White matter microglia morphological changes with aging in guinea pig offspring born growth restricted
Bibliographic record
Abstract
Abstract Fetal growth restriction is implicated in the programming of later-life neurodegeneration. We hypothesized that growth restricted offspring would show accelerated changes to microglial white matter morphology, relative to controls. Control guinea pig sows were fed ad libitum , while maternal nutrient restriction sows received 70% of control diet switched to 90% from mid-gestation. Offspring were sacrificed at ∼26 days (neonate) or ∼110 days (adult) postpartum. Coronal brain sections from the frontal cortex were subject to IBA1-staining for microglial detection and analyzed by machine learning software. At birth, total body weight of growth restricted offspring was reduced relative to control (p<0.0001) with postnatal catch-up growth observed. Microglial density was reduced in the corpus callosum of control (p<0.05) and growth restricted (p=0.13) adults, relative to neonates. Adults from both groups showed greater IBA1-positive area in the cingulum and periventricular white matter (p<0.05) and increased microglial fractal dimension in the corpus callosum (p<0.10) and periventricular white matter (p<0.05), relative to neonates. At the timepoints studied, we report age-related changes in white matter microglial morphology. However, maternal nutrient restriction leading to fetal growth restriction in guinea pigs does not appear to exacerbate these white matter microglia morphological changes as a marker for later-life neurodegeneration.
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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) | 0.002 | 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".