A publication written by Pérez-Medina C, Hak S, Reiner T, Fayad ZA, Nahrendorf M and Mulder WJM, 29 November 2017.
Biomedical engineering and its associated disciplines play a pivotal role in improving our understanding and management of disease. Motivated by past accomplishments, such as the clinical implementation of coronary stents, pacemakers or recent developments in antibody therapies, disease management now enters a new era in which precision imaging and nanotechnology-enabled therapeutics are maturing to clinical translation.
A review written by Bolker JA in Bioessays, 20 October 2017.
Leading animal models are powerful tools for translational research, but they also present obstacles. Poorly conducted preclinical research in animals is a common cause of translational failure, but even when such research is well-designed and carefully executed, challenges remain. In particular, dominant models may bias research directions, elide essential aspects of human disease, omit important context, or subtly shift research targets.
A publication written by Dodd S, White IR and Williamson P in Trials, 25 October 2017.
When a randomised trial is subject to deviations from randomised treatment, analysis according to intention-to-treat does not estimate two important quantities: relative treatment efficacy and effectiveness in a setting different from that in the trial. Even in trials of a predominantly pragmatic nature, there may be numerous reasons to consider the extent, and impact on analysis, of such deviations from protocol. Simple methods such as per-protocol or as-treated analyses, which exclude or censor patients on the basis of their adherence, usually introduce selection and confounding biases. However, there exist appropriate causal estimation methods which seek to overcome these inherent biases, but these methods remain relatively unfamiliar and are rarely implemented in trials.
A publication written by Seymour CW, Gomez H, Chang CH, Clermont G, Kellum JA, Kennedy J, Yende S and Angus DC in Critical Care, 18 October 2017.
All of medicine aspires to be precise, where a greater understanding of individual data will lead to personalized treatment and improved outcomes. Prompted by specific examples in oncology, the field of critical care may be tempted to envision that complex, acute syndromes could bend to a similar reductionist philosophy-where single mutations could identify and target our critically ill patients for treatment. However, precision medicine faces many challenges in critical care.
A review written by Bzdok D in Frontiers in Neuroscience, 6 October 2017.
Brain-imaging research has predominantly generated insight by means of classical statistics, including regression-type analyses and null-hypothesis testing using t-test and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity especially for applications in rich and complex data, including cross-validated out-of-sample prediction using pattern classification and sparsity-inducing regression.
A review written by Stamenovic M and Dobraca A in Acta Informatica Medica, 25 September 2017.
Aim of this paper is to describe some of models of outsourcing (numerous and response to different types of risks and increment of quality is based on individual problem and situation). Defining whether to outsource or not and whether to build or buy new information technology (IT) is question for contract research organization (CRO) and Pharma companies dealing with clinical trials, so the aim of this paper is to show business model that could make process of decision making less time consuming, less segmented and more efficient.