Ethnic Differences in Bladder Cancer Survival

ObjectiveTo examine trends in bladder cancer survival among whites, blacks, Hispanics, and Asian/Pacific Islanders in the United States over a 30-year period. Racial disparities in bladder cancer outcomes have been documented with poorer survival observed among blacks. Bladder cancer outcomes in other ethnic minority groups are less well described.MethodsFrom the Surveillance, Epidemiology and End Results cancer registry data, we identified patients diagnosed with transitional cell carcinoma of the bladder between 1975 and 2005. This cohort included 163,973 white, 7731 black, 7364 Hispanic, and 5934 Asian/Pacific Islander patients. We assessed the relationship between ethnicity and patient characteristics. Disease-specific 5-year survival was estimated for each ethnic group and for subgroups of stage and grade.ResultsBlacks presented with higher-stage disease than whites, Hispanics, and Asian/Pacific Islanders, although a trend toward earlier-stage presentation was observed in all groups over time. Five-year disease-specific survival was consistently worse for blacks than for other ethnic groups, even when stratified by stage and grade. Five-year disease-specific survival was 82.8% in whites compared with 70.2% in blacks, 80.7% in Hispanics, and 81.9% in Asian/Pacific Islanders. There was a persistent disease-specific survival disadvantage in black patients over time that was not seen in the other ethnic groups.ConclusionEthnic disparities in bladder cancer survival persist between whites and blacks, whereas survival in other ethnic minority groups appears similar to that of whites. Further study of access to care, quality of care, and treatment decision making among black patients is needed to better understand these disparities

Guidance for New Motivational Interviewing Trainers When Training Addiction Professionals

Evidence-based practices, such as motivational interviewing (MI), are not widely used in community alcohol and drug treatment settings. Successfully broadening the dissemination of MI will require numerous trainers and supervisors who are equipped to manage common barriers  to technology transfer. The aims of the our survey of 36 MI trainers were: 1) to gather opinions about the optimal format, duration, and content for beginning level addiction-focused MI training conducted by novice trainers and 2) to identify the challenges most likely to be encountered during provision of beginninglevel MI training and supervision, as well as the most highly recommended strategies for managing those challenges in addiction treatment sites. It is hoped that the findings of this survey will help beginning trainers equip themselves for successful training experience

Characteristics of the National Applicant Pool for Clinical Informatics Fellowships (2016-2017)

We conducted a national study to assess the numbers and diversity of applicants for 2016 and 2017 clinical informatics fellowship positions. In each year, we collected data on the number of applications that programs received from candidates who were ultimately successful vs. unsuccessful. In 2017, we also conducted an anonymous applicant survey. Successful candidates applied to an average of 4.2 and 5.5 programs for 2016 and 2017, respectively. In the survey, unsuccessful candidates reported applying to fewer programs. Assuming unsuccessful candidates submitted between 2-5 applications each, the total applicant pool numbered 42-69 for 2016 (competing for 24 positions) and 52-85 for 2017 (competing for 30 positions). Among survey respondents (n=33), 24% were female, 1 was black and none were Hispanic. We conclude that greater efforts are needed to enhance interest in clinical informatics among medical students and residents, particularly among women and members of underrepresented minority groups

Mechanosensing is critical for axon growth in the developing brain.

During nervous system development, neurons extend axons along well-defined pathways. The current understanding of axon pathfinding is based mainly on chemical signaling. However, growing neurons interact not only chemically but also mechanically with their environment. Here we identify mechanical signals as important regulators of axon pathfinding. In vitro, substrate stiffness determined growth patterns of Xenopus retinal ganglion cell axons. In vivo atomic force microscopy revealed a noticeable pattern of stiffness gradients in the embryonic brain. Retinal ganglion cell axons grew toward softer tissue, which was reproduced in vitro in the absence of chemical gradients. To test the importance of mechanical signals for axon growth in vivo, we altered brain stiffness, blocked mechanotransduction pharmacologically and knocked down the mechanosensitive ion channel piezo1. All treatments resulted in aberrant axonal growth and pathfinding errors, suggesting that local tissue stiffness, read out by mechanosensitive ion channels, is critically involved in instructing neuronal growth in vivo.This work was supported by the German National Academic Foundation (scholarship to D.E.K.), Wellcome Trust and Cambridge Trusts (scholarships to A.J.T.), Winston Churchill Foundation of the United States (scholarship to S.K.F.), Herchel Smith Foundation (Research Studentship to S.K.F.), CNPq 307333/2013-2 (L.d.F.C.), NAP-PRP-USP and FAPESP 11/50761-2 (L.d.F.C.), UK EPSRC BT grant (J.G.), Wellcome Trust WT085314 and the European Research Council 322817 grants (C.E.H.); an Alexander von Humboldt Foundation Feodor Lynen Fellowship (K.F.), UK BBSRC grant BB/M021394/1 (K.F.), the Human Frontier Science Program Young Investigator Grant RGY0074/2013 (K.F.), the UK Medical Research Council Career Development Award G1100312/1 (K.F.) and the Eunice Kennedy Shriver National Institute Of Child Health & Human Development of the National Institutes of Health under Award Number R21HD080585 (K.F.).This is the author accepted manuscript. The final version is available from Nature Publishing Group via https://doi.org/10.1038/nn.439

Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A

The major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all disorders classified as autoimmune. In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 and HLA-DRB1 (refs 1-3), but these genes cannot completely explain the association between type 1 diabetes and the MHC region. Owing to the region's extreme gene density, the multiplicity of disease-associated alleles, strong associations between alleles, limited genotyping capability, and inadequate statistical approaches and sample sizes, which, and how many, loci within the MHC determine susceptibility remains unclear. Here, in several large type 1 diabetes data sets, we analyse a combined total of 1,729 polymorphisms, and apply statistical methods - recursive partitioning and regression - to pinpoint disease susceptibility to the MHC class I genes HLA-B and HLA-A (risk ratios >1.5; Pcombined = 2.01 × 10-19 and 2.35 × 10-13, respectively) in addition to the established associations of the MHC class II genes. Other loci with smaller and/or rarer effects might also be involved, but to find these, future searches must take into account both the HLA class II and class I genes and use even larger samples. Taken together with previous studies, we conclude that MHC-class-I-mediated events, principally involving HLA-B*39, contribute to the aetiology of type 1 diabetes. ©2007 Nature Publishing Group

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead