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Record W2012813469 · doi:10.3389/fimmu.2012.00144

Adjuvants for Leishmania vaccines: from models to clinical application

2012· article· en· W2012813469 on OpenAlexafffund
Vanitha S. Raman, Malcolm S. Duthie, Christopher B. Fox, Greg Matlashewski, Steven G. Reed

Bibliographic record

VenueFrontiers in Immunology · 2012
Typearticle
Languageen
FieldMedicine
TopicResearch on Leishmaniasis Studies
Canadian institutionsMcGill University
FundersNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchNational Institutes of HealthDrugs for Neglected Diseases initiativeBill and Melinda Gates Foundation
KeywordsLeishmaniaAdjuvantMedicineVaccine adjuvantComputational biologyImmunologyVirologyBiologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.060
GPT teacher head0.360
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations78
Published2012
Admission routes2
Has abstractyes

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