Gender-sensitive reporting in medical research

Sex and gender differences influence the health and wellbeing of men and women. Although studies have drawn attention to observed differences between women and men across diseases, remarkably little research has been pursued to systematically investigate these underlying sex differences. Women continue to be underrepresented in clinical trials, and even in studies in which both men and women participate, systematic analysis of data to identify potential sex-based differences is lacking. Standards for reporting of clinical trials have been established to ensure provision of complete, transparent and critical information. An important step in addressing the gender imbalance would be inclusion of a gender perspective in the next Consolidated Standards of Reporting Trials (CONSORT) guideline revision. Uniform Requirements for Manuscripts Submitted to Biomedical Journals, as a set of well-recognized and widely used guidelines for authors and biomedical journals, should similarly emphasize the ethical obligation of authors to present data analyzed by gender as a matter of routine. Journal editors are also promoters of ethical research and adequate standards of reporting, and requirements for inclusion of gender analyses should be integrated into editorial policies as a matter of urgency.

3D reconstruction of medical images from slices automatically landmarked with growing neural models

In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. We present a brain ventricles fast reconstruction method. The method is based on the processing of brain sections and establishing a fixed number of landmarks onto those sections to reconstruct the ventricles 3D surface. Automated landmark extraction is accomplished through the use of the self-organising network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates the classical surface reconstruction and filtering processes. The proposed method offers higher accuracy compared to methods with similar efficiency as Voxel Grid.