“Time Is Brain” – for Cell Therapies
Bibliographic record
Abstract
The principle "time is brain" has long guided acute stroke treatment, emphasizing that earlier intervention improves outcomes. While this dictum applies to current gold-standard reperfusion therapies, its relevance to emerging regenerative approaches such as stem cell therapy remains to be established. A growing body of preclinical and clinical studies suggests that timing of cell delivery is a key determinant of graft survival, integration and therapeutic efficacy, largely through interactions with the evolving post-stroke microenvironment. Here, we discuss how early transplantation may access salvageable tissue but faces a hostile inflammatory microenvironment, whereas transplantation at the subacute or chronic phase benefits from a more permissive milieu but by then much of the tissue has been irreversibly lost. We further suggest the optimal window also depends on cell type and mechanism of action: neuroprotective or immunomodulatory grafts may benefit from earlier delivery, while cells requiring long-term survival and integration may perform better later. Thus, "time is brain" also applies to cell therapies, but it may require aligning graft delivery with the evolving post-stroke microenvironment rather than the acute therapeutic window. Identifying biomarkers that track inflammatory changes, vascular remodeling and brain damage could personalize this "window of receptivity" and guide tailored future clinical trials.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".