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In response to rising numbers of opioid overdose deaths, primary care providers have been called on to play a greater role in delivering buprenorphine treatment for opioid use disorder. However, policy makers and providers have raised concerns that expanding treatment access may reduce treatment quality and that primary care providers are not well equipped to deliver buprenorphine treatment. We investigated two research questions in response to these concerns: How did buprenorphine treatment use and quality change in North carolina Medicaid from 2014 to 2017, and how did buprenorphine treatment quality differ between primary care providers and specialists in North Carolina Medicaid during this period? We measured buprenorphine treatment quality as patients' retention in treatment and providers' adherence to treatment guidelines. We found that the number of enrollees receiving medication treatment for opioid use disorder increased substantially, but the percentage of enrollees with the disorder receiving treatment remained low. The quality of buprenorphine treatment increased during the study period, and primary care providers provided care of comparable or higher quality compared with that of other providers. Treatment quality for buprenorphine treatment is improving, but there remains room for improvement in both use and quality. Our results support the role of primary care providers in expanding treatment for opioid use disorder.

Developing practical, effective, and legally sustainable policies to separate firearms from people at risk of harm ing themselves or others presents a potentially important, but challenging, public health opportunity for gun violence prevention in the US. This article sketches the relevant policy landscape in order to demonstrate that point-of-purchase background checks are a necessary but insufficient component of a strategy to reduce gun violence in the US, and that risk-based preemptive gun removal schemes provide a complementary policy to bridge the gap. It then briefly recounts the history of enactment and gradual implementation of Connecticut's risk-based gun removal law, beginning with the high-profile homicide that drove public opinion to support the law.

Gun violence kills about ninety people every day in the United States, a toll measured in wasted and ruined lives and with an annual economic price tag exceeding $200 billion. Some policy makers suggest that reforming mental health care systems and improving point-ofpurchase background checks to keep guns from mentally disturbed people will address the problem. Epidemiological research shows that serious mental illness contributes little to the risk of interpersonal violence but is a strong factor in suicide, which accounts for most firearm fatalities. Meanwhile, the effectiveness of gun restrictions focused on mental illness remains poorly understood. This article examines gun-related suicide and violent crime in people with serious mental illnesses, and whether legal restrictions on firearm sales to people with a history of mental health adjudication are effective in preventing gun violence. Among the study population in two large Florida counties, we found that 62 percent of violent gun crime arrests and 28 percent of gun suicides involved individuals not legally permitted to have a gun at the time. Suggested policy reforms include enacting risk-based gun removal laws and prohibiting guns from people involuntarily detained in short-term psychiatric hospitalizations.

Colorectal cancer (CRC) is preventable through screening, with colonoscopy and fecal occult blood testing comprising the two most commonly used screening tests. Given the differences in complexity, risk, and cost, it is important to understand these tests' comparative effectiveness. The CONFIRM Study is a large, pragmatic, multicenter, randomized, parallel group trial to compare screening with colonoscopy vs. the annual fecal immunochemical test (FIT) in 50,000 average risk individuals. CONFIRM examines whether screening colonoscopy will be superior to a FIT-based screening program in the prevention of CRC mortality measured over 10 years. Eligible individuals 50-75 years of age and due for CRC screening are recruited from 46 Veterans Affairs (VA) medical centers. Participants are randomized to either colonoscopy or annual FIT. Results of colonoscopy are managed as per usual care and study participants are assessed for complications. Participants testing FIT positive are referred for colonoscopy. Participants are surveyed annually to determine if they have undergone colonoscopy or been diagnosed with CRC. The primary endpoint is CRC mortality. The secondary endpoints are (1) CRC incidence (2) complications of screening colonoscopy, and (3) the association between colonoscopists' characteristics and neoplasia detection, complications and post-colonoscopy CRC. CONFIRM leverages several key characteristics of the VA's integrated healthcare system, including a shared medical record with national databases, electronic CRC screening reminders, and a robust national research infrastructure with experience in conducting large-scale clinical trials. When completed, CONFIRM will be the largest intervention trial conducted within the VA (ClinicalTrials.gov identifier: NCT01239082).

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale 1 – 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 promoter 4 ; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation 5 , 6 ; analyses timings and patterns of tumour evolution 7 ; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity 8 , 9 ; and evaluates a range of more-specialized features of cancer genomes 8 , 10 – 18 . The flagship paper of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium describes the generation of the integrative analyses of 2,658 cancer whole genomes and their matching normal tissues across 38 tumour types, the structures for international data sharing and standardized analyses, and the main scientific findings from across the consortium studies.

In cancer, the primary tumour’s organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium , we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA. Some cancer patients first present with metastases where the location of the primary is unidentified; these are difficult to treat. In this study, using machine learning, the authors develop a method to determine the tissue of origin of a cancer based on whole sequencing data.

Abstract Among the 30 nonsynonymous nucleotide substitutions in the Omicron S-gene are 13 that have only rarely been seen in other SARS-CoV-2 sequences. These mutations cluster within three functionally important regions of the S-gene at sites that will likely impact (1) interactions between subunits of the Spike trimer and the predisposition of subunits to shift from down to up configurations, (2) interactions of Spike with ACE2 receptors, and (3) the priming of Spike for membrane fusion. We show here that, based on both the rarity of these 13 mutations in intrapatient sequencing reads and patterns of selection at the codon sites where the mutations occur in SARS-CoV-2 and related sarbecoviruses, prior to the emergence of Omicron the mutations would have been predicted to decrease the fitness of any virus within which they occurred. We further propose that the mutations in each of the three clusters therefore cooperatively interact to both mitigate their individual fitness costs, and, in combination with other mutations, adaptively alter the function of Spike. Given the evident epidemic growth advantages of Omicron overall previously known SARS-CoV-2 lineages, it is crucial to determine both how such complex and highly adaptive mutation constellations were assembled within the Omicron S-gene, and why, despite unprecedented global genomic surveillance efforts, the early stages of this assembly process went completely undetected.

Multi-omics datasets represent distinct aspects of the central dogma of molecular biology. Such high-dimensional molecular profiles pose challenges to data interpretation and hypothesis generation. ActivePathways is an integrative method that discovers significantly enriched pathways across multiple datasets using statistical data fusion, rationalizes contributing evidence and highlights associated genes. As part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types, we integrated genes with coding and non-coding mutations and revealed frequently mutated pathways and additional cancer genes with infrequent mutations. We also analyzed prognostic molecular pathways by integrating genomic and transcriptomic features of 1780 breast cancers and highlighted associations with immune response and anti-apoptotic signaling. Integration of ChIP-seq and RNA-seq data for master regulators of the Hippo pathway across normal human tissues identified processes of tissue regeneration and stem cell regulation. ActivePathways is a versatile method that improves systems-level understanding of cellular organization in health and disease through integration of multiple molecular datasets and pathway annotations. Multi-omics datasets pose major challenges to data interpretation and hypothesis generation owing to their high-dimensional molecular profiles. Here, the authors develop ActivePathways method, which uses data fusion techniques for integrative pathway analysis of multi-omics data and candidate gene discovery.