Modest phenotypic improvements in ASA-deficient mice with only one UDP-galactose:ceramide-galactosyltransferase gene

BACKGROUND: Arylsulfatase A (ASA)-deficient mice are a model for the lysosomal storage disorder metachromatic leukodystrophy. This lipidosis is characterised by the lysosomal accumulation of the sphingolipid sulfatide. Storage of this lipid is associated with progressive demyelination. We have mated ASA-deficient mice with mice heterozygous for a non-functional allele of UDP-galactose:ceramide-galactosyltransferase (CGT). This deficiency is known to lead to a decreased synthesis of galactosylceramide and sulfatide, which should reduce sulfatide storage and improve pathology in ASA-deficient mice. RESULTS: ASA-/- CGT+/- mice, however, showed no detectable decrease in sulfatide storage. Neuronal degeneration of cells in the spiral ganglion of the inner ear, however, was decreased. Behavioural tests showed small but clear improvements of the phenotype in ASA-/- CGT+/- mice. CONCLUSION: Thus the reduction of galactosylceramide and sulfatide biosynthesis by genetic means overall causes modest improvements of pathology

Transferability of Deep Learning Algorithms for Malignancy Detection in Confocal Laser Endomicroscopy Images from Different Anatomical Locations of the Upper Gastrointestinal Tract

Squamous Cell Carcinoma (SCC) is the most common cancer type of the epithelium and is often detected at a late stage. Besides invasive diagnosis of SCC by means of biopsy and histo-pathologic assessment, Confocal Laser Endomicroscopy (CLE) has emerged as noninvasive method that was successfully used to diagnose SCC in vivo. For interpretation of CLE images, however, extensive training is required, which limits its applicability and use in clinical practice of the method. To aid diagnosis of SCC in a broader scope, automatic detection methods have been proposed. This work compares two methods with regard to their applicability in a transfer learning sense, i.e. training on one tissue type (from one clinical team) and applying the learnt classification system to another entity (different anatomy, different clinical team). Besides a previously proposed, patch-based method based on convolutional neural networks, a novel classification method on image level (based on a pre-trained Inception V.3 network with dedicated preprocessing and interpretation of class activation maps) is proposed and evaluated. The newly presented approach improves recognition performance, yielding accuracies of 91.63% on the first data set (oral cavity) and 92.63% on a joint data set. The generalization from oral cavity to the second data set (vocal folds) lead to similar area-under-the-ROC curve values than a direct training on the vocal folds data set, indicating good generalization.Comment: Erratum for version 1, correcting the number of CLE image sequences used in one data se