For many intraoperative decisions surgeons depend on frozen section pathology, a technique developed over 150 y ago. Technical innovations that permit rapid molecular characterization of tissue samples at the time of surgery are needed. Here, using desorption electrospray ionization (DESI) MS, we rapidly detect the tumor metabolite 2-hydroxyglutarate (2-HG) from tissue sections of surgically resected gliomas, under ambient conditions and without complex or time-consuming preparation. With DESI MS, we identify isocitrate dehydrogenase 1-mutant tumors with both high sensitivity and specificity within minutes, immediately providing critical diagnostic, prognostic, and predictive information. Imaging tissue sections with DESI MS shows that the 2-HG signal overlaps with areas of tumor and that 2-HG levels correlate with tumor content, thereby indicating tumor margins. Mapping the 2-HG signal onto 3D MRI reconstructions of tumors allows the integration of molecular and radiologic information for enhanced clinical decision making. We also validate the methodology and its deployment in the operating room: We have installed a mass spectrometer in our Advanced Multimodality Image Guided Operating (AMIGO) suite and demonstrate the molecular analysis of surgical tissue during brain surgery. This work indicates that metabolite-imaging MS could transform many aspects of surgical care.T he microscopic review of tissue biopsies frequently remains the sole source of intraoperative diagnostic information, and many important surgical decisions such as the extent of tumor resection are based on this information. This approach is timeconsuming, requiring nearly 30 min between the moment a tissue is biopsied and the time the pathologist's interpretation is communicated back to the surgeon. Even after the report of the final pathologic diagnosis is issued days later, a lot of diagnostic, prognostic, and predictive information is left undiscovered and unexamined within the tissue. Tools that provide more immediate feedback to the surgeon and the pathologist and that also rapidly extract detailed molecular information could transform the management of care for cancer patients.MS offers the possibility for the in-depth analysis of the proteins and lipids that comprise tissues (1, 2). We have recently shown that desorption electrospray ionization (DESI) MS is a powerful methodology for characterizing lipids within tumor specimens (3-6). The intensity profile of lipids ionized from within tumors can be used for classifying tumors and for providing valuable prognostic information such as tumor subtype and grade. Because DESI MS is performed in ambient conditions with minimal pretreatment of the samples (7,8), there is the potential to provide diagnostic information rapidly within the operating room (4, 6, 9). The ability to quickly acquire such valuable diagnostic information from lipids prompted us to determine whether we could use DESI MS to detect additional molecules of diagnostic value within tumors, such as their metabolites.Recently,...
• GABA levels may decrease in patients with RRMS. • Lower GABA levels correlated with worse cognitive performance in patients with RRMS. • Dysfunctional GABAergic neurotransmission may have a role in cognitive impairment in RRMS.
Web-based instruction (WBI) programs, which have been increasingly developed in educational settings, are used by diverse learners. Therefore, individual differences are key factors for the development of WBI programs. Among various dimensions of individual differences, the study presented in this article focuses on cognitive styles. More specifically, this study investigates how cognitive styles affect students' learning patterns in a WBI program with an integrated approach, utilizing both traditional statistical and data-mining techniques. The former are applied to determine whether cognitive styles significantly affected students' learning patterns. The latter use clustering and classification methods. In terms of clustering, the K-means algorithm has been employed to produce groups of students that share similar learning patterns, and subsequently the corresponding cognitive style for each group is identified. As far as classification is concerned, the students' learning patterns are analyzed using a decision tree with which eight rules are produced for the automatic identification of students' cognitive styles based on their learning patterns. The results from these techniques appear to be consistent and the overall findings suggest that cognitive styles have important effects on students' learning patterns within WBI. The findings are applied to develop a model that can support the development of WBI programs.
To electrically control magnetic properties of material is promising toward spintronic applications, where the investigation of carrier doping effects on antiferromagnetic (AFM) materials remains challenging due to their zero net magnetization. In this work, the authors find electron doping dependent variation of magnetic orders of a 2D AFM insulator NiPS 3 , where doping concentration is tuned by intercalating various organic cations into the van der Waals gaps of NiPS 3 without introduction of defects and impurity phases. The doped NiPS 3 shows an AFM-ferrimagnetic (FIM) transition at a doping level of 0.2-0.5 electrons/cell and a FIM-AFM transition at a doping level of ≥0.6 electrons/cell. The authors propose that the found phenomenon is due to competition between Stoner exchange dominated inter-chain ferromagnetic order and super-exchange dominated AFM order at different doping level. The studies provide a viable way to exploit correlation between electronic structures and magnetic properties of 2D magnetic materials for realization of magnetoelectric effect.
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