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Record W3168728750 · doi:10.1186/s13287-021-02377-8

Optimising NK cell metabolism to increase the efficacy of cancer immunotherapy

2021· review· en· W3168728750 on OpenAlexaff
Chloe Choi, David K. Finlay

Bibliographic record

VenueStem Cell Research & Therapy · 2021
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsTrinity College
FundersIrish Research CouncilScience Foundation IrelandEuropean Research CouncilIrish Research Council for Science, Engineering and Technology
KeywordsCancer immunotherapyImmunotherapyChimeric antigen receptorImmune systemTumor microenvironmentAdoptive cell transferImmune checkpointBiologyCancer researchEffectorImmunologyCancer cellCellCell therapyCancerT cellStem cellCell biologyBiochemistry

Abstract

fetched live from OpenAlex

Immunotherapy has ushered in an exciting new era for cancer treatment. The recent discovery and success of immune checkpoint blockade and chimeric antigen receptor (CAR) T cell adoptive cell transfer has raised interest in using other immune cells, including Natural Killer (NK) cells, which might overcome some limitations with CAR T cell therapy. In this review article, we discuss the evidence that cellular metabolism is crucial for NK cell effector function. Additionally, potential strategies to optimise the metabolism of therapeutic NK cells for improved function within the metabolically adverse tumour microenvironment will be explored.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.093
GPT teacher head0.401
Teacher spread0.308 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations63
Published2021
Admission routes1
Has abstractyes

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