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Record W3215765083 · doi:10.3389/fmed.2021.756029

Cell Therapy: Types, Regulation, and Clinical Benefits

2021· review· en· W3215765083 on OpenAlex

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

VenueFrontiers in Medicine · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsUniversity of TorontoMcGill UniversityUniversité de MontréalMontreal Heart Institute
FundersUniversité de Montréal
KeywordsMulticellular organismCell therapyStem cellStem-cell therapyRegenerative medicineCellDiseaseCancer therapyMedicineBioinformaticsBiologyCancerPathologyInternal medicineCell biology

Abstract

fetched live from OpenAlex

century and continue to expand on investigational and investment grounds. Cell therapy includes stem cell- and non-stem cell-based, unicellular and multicellular therapies, with different immunophenotypic profiles, isolation techniques, mechanisms of action, and regulatory levels. Following the steps of their predecessor cell therapies that have become established or commercialized, investigational and premarket approval-exempt cell therapies continue to provide patients with promising therapeutic benefits in different disease areas. In this review article, we delineate the vast types of cell therapy, including stem cell-based and non-stem cell-based cell therapies, and create the first-in-literature compilation of the different "multicellular" therapies used in clinical settings. Besides providing the nuts and bolts of FDA policies regulating their use, we discuss the benefits of cell therapies reported in 3 therapeutic areas-regenerative medicine, immune diseases, and cancer. Finally, we contemplate the recent attention shift toward combined therapy approaches, highlighting the factors that render multicellular therapies a more attractive option than their unicellular counterparts.

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 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 score0.851
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

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

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 head0.048
GPT teacher head0.363
Teacher spread0.315 · 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