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https://www.selleckchem.com/products/Chlorogenic-acid.html Despite the significant achievements in the diagnosis and treatment of metastatic breast cancer (MBC), this condition remains substantially an incurable disease. In recent years, several clinical studies have aimed to identify novel molecular targets, therapeutic strategies, and predictive biomarkers to improve the outcome of women with MBC. Overall, ~40% of hormone receptor (HR)+/HER2- MBC cases harbor alterations affecting the (PI3K)/Akt/mammalian target of rapamycin (mTOR) pathway. This pathway is a major target in oncogenesis, as it regulates growth, proliferation, cell survival, and angiogenesis. Lately, the pharmacologic targeting of PIK3CA in HR+/HER2- MBC has shown significant benefits after the occurrence of endocrine therapy resistance. The orally available α-selective PIK3CA inhibitor, alpelisib, has been approved in this setting. To perform an optimal patients' selection for this drug, it is crucial to adopt a tailored methodology. Clinically relevant PIK3CA alterations may be detected in several biospecimens (e.g. tissue samples and liquid biopsy) using different techniques (e.g. real-time PCR and next-generation sequencing). In this study, we provide an overview of the role of PIK3CA in breast cancer and of the characterization of its mutational status for appropriate clinical management.Functional lung avoidance radiation therapy aims to minimize dose delivery to the normal lung tissue while favoring dose deposition in the defective lung tissue based on the regional function information. However, the clinical acquisition of pulmonary functional images is resource-demanding, inconvenient, and technically challenging. This study aims to investigate the deep learning-based lung functional image synthesis from the CT domain. Forty-two pulmonary macro-aggregated albumin SPECT/CT perfusion scans were retrospectively collected from the hospital. A deep learning-based framework (including image prepar

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