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Record W2887911630 · doi:10.1093/intimm/dxy051

Producing proT cells to promote immunotherapies

2018· review· en· W2887911630 on OpenAlexafffund
Jastaranpreet Singh, Juan Carlos Zúñiga‐Pflücker

Bibliographic record

VenueInternational Immunology · 2018
Typereview
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersCanadian Institutes of Health ResearchCanada First Research Excellence FundOntario Institute for Regenerative MedicineKrembil FoundationUniversity of Toronto
KeywordsImmunotherapyChimeric antigen receptorCancer immunotherapyInduced pluripotent stem cellImmune systemT cellCell therapyImmunologyProgenitor cellAntigen-presenting cellAntigenBiologyMajor histocompatibility complexCancer researchStem cellCell biologyEmbryonic stem cell

Abstract

fetched live from OpenAlex

T lymphocytes are critical mediators of the adaptive immune system and they can be harnessed as therapeutic agents against pathogens and in cancer immunotherapy. T cells can be isolated and expanded from patients and potentially generated in vitro using clinically relevant systems. An ultimate goal for T-cell immunotherapy is to establish a safe, universal effector cell type capable of transcending allogeneic and histocompatibility barriers. To this end, human pluripotent stem cells offer an advantage in generating a boundless supply of T cells that can be readily genetically engineered. Here, we review emerging T-cell therapeutics, including tumor-infiltrating lymphocytes, chimeric antigen receptors and progenitor T cells (proT cells) as well as feeder cell-free in vitro systems for their generation. Furthermore, we explore their potential for adoption in the clinic and highlight the challenges that must be addressed to increase the therapeutic success of a universal 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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.066
GPT teacher head0.405
Teacher spread0.338 · 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

Citations13
Published2018
Admission routes2
Has abstractyes

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