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Thyroid malignancy neoplasm-associated biomarkers as targets for oncolytic virotherapy

Overview of attention for article published in Oncolytic Virotherapy, June 2016
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Title
Thyroid malignancy neoplasm-associated biomarkers as targets for oncolytic virotherapy
Published in
Oncolytic Virotherapy, June 2016
DOI 10.2147/ov.s99856
Pubmed ID
Authors

Mingxu Guan, Yanping Ma, Sahil Rajesh Shah, Gaetano Romano

Abstract

Biomarkers associated with thyroid malignant neoplasm (TMN) have been widely applied in clinical diagnosis and in research oncological programs. The identification of novel TMN biomarkers has greatly improved the efficacy of clinical diagnosis. A more accurate diagnosis may lead to better clinical outcomes and effective treatments. However, the major deficiency of conventional chemotherapy and radiotherapy is lack of specificity. Due to the macrokinetic interactions, adverse side effects will occur, including chemotherapy and radiotherapy resistance. Therefore, a new treatment is urgently needed. As an alternative approach, oncolytic virotherapy may represent an opportunity for treatment strategies that can more specifically target tumor cells. In most cases, viral entry requires the expression of specific receptors on the surface of the host cell. Currently, molecular virologists and gene therapists are working on engineering oncolytic viruses with altered tropism for the specific targeting of malignant cells. This review focuses on the strategy of biomarkers for the production of novel TMN oncolytic therapeutics, which may improve the specificity of targeting of tumor cells and limit adverse effects in patients.

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The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 38%
Student > Ph. D. Student 1 13%
Professor > Associate Professor 1 13%
Other 1 13%
Unknown 2 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 1 13%
Immunology and Microbiology 1 13%
Psychology 1 13%
Medicine and Dentistry 1 13%
Engineering 1 13%
Other 0 0%
Unknown 3 38%