Cell Therapy: Types, Regulation, and Clinical Benefits
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".