Melanoma neuronal learning

INTRODUCTION: The human brain processes shape and texture information separately through different neurons in the visual system. In intelligent computer-aided imaging diagnosis, pre-trained feature extractors are commonly used in various medical image recognition methods, common pre-training datasets such as ImageNet tend to improve the texture representation of the model but make it ignore many shape features. Weak shape feature representation is disadvantageous for some tasks that focus on shape features in medical image analysis...

Basal cell carcinoma (BCC), squamous cell carcinoma (SqCC) and melanoma are among the most common cancer types. Correct diagnosis based on histological evaluation after biopsy or excision is paramount for adequate therapy stratification. Deep learning on histological slides has been suggested to complement and improve routine diagnostics, but publicly available curated and annotated data and usable models trained to distinguish common skin tumors are rare and often lack heterogeneous non-tumor categories. A total of 16 classes from 386 cases were manually annotated on scanned histological slides, 129,364 100 x 100 µm (~395 x 395 px) image tiles were extracted and split into a training, validation and test set...

Recently, convolutional neural networks have greatly outperformed previous systems based on handcrafted features once the size of public databases has increased. However, these algorithms learn feature representations that are difficult to interpret and analyse. On the other hand, experts require automatic systems to explain their decisions according to clinical criteria which, in the field of melanoma diagnosis, are related to the analysis of dermoscopic features found in the lesions. In recent years, the interpretability of deep networks has been explored using methods that obtain visual features highlighted by neurones or analyse activations to extract more useful information...

BACKGROUND: Skin cutaneous melanoma (SKCM) is one of the most highly prevalent and complicated malignancies. Glycolysis and cholesterogenesis pathways both play important roles in cancer metabolic adaptations. The main aims of this study are to subtype SKCM based on glycolytic and cholesterogenic genes and to build a clinical outcome predictive algorithm based on the subtypes. METHODS: A dataset with 471 SKCM specimens was downloaded from The Cancer Genome Atlas (TCGA) database...

Although over one-third of stroke patients may develop post-stroke cognitive impairment (PSCI), the mechanisms underlying PSCI remain unclear. We explored here, the involvement of post-stroke inflammasomes in long-term PSCI development, using a 45 min-middle cerebral artery occlusion (MCAO)/reperfusion-induced PSCI model. Immunohistological assessment on day 1, 3, and 7 was followed by cognitive function test 28 days post-stroke. Evaluation of inflammasome sensor gene expression in aged mouse brains showed dominant expression of absent in melanoma 2 (Aim2) in 6-, 12-, and 18-month-old mouse brains...

BACKGROUND: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measured to date. This study compares the performance of a convolutional neuronal network trained with dermoscopic images exclusively for identifying melanoma in clinical photographs with the manual grading of the same images by dermatologists...

Maged1 is a member of the type II melanoma antigen (MAGE) family of proteins, which is highly conserved in the brain between mouse and human. Recently, Maged1 has been reported to be involved in depression and impaired sexual behavior. However, the role of Maged1 in learning and memory remains unknown. The aim of the present study was therefore to investigate whether Maged1 deficiency can impair learning and memory formation. By behavioral tests and electrophysiological recording, we observed that 5-6-month-old Maged1 knockout mice displayed the reduced basal synaptic transmission, pronounced hippocampal dysfunction, impaired spatial learning, and a deficit in long-term potentiation induction...

Deficient repair of ubiquitous errors in the genome risks faulty transcription or replication. Its direct and indirect phenotypic consequences are rare, complex, dementing, lethal disorders of children with inadequately understood overlapping genotypes and variable severity. Mutations of CSA or CSB responsible for impaired transcription-coupled repair cause Cockayne syndrome (CS). Its characteristics are (1) profound growth deficiency affecting all tissues, including the brain, (2) premature aging marked by cachexia, vascular disease, exocrine deficiency, and osteopenia, but not cancer, and (3) a selective degenerative disorder of central and peripheral myelin and by neuronal loss in the retina and inner ear, and in the cerebellum and basal ganglia where it is associated with calcification...