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Astrocytic gliomas are the most common and lethal form of intracranial tumors. These tumors are characterized by a significant heterogeneity in terms of cytopathological, transcriptional, and (epi)genomic features. This heterogeneity has made these cancers one of the most challenging types of cancers to study and treat. To uncover these complexities and to have better understanding of the disease initiation and progression, identification, and characterization of underlying cellular and molecular pathways related to (epi)genetics of astrocytic gliomas is crucial. Here, we discuss and summarize molecular and (epi)genetic mechanisms that provide clues as to the pathogenesis of astrocytic gliomas.

Keratin intermediate filaments play an important role in maintaining the integrity of the skin structure. Understanding the importance of this subject is possible with the investigation of keratin defects in epidermolysis bullosa simplex (EBS). Nowadays, in addition to clinical criteria, new molecular diagnostic methods, such as next generation sequencing, can help to distinguish the subgroups of EBS more precisely. Because the most important and most commonly occurring molecular defects in these patients are the defects of keratins 5 and14 (KRT5 and KRT14), comprehending the nature structure of these proteins and their involved processes can be very effective in understanding the pathophysiology of this disease and providing new and effective therapeutic platforms to treat it. Here, we summarized the various aspects of the presence of KRT5 and KRT14 in the epidermis, their relation to the incidence and severity of EBS phenotypes, and the processes with which these proteins can affect them.

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The multiple linear regression (MLR) was used to build the linear quantitative structure-property relationship (QSPR) model for the prediction of the molar diamagnetic susceptibility (χ m ) for 140 diverse organic compounds using the three significant descriptors calculated from the molecular structures alone and selected by stepwise regression method. Stepwise regression was employed to develop a regression equation based on 100 training compounds, and predictive ability was tested on 40 compounds reserved for that purpose. The stability of the proposed model was validated using Leave-One-Out cross-validation and randomization test. Application of the developed model to a testing set of 40 organic compounds demonstrates that the new model is reliable with good predictive accuracy and simple formulation. By applying MLR method we can predict the test set (40 compounds) with Q 2 ext of 0.9894 and average root mean square error (RMSE) of 2.2550. The model applicability domain was always verified by the leverage approach in order to propose reliable predicted data. The prediction results are in good agreement with the experimental values.