Integrins are the adhesion molecules and receptors of extracellular matrix (ECM). They mediate the interactions between cells-cells and cells-ECM. The crosstalk between cancer cells and their microenvironment triggers a variety of critical signaling cues and promotes the malignant phenotype of cancer. As a type of transmembrane protein, integrin-mediated cell adhesion is essential in regulating various biological functions of cancer cells. Recent evidence has shown that integrins present on tumor cells or tumorassociated stromal cells are involved in ECM remodeling, and as mechanotransducers sensing changes in the biophysical properties of the ECM, which contribute to cancer metastasis, stemness and drug resistance. In this review, we outline the mechanism of integrin-mediated effects on biological changes of cancers and highlight the current status of clinical treatments by targeting integrins.

Various algorithms comparing 2D NMR spectra have been explored for their ability to dereplicate natural products as well as determine molecular structures. However, spectroscopic artefacts, solvent effects, and the interactive effect of functional group(s) on chemical shifts combine to hinder their effectiveness. Here, we leveraged Non-Uniform Sampling (NUS) 2D NMR techniques and deep Convolutional Neural Networks (CNNs) to create a tool, SMART, that can assist in natural products discovery efforts. First, an NUS heteronuclear single quantum coherence (HSQC) NMR pulse sequence was adapted to a state-of-the-art nuclear magnetic resonance (NMR) instrument, and data reconstruction methods were optimized, and second, a deep CNN with contrastive loss was trained on a database containing over 2,054 HSQC spectra as the training set. To demonstrate the utility of SMART, several newly isolated compounds were automatically located with their known analogues in the embedded clustering space, thereby streamlining the discovery pipeline for new natural products.

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