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- Tabiri, S, et al.
(author)
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- 2021
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swepub:Mat__t
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- Bravo, L, et al.
(author)
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- 2021
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swepub:Mat__t
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- Kanai, M, et al.
(author)
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- 2023
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swepub:Mat__t
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- Campbell, PJ, et al.
(author)
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Pan-cancer analysis of whole genomes
- 2020
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In: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 578:7793, s. 82-
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Journal article (peer-reviewed)abstract
- Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1–3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4–5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10–18.
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- Thoma, B, et al.
(author)
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An international, interprofessional investigation of the self-reported podcast listening habits of emergency clinicians: A METRIQ Study
- 2020
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In: CJEM. - : Springer Science and Business Media LLC. - 1481-8043 .- 1481-8035. ; 22:1, s. 112-117
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Journal article (peer-reviewed)abstract
- ObjectivesPodcasts are increasingly being used for medical education. A deeper understanding of usage patterns would inform both producers and researchers of medical podcasts. We aimed to determine how and why podcasts are used by emergency medicine and critical care clinicians.MethodsAn international interprofessional sample (medical students, residents, physicians, nurses, physician assistants, and paramedics) was recruited through direct contact and a multimodal social media (Twitter and Facebook) campaign. Each participant completed a survey outlining how and why they utilize medical podcasts. Recruitment materials included an infographic and study website.Results390 participants from 33 countries and 4 professions (medicine, nursing, paramedicine, physician assistant) completed the survey. Participants most frequently listened to medical podcasts to review new literature (75.8%), learn core material (75.1%), and refresh memory (71.8%). The majority (62.6%) were aware of the ability to listen at increased speeds, but most (76.9%) listened at 1.0 x (normal) speed. All but 25 (6.4%) participants concurrently performed other tasks while listening. Driving (72.3%), exercising (39.7%), and completing chores (39.2%) were the most common. A minority of participants used active learning techniques such as pausing, rewinding, and replaying segments of the podcast. Very few listened to podcasts multiple times.ConclusionsAn international cohort of emergency clinicians use medical podcasts predominantly for learning. Their listening habits (rarely employing active learning strategies and frequently performing concurrent tasks) may not support this goal. Further exploration of the impact of these activities on learning from podcasts is warranted.
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- Klonoff, D. C., et al.
(author)
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A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings
- 2022
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In: Journal of Diabetes Science and Technology. - : SAGE Publications. - 1932-2968.
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Journal article (peer-reviewed)abstract
- Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.
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