Radiation-induced skin injury in the animal model of scleroderma: implications for post-radiotherapy fibrosis

<p>Abstract</p> <p>Background</p> <p>Radiation therapy is generally contraindicated for cancer patients with collagen vascular diseases (CVD) such as scleroderma due to an increased risk of fibrosis. The tight skin (TSK) mouse has skin which, in some respects, mimics that of patients with scleroderma. The skin radiation response of TSK mice has not been previously reported. If TSK mice are shown to have radiation sensitive skin, they may prove to be a useful model to examine the mechanisms underlying skin radiation injury, protection, mitigation and treatment.</p> <p>Methods</p> <p>The hind limbs of TSK and parental control C57BL/6 mice received a radiation exposure sufficient to cause approximately the same level of acute injury. Endpoints included skin damage scored using a non-linear, semi-quantitative scale and tissue fibrosis assessed by measuring passive leg extension. In addition, TGF-β1 cytokine levels were measured monthly in skin tissue.</p> <p>Results</p> <p>Contrary to our expectations, TSK mice were more resistant (i.e. 20%) to radiation than parental control mice. Although acute skin reactions were similar in both mouse strains, radiation injury in TSK mice continued to decrease with time such that several months after radiation there was significantly less skin damage and leg contraction compared to C57BL/6 mice (p < 0.05). Consistent with the expected association of transforming growth factor beta-1 (TGF-β1) with late tissue injury, levels of the cytokine were significantly higher in the skin of the C57BL/6 mouse compared to TSK mouse at all time points (p < 0.05).</p> <p>Conclusion</p> <p>TSK mice are not recommended as a model of scleroderma involving radiation injury. The genetic and molecular basis for reduced radiation injury observed in TSK mice warrants further investigation particularly to identify mechanisms capable of reducing tissue fibrosis after radiation injury.</p

PEDIA: prioritization of exome data by image analysis

Purpose Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists. Methods Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds. Results The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20–89% and the top 10 accuracy rate by more than 5–99% for the disease-causing gene. Conclusion Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis

Preferences of the public for sharing health data: discrete choice experiment

Background: Digital technological development in the last 20 years has led to significant growth in digital collection, use, and sharing of health data. To maintain public trust in the digital society and to enable acceptable policy-making in the future, it is important to investigate people’s preferences for sharing digital health data. Objective: The aim of this study is to elicit the preferences of the public in different Northern European countries (the United Kingdom, Norway, Iceland, and Sweden) for sharing health information in different contexts. Methods: Respondents in this discrete choice experiment completed several choice tasks, in which they were asked if data sharing in the described hypothetical situation was acceptable to them. Latent class logistic regression models were used to determine attribute-level estimates and heterogeneity in preferences. We calculated the relative importance of the attributes and the predicted acceptability for different contexts in which the data were shared from the estimates. Results: In the final analysis, we used 37.83% (1967/5199) questionnaires. All attributes influenced the respondents’ willingness to share health information (P<.001). The most important attribute was whether the respondents were informed about their data being shared. The possibility of opting out from sharing data was preferred over the opportunity to consent (opt-in). Four classes were identified in the latent class model, and the average probabilities of belonging were 27% for class 1, 32% for class 2, 23% for class 3, and 18% for class 4. The uptake probability varied between 14% and 85%, depending on the least to most preferred combination of levels. Conclusions: Respondents from different countries have different preferences for sharing their health data regarding the value of a review process and the reason for their new use. Offering respondents information about the use of their data and the possibility to opt out is the most preferred governance mechanism