Glioblastoma (GBM) is the most common and highly lethal primary malignant brain tumor in adults. There is a dire need for easily accessible, noninvasive biomarkers that can delineate underlying molecular activities and predict response to therapy. To this end, we sought to identify subtypes of GBM, differentiated solely by quantitative MR imaging features, that could be used for better management of GBM patients. Quantitative image features capturing the shape, texture, and edge sharpness of each lesion were extracted from MR images of 121 patients with de novo, solitary, unilateral GBM. Three distinct phenotypic “clusters” emerged in the development cohort using consensus clustering with 10,000 iterations on these image features. These three clusters—pre-multifocal, spherical, and rim-enhancing, names reflecting their image features—were validated in an independent cohort consisting of 144 multi-institution patients with similar tumor characteristics from The Cancer Genome Atlas (TCGA). Each cluster mapped to a unique set of molecular signaling pathways using pathway activity estimates derived from analysis of TCGA tumor copy number and gene expression data with the PARADIGM algorithm. Distinct pathways, such as c-Kit and FOXA, were enriched in each cluster, indicating differential molecular activities as determined by image features. Each cluster also demonstrated differential probabilities of survival, indicating prognostic importance. Our imaging method offers a noninvasive approach to stratify GBM patients and also provides unique sets of molecular signatures to inform targeted therapy and personalized treatment of GBM.

We have determined the high-resolution strand-specific transcriptome of the fission yeast S. pombe under multiple growth conditions using a novel RNA-DNA hybridization mapping (HybMap) technique. HybMap uses an antibody against an RNA-DNA hybrid to detect RNA molecules hybridized to a high-density DNA oligonucleotide tiling microarray. HybMap showed exceptional dynamic range and reproducibility, and allowed us to identify strand-specific coding, noncoding and structural RNAs, as well as previously unknown RNAs conserved in distant yeast species. Notably, we found that virtually the entire euchromatic genome (including intergenics) is transcribed, with heterochromatin dampening intergenic transcription. We identified features including large numbers of condition-specific noncoding RNAs, extensive antisense transcription, new properties of antisense transcripts and induced divergent transcription. Furthermore, our HybMap data informed the efficiency and locations of RNA splicing genome-wide. Finally, we observed strand-specific transcription islands around tRNAs at heterochromatin boundaries inside centromeres. Here, we discuss these new features in terms of organism fitness and transcriptome evolution.

OBJECTIVEAugmented reality (AR) is a novel technology that has the potential to increase the technical feasibility, accuracy, and safety of conventional manual and robotic computer-navigated pedicle insertion methods. Visual data are directly projected to the operator’s retina and overlaid onto the surgical field, thereby removing the requirement to shift attention to a remote display. The objective of this study was to assess the comparative accuracy of AR-assisted pedicle screw insertion in comparison to conventional pedicle screw insertion methods.METHODSFive cadaveric male torsos were instrumented bilaterally from T6 to L5 for a total of 120 inserted pedicle screws. Postprocedural CT scans were obtained, and screw insertion accuracy was graded by 2 independent neuroradiologists using both the Gertzbein scale (GS) and a combination of that scale and the Heary classification, referred to in this paper as the Heary-Gertzbein scale (HGS). Non-inferiority analysis was performed, comparing the accuracy to freehand, manual computer-navigated, and robotics-assisted computer-navigated insertion accuracy rates reported in the literature. User experience analysis was conducted via a user experience questionnaire filled out by operators after the procedures.RESULTSThe overall screw placement accuracy achieved with the AR system was 96.7% based on the HGS and 94.6% based on the GS. Insertion accuracy was non-inferior to accuracy reported for manual computer-navigated pedicle insertion based on both the GS and the HGS scores. When compared to accuracy reported for robotics-assisted computer-navigated insertion, accuracy achieved with the AR system was found to be non-inferior when assessed with the GS, but superior when assessed with the HGS. Last, accuracy results achieved with the AR system were found to be superior to results obtained with freehand insertion based on both the HGS and the GS scores. Accuracy results were not found to be inferior in any comparison. User experience analysis yielded “excellent” usability classification.CONCLUSIONSAR-assisted pedicle screw insertion is a technically feasible and accurate insertion method.