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  1. 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.

     
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  2. 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.

     
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    Free, publicly-accessible full text available January 1, 2026
  3. Free, publicly-accessible full text available October 1, 2025
  4. Azido-lipid enables simultaneous delivery of mRNA and metabolic tagging of cell membranes.

     
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    Free, publicly-accessible full text available August 6, 2025
  5. Free, publicly-accessible full text available September 1, 2025
  6. Free, publicly-accessible full text available July 1, 2025
  7. Abstract Background

    Alzheimer’s Disease (AD) is a progressive memory disorder that causes irreversible cognitive decline. Given that there is currently no cure, it is critical to detect AD in its early stage during the disease progression. Recently, many statistical learning methods have been presented to identify cognitive decline with temporal data, but few of these methods integrate heterogeneous phenotype and genetic information together to improve the accuracy of prediction. In addition, many of these models are often unable to handle incomplete temporal data; this often manifests itself in the removal of records to ensure consistency in the number of records across participants.

    Results

    To address these issues, in this work we propose a novel approach to integrate the genetic data and the longitudinal phenotype data to learn a fixed-length “enriched” biomarker representation derived from the temporal heterogeneous neuroimaging records. Armed with this enriched representation, as a fixed-length vector per participant, conventional machine learning models can be used to predict clinical outcomes associated with AD.

    Conclusion

    The proposed method shows improved prediction performance when applied to data derived from Alzheimer’s Disease Neruoimaging Initiative cohort. In addition, our approach can be easily interpreted to allow for the identification and validation of biomarkers associated with cognitive decline.

     
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  8. Abstract

    Ferroptosis has been shown to play a crucial role in preventing cancer development, but the underlying mechanisms of dysregulated genes and genetic alternations driving cancer development by regulating ferroptosis remain unclear. Here, we showed that the synergistic role of ELF3 overexpression and PTEN deficiency in driving lung cancer development was highly dependent on the regulation of ferroptosis. HumanELF3(hELF3) overexpression in murine lung epithelial cells only caused hyperplasia with increased proliferation and ferroptosis. hELF3overexpression andPtengenetic disruption significantly induced lung tumor development with increased proliferation and inhibited ferroptosis. Mechanistically, we found it was due to the induction of SCL7A11, a typical ferroptosis inhibitor, and ELF3 directly and positively regulated SCL7A11 in the PTEN-deficient background. Erastin-mediated inhibition of SCL7A11 induced ferroptosis in cells with ELF3 overexpression and PTEN deficiency and thus inhibited cell colony formation and tumor development. Clinically, human lung tumors showed a negative correlation betweenELF3andPTENexpression and a positive correlation betweenELF3andSCL7A11in a subset of human lung tumors withPTEN-low expression.ELF3andSCL7A11expression levels were negatively associated with lung cancer patients’ survival rates. In summary, ferroptosis induction can effectively attenuate lung tumor development induced byELF3overexpression andPTENdownregulation or loss-of-function mutations.

     
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    Free, publicly-accessible full text available December 1, 2025
  9. Free, publicly-accessible full text available April 1, 2025