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Differential immune effects mediated by Toll‐like receptors stimulation in precursor B‐cell acute lymphoblastic leukaemia

2005· article· en· W2062191416 on OpenAlexaff
Sophie L. Corthals, Kristin Wynne, Kevin She, Hiromi Shimizu, Darko Curman, Kristy Garbutt, Gregor S. D. Reid

Bibliographic record

VenueBritish Journal of Haematology · 2005
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsTLR9TLR2Immune systemImmunologyReceptorT cellImmunogenicityToll-like receptorBiologyImmune toleranceCancer researchInnate immune systemMedicineInternal medicineGene expression

Abstract

fetched live from OpenAlex

Acute lymphoblastic leukaemia (ALL) is the most common paediatric malignancy and, although current therapy is widely effective, relapse remains a significant clinical problem for which new treatment strategies are required. The ligation of Toll-like receptors (TLR) on antigen-presenting cells stimulates the generation of strong T-cell helper type 1 (Th1) adaptive immune responses. Although TLR9 ligation has been shown to enhance immunogenicity of a number of leukaemia cell types, there have been few reports of the effects mediated through other TLR. In this study we analysed both the expression of TLR by B-cell precursor ALL cell lines and the effects of individual TLR ligation on the ability of ALL cells to stimulate allogeneic T cells. While ligation of TLR2, TLR 7 and TLR9 led to detectable changes in ALL costimulatory molecule expression, only TLR2 and TLR9 stimulation influenced T-cell responses. The TLR2 ligand Pam3CysSerLys4 provoked the most significant changes in T-cell response, dramatically augmenting interferon-gamma production. These results suggest that TLR ligands, in addition to TLR9 agonists, may provide a strategy to enhance the generation of anti-ALL immune activity by skewing responding T cells towards a Th1 response.

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 score0.066
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.205
Teacher spread0.202 · 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

Citations29
Published2005
Admission routes1
Has abstractyes

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