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Record W2752338863 · doi:10.1186/s12981-017-0165-8

Development of targeted adjuvants for HIV-1 vaccines

2017· review· en· W2752338863 on OpenAlexafffund
Jun Liu, Mario Ostrowski

Bibliographic record

VenueAIDS Research and Therapy · 2017
Typereview
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsSt. Michael's HospitalCanada Research ChairsUniversity of Toronto
FundersCanadian Institutes of Health ResearchOntario HIV Treatment Network
KeywordsAdjuvantMedicineContext (archaeology)Human immunodeficiency virus (HIV)ImmunologyHIV vaccineVaccine adjuvantBiology

Abstract

fetched live from OpenAlex

Finding new adjuvants is an integrated component of the efforts in developing an effective HIV-1 vaccine. Compared with traditional adjuvants, a modern adjuvant in the context of HIV-1 prevention would elicit a durable and potent memory response from B cells, CD8 + T cells, and NK cells but avoid overstimulation of HIV-1 susceptible CD4 + T cells, especially at genital and rectal mucosa, the main portals for HIV-1 transmission. We briefly review recent advances in the studies of such potential targeted adjuvants, focusing on three classes of molecules that we study: TNFSF molecules, TLRs agonists, and NODs agonists.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.245
GPT teacher head0.465
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations15
Published2017
Admission routes2
Has abstractyes

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