Gestational physical activity alters offspring brain APP processing in an age-specific manner
Bibliographic record
Abstract
Maternal exercise is beneficial for offspring brain development. Amyloid precursor protein (APP) influences neurogenesis and synaptic plasticity. Cleavage products of APP are implicated in the proliferation of neural progenitor cells and neuronal network development. Our study aimed to investigate differences in APP processing in active or sedentary offspring of dams who were exposed to voluntary wheel running with and without a western diet throughout gestation. Female Wistar rats (7-8 weeks old) were fed a normal chow or western diet and randomized into voluntary wheel run or sedentary conditions. Dams returned to sedentary conditions post-parturition. The pups were weaned at 6 weeks after which point half of the samples were collected, while the rest of the pups remained on a normal diet, separated into sedentary or voluntary wheel run groups, and collected 12 weeks later. In utero exposure to maternal exercise was associated with higher neuronal nuclear protein, higher soluble APPα and lower soluble APPβ in offspring prefrontal cortex tissue at 6, but not 18 weeks of age. Neuronal nuclear protein is exclusive to mature neurons implying that offspring of mothers who exercised could have more neuron maturation potentially influenced by the higher APPα content at this early developmental stage. The voluntary wheel run offspring groups had a higher mature/pro brain derived neurotrophic factor ratio compared to the sedentary counterparts. The maternal effects were isolated to the juvenile 6-week-old pups, while the differences in the adult offspring were caused by their own exercise status.
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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".