Current Issue : October-December Volume : 2022 Issue Number : 4 Articles : 5 Articles
Digital pathology analysis using deep learning has been the subject of several studies. As with other medical data, pathological data are not easily obtained. Because deep learning-based image analysis requires large amounts of data, augmentation techniques are used to increase the size of pathological datasets. This study proposes a novel method for synthesizing brain tumor pathology data using a generative model. For image synthesis, we used embedding features extracted from a segmentation module in a general generative model. We also introduce a simple solution for training a segmentation model in an environment in which the masked label of the training dataset is not supplied. As a result of this experiment, the proposed method did not make great progress in quantitative metrics but showed improved results in the confusion rate of more than 70 subjects and the quality of the visual output....
Integrating Artificial Intelligence (AI) tools in the tissue diagnostic workflow will benefit the pathologist and, ultimately, the patient. The generation of such AI tools has two parallel yet interconnected processes, namely the definition of the pathologist’s task to be delivered in silico, and the software development requirements. In this review paper, we demystify this process, from a viewpoint that joins experienced pathologists and data scientists, by proposing a general pathway and describing the core steps to build an AI digital pathology tool. In doing so, we highlight the importance of the collaboration between AI scientists and pathologists, from the initial formulation of the hypothesis to the final, ready-to-use product....
Concomitant schwannomas and benign meningothelial proliferations, including meningothelial hyperplasia or meningioma, rarely occur at the same location outside the setting of neurofibromatosis. Herein, we present a rare case of concurrent schwannoma and benign meningothelial hyperplasia concomitantly occurring in the cervical spine of a 69-year-old male patient with no history of any genetic disorder....
Similar to other malignancies, TCGA network efforts identified the detailed genomic picture of skin melanoma, laying down the basis of molecular classification. On the other hand, genome- wide association studies discovered the genetic background of the hereditary melanomas and the susceptibility genes. These genetic studies helped to fine-tune the differential diagnostics of malignant melanocytic lesions, using either FISH tests or the myPath gene expression signature. Although the original genomic studies on skin melanoma were mostly based on primary tumors, data started to accumulate on the genetic diversity of the progressing disease. The prognostication of skin melanoma is still based on staging but can be completed with gene expression analysis (DecisionDx). Meanwhile, this genetic knowledge base of skin melanoma did not turn to the expected wide array of target therapies, except the BRAF inhibitors. The major breakthrough of melanoma therapy was the introduction of immune checkpoint inhibitors, which showed outstanding efficacy in skin melanoma, probably due to their high immunogenicity. Unfortunately, beyond BRAF, KIT mutations and tumor mutation burden, no clinically validated predictive markers exist in melanoma, although several promising biomarkers have been described, such as the expression of immune- related genes or mutations in the IFN-signaling pathway. After the initial success of either target or immunotherapies, sooner or later, relapses occur in the majority of patients, due to various induced genetic alterations, the diagnosis of which could be developed to novel predictive genetic markers....
Viral infections are influenced by various microorganisms in the environment surrounding the target tissue, and the correlation between the type and balance of commensal microbiota is the key to establishment of the infection and pathogenicity. Some commensal microorganisms are known to resist or promote viral infection, while others are involved in pathogenicity. It is also becoming evident that the profile of the commensal microbiota under normal conditions influences the progression of viral diseases. Thus, to understand the pathogenesis underlying viral infections, it is important to elucidate the interactions among viruses, target tissues, and the surrounding environment, including the commensal microbiota, which should have different relationships with each virus. In this review, we outline the role of microorganisms in viral infections. Particularly, we focus on gaining an in-depth understanding of the correlations among viral infections, target tissues, and the surrounding environment, including the commensal microbiota and the gut virome, and discussing the impact of changes in the microbiota (dysbiosis) on the pathological progression of viral infections....
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