Neural Regeneration Research (NRR; ISSN 1673-5374) is an open-access (www.nrronline.org), peer-reviewed international journal focusing exclusively on the exciting field of neural regeneration research, with 36 issues published per year. NRR is devoted to publishing basic research, translational medicine and randomized clinical trial papers, as well as prospective reviews written by invited experts and academic discussion papers in the field of neural regeneration. NRR aims to publish timely, innovative and creative basic and clinical research with the highest standards in neural regeneration research. NRR publishes a diverse variety of topics in neural regeneration, including brain, spinal cord and peripheral nerve injury, traditional Chinese medicine, acupuncture and moxibustion, stem cells, tissue engineering, inflammation, the glial scar, gene therapy, biological factors, neurorehabilitation, neuroimaging, neurodegenerative diseases, neuroplasticity and neurogenesis.
NRR especially encourages publication of innovative studies by young scientific scholars. In addition, high-quality experimental studies from pharmaceutical companies and manufacturers of medical equipment will also be considered for publication in NRR.
NRR publishes original research, clinical studies and studies in evidence-based medicine, as well as technique and methodology papers, reviews and commentaries. Reviews and commentaries are written by invited experts. A few case reports are also published in NRR. These should provide significant novel insight and help guide basic and clinical research. Furthermore, case reports should present rare clinical cases with direct relevance to neural regeneration.
NRR has excellent international coverage. Since it began publication, the journal has been indexed by Science Citation Index Expanded (SCI-E), BIOSIS Previews (BP), Chemical Abstracts (CA), Scopus, Excerpta Medica (EM), Index of Copernicus (IC), Chinese Science Citation Database, Source Periodical for Chinese Scientific and Technical Papers (English version) and OvidSP.