Wielding the Double‐Edged Sword of Inflammation: Building Biomaterial‐Based Strategies for Immunomodulation in Ischemic Stroke Treatment
Bibliographic record
Abstract
Abstract Stroke is a leading cause of disability with no current treatment to regenerate lost brain tissue. Innovative preclinical and clinical trials have attempted to improve stroke recovery by promoting cell survival, downregulating astrogliosis and inflammation, and improving neurogenesis and angiogenesis; however, the complexity of stroke pathophysiology raises many challenges. Previous attempts to grossly inhibit the inflammatory reaction failed to improve stroke outcomes, prompting scientists to explore selective modulation rather than unbiased inhibition. Although experimental studies involving immunomodulation are successful, strategies have largely failed in the clinic. Some of these approaches are hindered by poor delivery efficiency or cell survival, challenges that could be at least partially overcome using biomaterials. Biomaterials may enhance immunomodulatory processes by improving drug and cell delivery to the injured tissue. Furthermore, the materials themselves can support healing and may be designed to act as immunomodulators, thereby contributing to tissue regeneration and endogenous repair processes. Described here are novel biomaterial‐based strategies to modulate the immune response after ischemic stroke, with an emphasis on extracellular matrix mimetics and hydrogels for local delivery of drugs and cells. Finally, a future perspective is described, highlighting the potential of these therapies to achieve clinical translation and improve patients’ functional repair.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".