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Lung cancer represents a significant global health burden, with non-small cell lung cancer (NSCLC) being the most common subtype. The current standard of care for NSCLC has limited efficacy, highlighting the necessity for innovative treatment options. Lidocaine, traditionally recognized as a local anesthetic, has emerged as a compound with potential antitumor and anti-inflammatory capabilities.

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Neuroendocrine neoplasms (NENs) encompass a diverse set of malignancies with limited precision therapy options. Recently, therapies targeting DLL3 have shown clinical efficacy in aggressive NENs, including small cell lung cancers and neuroendocrine prostate cancers. Given the continued development and expansion of DLL3-targeted therapies, we sought to characterize the expression of DLL3 and identify its clinical and molecular correlates across diverse neuroendocrine and non-neuroendocrine cancers.

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Background: Human Lung Carcinoma (LC) is among the most diagnosed cancers across the world among those non-small cell lung cancer (NSCLC) comprises about 85%. Next Generation Sequencing based detection of mutations are now well established in molecular oncology. With the advent of modern diagnostic methods, it is now well known that there are several mutations and gene rearrangements which are associated with the development of LC.

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Extracellular vesicles (EVs) are emerging as crucial biomarkers in cancer diagnostics and therapeutics with their heterogeneity presenting both challenges and opportunities in prostate cancer research. However, existing methods for isolating and characterizing EV subtypes have been limited by inefficient separation and inadequate proteomic analysis. Here we show an optimized centrifugal microfluidic device, Exodisc, that efficiently isolates large quantities of EV subtypes from particle-enriched medium, enabling comprehensive proteomic analysis of small (EV-S, 20-200 nm) and large (EV-L, >200 nm) EVs.

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