Immunotargeting with CD154 (CD40 Ligand) Enhances DNA Vaccine Responses in Ducks
Bibliographic record
Abstract
Engagement of CD154 on activated T cells with CD40 on antigen-presenting cells (APCs) potentiates adaptive immune responses in mammals. Soluble multimeric forms of CD154 have been used as an adjuvant or in immunotargeting strategies to enhance vaccine responses. The objective of our study was to examine the ability of duck CD154 (DuCD154) to enhance DNA vaccine responses in the duck hepatitis B model. Constructs were generated to express the functional domain of DuCD154 (tCD154), truncated duck hepatitis B virus (DHBV) core antigen (tcore) and chimera of tcore fused to tCD154 (tcore-tCD154). Expression in LMH cells demonstrated that all proteins were secreted and that tCD154 and tcore-tCD154 formed multimers. Ducks immunized with the plasmid ptcore-tCD154 developed accelerated and enhanced core-specific antibody responses compared to ducks immunized with ptcore or ptcore plus ptCD154. Antibody responses were better sustained in both ptcore-tCD154- and ptcore plus ptCD154-immunized ducks. Core-specific proliferative responses of duck peripheral blood mononuclear cells were enhanced in ducks immunized with ptcore-tCD154 or ptcore alone. This study suggests that the role of CD154 in the regulation of adaptive immune responses had already evolved before the divergence of birds and mammals. Thus, targeting of antigens to APCs with CD154 is an effective strategy to enhance DNA vaccine responses not only in mammalian species but also in avian species.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".