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  • Xu, Junjie  (2)
  • Unknown  (2)
  • 1
    Online Resource
    Online Resource
    Frontiers Media SA ; 2022
    In:  Frontiers in Immunology Vol. 13 ( 2022-10-6)
    In: Frontiers in Immunology, Frontiers Media SA, Vol. 13 ( 2022-10-6)
    Abstract: Adjuvants are crucial components of vaccines that can enhance and modulate antigen-specific immune responses. Herein, we reported for the first time that human metallothionein-3 (MT3), a low molecular weight cysteine-rich metal-binding protein, was a novel promising adjuvant candidate that could help protein antigens to induce rapid, effective, and durable antigen-specific immune responses. In the present study, MT3 was fused to outer membrane protein 19 (Omp19) of Brucella abortus (MT3-Omp19, MO) and C fragment heavy chain (Hc) of tetanus neurotoxin (MT3-Hc, MH), respectively. The results showed that MT3 as a built-in adjuvant increased the Omp19- or Hc-specific antibody responses by 100-1000 folds in seven days after primary immunization. Compared to other commercially available adjuvants, MT3 could stimulate earlier (4 days after primary injection) and stronger (10-100 folds) antibody response with lower antigen dose, and its adjuvanticity relied on fusion to antigen. Although the mechanism was not clear yet, the fusion protein MO was observed to directly activate DCs, promote germinal center formation and improve the speed of Ig class switching. Interestingly, our subsequent study found that other members of the mammalian MT family (human MT1 or murine MT3 for examples) also had potential adjuvant effects, but their effects were lower than human MT3. Overall, this study explored a new function of human MT3 as a novel built-in adjuvant, which may have important clinical application potential in vaccine development against global pandemics.
    Type of Medium: Online Resource
    ISSN: 1664-3224
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2606827-8
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  • 2
    Online Resource
    Online Resource
    Association for the Advancement of Artificial Intelligence (AAAI) ; 2021
    In:  Proceedings of the AAAI Conference on Artificial Intelligence Vol. 35, No. 18 ( 2021-05-18), p. 16060-16062
    In: Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Vol. 35, No. 18 ( 2021-05-18), p. 16060-16062
    Abstract: We present TODS, an automated Time Series Outlier Detection System for research and industrial applications. TODS is a highly modular system that supports easy pipeline construction. The basic building block of TODS is primitive, which is an implementation of a function with hyperparameters. TODS currently supports 70 primitives, including data processing, time series processing, feature analysis, detection algorithms, and a reinforcement module. Users can freely construct a pipeline using these primitives and perform end- to-end outlier detection with the constructed pipeline. TODS provides a Graphical User Interface (GUI), where users can flexibly design a pipeline with drag-and-drop. Moreover, a data-driven searcher is provided to automatically discover the most suitable pipelines given a dataset. TODS is released under Apache 2.0 license at https://github.com/datamllab/tods. A video is available on YouTube (https://youtu.be/JOtYxTclZgQ)
    Type of Medium: Online Resource
    ISSN: 2374-3468 , 2159-5399
    Language: Unknown
    Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
    Publication Date: 2021
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