Immunomodulation With Local, Sustained Delivery of Pituitary Adenylate Cyclase Activating Polypeptide Results in Improved Functional Recovery in Stroke‐Injured Mice
Bibliographic record
Abstract
Abstract Following ischemic stroke, astrocytes and microglia become activated and create a hostile microenvironment that can exacerbate brain damage, yet these cells also contribute to tissue regeneration. Pituitary adenylate cyclase activating polypeptide (PACAP) is a promising neuroprotective peptide that modulates microglia toward a pro‐reparative phenotype, however, its short half‐life in vivo and dose‐limited off‐target effects have made systemic delivery untenable. Local delivery presents a promising alternative. To this end, we developed a hydrogel‐nanoparticle composite for the minimally invasive, local delivery of PACAP to the brain and tested this strategy in chemically‐induced, endothelin‐1 stroke‐injured mice. We demonstrate that prolonged delivery of PACAP improved the physical strength and mobility of mice for up to 28 days after stroke. The treatment decreased the number of apoptotic neurons in the stroke microenvironment, increased neuron survival at 28 days post‐stroke, and attenuated reactive astrogliosis and microglia activation. PACAP stimulation resulted in increased Iba1+Arg1+ pro‐reparative microglia and decreased Iba1+CD86+ pro‐inflammatory microglia. Furthermore, PACAP stimulation significantly decreased pro‐inflammatory GFAP+LCN2+ and GFAP+S100β+ astrocytes versus controls. This phenotypic shift in microglia and astrocytes may account for the functional improvements post stroke and paves the way for local delivery of new therapeutic strategies targeting the immune response for stroke treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".