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Record W2904657629 · doi:10.1002/adhm.201801091

Dendritic Cell Membrane Vesicles for Activation and Maintenance of Antigen‐Specific T Cells

2018· article· en· W2904657629 on OpenAlexfundno aff
Lukasz J. Ochyl, James J. Moon

Bibliographic record

VenueAdvanced Healthcare Materials · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsnot available
FundersNational Institute of Biomedical Imaging and BioengineeringCongressionally Directed Medical Research ProgramsNational Institute of Allergy and Infectious DiseasesAlberta Emerald FoundationMelanoma Research AllianceEmerald FoundationU.S. Department of DefenseDOD Peer Reviewed Cancer Research ProgramNational Cancer InstituteNational Institutes of HealthNational Science Foundation
KeywordsVesicleCell biologyAntigenCellMembraneAntigen-presenting cellDendritic cellCell membraneT cellChemistryBiophysicsImmunologyBiologyImmune systemBiochemistry

Abstract

fetched live from OpenAlex

Cell membranes have recently gained attention as a promising drug delivery system. Here, dendritic cell membrane vesicles (DC-MVs) are examined as a platform to promote T cell responses. Nanosized DC-MVs are derived from DCs pretreated with monophosphoryl lipid A (MPLA), a FDA-approved immunostimulatory adjuvant. These "mature" DC-MVs activate DCs in vitro and increase their expression of costimulatory markers. DC-MVs also promote cross-priming of antigen-specific T cells in vitro, increasing their survival and CD25 expression. In addition, these mature DC-MVs potently augment the expansion of adoptively transferred CD8+ T cells in vivo, generating twofold to fourfold higher frequency of antigen-specific T cells, compared with other control formulations, including "immature" DC-MVs obtained without the MPLA pretreatment. Taken together, these results suggest that DC-MVs are an effective delivery platform for T cell activation and may serve as a potential delivery system for improving adoptive T cell therapy.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.016
GPT teacher head0.271
Teacher spread0.255 · 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 designBench or experimental
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

Citations49
Published2018
Admission routes1
Has abstractyes

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