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Record W1979255880 · doi:10.2217/imt.10.44

Emerging applications of lentiviral vectors in dendritic cell-based immunotherapy

2010· review· en· W1979255880 on OpenAlexafffund
Huiming Wang, Liang Zhang, Sam K. P. Kung

Bibliographic record

VenueImmunotherapy · 2010
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsUniversity of Manitoba
FundersCanadian Institutes of Health Research
KeywordsImmunotherapyDendritic cellImmunologyViral vectorImmune systemCancer immunotherapyAntigenBiologyAntigen-presenting cellT cellAutoimmunityCytotoxic T cellMedicineIn vitroRecombinant DNA

Abstract

fetched live from OpenAlex

Dendritic cells are professional antigen-presenting cells that initiate, regulate and shape the induction of specific immune responses. The ability to use dendritic cells in the induction of antigen-specific tolerance, antigen-specific immunity or specific differentiation of T-helper subsets holds great promise in dendritic cell-based immunotherapy of various diseases such as cancer, viral infections, allergy, as well as autoimmunity. Replication-incompetent HIV-1-based lentiviral vector is now emerging as a promising delivery system to genetically modify dendritic cells through antigen recognition, costimulatory molecules and/or polarization signals for the manipulation of antigen-specific immunity in vivo. This article discusses some of the recent advances in the uses of lentiviral vectors in dendritic cell-based immunotherapy.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.962
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.018
GPT teacher head0.344
Teacher spread0.326 · 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.

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

Citations8
Published2010
Admission routes2
Has abstractyes

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