Enrollment of adolescents and young adults onto SWOG cancer research network clinical trials: A comparative analysis by treatment site and era.

BackgroundFew adolescents and young adults (AYAs, 15-39 years old) enroll onto cancer clinical trials, which hinders research otherwise having the potential to improve outcomes in this unique population. Prior studies have reported that AYAs are more likely to receive cancer care in community settings. The National Cancer Institute (NCI) has led efforts to increase trial enrollment through its network of NCI-designated cancer centers (NCICC) combined with community outreach through its Community Clinical Oncology Program (CCOP; replaced by the NCI Community Oncology Research Program in 2014).MethodsUsing AYA proportional enrollment (the proportion of total enrollments who were AYAs) as the primary outcome, we examined enrollment of AYAs onto SWOG therapeutic trials at NCICC, CCOP, and non-NCICC/non-CCOP sites from 2004 to 2013 by type of site, study period (2004-08 vs 2009-13), and patient demographics.ResultsOverall, AYA proportional enrollment was 10.1%. AYA proportional enrollment decreased between 2004-2008 and 2009-2013 (13.1% vs 8.5%, P < .001), and was higher at NCICCs than at CCOPs and non-NCICC/non-CCOPs (14.1% vs 8.3% and 9.2%, respectively; P < .001). AYA proportional enrollment declined significantly at all three site types. Proportional enrollment of AYAs who were Black or Hispanic was significantly higher at NCICCs compared with CCOPs or non-NCICC/non-CCOPs (11.5% vs 8.8, P = .048 and 11.5% vs 8.6%, P = .03, respectively).ConclusionNot only did community sites enroll a lower proportion of AYAs onto cancer clinical trials, but AYA enrollment decreased in all study settings. Initiatives aimed at increasing AYA enrollment, particularly in the community setting with attention to minority status, are needed

Comments on alternative calculations of the broadening of spectral lines of neutral sodium by H-atom collisions

With the exception of the sodium D-lines recent calculations of line broadening cross-sections for several multiplets of sodium by Leininger et al (2000) are in substantial disagreement with cross-sections interpolated from the tables of Anstee and O'Mara (1995) and Barklem and O'Mara (1997). The discrepancy is as large as a factor of three for the 3p-4d multiplet. The two theories are tested by using the results of each to synthesize lines in the solar spectrum. It is found that generally the data from the theory of Anstee, Barklem and O'Mara produce the best match to the observed solar spectrum. It is found, using a simple model for reflection of the optical electron by the potential barrier between the two atoms, that the reflection coefficient is too large for avoided crossings with the upper states of subordinate lines to contribute to line broadening, supporting the neglect of avoided ionic crossings by Anstee, Barklem and O'Mara for these lines. The large discrepancies between the two sets of calculations is a result of an approximate treatment of avoided ionic crossings for these lines by Leininger et al (2000).Comment: 18 pages, 5 ps figures included, to appear in J Phys B: At. Mol. Opt. Phy

Genome-Wide Association Studies of Endometrial Cancer: Latest Developments and Future Directions.

Endometrial cancer, the most commonly diagnosed cancer of the female reproductive tract in developed countries, has a heritable component. To date, 16 genetic risk regions have been robustly discovered by genome-wide association studies (GWAS) of endometrial cancer. Post-GWAS analyses including expression quantitative trait loci analysis and laboratory-based functional studies have been successful in identifying genes and pathways involved in endometrial carcinogenesis. Mendelian randomization analysis studies have confirmed factors causal for endometrial cancer risk, including increased body mass index and early onset of menarche. In this review, we summarize findings from GWAS and post-GWAS analyses of endometrial cancer. We discuss clinical implications of these findings, current knowledge gaps, and future directions for the study of endometrial cancer genetics.TAO’M is supported by a National Health and Medical Research Council (NHMRC) Early Career Fellowship (APP1111246), PFK is supported by an Australian Government Research Training Program PhD Scholarship and QIMR Berghofer Postgraduate Top-Up Scholarship, ABS is supported by an NHMRC Senior Research Fellowship (APP1061779)

Assessing the Role of Selenium in Endometrial Cancer Risk: A Mendelian Randomization Study.

