Human Apolipoprotein E ε4 Allele Modulates Energy Substrate Availability, Seizure Burden, Mortality and Hippocampal Injury, Cell Death, and Inflammation after Neonatal Hypoxic-Ischemic Brain Injury
Bibliographic record
Abstract
INTRODUCTION: Human apolipoprotein E allele ε4 (ApoE4) is the strongest genetic risk factor for some forms of adulthood neurodegeneration linked to energetic disturbances and inflammation. We hypothesized that ApoE4 also influences neonatal brain neurodegeneration after a hypoxic-ischemic (HI) insult, resulting in energy substrates (i.e., glucose, ketone bodies [KBs]) disturbances, hippocampal injury, cell death, and inflammation. METHODS: Right-sided brain HI was induced at P10 in wild-type (wt, C57BL6) and humanized ApoE3 and ApoE4 mice with sham anesthesia-exposed littermates as controls. Seizure-like activity, survival, blood glucose (BG), and KB were determined immediately after the HI insult. The hippocampi were assessed 24 h and 72 h after the HI insult for residual volume, cell death (α-fodrin breakdown), inflammatory markers, and transcriptomics (RNAseq). RESULTS: Wt, ApoE3, and ApoE4 mice were congenic (>99.8% transcriptome similarity). Female ApoE4 mice had worse seizures, lower survival, and smaller residual hippocampal volumes than the ApoE3 mice. All three strains had lower BG after HI. ApoE4 mice also had lower KB. Low BG was associated with higher proinflammatory and cell death markers in the hippocampus in all HI genotype groups at 24 h but more robustly in ApoE4 mice, and in combination with high KB, was strongly linked to cell death (greater α-fodrin breakdown). CONCLUSION: Humanized ApoE4, compared to ApoE3, causes greater hippocampal injury, cell death, and inflammation after a neonatal HI insult in association with low BG and underutilized KB. The mechanisms behind these associations need further investigation.
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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".