Engineering Stem Cells for Biomedical Applications
Bibliographic record
Abstract
Stem cells are characterized by a number of useful properties, including their ability to migrate, differentiate, and secrete a variety of therapeutic molecules such as immunomodulatory factors. As such, numerous pre-clinical and clinical studies have utilized stem cell-based therapies and demonstrated their tremendous potential for the treatment of various human diseases and disorders. Recently, efforts have focused on engineering stem cells in order to further enhance their innate abilities as well as to confer them with new functionalities, which can then be used in various biomedical applications. These engineered stem cells can take on a number of forms. For instance, engineered stem cells encompass the genetic modification of stem cells as well as the use of stem cells for gene delivery, nanoparticle loading and delivery, and even small molecule drug delivery. The present Review gives an in-depth account of the current status of engineered stem cells, including potential cell sources, the most common methods used to engineer stem cells, and the utilization of engineered stem cells in various biomedical applications, with a particular focus on tissue regeneration, the treatment of immunodeficiency diseases, and cancer.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".