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Available: https:\/\/www.empowerpharmacy.com\/drugs\/liothyronine-sodium-sr-capsules.html#footnote22_c6u8axd<\/ext-link>"},{"key":"ref45","first-page":"224","article-title":"J Med Libr Assoc","volume":"95","author":"V. 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Kunz","year":"1996","journal-title":"Proc Natl Acad Sci"},{"key":"ref49","first-page":"e85995","article-title":"Arsenic Trioxide Overcomes Rapamycin-Induced Feedback Activation of AKT and ERK Signaling to Enhance the Anti-Tumor Effects in Breast Cancer","volume":"8","author":"C Guilbert","year":"2013"},{"key":"ref50","first-page":"881","article-title":"Prolongation of the QT Interval and Ventricular Tachycardia in Patients Treated with Arsenic Trioxide for Acute Promyelocytic Leukemia","volume":"133","author":"O Kazunori","year":"2001","journal-title":"Ann Intern Med"},{"key":"ref51","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.molcel.2016.06.022","article-title":"A network of conserved synthetic lethal interactions for exploration of precision cancer therapy","volume":"63","author":"R Srivas","year":"2016","journal-title":"Mol Cell"},{"key":"ref52","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1161\/CIRCRESAHA.116.303975","article-title":"Control of 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