Increased expression of insulin‐like growth factor I augments the progressive phase of synaptogenesis without preventing synapse elimination in the hypoglossal nucleus
Bibliographic record
Abstract
The in vivo actions of insulin-like growth factor I (IGF-I) on synaptogenesis in the hypoglossal nucleus were investigated in transgenic mice that overexpress IGF-I in the brain postnatally and in normal nontransgenic littermate controls. In a previous study using these mice, we found that IGF-I increases the total volume of the hypoglossal nucleus by increasing the volume of neuropil rather than by increasing total neuron number; therefore, the progressive and regressive phases of synaptogenesis could be evaluated without the confounding effects of altered neuron number. The volume of the hypoglossal nucleus was significantly increased by 28% to 59% in transgenic mice after postnatal day (P) 7, whereas the total number of hypoglossal neurons did not differ significantly from controls. The numerical density of neurons was significantly decreased by 21% to 38% after P7, and the density of myelinated axons was significantly increased by 19%. Although the numerical density of synapses did not differ between groups at any age, the total number of synapses in transgenic mice was increased by 42% to 52% after P14. Total synapse number in controls increased from P7 (7.9 million) to peak values at P21 (36.0 million), followed by a significant decrease (33%) at P130 (24.2 million). In transgenic mice, total synapses increased from 8.2 million on P7 to 51.1 million on P21, followed by a significant decrease (28%) to 36.7 million at P130. Our results demonstrated that IGF-I can stimulate a persistent increase in the number of hypoglossal synapses, thereby augmenting the progressive phase of synaptogenesis without preventing synapse elimination during the regressive phase.
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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".