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Record W3091505034 · doi:10.1111/bjh.17125

Use of an anti‐CD200‐blocking antibody improves immune responses to AML <i>in vitro</i> and <i>in vivo</i>

2020· article· en· W3091505034 on OpenAlexaff
Namrata Rastogi, Sarah M. Baker, Stephen Man, Robert A. Uger, Mark Wong, Steven Coles, Marie Hodges, Amanda Gilkes, Steven Knapper, Richard L. Darley, Alex Tonks

Bibliographic record

VenueBritish Journal of Haematology · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsTrillium Therapeutics (Canada)
FundersEuropean Regional Development FundMedical Research CouncilBlood Cancer UKCardiff UniversityCancer Research UKCancer Research WalesNational Cancer Research Institute
KeywordsIn vivoImmune systemIn vitroAntibodyImmunologyMedicineEffectorCancer researchBlocking antibodyMyeloidBiology

Abstract

fetched live from OpenAlex

Treatment of relapsed/resistant acute myeloid leukaemia (AML) remains a significant area of unmet patient need, the outlook for most patients remaining extremely poor. A promising approach is to augment the anti-tumour immune response in these patients; most cancers do not activate immune effector cells because they express immunosuppressive ligands. We have previously shown that CD200 (an immunosuppressive ligand) is overexpressed in AML and confers an inferior overall survival compared to CD200low/neg patients. Here we show that a fully human anti-CD200 antibody (TTI-CD200) can block the interaction of CD200 with its receptor and restore AML immune responses in vitro and in vivo.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.282
Teacher spread0.245 · 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
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

Citations27
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueBritish Journal of HaematologySame topicNeuroinflammation and Neurodegeneration MechanismsFrench-language works237,207