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Record W3212697406 · doi:10.1021/acsptsci.1c00188

Enhancing CAR-T Cell Therapy with Functional Nucleic Acids

2021· review· en· W3212697406 on OpenAlexafffund
Bruktawit Maru, Lea Nadeau, Maureen McKeague

Bibliographic record

VenueACS Pharmacology & Translational Science · 2021
Typereview
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsNucleic acidChimeric antigen receptorRibozymeImmunotherapyAptamerComputational biologyCell therapyCancer therapyBiologyCellMedicineRNACancerImmunologyImmune systemBiochemistryMolecular biologyGenetics

Abstract

fetched live from OpenAlex

Chimeric antigen receptor (CAR) T cell therapy is a relatively new form of immunotherapy that has had success in treating patients with hematologic malignancies, leading to three recent United States Food and Drug Administration approvals. However, several challenges hinder the widespread use of CAR-T therapy. Here, we review the application of functional nucleic acids such as aptamers and ribozymes as novel tools to improve a variety of steps in CAR-T cell therapy development. We critically examine key studies that highlight the benefits of functional nucleic acids at different stages of cell-based therapy and discuss the feasibility of their practical clinical application. Finally, we offer insights into potential opportunities where chemists can significantly contribute to the innovative incorporation of functional nucleic acids to overcome challenges associated with this cutting-edge immunotherapy.

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: 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
Metaresearch0.0000.000
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 science0.0000.001
Research integrity0.0010.001
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 head0.074
GPT teacher head0.398
Teacher spread0.324 · 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

Citations10
Published2021
Admission routes2
Has abstractyes

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