Insulin-like growth factor-I and its receptor in the frontal cortex, hippocampus, and cerebellum of normal human and Alzheimer disease brains
Bibliographic record
Abstract
Assimilated evidence indicates that the neurotoxic potential of amyloid beta (Abeta) peptide and an alteration in the level of growth factor(s) may possibly be involved in the loss of neurons observed in the brain of patients suffering from Alzheimer disease (AD), the prevalent cause of dementia in the elderly. In the present study, using receptor binding assays and immunocytochemistry, we evaluated the pharmacological profile of insulin-like growth factor-I (IGF-I) receptors and the distribution of IGF-I immunoreactivity in the frontal cortex, hippocampus, and cerebellum of AD and age-matched control brains. In control brains, [(125)I]IGF-I binding was inhibited more potently by IGF-I than by Des(1-3)IGF-I, IGF-II or insulin. The IC(50) values for IGF-I in the frontal cortex, hippocampus, and cerebellum of the normal brain did not differ significantly from the corresponding regions of the AD brain. Additionally, neither K(D) nor B(max) values were found to differ in the hippocampus of AD brains from the controls. At the regional levels, [(125)I]IGF-I binding sites in the AD brain also remained unaltered compared to the controls. As for the peptide itself, IGF-I immunoreactivity, in normal control brains, was evident primarily in a subpopulation of astrocytes in the frontal cortex and hippocampus, and in certain Purkinje cells of the cerebellum. In AD brains, a subset of Abeta-containing neuritic plaques, apart from astrocytes, exhibit IGF-I immunoreactivity. These results, taken together, suggest a role for IGF-I in compensatory plasticity and/or survival of the susceptible neurons in AD brains.
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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 | 0.001 | 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 | 0.000 |
| 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".