Translational Medicine

The National Center for Advancing Translational Sciences, NIH, defines translation as “the process of turning observations in the laboratory, clinic, and community into interventions that improve the health of individuals and populations – from diagnostics and therapeutics to medical procedures and behavioral interventions”. Translational sciences aim to translate results obtained from basic research into changes that improve the health of individual patients, as well as public health. Several medical branches and disciplines, including (but not limited to) clinical and surgical pathology, genetics, medical and radiation oncology, surgery, and population health, are involved in the development of a real-life impact on health status.

In this section of Medical Sciences, we aim to rapidly publish contributions on all aspects of translational sciences. We encourage the submission of Research Articles, Reviews, Mini Reviews, Opinions, Guidelines, Recommendations, Point/Counterpoint Articles, Letters to the Editor, and Editorials describing the results from in vitro and in vivo pre-clinical studies with a potential clinical impact, as well as data from clinical trials and health community interventions. We also welcome manuscripts examining current critical issues in public health, applications of artificial intelligence, and evidence-based research.

The main topics to be covered in this section are:

• The development and/or validation of diagnostic and prognostic biomarkers;
• Clinical algorithms in medicine and surgery;
• Novel or improved research-based therapeutic and diagnostic procedures;
• Clinical research efficiency;
• Artificial intelligence applied to pathology, radiology, clinical medicine, and surgery;
• Multidisciplinary approaches to healthcare;
• Chemical genomics and drug discovery;
• Data mining and medical informatics;
• Preclinical models of human diseases;
• Health intervention research.

Keywords

• translation
• biomarkers
• health intervention
• medical informatics
• clinical algorithms
• evidence-based research
• chemical genomics
• drug discovery