Endometrial cancer is the most commonly diagnosed gynecological cancer in developed countries. Based on evidence from observational studies which suggest selenium inhibits the development of several cancers (including lung and prostate cancer), selenium supplementation has been touted as a potential cancer preventative agent. However, randomized controlled trials have not reported benefit for selenium supplementation in reducing cancer risk. For endometrial cancer, limited observational studies have been conducted assessing whether selenium intake, or blood selenium levels, associated with reduced risk, and no randomized controlled trials have been conducted. We performed a two-sample Mendelian randomization analysis to examine the relationship between selenium levels (using a composite measure of blood and toenail selenium) and endometrial cancer risk, using summary statistics for four genetic variants associated with selenium levels at genome-wide significance levels (P < 5 × 10-8), from a study of 12,906 endometrial cancer cases and 108,979 controls, all of European ancestry. Inverse variance weighted (IVW) analysis indicated no evidence of a causal role for selenium levels in endometrial cancer development (OR per unit increase in selenium levels Z-score = 0.99, 95% CI = 0.87-1.14). Similar results were observed for sensitivity analyses robust to the presence of unknown pleiotropy (OR per unit increase in selenium levels Z-score = 0.98, 95% CI 0.89-1.08 for weighted median; OR per unit increase in selenium levels Z-score = 0.90, 95% CI = 0.53-1.50 for MR-Egger). In conclusion, these results do not support the use of selenium supplementation to prevent endometrial cancer.This work was supported by a National Health and Medical Research Council (NHMRC) Project Grant (APP1109286). PFK is supported by an Australian Government Research Training Program PhD Scholarship and QIMR Berghofer Postgraduate Top-Up Scholarship, TAO’M is supported by an NHMRC Early Career Fellowship (APP1111246), ABS is supported by an NHMRC Senior Research Fellowship (APP1061779)

Creating a database of internet-based clinical trials to support a public-led research programme: A descriptive analysis

Background: Online trials are rapidly growing in number, offering potential benefits but also methodological, ethical and social challenges. The International Network for Knowledge on Well-being (ThinkWell™) aims to increase public and patient participation in the prioritisation, design and conduct of research through the use of technologies. Objective: We aim to provide a baseline understanding of the online trial environment, determining how many trials have used internet-based technologies; how they have been used; and how use has developed over time. Methods: We searched a range of bibliographic databases to March 2015, with no date limits, supplemented by citation searching and references provided by experts in the field. Results were screened against inclusion and exclusion criteria, and included studies mapped against a number of key dimensions, with key themes developed iteratively throughout the process. Results: We identified 1992 internet-based trials to March 2015. The number of reported studies increased substantially over the study timeframe. The largest number of trials were conducted in the USA (49.7%), followed by The Netherlands (10.2%); Australia (8.5%); the United Kingdom (5.8%); Sweden (4.6%); Canada (4%); and Germany (2.6%). South Korea (1.5%) has the highest number of reported trials for other continents. There is a predominance of interventions addressing core public health challenges including obesity (8.6%), smoking cessation (5.9%), alcohol abuse (7.7%) and physical activity (10.2%); in mental health issues such as depression (10.9%) and anxiety (5.6%); and conditions where self-management (16.6%) or monitoring (8.1%) is a major feature of care. Conclusions: The results confirm an increase in the use of the internet in trials. Key themes have emerged from the analysis and further research will be undertaken in order to investigate how the data can be used to improve trial design and recruitment, and to build an open access resource to support the public-led research agenda

An examination of business occupier relocation decision making : distinguishing small and large firm behaviour

This paper explores how business occupiers decide whether and where to relocate. It captures the experience and behaviour of a range of sizes and types of business occupier and subjects their decision-making processes to detailed scrutiny. A linear three-stage decision model is used to sequence and structure interviews with individuals who have intimate involvement with the relocation of 28 firms and organizations in Tyne and Wear, in the north-east of England. The 'constant comparative' method is used to analyse the interview data, from which emerges 18 key concepts, comprising 51 characteristic components. Using an axial approach, these are organized into 10 cross-cutting themes that represent the main areas of consideration or influence on the thinking of the people involved in determining whether a firm or organization should relocate and, if so, where to. The resulting analysis finds that organizations adopt varying degrees of sophistication when making relocation decisions; small firms are more inclined to make decisions based on constrained information; larger organizations adopt a more complex approach. Regardless of firm size, key individuals exert considerable influence over the decision-making process and its outcome

The Human Behaviour-Change Project: Harnessing the power of Artificial Intelligence and Machine Learning for evidence synthesis and interpretation

