Treating large established tumors is challenging for dendritic cell (DC)-based immunotherapy. DC activation with tumor cell-derived exosomes (TEXs) carrying multiple tumor-associated antigen can enhance tumor recognition. Adding a potent adjuvant, high mobility group nucleosome-binding protein 1 (HMGN1), boosts DCs' ability to activate T cells and improves vaccine efficiency. Here, we demonstrate that TEXs painted with the functional domain of HMGN1 (TEX-N1ND) via an exosomal anchor peptide potentiates DC immunogenicity. TEX-N1ND pulsed DCs (DC) elicit long-lasting antitumor immunity and tumor suppression in different syngeneic mouse models with large tumor burdens, most notably large, poorly immunogenic orthotopic hepatocellular carcinoma (HCC). DC show increased homing to lymphoid tissues and contribute to augmented memory T cells. Importantly, N1ND-painted serum exosomes from cancer patients also promote DC activation. Our study demonstrates the potency of TEX-N1ND to strengthen DC immunogenicity and to suppress large established tumors, and thus provides an avenue to improve DC-based immunotherapy.
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http://dx.doi.org/10.1038/s41467-020-15569-2 | DOI Listing |
ACS Nano
January 2025
State Key Laboratory of Materials Processing and Die & Mould Technology, and School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, P. R. China.
Intensifying the severity of electromagnetic (EM) pollution in the environment represents a significant threat to human health and results in considerable energy wastage. Here, we provide a strategy for electricity generation from heat generated by electromagnetic wave radiation captured from the surrounding environment that can reduce the level of electromagnetic pollution while alleviating the energy crisis. We prepared a porous, elastomeric, and lightweight BiTe/carbon aerogel (CN@BiTe) by a simple strategy of induced in situ growth of BiTe nanosheets with three-dimensional (3D) carbon structure, realizing the coupling of electromagnetic wave absorption (EMA) and thermoelectric (TE) properties.
View Article and Find Full Text PDFNat Methods
January 2025
Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
The surge in genome data, with ongoing efforts aiming to sequence 1.5 M eukaryotes in a decade, could revolutionize genomics, revealing the origins, evolution and genetic innovations of biological processes. Yet, traditional genomics methods scale poorly with such large datasets.
View Article and Find Full Text PDFNat Ecol Evol
January 2025
Centre for Biological Diversity, School of Biology, University of St Andrews, St Andrews, UK.
Rapid growth in bio-logging-the use of animal-borne electronic tags to document the movements, behaviour, physiology and environments of wildlife-offers opportunities to mitigate biodiversity threats and expand digital natural history archives. Here we present a vision to achieve such benefits by accounting for the heterogeneity inherent to bio-logging data and the concerns of those who collect and use them. First, we can enable data integration through standard vocabularies, transfer protocols and aggregation protocols, and drive their wide adoption.
View Article and Find Full Text PDFJ Math Biol
January 2025
Instituto de Ingeniería Matemática, Universidad de Valparaíso, Valparaíso, Chile.
We study the large-time behavior of an ensemble of entities obeying replicator-like stochastic dynamics with mean-field interactions as a model for a primordial ecology. We prove the propagation-of-chaos property and establish conditions for the strong persistence of the N-replicator system and the existence of invariant distributions for a class of associated McKean-Vlasov dynamics. In particular, our results show that, unlike typical models of neutral ecology, fitness equivalence does not need to be assumed but emerges as a condition for the persistence of the system.
View Article and Find Full Text PDFSci Rep
January 2025
State-owned Jiaozuo Forest Farm, Jiaozuo, 454000, Henan, China.
Accurately estimating forest carbon sink and exploring their climate-driven mechanisms are critical to achieving carbon neutrality and sustainable development. Fewer studies have used machine learning-based dynamic models to estimate forest carbon sink. The climate-driven mechanisms in Shangri-La have yet to be explored.
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