. a generational gap). Cross-district patterns revealed transmissions were likely to occur between districts that are nearest geographically but also followed social trends. Furthermore, the study identified vulnerable occupations. Outdegree, representing the number of secondary infections caused by an individual, was also investigated. As the pandemic progressed, a decreased outdegree among older age groups (50 +) likely reflected the positive effect of vaccinations and immunity. In contrast, rising values among younger individuals probably reflected fewer vaccinations and more active social interactions. Age, district, and occupation patterns of transmission and super-spreader concentrations can guide targeted intervention strategies, prioritize vaccination efforts, and support decision-making for effective control and prevention of pandemics.

Background: Chronic kidney disease (CKD) affects over 10% of the global population and imposes a growing burden on healthcare systems. Aim: To explore nurses’ perceptions of their roles in CKD care and identify factors influencing role implementation. Methods: An Interpretative Phenomenological Approach (IPA) was employed, involving semi-structured interviews with 16 purposively selected nurses from all district hospitals in the Republic of Cyprus. Thematic analysis was conducted on the transcribed data. Findings: Nurses identified five core roles in CKD care: machine operator, holistic caregiver, bureaucratic coordinator, patient educator, and emotional supporter. These roles varied by setting. Key influencing factors included nurse training, organizational challenges, barriers to patient education, patient behavior, and nurses’ coping strategies. Conclusions: Nurses are essential to quality CKD care, particularly in patient education. A framework was developed to address barriers and support nurses, healthcare organizations, and patients in improving care delivery.