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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 OpenAlex
Bruktawit Maru, Lea Nadeau, Maureen McKeague

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0160.000

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 head0.074
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