Manipulation of dendritic cells for host defence against intracellular infections
Bibliographic record
Abstract
Dendritic cells (DCs) are an important innate immune cell type which is the bridge between innate and adaptive immunity. Mounting experimental evidence suggests that manipulating DCs represents a powerful means to enhance host defence against intracellular infectious diseases. We have developed several strategies to manipulate DCs either in vivo or in vitro for the purpose of enhancing the effect of vaccination or immunotherapeutics. In vivo delivery of transgene encoding GM-CSF (granulocyte/macrophage colony-stimulating factor), a DC-activating cytokine, increases the number and activation status of DCs at various tissue sites and enhances antimicrobial immune responses in murine models. Co-expression or co-delivery of GM-CSF gene transfer vector with an antimicrobial vaccine enhances microbial antigen-specific T-cell responses and immune protection. Murine bone marrow-derived DCs are being manipulated in vitro and exploited as a vaccine delivery system. Transduction of DCs with a virus-vectored tuberculosis vaccine is a powerful way to activate T-cells in vivo. Such genetically modified DC vaccines can be administered either parenterally or mucosally via the respiratory tract.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".