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Cited by 304

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Carrier-induced epitopic suppression, a major issue for future synthetic vaccines.

Schutze, M P, Leclerc, C, Jolivet, M, Audibert, F, Chedid, L·1985·The Journal of Immunology

Abstract

Synthetic antigens have been shown, in experimental models, to induce protective immunity against a variety of pathogens. These studies have demonstrated that, due to their low immunogenicity, these synthetic antigens required conjugation to carrier molecules. Therefore, the choice of appropriate carriers for human immunization by future synthetic vaccines is a major issue. Tetanus toxoid is generally considered to be an effective potential carrier devoid of side-effects. However, the present study performed in mice with two synthetic vaccine models demonstrates that the immune response against the synthetic epitopes conjugated to tetanus toxoid can be suppressed by pre-existing immunity against this same carrier. Because most humans have been exposed to this antigen, this effect may have important implications for the development of synthetic vaccines.

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Metadata

Authors
Schutze, M. P., Leclerc, C., Jolivet, M., Audibert, F., & Chedid, L.
Venue
The Journal of Immunology
Published
1985-10-01
Volume
135
Issue
4
Pages
2319-2322
ISSN
  • 0022-1767 (ISSN-L)
  • 1550-6606
OpenAlex
W1781631981
Cited by
304
References
0
FWCI
3.73
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Carrier-induced epitopic suppression, a major issue for future synthetic vaccines. https://app.cochat.ai/papers/10.4049/jimmunol.135.4.2319
The paper
Schutze, M. P., Leclerc, C., Jolivet, M., Audibert, F., & Chedid, L. (1985). Carrier-induced epitopic suppression, a major issue for future synthetic vaccines. The Journal of Immunology. https://doi.org/10.4049/jimmunol.135.4.2319

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.