Loss-of-function of the lysosomal enzyme galactosyl-ceramidase (GALC) causes the accumulation of the lipid raft-associated sphingolipid psychosine, the disruption of postnatal myelination, neurodegeneration and early death in most cases of infantile Krabbe disease. This work presents a first study towards understanding the progression of axonal defects in this disease using the Twitcher mutant mouse. Axonal swellings were detected in axons within the mutant spinal cord as early as one week after birth. As the disease progressed, more axonopathic profiles were found in other regions of the nervous system, including peripheral nerves and various brain areas. Isolated mutant neurons recapitulated axonal and neuronal defects in the absence of mutant myelinating glia, suggesting an autonomous neuronal defect. Psychosine was sufficient to induce axonal defects and cell death in cultures of acutely isolated neurons. Interestigly, axonopathy in young Twitcher mice occured in the absence of demyelination and of neuronal apoptosis. Neuronal damage occurred at later stages, when mutant mice were moribund and demyelinated. Altogether, these findings suggest a progressive dying-back neuronal dysfunction in Twitcher mutants.

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Background Market-applicable concurrent electrocardiogram (ECG) diagnosis for multiple heart abnormalities that covers a wide range of arrhythmias, with better-than-human accuracy, has not yet been developed. We therefore aimed to engineer a deep learning approach for the automated multilabel diagnosis of heart rhythm or conduction abnormalities by real-time ECG analysis. MethodsWe used a dataset of ECGs (standard 10 s, 12-channel format) from adult patients (aged ≥18 years), with 21 distinct rhythm classes, including most types of heart rhythm or conduction abnormalities, for the diagnosis of arrhythmias at multilabel level. The ECGs were collected from three campuses of Tongji Hospital (Huazhong University of Science and Technology, Wuhan, China) and annotated by cardiologists. We used these datasets to develop a convolutional neural network approach to generate diagnoses of arrythmias. We collected a test dataset of ECGs from a new group of patients not included in the training dataset. The test dataset was annotated by consensus of a committee of board-certified, actively practicing cardiologists. To evaluate the performance of the model we assessed the F1 score and the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, as well as quantifying sensitivity and specificity. To validate our results, findings for the test dataset were compared with diagnoses made by 53 ECG physicians working in cardiology departments who had a wide range of experience in ECG interpretation (range 0 to >12 years). An external public validation dataset of 962 ECGs from other hospitals was used to study generalisability of the diagnostic model.