Patient preferences, knowledge and beliefs about kidney allocation: qualitative findings from the UK-wide ATTOM programme.

OBJECTIVE: To explore how patients who are wait-listed for or who have received a kidney transplant understand the current UK kidney allocation system, and their views on ways to allocate kidneys in the future. DESIGN: Qualitative study using semistructured interviews and thematic analysis based on a pragmatic approach. PARTICIPANTS: 10 deceased-donor kidney transplant recipients, 10 live-donor kidney transplant recipients, 12 participants currently wait-listed for a kidney transplant and 4 participants whose kidney transplant failed. SETTING: Semistructured telephone interviews conducted with participants in their own homes across the UK. RESULTS: Three main themes were identified: uncertainty of knowledge of the allocation scheme; evaluation of the system and participant suggestions for future allocation schemes. Most participants identified human leucocyte anitgen matching as a factor in determining kidney allocation, but were often uncertain of the accuracy of their knowledge. In the absence of information that would allow a full assessment, the majority of participants consider that the current system is effective. A minority of participants were concerned about the perceived lack of transparency of the general decision-making processes within the scheme. Most participants felt that people who are younger and those better matched to the donor kidney should be prioritised for kidney allocation, but in contrast to the current scheme, less priority was considered appropriate for longer waiting patients. Some non-medical themes were also discussed, such as whether parents of dependent children should be prioritised for allocation, and whether patients with substance abuse problems be deprioritised. CONCLUSIONS: Our participants held differing views about the most important factors for kidney allocation, some of which were in contrast to the current scheme. Patient participation in reviewing future allocation policies will provide insight as to what is considered acceptable to patients and inform healthcare staff of the kinds of information patients would find most useful

Contrasting patterns of population structure and gene flow facilitate exploration of connectivity in two widely distributed temperate octocorals

This is the final version of the article. Available from Springer Nature via the DOI in this record.Connectivity is an important component of metapopulation dynamics in marine systems and can influence population persistence, migration rates and conservation decisions associated with Marine Protected Areas (MPAs). In this study, we compared the genetic diversity, gene flow and population structure of two octocoral species, Eunicella verrucosa and Alcyonium digitatum, in the northeast Atlantic (ranging from the northwest of Ireland and the southern North Sea, to southern Portugal), using two panels of thirteen and eight microsatellite loci, respectively. Our results identified regional genetic structure in E. verrucosa partitioned between populations from southern Portugal, northwest Ireland, and Britain/France; subsequent hierarchical analysis of population structure also indicated reduced gene flow between southwest Britain and northwest France. However, over a similar geographical area, A. digitatum showed little evidence of population structure, suggesting high gene flow and/or a large effective population size; indeed, the only significant genetic differentiation detected in A. digitatum occurred between North Sea samples and those from the English Channel/northeast Atlantic. In both species the vast majority of gene flow originated from sample sites within regions, with populations in southwest Britain being the predominant source of contemporary exogenous genetic variants for the populations studied. Unsurprisingly, historical patterns of gene flow appeared more complex, though again southwest Britain appeared an important source of genetic variation for both species. Our findings have major conservation implications, particularly for E. verrucosa, a protected species in UK waters and listed by the IUCN as ‘Vulnerable’, and for the designation and management of European MPAs.We thank Natural England (project No. RP0286, contract No. SAE 03-02-146), the NERC (grant No. NE/L002434/1) and the University of Exeter for funding this research. Additional funding for sample collection, travel and microsatellite development was provided by the EU Framework 7 ASSEMBLE programme, agreement no. 227799, and NERC grant No. NBAF-362

Diet and asthma: looking back, moving forward

Asthma is an increasing global health burden, especially in the western world. Public health interventions are sought to lessen its prevalence or severity, and diet and nutrition have been identified as potential factors. With rapid changes in diet being one of the hallmarks of westernization, nutrition may play a key role in affecting the complex genetics and developmental pathophysiology of asthma. The present review investigates hypotheses about hygiene, antioxidants, lipids and other nutrients, food types and dietary patterns, breastfeeding, probiotics and intestinal microbiota, vitamin D, maternal diet, and genetics. Early hypotheses analyzed population level trends and focused on major dietary factors such as antioxidants and lipids. More recently, larger dietary patterns beyond individual nutrients have been investigated such as obesity, fast foods, and the Mediterranean diet. Despite some promising hypotheses and findings, there has been no conclusive evidence about the role of specific nutrients, food types, or dietary patterns past early childhood on asthma prevalence. However, diet has been linked to the development of the fetus and child. Breastfeeding provides immunological protection when the infant's immune system is immature and a modest protective effect against wheeze in early childhood. Moreover, maternal diet may be a significant factor in the development of the fetal airway and immune system. As asthma is a complex disease of gene-environment interactions, maternal diet may play an epigenetic role in sensitizing fetal airways to respond abnormally to environmental insults. Recent hypotheses show promise in a biological approach in which the effects of dietary factors on individual physiology and immunology are analyzed before expansion into larger population studies. Thus, collaboration is required by various groups in studying this enigma from epidemiologists to geneticists to immunologists. It is now apparent that this multidisciplinary approach is required to move forward and understand the complexity of the interaction of dietary factors and asthma

PALB2, CHEK2 and ATM rare variants and cancer risk: data from COGS

Background: The rarity of mutations in PALB2, CHEK2 and ATM make it difficult to estimate precisely associated cancer risks. Population-based family studies have provided evidence that at least some of these mutations are associated with breast cancer risk as high as those associated with rare BRCA2 mutations. We aimed to estimate the relative risks associated with specific rare variants in PALB2, CHEK2 and ATM via a multicentre case-control study.Methods: We genotyped 10 rare mutations using the custom iCOGS array: PALB2 c.1592delT, c.2816T&gt;G and c.3113G&gt;A, CHEK2c.349A&gt;G, c.538C&gt;T, c.715G&gt;A, c.1036C&gt;T, c.1312G&gt;T, and c.1343T&gt;G and ATM c.7271T&gt;G. We assessed associations with breast cancer risk (42 671 cases and 42 164 controls), as well as prostate (22 301 cases and 22 320 controls) and ovarian (14 542 cases and 23 491 controls) cancer risk, for each variant.Results: For European women, strong evidence of association with breast cancer risk was observed for PALB2 c.1592delT OR 3.44 (95% CI 1.39 to 8.52, p=7.1×10−5), PALB2 c.3113G&gt;A OR 4.21 (95% CI 1.84 to 9.60, p=6.9×10−8) and ATM c.7271T&gt;G OR 11.0 (95% CI 1.42 to 85.7, p=0.0012). We also found evidence of association with breast cancer risk for three variants in CHEK2, c.349A&gt;G OR 2.26 (95% CI 1.29 to 3.95), c.1036C&gt;T OR 5.06 (95% CI 1.09 to 23.5) and c.538C&gt;T OR 1.33 (95% CI 1.05 to 1.67) (p≤0.017). Evidence for prostate cancer risk was observed for CHEK2 c.1343T&gt;G OR 3.03 (95% CI 1.53 to 6.03, p=0.0006) for African men and CHEK2 c.1312G&gt;T OR 2.21 (95% CI 1.06 to 4.63, p=0.030) for European men. No evidence of association with ovarian cancer was found for any of these variants.Conclusions: This report adds to accumulating evidence that at least some variants in these genes are associated with an increased risk of breast cancer that is clinically important.</p

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation