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Record W2528454353 · doi:10.2177/jsci.39.312

[no title]

2016· article· en· W2528454353 on OpenAlexaff
中津川 宗秀, Butler Marcus O., 鳥越 俊彦, Naoto Hirano

Bibliographic record

VenueJapanese Journal of Clinical Immunology · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicT-cell and B-cell Immunology
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

TCR遺伝子導入養子免疫治療は,実行可能かつ有望ながん免疫治療である.しかしながら胸腺において腫瘍抗原を含む自己抗原に対してネガティブセレクションを受けた末梢血T細胞から高親和性かつ腫瘍を認識するTCRを単離するのは容易ではない.我々は腫瘍認識高親和性TCRを効率よく単離する新規方法を開発した.末梢血T細胞にがん抗原特異的TCRα鎖或いはβ鎖のみ(TCR片鎖)を遺伝子導入すると内因性に発現するTCRとペアリングし,胸腺セレクションを経験しないTCRレパトアが作製される.その胸腺非選択性TCRレパトアには高親和性TCRが含まれ,抗原特異的に増幅することで腫瘍抗原特異的高親和性TCRを単離することが可能となる.この方法によって末梢血CD8+T細胞のみならず,CD4+T細胞からもClass I拘束性高親和性TCRを単離することが可能であった.それら高親和性TCR導入T細胞はCD8分子非依存性に腫瘍を認識した.胸腺セレクションを受けたTCRは,通常ペプチド/MHC複合体に対して親和性は低く,マルチマー化していないペプチド/MHCモノマーでは染色されない.驚くべきことにTCR片鎖導入CD4+T細胞からはペプチド/MHCモノマーで染色可能な極めて高い親和性を有するTCRが単離可能であった.ただしCD4+T細胞から単離されたTCRは標的ペプチドと類似配列のペプチドに対する交差反応性が高い傾向にあり,臨床応用に関しては十分注意してTCRを選択する必要がある.

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)0.006
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.149
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.000
Science and technology studies0.0000.004
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0020.002
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 head0.039
GPT teacher head0.326
Teacher spread0.287 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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