Induced Pluripotent Stem Cells for the Treatment of Stroke: The Potential and the Pitfalls
Bibliographic record
Abstract
The extraordinary discovery of induced pluripotent stem cells (iPSCs) has led to the very real possibility that patient-specific cell therapy can be realized. The potential to develop cell replacement therapies outside the ethical and legal limitations, has initiated a new era of hope for regenerative strategies to treat human neurological disease including stroke. In this article, we will review and compare the current approaches to derive iPSCs from different somatic cells, and the induction into neuronal phenotypes, considering the advantages and disadvantages to the methodologies of derivation. We will highlight the work relating to the use of iPSC-based therapies in models of stroke and their potential use in clinical trials. Finally, we will consider future directions and areas of exploration which may promote the realization of iPSC-based cell replacement strategies for the treatment of stroke.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".