Adjuvants for Leishmania vaccines: from models to clinical application
Bibliographic record
Abstract
Two million new cases of leishmaniasis occur every year, with the cutaneous leishmaniasis (CL) presentation accounting for approximately two-thirds of all cases. Despite the high incidence rates and geographic expansion of the disease, CL remains a neglected tropical disease without effective intervention strategies. Efforts to address this deficit have given rise to the experimental murine model of CL. By virtue of its simplicity and pliability, the CL model has been used to provide substantial information regarding cellular immunity, as well as in the discovery and evaluation of various vaccine adjuvants. The CL model has facilitated in vivo studies of the mechanism of action of many adjuvants, including the TLR4 agonist monophosphoryl lipid A, the TLR7/8 agonist imiquimod, the TLR9 agonist CpG, adenoviral vectors, and the immunostimulatory complexes. Together, these studies have helped to unveil the requirement for certain types of immune responses at specific stages of CL disease and provide a basis to aid the design of effective second-generation vaccines for human CL. This review focuses on adjuvants that have been tested in experimental CL, outlining how they have helped advance our understanding of the disease and ultimately, how they have performed when applied within clinical trials against human CL.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".