Induced Cell Turnover: A Novel Therapeutic Modality for <i>In Situ</i> Tissue Regeneration
Bibliographic record
Abstract
Induced cell turnover (ICT) is a theoretical intervention in which the targeted ablation of damaged, diseased, and/or nonfunctional cells is coupled with replacement by partially differentiated induced pluripotent stem cells in a gradual and multiphasic manner. Tissue-specific ablation can be achieved using pro-apoptotic small molecule cocktails, peptide mimetics, and/or tissue-tropic adeno-associated virus-delivered suicide genes driven by cell type-specific promoters. Replenishment with new cells can be mediated by systemic administration of cells engineered for homing, robustness, and even enhanced function and disease resistance. Otherwise, the controlled release of cells can be achieved using implanted biodegradable scaffolds, hydrogels, and polymer matrixes. In theory, ICT would enable in situ tissue regeneration without the need for surgical transplantation of organs produced ex vivo, and addresses non-transplantable tissues (such as the vasculature, lymph nodes, and the nervous system). This article outlines several complimentary strategies for overcoming barriers to ICT in an effort to stimulate further research at this promising interface of cell therapy, tissue engineering, and regenerative medicine.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".