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Cancers of unknown primary (CUP) are metastatic cancers for which the primary tumor is not found despite thorough diagnostic investigations. Multiple molecular assays have been proposed to identify the tissue of origin (TOO) and inform clinical care; however, none has been able to combine accuracy, interpretability, and easy access for routine use. We developed a classifier tool based on the training of a variational autoencoder to predict tissue of origin based on RNA-sequencing data. We used as training data 20,918 samples corresponding to 94 different categories, including 39 cancer types and 55 normal tissues. The TransCUPtomics classifier was applied to a retrospective cohort of 37 CUP patients and 11 prospective patients. TransCUPtomics exhibited an overall accuracy of 96% on reference data for TOO prediction. The TOO could be identified in 38 (79%) of 48 CUP patients. Eight of 11 prospective CUP patients (73%) could receive first-line therapy guided by TransCUPtomics prediction, with responses observed in most patients. The variational autoencoder added further utility by enabling prediction interpretability, and diagnostic predictions could be matched to detection of gene fusions and expressed variants. TransCUPtomics confidently predicted TOO for CUP and enabled tailored treatments leading to significant clinical responses. The interpretability of our approach is a powerful addition to improve the management of CUP patients., (Copyright © 2021 Association for Molecular Pathology and American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.)

Background & Aims: Assessment of dietary intake using a 3-day dietary record may delay the management of undernutrition. Methods allowing a quick estimation of dietary intake are needed. We aimed to determine the feasibility of assessing dietary intake using two 10-point verbal (AVeS) and visual (AViS) analogue scales, to assess the correlations of both scales with energy intake, and to determine the accuracy of AVeS for assessing undernutrition., Methods: We prospectively recruited 114 patients undernourished or nutritionally at-risk in two French University Hospitals. Undernutrition was defined as a Nutritional Risk Index <97.5. AVeS and AViS were performed by one interviewer and mean daily energy intake was calculated from 3-day dietary records by one dietician., Results: The feasibility of AVeS and AViS was 98% and 96%, respectively. Both verbal and visual scales were statistically correlated with calculated energy intake (rho=0.66 and rho=0.74, P<0.0001), especially in undernourished patients (rho=0.82, P<0.0001, for AVeS). Sensitivity, specificity, positive and negative predictive values of an AVeS score less than 7 for assessing undernutrition were 57%, 81%, 86% and 46%, respectively., Conclusion: AVeS and AViS could be used for a quick assessment of dietary intake in clinical practice, particularly in undernourished in-patients. Thus, both verbal and visual analogue scales could be particularly useful for the management of hospital undernutrition.

The medium chain acyl-CoA deshydrogenase defect: a new inherited metabolic disorder. This enzymatic defect blocks the catabolism of non esterified fatty acids during fasting. Thus, this disease is revealed by a coma due to hypoglycemia in a young child; the presence of dicarboxylic aciduria in such a situation is the main evidence for this diagnosis. Finally, the enzymatic studies performed on skin fibroblasts show a defect in medium chain acyl-CoA deshydrogenase. When a child is investigated away from a coma episode, the ketotic diet induces dicarboxylic aciduria but must be performed in an intensive care unit for its dangers.

Variables describing unit discharges from multiunit extracellular records were treated, using multivariate statistical analysis for further automatic spike separation. A spike was described by 8 parameters (3 amplitude values, 3 time values and 2 slopes). These 8 parameters were summarized by 2 new independent factors, the principal components (PC). Examination of the 2 PCs from each spike allowed the observer to determine PC families corresponding to spike populations. From these the spikes were automatically classified. PC analysis showed groups of redundant parameters. Elimination of redundancy decreased the computing time without lowering the separation efficiency of the method. Three parameters turned out to be sufficient for spike discrimination: the maximum and the minimum amplitudes and the time between these. This agrees well with the physical basis of current propagation in the conducting medium, from the firing neurone to the recording electrode.