Archives

  • 2019-10
  • 2019-11
  • 2020-03
  • 2020-07
  • 2020-08
  • br Sharma RA Cummins C Crellin A Selective

    2020-08-12


    [4] Sharma RA, Cummins C, Crellin A. Selective internal radio-therapy of the liver: at the crossroads of interventional oncology research and National Health Service commis-sioning. Clin Oncol 2014;26(12):733e735.
    [5] Kennedy A, Nag S, Salem R, Murthy R, McEwan AJ, Nutting C, et al. Recommendations for radioembolization of hepatic malignancies using yttrium-90 microsphere brachytherapy: BafilomycinA1 consensus panel report from the radioembolization brachy-therapy oncology consortium. Int J Radiat Oncol Biol Phys 2007;68(1):13e23.
    [6] NHS England. Independent evaluation of the selective internal ra-diation therapy commissioning through evaluation scheme: evalu-ation report 2017. Available at: https://www.england.nhs.uk/ publication/independent-evaluation-of-the-selectiveinternal-radiation-therapy-commissioning-through-evaluation-scheme/. [7] Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al. New response evaluation criteria in solid tu-mours: revised RECIST guideline (version 1.1). Eur J Cancer 2009;45(2):228e247.
    [10] Hickish T, Andre T, Wyrwicz L, Saunders M, Sarosiek T, Kocsis J, et al. MABp1 as a novel antibody treatment for advanced colorectal cancer: a randomised, double-blind, 
    [13] Bester L, Meteling B, Pocock N, Pavlakis N, Chua TC, Saxena A, et al. Radioembolization versus standard care of hepatic me-tastases: comparative retrospective cohort study of survival outcomes and adverse events in salvage patients. J Vasc Interv Radiol 2012;23(1):96e105.
    [14] Seidensticker R, Denecke T, Kraus P, Seidensticker M, Mohnike K, Fahlke J, et al. Matched-pair comparison of 12 radioembolization plus best supportive care versus best supportive care alone for chemotherapy refractory liver-dominant colorectal metastases. Cardiovasc Interv Radiol 2012;35(5):1066e1073.
    [15] Hendlisz A, Van den Eynde M, Peeters M, Maleux G, Lambert B, Vannoote J, et al. Phase III trial comparing pro-tracted intravenous fluorouracil infusion alone or with yttrium-90 resin microspheres radioembolization for liver-limited metastatic colorectal cancer refractory to standard chemotherapy. J Clin Oncol 2010;28(23):3687e3694.
    [16] Sridhara R, Mandrekar SJ, Dodd LE. Missing data and mea-surement variability in assessing progression-free survival endpoint in randomized clinical trials. Clin Cancer Res 2013; 19(10):2613e2620.
    [17] Zacharias AJ, Jayakrishnan TT, Rajeev R, Rilling WS, Thomas JP, George B, et al. Comparative effectiveness of hepatic artery based therapies for unresectable colorectal liver metastases: a meta-analysis. PLoS One 2015;10(10):e0139940.
    [18] Saxena A, Bester L, Shan L, Perera M, Gibbs P, Meteling B, et al. A systematic review on the safety and efficacy of yttrium-90 radioembolization for unresectable, chemorefractory colo-rectal cancer liver metastases. J Cancer Res Clin Oncol 2014; 140(4):537e547.
    [19] Wasan HS, Gibbs P, Sharma NK, Taieb J, Heinemann V, Ricke J, et al. First-line selective internal radiotherapy plus chemo-therapy versus chemotherapy alone in patients with liver metastases from colorectal cancer (FOXFIRE, SIRFLOX, and FOXFIRE-Global): a combined analysis of three multicentre, randomised, phase 3 trials. Lancet Oncol 2017;18(9):11591171. Gene 685 (2019) 62–69
    Contents lists available at ScienceDirect
    Gene
    journal homepage: www.elsevier.com/locate/gene
    Research paper
    Analysis of cancer gene attributes using electrical sensor T
    Tanusree Roy
    University of Engineering and Management, Kolkata 700156, India
    Keywords:
    Genomics
    Modeling
    Cancer
    Network simulation
    Sensor
    Gene 
    Prediction of cancer gene attributes by their primary structure (long amino acid sequence) is instrumental in large-scale genomics projects, especially in the field of genomics. Various methods are proposed to predict gene characteristics from its primary structure, but accurate prediction of it is still very challenging task. Here we introduce an electrical network based sensor or detector to discriminate cancer and non-cancer cells based on two features i.e. amino acid sequence length, and hydrophilic/hydrophobic property. The electrical circuit consists of resistors, capacitors and inductors, is used for modeling individual amino acid and cascaded to form gene system. Corresponding electrical responses are judged using Bode and Nyquist analyzers and achieve 89.55% accuracy with 87.06% True Positive rate and 95.42% True Negative rate, demonstrate that proposed electrical sensor model is very promising for predicting the cancer gene attributes as well as non-cancer gene in the field of large-scale genomics study.
    1. Introduction
    In recent years, complex network modeling is gaining importance in nanobioscience and genomic research. Several researchers proposed different type of complex network approaches to model deoxyr-ibonucleic acid (DNA)/ribonucleic acid (RNA), amino acid and protein structure. Resistor-capacitor ladder network is used to develop model for DNA/RNA strings at nucleotide level (Marshall, 2010a). Passive analog electrical circuits are designed for protein structure (Sampath, 2006). The secondary structure and the secondary structure linkages are modeled using resistor, capacitor and inductor (Marshall, 2010b). Electrical network models are also developed to study the structural and electrical properties of DNA, protein, gene etc. (Hodzic and Newcomb, BafilomycinA1 2007; Alfinito et al., 2008; Roy et al., 2014; Roy and Barman, 2014).