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Epitope DNA Vaccines Against Tuberculosis: Spacers and Ubiquitin Modulates Cellular Immune Responses Elicited by Epitope DNA Vaccine (2004)

Wang, Q.-M. ; Sun, S.-H. ; Hu, Z.-L. ; [et al.]

Oxford, UK; Malden, USA : Blackwell Science Ltd

Scandinavian journal of immunology 60 (2004), S. 0

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ISSN: 1365-3083

Source: Blackwell Publishing Journal Backfiles 1879-2005

Topics: Medicine

Notes: Cell-mediated immune responses are crucial in the protection against tuberculosis. In this study, we constructed epitope DNA vaccines (p3-M-38) encoding cytotoxic T lymphocyte (CTL) epitopes of MPT64 and 38 kDa proteins of Mycobacterium tuberculosis. In order to observe the influence of spacer sequence (Ala-Ala-Tyr) or ubiquitin (UbGR) on the efficacy of the two CTL epitopes, we also constructed DNA vaccines, p3-M-S(spacer)-38, p3-Ub (UbGR)-M-S-38 and p3-Ub-M-38. The immune responses elicited by the four DNA vaccines were tested in C57BL/6 (H-2b) mice. The cytotoxicity of T cells was detected by LDH-release method and by enzyme-linked immunospot assay for epitope-specific cells secreting interferon-γ. The results showed that DNA immunization with p3-M-38 vaccine could induce epitope-specific CD8+ CTL response and that the spacer sequence (AAY) only enhanced M epitope presentation. The protein-targeting sequence (UbGR) enhanced the immunogenicity of the two epitopes. The finding that defined spacer sequences at C-terminus and protein-targeting degradation modulated the immune response of epitope string DNA vaccines will be of importance for the further development of multi-epitope DNA vaccines against tuberculosis.

Type of Medium: Electronic Resource

URL: http://dx.doi.org/10.1111/j.0300-9475.2004.01442.x

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Nonmonotonic trust region algorithm (1993)

Deng, N. Y. ; Xiao, Y. ; Zhou, F. J.

Springer

Journal of optimization theory and applications 76 (1993), S. 259-285

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ISSN: 1573-2878

Keywords: Unconstrained optimization ; trust region method ; nonmonotone method ; convergence

Source: Springer Online Journal Archives 1860-2000

Topics: Mathematics

Notes: Abstract A nonmonotonic trust region method for unconstrained optimization problems is presented. Although the method allows the sequence of values of the objective function to be nonmonotonic, convergence properties similar to those for the usual trust region method are proved under certain conditions, including conditions on the approximate solutions to the subproblem. To make the solution satisfy these conditions, an algorithm to solve the subproblem is also established. Finally, some numerical results are reported which show that the nonmonotonic trust region method is superior to the usual trust region method according to both the number of gradient evaluations and the number of function evaluations.