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Abstract Dendritic cells (DCs), the main type of antigen-presenting cells in the body, act as key mediators of adaptive immunity by sampling antigens from diseased cells for the subsequent priming of antigen-specific T and B cells. While DCs can secrete a diverse array of cytokines that profoundly shape the immune milieu, exogenous cytokines are often needed to maintain the survival, proliferation, and differentiation of DCs, T cells, and B cells. However, conventional cytokine therapies for cancer treatment are limited by their low therapeutic benefit and severe side effects. The overexpression of cytokines in DCs, followed by paracrine release or membrane display, has emerged as a viable approach for controlling the exposure of cytokines to interacting DCs and T/B cells. This approach can potentially reduce the necessary dose of cytokines and associated side effects to achieve comparable or enhanced antitumor efficacy. Various strategies have been developed to enable the overexpression or chemical conjugation of cytokines on DCs for the subsequent modulation of DC–T/B-cell interactions. This review provides a brief overview of strategies that enable the overexpression of cytokines in or on DCs via genetic engineering or chemical modification methods and discusses the promise of cytokine-overexpressing DCs for the development of new-generation cancer immunotherapy. 

Here, we report metabolic glycan labeling of adipocytes and targeted modulation via click chemistry, offering a novel platform to manipulate adipocyte interactions with other cells. 

Azido-lipid enables simultaneous delivery of mRNA and metabolic tagging of cell membranes. 

Chest X-ray (CXR) analysis plays an important role in patient treatment. As such, a multitude of machine learning models have been applied to CXR datasets attempting automated analysis. However, each patient has a differing number of images per angle, and multi-modal learning should deal with the missing data for specific angles and times. Furthermore, the large dimensionality of multi-modal imaging data with the shapes inconsistent across the dataset introduces the challenges in training. In light of these issues, we propose the Fast Multi-Modal Support Vector Machine (FMMSVM) which incorporates modality-specific factorization to deal with missing CXRs in the specific angle. Our model is able to adjust the fine-grained details in feature extraction and we provide an efficient optimization algorithm scalable to a large number of features. In our experiments, FMMSVM shows clearly improved classification performance.