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  • Norfloxacin br Overall it can be concluded that the Fe HT

    2022-05-06


    Overall, it can be concluded that the 0.2Fe-HT powder did not ne-gatively affect cellular growth over 7 days of culture and also improved cell adhesion and resulted in cellular growth.
    The metabolic activity of Norfloxacin seeded on different samples (control, 0.2Fe-HT, and magnetite) under AMF was also assessed using PrestoBlue assay (Fig. 8(d)). As can be seen, the metabolic activity of Saos-2 cells significantly decreased when magnetic samples were ex-posed to AMF (p < 0.01). For more comparison, cell counts from obtained fluorescence 
    images were done by Image J software and cell viability was reported by dividing the live cell count by the total cell count (Fig. 8(b) and (e)). As can be seen from Fig. 8(b), the hMSCs viability for HT and 0.2Fe-HT samples is significantly more than magnetite sample in each time point. The higher proliferation rate in HT and 0.2Fe-HT powder samples than the magnetite sample confirmed that HT and 0.2Fe-HT promoted the proliferation of cells more than the magnetite. The results of cell counts showed that both 0.2Fe-HT and magnetite samples have a negative effect on Saos-2 cells under magnetic field (Fig. 8(e)). This confirmed high hyperthermia ability of synthesized 0.2Fe-HT like magnetite.
    Fig. 8(c) and (f) show illustration of cell culturing on the samples for evaluation of their tissue engineering and hyperthermia abilities, re-spectively.
    The results suggest that the 0.2Fe-HT studied in this paper shows a better hyperthermic response in addition to extended biocompatibility and bioactivity as compared to the conventional materials such as magnetite. The introduction of this magnetic biomaterial opens up the possibility of introducing hyperthermia responsive materials that are safe for the healthy bone tissue and additionally enhances bone re-generation.
    4. Conclusion
    Hyperthermia - is one of the applications of MNPs for destroying cancer cells such as malignant bone tumor cells through the heat gen-eration under AMF. Developing a novel biomaterial with properties such as acceptable magnetic hyperthermia performance, desirable biocompatibility and bioactivity, proven ability to support cell differ-entiation and growth is considered crucial for increasing the efficiency of treatment. In this research, we have shown that despite high hy-perthermia ability of magnetite as an attractive bioceramic used in hyperthermia applications (increasing temperature up to 53 °C in 250 s), it was not non-toxic and could not induce bone-like apatite formation. We also showed that synthesized nano-structured 0.2Fe-HT powder (Ca1.8Fe0.2ZnSi2O7) had a milder hyperthermia effect compared
    to magnetite; temperature increased to 40 °C in 250 s, however, it was completely biocompatible and also could enhance cellular proliferation with a good apatite formation ability. Furthermore, in vitro cell culture experiments showed that the 0.2Fe-HT powder could kill cancer cells under AMF like magnetite powder. Therefore, the synthesized multi-functional Fe-HT powder can be widely employed as filling biomaterial in critical size bone defects after removing malignant bone tumors to treat bone tumor cells and simultaneously to support bone cellular growth.
    References
    E. Verné, Composite bone cements for hyperthermia: modeling and characterization of magnetic, calorimetric and in vitro heating properties, Ceram. Int. 43 (2017) 4831–4840. [10] R. Petca, S. Gavriliu, G. Burnei, Retrospective clinicopathological study of malig-nant bone tumors in children and adolescents in Romania–single center experience, J. Med. Life 9 (2016) 205.
    Contents lists available at ScienceDirect
    Journal of Biomedical Informatics: X
    journal homepage: www.journals.elsevier.com/journal-of-biomedical-informatics-x
    Comparing breast cancer treatments using automatically detected surrogate T and clinically relevant outcomes entities from text
    Catherine Blakea, , Rebecca Kehmb
    a School of Information Sciences and Department of Computer Science, University of Illinois at Urbana Champaign, United States b Mailman School of Public Health, Columbia University, United States
    Keywords:
    Evidence-based medicine
    Outcome extraction
    Text mining
    Machine learning
    Systematic reviews
    Breast cancer outcomes 
    Population, intervention, comparison and outcome (PICO) facets of clinical studies are required both for phy-sicians in a clinical setting and for reviewers as they compare the effectiveness of different treatment strategies. Automated methods developed for the first three of these facets identify entities, but outcome detection has been limited to identifying the entire sentence. We frame outcome detection as a noun phrase prediction task and use semi-supervised learning to detect new outcomes (aka endpoints) from the method section of 88 K MEDLINE abstracts. A manual analysis showed that 96.7% of all outcomes can be captured using a noun phrase re-presentation. With respect to the machine learning classifiers, the Support Vector Machine produced higher precision, F1-score, and accuracy than the General Linear Model when evaluated with respect to the initial gold standard of survivorship seed terms and a manual gold standard that considered all outcomes. However, the best model does not employ machine learning, but rather leverages list structure and resulted in 90.14 precision, 60.69 recall, 75.41 F1-score, and 92.60 accuracy with respect to the manual gold standard of all outcomes. Finally we developed a silver standard with a precision of 89.28 and recall of 86.77 compared to the manual gold standard and used the silver standard to identify all outcomes reported for five breast cancer treatments. The increased precision afforded by this approach reveals that in contrast to chemotherapy and targeted therapy, the surrogate outcome disease free survival (DFS) is reported more frequently than the clinically relevant outcome overall survival (OS) for hormone therapies, which is consistent with findings that DFS translates into firm OS improvements in a hormone therapy setting.