Background Behaviour change is key to addressing both the challenges facing human health and wellbeing and to promoting the uptake of research findings in health policy and practice. We need to make better use of the vast amount of accumulating evidence from behaviour change intervention (BCI) evaluations and promote the uptake of that evidence into a wide range of contexts. The scale and complexity of the task of synthesising and interpreting this evidence, and increasing evidence timeliness and accessibility, will require increased computer support. The Human Behaviour-Change Project (HBCP) will use Artificial Intelligence and Machine Learning to (i) develop and evaluate a ‘Knowledge System’ that automatically extracts, synthesises and interprets findings from BCI evaluation reports to generate new insights about behaviour change and improve prediction of intervention effectiveness and (ii) allow users, such as practitioners, policy makers and researchers, to easily and efficiently query the system to get answers to variants of the question ‘What works, compared with what, how well, with what exposure, with what behaviours (for how long), for whom, in what settings and why?’. Methods The HBCP will: a) develop an ontology of BCI evaluations and their reports linking effect sizes for given target behaviours with intervention content and delivery and mechanisms of action, as moderated by exposure, populations and settings; b) develop and train an automated feature extraction system to annotate BCI evaluation reports using this ontology; c) develop and train machine learning and reasoning algorithms to use the annotated BCI evaluation reports to predict effect sizes for particular combinations of behaviours, interventions, populations and settings; d) build user and machine interfaces for interrogating and updating the knowledge base; and e) evaluate all the above in terms of performance and utility. Discussion The HBCP aims to revolutionise our ability to synthesise, interpret and deliver evidence on behaviour change interventions that is up-to-date and tailored to user need and context. This will enhance the usefulness, and support the implementation of, that evidence.The project is funded by a Wellcome Trust collaborative award [The Human Behaviour-Change Project: Building the science of behaviour change for complex intervention development’, 201,524/Z/16/Z]. During the preparation of the manuscript RW’s salary was funded by Cancer Research UK

Comparison of gene targets and sampling regimes for SARS-CoV-2 quantification for wastewater epidemiology in UK prisons

Prisons are high-risk settings for infectious disease transmission, due to their enclosed and semi-enclosed environments. The proximity between prisoners and staff, and the diversity of prisons reduces the effectiveness of non-pharmaceutical interventions, such as social distancing. Therefore, alternative health monitoring methods, such as wastewater-based epidemiology (WBE), are needed to track pathogens, including SARS-CoV-2. This pilot study assessed WBE to quantify SARS-CoV-2 prevalence in prison wastewater to determine its utility within a health protection system for residents. The study analysed 266 samples from six prisons in England over a 12-week period for nucleoprotein 1 (N1 gene) and envelope protein (E gene) using quantitative reverse transcriptase-polymerase chain reaction. Both gene assays successfully detected SARS-CoV-2 fragments in wastewater samples, with both genes significantly correlating with COVID-19 case numbers across the prisons (p &lt; 0.01). However, in 25% of the SARS-positive samples, only one gene target was detected, suggesting that both genes be used to reduce false-negative results. No significant differences were observed between 14- and 2-h composite samples, although 2-h samples showed greater signal variance. Population normalisation did not improve correlations between the N1 and E genes and COVID-19 case data. Overall, WBE shows considerable promise for health protection in prison settings

The Human Behaviour-Change Project: An artificial intelligence system to answer questions about changing behaviour [version 1; peer review: not peer reviewed]

Changing behaviour is necessary to address many of the threats facing human populations. However, identifying behaviour change interventions likely to be effective in particular contexts as a basis for improving them presents a major challenge. The Human Behaviour-Change Project harnesses the power of artificial intelligence and behavioural science to organise global evidence about behaviour change to predict outcomes in common and unknown behaviour change scenarios

Prioritising references for systematic reviews with RobotAnalyst: A user study

Screening references is a time-consuming step necessary for systematic reviews and guideline development. Previous studies have shown that human effort can be reduced by using machine learning software to prioritise large reference collections such that most of the relevant references are identified before screening is completed. We describe and evaluate RobotAnalyst, a Web-based software system that combines text-mining and machine learning algorithms for organising references by their content and actively prioritising them based on a relevancy classification model trained and updated throughout the process. We report an evaluation over 22 reference collections (most are related to public health topics) screened using RobotAnalyst with a total of 43 610 abstract-level decisions. The number of references that needed to be screened to identify 95% of the abstract-level inclusions for the evidence review was reduced on 19 of the 22 collections. Significant gains over random sampling were achieved for all reviews conducted with active prioritisation, as compared with only two of five when prioritisation was not used. RobotAnalyst's descriptive clustering and topic modelling functionalities were also evaluated by public health analysts. Descriptive clustering provided more coherent organisation than topic modelling, and the content of the clusters was apparent to the users across a varying number of clusters. This is the first large-scale study using technology-assisted screening to perform new reviews, and the positive results provide empirical evidence that RobotAnalyst can accelerate the identification of relevant studies. The results also highlight the issue of user complacency and the need for a stopping criterion to realise the work savings