Use of defined TLR ligands as adjuvants within human vaccines
Bibliographic record
Abstract
Our improved understanding of how innate immune responses can be initiated and how they can shape adaptive B- and T-cell responses is having a significant impact on vaccine development by directing the development of defined adjuvants. Experience with first generation vaccines, as well as rapid advances in developing defined vaccines containing Toll-like receptor ligands (TLRLs), indicate that an expanded number of safe and effective vaccines containing such molecules will be available in the future. In this review, we outline current knowledge regarding TLRs, detailing the different cell types that express TLRs, the various signaling pathways TLRs utilize, and the currently known TLRLs. We then discuss the current status of TLRLs within vaccine development programs, including the importance of appropriate formulation, and how recent developments can be used to better define the mechanisms of action of vaccines. Finally, we introduce the possibility of using TLRLs, either in combination or with non-TLRLs, to synergistically potentiate vaccine-induced responses to provide not only prophylactic, but therapeutic protection against infectious diseases and cancer.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".