Type 1 insulin‐like growth factor receptor signaling is essential for the development of the hippocampal formation and dentate gyrus
Bibliographic record
Abstract
Type 1 insulin-like growth factor receptor (IGF1R) signaling in neuronal development was studied in mutant mice with blunted igf1r gene expression in nestin-expressing neuronal precursors. At birth [postnatal (P) day 0] brain weights were reduced to 37% and 56% of controls in mice homozygous (nes-igf1r(-/-)) and heterozygous (nes-igf1r(-/Wt)) for the null mutation, respectively, and this brain growth retardation persisted postnatally. Stereological analysis demonstrated that the volumes of the hippocampal formation, CA fields 1-3, dentate gyrus (DG), and DG granule cell layer (GCL) were decreased by 44-54% at P0 and further by 65-69% at P90 in nes-igf1r(-/Wt) mice. In nes-igf1r(-/-) mice, volumes were 29-31% of controls at P0 and, in the two mice that survived to P90, 6-19% of controls, although the hilus could not be identified. Neuron density did not differ among the mice at any age studied; therefore, decreased volumes were due to reduced cell number. In postnatal nes-igf1r(-/Wt) mice, the percentage of apoptotic cells, as judged by activated caspase-3 immunostaining, was increased by 3.5-5.3-fold. The total number of proliferating DG progenitors (labeled by BrdU incorporation and Ki67 staining) was reduced by approximately 50%, but the percentage of these cells was similar to the percentages in littermate controls. These findings suggest that 1) the postnatal reduction in DG size is due predominantly to cell death, pointing to the importance of the IGF1R in regulating postnatal apoptosis, 2) surviving DG progenitors remain capable of proliferation despite reduced IGF1R expression, and 3) IGF1R signaling is necessary for normal embryonic brain development.
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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 | 0.000 |
| 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".