The human RECQ1 helicase is highly expressed in glioblastoma and plays an important role in tumor cell proliferation

<p>Abstract</p> <p>Background</p> <p>RecQ helicases play an essential role in the maintenance of genome stability. In humans, loss of RecQ helicase function is linked with predisposition to cancer and/or premature ageing. Current data show that the specific depletion of the human RECQ1 helicase leads to mitotic catastrophe in cancer cells and inhibition of tumor growth in mice.</p> <p>Results</p> <p>Here, we show that RECQ1 is highly expressed in various types of solid tumors. However, only in the case of brain gliomas, the high expression of RECQ1 in glioblastoma tissues is paralleled by a lower expression in the control samples due to the poor expression of RECQ1 in non-dividing tissues. This conclusion is validated by immunohistochemical analysis of a tissue microarray containing 63 primary glioblastomas and 19 perilesional tissue samples, as control. We also show that acute depletion of RECQ1 by RNAi results in a significant reduction of cellular proliferation, perturbation of S-phase progression, and spontaneous γ-H2AX foci formation in T98G and U-87 glioblastoma cells. Moreover, RECQ1 depleted T98G and U-87 cells are hypersensitive to HU or temozolomide treatment.</p> <p>Conclusions</p> <p>Collectively, these results indicate that RECQ1 has a unique and important role in the maintenance of genome integrity. Our results also suggest that RECQ1 might represent a new suitable target for anti cancer therapies aimed to arrest cell proliferation in brain gliomas.</p

Analysis of the human RECQ1 helicase function in cells

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Applications of machine learning approaches to combat COVID-19: A survey

Machine learning (ML) and artificial intelligence (AI) approaches are prominent and well established in the field of health-care informatics. Because they have a more productive ability to predict, they are successfully applied in several health-care applications. ML approaches are needed thanks to the unsatisfactory experience of the novel virus, considerable ambiguity, complicated social circumstances, and inadequate accessible data. Several approaches have been applied as a tool to combat and protect against the new diseases. The COVID-19 outbreak has rapid growth, so it is not easy to predict the patients and resources within a specified time. ML is a strong approach in the fighting against the pandemic such as COVID-19. It is found significant to predict the susceptible, infected, recovered, or exposed persons and can assist the control strategies to block the spread of infections. This study critically examines the appropriateness and contribution of AI/ML methods on COVID-19 datasets, enhancing the understanding to apply these methods for quick analysis and verification of pandemic databases. © 2022 Elsevier Inc. All rights reserved