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https://www.selleckchem.com/products/deg-77.html RESULTS After histologic comparison, the DTI sensitivity and specificity to predict disrupted fiber tracts were respectively of 89% and 90%. The positive and negative predicted values of DTI were 80% and 95%. The DTI data were in line with the histologic myelin fiber orientation in 90% of patients. In our series, the prevalence of destructed fiber was 31%. Glioblastoma WHO grade IV harbored a higher proportion of destructed white matter tracts. Lower WHO grades were associated with higher preservation of subcortical fiber tracts. CONCLUSION This DTI/histology study of "en bloc"-resected gliomas reported a high and reproducible concordance of the visual color-coded FA map with the histologic examination to predict subcortical fiber tract disruption. Our series brought consistency to the DTI data that could be performed routinely for glioma surgery to predict the tumor grade and the postoperative clinical outcomes.BACKGROUND The objective recording of subjectively experienced pain is a problem that has not been sufficiently solved to date. In recent years, data sets have been created to train artificial intelligence algorithms to recognize patterns of pain intensity. The multimodal recognition of pain with machine learning could provide a way to reduce an over- or undersupply of analgesics, explicitly in patients with limited communication skills. OBJECTIVES This study investigated the methodology of automated multimodal recognition of pain intensity and modality using machine-learning techniques of artificial intelligence. Multimodal recognition rates of experimentally induced phasic electrical and heat pain stimuli were compared with uni- and bimodal recognition rates. MATERIAL AND METHODS On the basis of the X‑ITE Pain Database, healthy subjects were stimulated with phasic electro-induced pain and heat pain, and their corresponding pain responses were recorded with multimodal sensors (acoustic, video-based, physiolod

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