Deleting IGF-1 receptor from forebrain neurons confers neuroprotection during stroke and upregulates endocrine somatotropin
Bibliographic record
Abstract
Insulin-like growth factors control numerous processes, namely somatic growth, metabolism and stress resistance, connecting this pathway to aging and age-related diseases. Insulin-like growth factor signaling also impacts on neurogenesis, neuronal survival and structural plasticity. Recent reports demonstrated that diminished insulin-like growth factor signaling confers increased stress resistance in brain and other tissues. To better understand the role of neuronal insulin-like growth factor signaling in neuroprotection, we inactivated insulin-like growth factor type-1-receptor in forebrain neurons using conditional Cre-LoxP-mediated gene targeting. We found that brain structure and function, including memory performance, were preserved in insulin-like growth factor receptor mutants, and that certain characteristics improved, notably synaptic transmission in hippocampal neurons. To reveal stress-related roles of insulin-like growth factor signaling, we challenged the brain using a stroke-like insult. Importantly, when charged with hypoxia-ischemia, mutant brains were broadly protected from cell damage, neuroinflammation and cerebral edema. We also found that in mice with insulin-like growth factor receptor knockout specifically in forebrain neurons, a substantial systemic upregulation of growth hormone and insulin-like growth factor-I occurred, which was associated with significant somatic overgrowth. Collectively, we found strong evidence that blocking neuronal insulin-like growth factor signaling increases peripheral somatotropic tone and simultaneously protects the brain against hypoxic-ischemic injury, findings that may contribute to developing new therapeutic concepts preventing the disabling consequences of stroke.
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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) | 0.001 | 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) | 0.002 | 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".