Insulin-Like Growth Factor-I Promotes Neurogenesis and Synaptogenesis in the Hippocampal Dentate Gyrus during Postnatal Development
Bibliographic record
Abstract
The in vivo actions of insulin-like growth factor-I (IGF-I) on the growth and development of the hippocampal dentate gyrus were investigated in transgenic mice that overexpress IGF-I postnatally in the brain and in normal nontransgenic littermate controls. Stereological analyses of the dentate gyrus were performed by light and electron microscopy on days 7, 14, 21, 28, 35, and 130 to determine postnatal changes in the numerical density and total number of neurons and synapses. The volumes of both the granule cell layer and the molecular layer of the dentate gyrus were significantly increased by 27-69% in transgenic mice after day 7, with the greatest relative increases occurring by day 35. Although the numerical density of neurons in the granule cell layer did not differ significantly between transgenic and control mice at any age studied, the total number of neurons was significantly greater in transgenic mice by 29-61% beginning on day 14. The total number of synapses in the molecular layer was significantly increased by 42-105% in transgenic mice from day 14 to day 130. A transient increase in the synapse-to-neuron ratio was found in transgenic mice at postnatal days 28 and 35 but not at day 130. This finding indicates a disproportionate increase in synaptogenesis, exceeding that expected for the observed increase in neuron number. Our results demonstrate that IGF-I overexpression produces persistent increases in the total number of neurons and synapses in the dentate gyrus, indicating that IGF-I promotes both neurogenesis and synaptogenesis in the developing hippocampus in vivo.
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) | 0.000 | 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 | 0.000 |
| 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 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".