• 2019-10
  • 2019-11
  • 2020-03
  • 2020-07
  • 2020-08
  • 2021-03
  • Gefitinib br Conflict of interest statement br The authors h


    Conflict of interest statement
    The authors have no conflicts of interests with other persons or organizations.
    This study Gefitinib sponsored in part by three research grants as follows: HR15-016 from Oklahoma Center for the Advancement of Science and Technology (OCAST), Grant R01 CA197150 from the National Cancer Institute, National Institutes of Health, USA, and SCC research award from Stephenson Cancer Center at the University of Oklahoma Health Sciences Center.
    10. Gundreddy, R.R., et al., Assessment of performance and reproducibility of applying a content-based image retrieval scheme for classification of breast lesions. Medical Physics, 2015. 42(7): p. 4241-4249.
    12. Danala, G., et al., Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms. Annals of Biomedical Engineering, 2018. 46(9): p. 1419-1431.
    13. Carney, P.A., et al., Individual and Combined Effects of Age, Breast Density, and Hormone Replacement Therapy Use on the Accuracy of Screening Mammography. Annals of Internal Medicine, 2003. 138(3): p. 168-175.
    14. Qiu, Y., et al., A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology. Journal of X-ray science and technology, 2017. 25(5): p. 751-763.
    15. Gulshan, V., et al., Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus PhotographsAccuracy of a Deep Learning Algorithm for Detection of Diabetic RetinopathyAccuracy of a Deep Learning Algorithm for Detection of Diabetic Retinopathy. JAMA, 2016. 316(22): p. 2402-2410.
    16. Yan, S., et al., Improving Performance of Breast Cancer Risk Prediction by Incorporating Optical Density Image Feature Analysis: An Assessment. Academic Radiology, 2017.
    18. Wang, X., et al., Computerized prediction of risk for developing breast cancer based on bilateral mammographic breast tissue asymmetry. Medical engineering & physics, 2011. 33(8): p. 934-942.
    21. Kourou, K., et al., Machine learning applications in cancer Gefitinib prognosis and prediction. Computational and structural biotechnology journal, 2014. 13: p. 8-17.
    23. Tan, M., J. Pu, and B. Zheng, Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model. International journal of computer assisted radiology and surgery, 2014. 9(6): p. 1005-1020.
    24. Lederman, D., et al., Improving Breast Cancer Risk Stratification Using Resonance-Frequency Electrical Impedance Spectroscopy Through Fusion of Multiple Classifiers. Annals of Biomedical Engineering, 2011. 39(3): p. 931-945.
    25. Kennedy, J. and R. Eberhart. Particle swarm optimization. in Proceedings of ICNN'95 - International Conference on Neural Networks. 1995.
    28. Giger, M.L., N. Karssemeijer, and J.A. Schnabel, Breast Image Analysis for Risk Assessment, Detection, Diagnosis, and Treatment of Cancer, in Annual Review of Biomedical Engineering, Vol 15, M.L. Yarmush, Editor. 2013, Annual Reviews: Palo Alto. p. 327-357.
    34. Battiti, R., Using mutual information for selecting features in supervised neural net learning. IEEE Transactions on Neural Networks, 1994. 5(4): p. 537-550.
    35. Constantinidis, A.S., M.C. Fairhurst, and A.F.R. Rahman, A new multi-expert decision combination algorithm and its application to the detection of circumscribed masses in digital mammograms. Pattern Recognition, 2001. 34(8): p. 1527-1537.
    Contents lists available at ScienceDirect
    International Journal of Biological Macromolecules
    Aptamer functionalized curcumin-loaded human serum albumin (HSA) nanoparticles for targeted delivery to HER-2 positive breast cancer cells
    Tayebeh Saleh, Tooba Soudi, Seyed Abbas Shojaosadati
    Biotechnology Group, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, PO Box: 14155-114, Iran
    Article history:
    Human serum albumin nanoparticle (HSA NP) Curcumin
    Human epithelial growth factor receptor 2
    Active targeting 
    In this study, an HER2 aptamer-decorated curcumin-loaded human serum albumin nanoparticle (Apt-HSA/CCM NP) was developed and characterized as a new anticancer formulation for targeted delivery to human epithelial growth factor receptor 2 (HER2) overexpressing breast cancer cells. Conjugation of HER2 Apt to the surface of HSA NPs was confirmed by gel electrophoresis and FTIR analysis. The obtained NPs have the hydrodynamic diam-eter of 281.1 ± 11.1 nm and zeta potential of −33.3 ± 2.5 mV. The data demonstrated that encapsulation of curcumin in HSA NPs by desolvation method has increased water solubility by 400 folds. Fluorescent microscopy image demonstrated remarkable cytoplasmic uptake of Apt-HSA/CCM NPs in HER2-overexpressing SK-BR-3 cells compared to unconjugated counterparts. Cytotoxicity experiments demonstrated no significant difference be-tween cytotoxic effect of free curcumin and non-targeted HSA/CCM NPs in both HER2 positive and HER2 negative cell lines. However, the toxicity of Apt-HSA/CCM NPs was significantly higher and cell viability reached 36% after 72 h in SK-BR3 cell line. These results suggest that this targeted delivery system has the potential to be considered as a promising candidate for the treatment of HER2 positive cancer cells.