Neuronal and Astrocyte Insulin-like Growth Factor-1 Signaling Differentially Modulates Ischemic Stroke Damage
Bibliographic record
Abstract
Ischemic stroke is a leading cause of death and disability, as therapeutic options for mitigating the long-term deficits precipitated by the event remain limited. Acute administration of the neuroendocrine modulator insulin-like growth factor-1 (IGF-1) attenuates ischemic stroke damage in preclinical models, and clinical studies suggest IGF-1 can reduce the risk of stroke and improve overall outcomes. The cellular mechanism by which IGF-1 exerts this protection is poorly defined, as all cells within the neurovascular unit express the IGF-1 receptor. We hypothesize that the functional regulation of both neurons and astrocytes by IGF-1 is critical in minimizing damage in ischemic stroke. To test this, we utilized inducible astrocyte-specific or neuron-specific transgenic mouse models to selectively reduce IGF-1R in the adult mouse brain prior to photothrombotic stroke. Acute changes in blood brain barrier permeability, microglial activation, systemic inflammation, and biochemical composition of the brain were assessed 3 hours following photothrombosis, and significant protection was observed in mice deficient in neuronal and astrocytic IGF-1R. When the extent of tissue damage and sensorimotor dysfunction was assessed for 3 days following stroke, only the neurological deficit score continued to show improvements, and the extent of improvement was enhanced with additional IGF-1 supplementation. Overall, results indicate that neuronal and astrocytic IGF-1 signaling influences stroke damage but IGF-1 signaling within these individual cell types is not required for minimizing tissue damage or behavioral outcome.
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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 | 0.000 | 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 | 0.000 |
| 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".