Potential of Stem Cell-Based Therapy for Ischemic Stroke
Bibliographic record
Abstract
Ischemic stroke is one of the major health problems worldwide. The only FDA approved antithrombotic drug for acute ischemic stroke is the tissue plasminogen activator (tPA). The very narrow time window of 3-5 hours required to ensure its effectiveness, but tPA’s potential to exacerbate blood-brain barrier (BBB) leakage (thereby increasing hemorrhagic incidents) necessitates a search for therapeutic alternatives. Stem cell based therapy for ischemic stroke seems to be promising candidate. Several studies have been devoted to assessing the therapeutic potential of different types of stem cells such as neural stem cells (NSC), mesenchymal stem cells (MSC), embryonic stem cells (ESC), and human induced pluripotent stem cell-derived neural stem cells (hiPSC-NSCs) as treatments for ischemic stroke. The results of these studies are intriguing but many of them have presented conflicting results. Additionally, the mechanism(s) by which engrafted stem/progenitor cells exert their actions are to a large extent unknown. In this review, we will provide a synopsis of different preclinical and clinical studies related to the use of stem cell based stroke therapy, and explore possible beneficial/ detrimental outcomes associated with the use of different types of stem cells. Moreover, a list of completed and ongoing preclinical and clinical studies is provided, and their major outcomes are discussed. Due to limited/short time window implemented in most of the recorded clinical trials about the use of stem cells as potential therapeutic intervention for stroke, further clinical trials evaluating the efficacy of the intervention in a longer time window after cellular engraftment are still needed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".