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Record W2410632674 · doi:10.1385/1-59259-688-6:133

The Use of Bone Marrow-Chimeric Mice in Determining the MHC Restriction of Epitope-Specific Cytotoxic T Lymphocytes

2003· article· en· W2410632674 on OpenAlexaff
Akiko Iwasaki, Brian H. Barber

Bibliographic record

VenueHumana Press eBooks · 2003
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCytotoxic T cellCTL*Priming (agriculture)TransfectionBiologyAntigenMHC class IDNA vaccinationNaked DNAEpitopeImmunologyMajor histocompatibility complexMolecular biologyVirologyCD8ImmunizationGeneIn vitroGenetics

Abstract

fetched live from OpenAlex

Plasmid DNA immunization has emerged as a promising vaccine strategy against infectious agents, as well as a potential intervention for the treatment of cancer, autoimmunity, and allergy (1). Until recently, however, the cellular events by which injected plasmid DNA elicits potent antibody and cytotoxic T-lymphocyte (CTL) responses were largely unknown. Upon intramuscular (i.m.) injection of naked DNA, predominant expression of transfected DNA occurs in the myofibers (2), but no direct transfection of antigen presenting cells (APC) has been reported. There are essentially three different mechanisms by which CTLs can be primed by the injected DNA (3). The first possibility is that the transfected muscle cells directly activate CTLs by presenting the antigenic peptide on their MHC class I molecules. Alternatively, the priming of CTLs may be mediated by professional APC taking up antigen released from muscle cells. Finally, CTL priming may involve direct transfection of APC occurring, albeit at low level, and that the CTLs are activated by the transfected APC.

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)0.003
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.071
GPT teacher head0.252
Teacher spread0.181 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations0
Published2003
Admission routes1
Has abstractyes

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