一种新型的级联深度学习分类器在胸片中诊断covid - 19和肺炎

Q4 Medicine
S. AzarZochedhA, S. AsathBahadur, T. Kathiresan
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引用次数: 0

摘要

乳腺癌是最常见的女性癌症之一。目前可用的治疗方法是诱导癌细胞死亡并对健康细胞产生副作用。为了克服化疗的挑战,基于植物的植物化学物质和营养药品具有有效的抗癌活性。柚皮苷是一种植物衍生的类黄酮,天然存在于柑橘类水果中,具有抗癌活性。在本研究中,我们分析了分子的化学势和稳定性的前沿分子轨道(HOMO-LUMO),并对柚皮苷进行了分子对接研究,以发现配体与乳腺癌标记蛋白的结合效率。蛋白质的三维结构从RCSB蛋白质数据库中检索,autodock vina工具有助于分子对接研究。在7种靶蛋白中,PR具有高效结合能(-9.3 Kcal/mol),证实了其靶向乳腺癌的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel cascaded-deep learning classifier for diagnosis of Covid19 and pneumonia disease in chest X-Ray
Breast cancer is one of the most frequent female cancer. The currently available therapeutics are induces cancer cell death and also developing side effects in healthy cells. To overcome the chemotherapeutic challenges, plant-based phytochemicals and nutraceuticals are consist efficient anticancer activity. Naringin is one of the plant derived flavonoids, naturally present in citrus fruits with anticancer activity. In this present study, we analyze the frontier molecular orbital (HOMO-LUMO) for chemical potential and stability of molecules and also the molecular docking studies of naringin to find the binding efficiency of ligand and breast cancer marker proteins. The 3D structure of proteins is retrieved from the RCSB protein data bank and the tool autodock vina helps in molecular docking studies. Among the seven target proteins, PR have efficient binding energy (-9.3 Kcal/mol), which confirms the potential to target breast cancer.
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来源期刊
Journal of Cardiovascular Disease Research
Journal of Cardiovascular Disease Research Medicine-Cardiology and Cardiovascular Medicine
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