Comprehensive analysis of clinical images contributions for melanoma classification using convolutional neural networks.

BACKGROUND: Timely diagnosis plays a critical role in determining melanoma prognosis, prompting the development of deep learning models to aid clinicians. Questions persist regarding the efficacy of clinical images alone or in conjunction with dermoscopy images for model training. This study aims to compare the classification performance for melanoma of three types of CNN models: those trained on clinical images, dermoscopy images, and a combination of paired clinical and dermoscopy images from the same lesion. MATERIALS AND METHODS: We divided 914 image pairs into training, validation, and test sets. Models were built using pre-trained Inception-ResNetV2 convolutional layers for feature extraction, followed by binary classification. Training comprised 20 models per CNN type using sets of random hyperparameters. Best models were chosen based on validation AUC-ROC. RESULTS: Significant AUC-ROC differences were found between clinical versus dermoscopy models (0.661 vs. 0.869, p < 0.001) and clinical versus clinical + dermoscopy models (0.661 vs. 0.822, p = 0.001). Significant sensitivity differences were found between clinical and dermoscopy models (0.513 vs. 0.799, p = 0.01), dermoscopy versus clinical + dermoscopy models (0.799 vs. 1.000, p = 0.02), and clinical versus clinical + dermoscopy models (0.513 vs. 1.000, p < 0.001). Significant specificity differences were found between dermoscopy versus clinical + dermoscopy models (0.800 vs. 0.288, p < 0.001) and clinical versus clinical + dermoscopy models (0.650 vs. 0.288, p < 0.001). CONCLUSION: CNN models trained on dermoscopy images outperformed those relying solely on clinical images under our study conditions. The potential advantages of incorporating paired clinical and dermoscopy images for CNN-based melanoma classification appear less clear based on our findings.

Network pharmacology reveals multitarget mechanism of action of drugs to be repurposed for COVID-19.

The coronavirus disease 2019 pandemic accelerated drug/vaccine development processes, integrating scientists all over the globe to create therapeutic alternatives against this virus. In this work, we have collected information regarding proteins from SARS-CoV-2 and humans and how these proteins interact. We have also collected information from public databases on protein-drug interactions. We represent this data as networks that allow us to gain insights into protein-protein interactions between both organisms. With the collected data, we have obtained statistical metrics of the networks. This data analysis has allowed us to find relevant information on which proteins and drugs are the most relevant from the network pharmacology perspective. This method not only allows us to focus on viral proteins as the main targets for COVID-19 but also reveals that some human proteins could be also important in drug repurposing campaigns. As a result of the analysis of the SARS-CoV-2-human interactome, we have identified some old drugs, such as disulfiram, auranofin, gefitinib, suloctidil, and bromhexine as potential therapies for the treatment of COVID-19 deciphering their potential complex mechanism of action.

Human recombinant lysosomal ß-Hexosaminidases produced in Pichia pastoris efficiently reduced lipid accumulation in Tay-Sachs fibroblasts.

GM2 gangliosidosis, Tay-Sachs and Sandhoff diseases, are lysosomal storage disorders characterized by the lysosomal accumulation of GM2 gangliosides. This accumulation is due to deficiency in the activity of the ß-hexosaminidases Hex-A or Hex-B, which are dimeric hydrolases formed by αß or ßß subunits, respectively. These disorders show similar clinical manifestations that range from mild systemic symptoms to neurological damage and premature death. There is still no effective therapy for GM2 gangliosidoses, but some therapeutic alternatives, as enzyme replacement therapy, have being evaluated. Previously, we reported the production of active human recombinant ß-hexosaminidases (rhHex-A and rhHex-B) in the methylotrophic yeast Pichia pastoris. In this study, we evaluated in vitro the cellular uptake, intracellular delivery to lysosome, and reduction of stored substrates. Both enzymes were taken-up via endocytic pathway mediated by mannose and mannose-6-phosphate receptors and delivered to lysosomes. Noteworthy, rhHex-A diminished the levels of stored lipids and lysosome mass in fibroblasts from Tay-Sachs patients. Overall, these results confirm the potential of P. pastoris as host to produce recombinant ß-hexosaminidases intended to be used in the treatment of GM2 gangliosidosis.