Concept: Medical research
- Proceedings of the National Academy of Sciences of the United States of America
- Published over 6 years ago
A cornerstone of modern biomedical research is the use of mouse models to explore basic pathophysiological mechanisms, evaluate new therapeutic approaches, and make go or no-go decisions to carry new drug candidates forward into clinical trials. Systematic studies evaluating how well murine models mimic human inflammatory diseases are nonexistent. Here, we show that, although acute inflammatory stresses from different etiologies result in highly similar genomic responses in humans, the responses in corresponding mouse models correlate poorly with the human conditions and also, one another. Among genes changed significantly in humans, the murine orthologs are close to random in matching their human counterparts (e.g., R(2) between 0.0 and 0.1). In addition to improvements in the current animal model systems, our study supports higher priority for translational medical research to focus on the more complex human conditions rather than relying on mouse models to study human inflammatory diseases.
As social scientists have investigated the political and social factors influencing public opinion in science-related policy debates, there has been growing interest in the implications of this research for public communication and outreach. Given the level of political polarization in the United States, much of the focus has been on partisan differences in public opinion, the strategies employed by political leaders and advocates that promote those differences, and the counter-strategies for overcoming them. Yet this focus on partisan differences tends to overlook the processes by which core beliefs about science and society impact public opinion and how these schema are often activated by specific frames of reference embedded in media coverage and popular discourse. In this study, analyzing cross-sectional, nationally representative survey data collected between 2002 and 2010, we investigate the relative influence of political partisanship and science-related schema on Americans' support for embryonic stem cell research. In comparison to the influence of partisan identity, our findings suggest that generalized beliefs about science and society were more chronically accessible, less volatile in relation to media attention and focusing events, and an overall stronger influence on public opinion. Classifying respondents into four unique audience groups based on their beliefs about science and society, we additionally find that individuals within each of these groups split relatively evenly by partisanship but differ on other important dimensions. The implications for public engagement and future research on controversies related to biomedical science are discussed.
Next-generation sequencing (NGS) is increasingly being adopted as the backbone of biomedical research. With the commercialization of various affordable desktop sequencers, NGS will be reached by increasing numbers of cellular and molecular biologists, necessitating community consensus on bioinformatics protocols to tackle the exponential increase in quantity of sequence data. The current resources for NGS informatics are extremely fragmented. Finding a centralized synthesis is difficult. A multitude of tools exist for NGS data analysis; however, none of these satisfies all possible uses and needs. This gap in functionality could be filled by integrating different methods in customized pipelines, an approach helped by the open-source nature of many NGS programmes. Drawing from community spirit and with the use of the Wikipedia framework, we have initiated a collaborative NGS resource: The NGS WikiBook. We have collected a sufficient amount of text to incentivize a broader community to contribute to it. Users can search, browse, edit and create new content, so as to facilitate self-learning and feedback to the community. The overall structure and style for this dynamic material is designed for the bench biologists and non-bioinformaticians. The flexibility of online material allows the readers to ignore details in a first read, yet have immediate access to the information they need. Each chapter comes with practical exercises so readers may familiarize themselves with each step. The NGS WikiBook aims to create a collective laboratory book and protocol that explains the key concepts and describes best practices in this fast-evolving field.
BACKGROUND: MEDLINE®/PubMed® indexes over 20 million biomedical articles, providing curated annotation of contents using a controlled vocabulary known as Medical Subject Headings (MeSH). The MeSH vocabulary, developed over 50+ years, provides a broad coverage of topics across biomedical research. Distilling the essential biomedical themes for a topic of interest from the relevant literature is important to both understand the importance of related concepts and discover new relationships. RESULTS: We introduce a novel method for determining enriched curator-assigned MeSH annotations in a set of papers associated to a topic, such as a gene, an author or a disease. We generate MeSH Over-representation Profiles (MeSHOPs) to quantitatively summarize the annotations in a form convenient for further computational analysis and visualization. Based on a hyper geometric distribution of assigned terms, MeSHOPs statistically account for the prevalence of the associated biomedical annotation while highlighting unusually prevalent terms based on a specified background. MeSHOPs can be visualized using word clouds, providing a succinct quantitative graphical representation of the relative importance of terms. Using the publication dates of articles, MeSHOPs track changing patterns of annotation over time. Since MeSHOPs are quantitative vectors, MeSHOPs can be compared using standard techniques such as hierarchical clustering. The reliability of MeSHOP annotations is assessed based on the capacity to re-derive the subset of the Gene Ontology annotations with equivalent MeSH terms. CONCLUSIONS: MeSHOPs allows quantitative measurement of the degree of association between any entity and the annotated medical concepts, based directly on relevant primary literature. Comparison of MeSHOPs allows entities to be related based on shared medical themes in their literature. A web interface is provided for generating and visualizing MeSHOPs.
Rodent models produce data which underpin biomedical research and non-clinical drug trials, but translation from rodents into successful clinical outcomes is often lacking. There is a growing body of evidence showing that improving experimental design is key to improving the predictive nature of rodent studies and reducing the number of animals used in research. Age, one important factor in experimental design, is often poorly reported and can be overlooked. The authors conducted a survey to assess the age used for a range of models, and the reasoning for age choice. From 297 respondents providing 611 responses, researchers reported using rodents most often in the 6-20 week age range regardless of the biology being studied. The age referred to as ‘adult’ by respondents varied between six and 20 weeks. Practical reasons for the choice of rodent age were frequently given, with increased cost associated with using older animals and maintenance of historical data comparability being two important limiting factors. These results highlight that choice of age is inconsistent across the research community and often not based on the development or cellular ageing of the system being studied. This could potentially result in decreased scientific validity and increased experimental variability. In some cases the use of older animals may be beneficial. Increased scientific rigour in the choice of the age of rodent may increase the translation of rodent models to humans.
Esteban Gonzalez Burchard and colleagues explore how making medical research more diverse would aid not only social justice but scientific quality and clinical effectiveness, too.
Evidence shows that research abstracts are commonly inconsistent with their corresponding full reports, and may mislead readers. In this scoping review, which is part of our series on the state of reporting of primary biomedical research, we summarized the evidence from systematic reviews and surveys, to investigate the current state of inconsistent abstract reporting, and to evaluate factors associated with improved reporting by comparing abstracts and their full reports.
In the scientific literature, spin refers to reporting practices that distort the interpretation of results and mislead readers so that results are viewed in a more favourable light. The presence of spin in biomedical research can negatively impact the development of further studies, clinical practice, and health policies. This systematic review aims to explore the nature and prevalence of spin in the biomedical literature. We searched MEDLINE, PreMEDLINE, Embase, Scopus, and hand searched reference lists for all reports that included the measurement of spin in the biomedical literature for at least 1 outcome. Two independent coders extracted data on the characteristics of reports and their included studies and all spin-related outcomes. Results were grouped inductively into themes by spin-related outcome and are presented as a narrative synthesis. We used meta-analyses to analyse the association of spin with industry sponsorship of research. We included 35 reports, which investigated spin in clinical trials, observational studies, diagnostic accuracy studies, systematic reviews, and meta-analyses. The nature of spin varied according to study design. The highest (but also greatest) variability in the prevalence of spin was present in trials. Some of the common practices used to spin results included detracting from statistically nonsignificant results and inappropriately using causal language. Source of funding was hypothesised by a few authors to be a factor associated with spin; however, results were inconclusive, possibly due to the heterogeneity of the included papers. Further research is needed to assess the impact of spin on readers' decision-making. Editors and peer reviewers should be familiar with the prevalence and manifestations of spin in their area of research in order to ensure accurate interpretation and dissemination of research.
- Proceedings of the National Academy of Sciences of the United States of America
- Published over 2 years ago
Young researchers are crucially important for basic science as they make unexpected, fundamental discoveries. Since 1982, we find a steady drop in the number of grant-eligible basic-science faculty [principal investigators (PIs)] younger than 46. This fall occurred over a 32-y period when inflation-corrected congressional funds for NIH almost tripled. During this time, the PI success ratio (fraction of basic-science PIs who are R01 grantees) dropped for younger PIs (below 46) and increased for older PIs (above 55). This age-related bias seems to have caused the steady drop in the number of young basic-science PIs and could reduce future US discoveries in fundamental biomedical science. The NIH recognized this bias in its 2008 early-stage investigator (ESI) policy to fund young PIs at higher rates. We show this policy is working and recommend that it be enhanced by using better data. Together with the National Institute of General Medical Sciences (NIGMS) Maximizing Investigators' Research Award (MIRA) program to reward senior PIs with research time in exchange for less funding, this may reverse a decades-long trend of more money going to older PIs. To prepare young scientists for increased demand, additional resources should be devoted to transitional postdoctoral fellowships already offered by NIH.
The National Institutes of Health needs to make radical changes to ensure that biomedical research continues to thrive in the United States.