The Effects of Vascular Cognitive Impairment on the Thickness of the Granular Cell Layer of the Dentate Gyrus within the Hippocampus in a Mouse Model
Bibliographic record
Abstract
Vascular Cognitive Impairment (VCI) is a form of dementia, most prevalent after Alzheimer’s Disease. However, VCI remains the second leading cause for dementia because it restricts blood flow to the brain and there are currently no treatments. There has been a positive correlation between VCI and hippocampal atrophy reported. Diet such as deficiencies in folic acid, is a modifiable risk factor for neurodegeneration. In the present study, a mouse model of VCI was combined with a deficiency in folic acid to assess hippocampal morphology. Animals were split into 4 experimental groups where they were placed into a control diet (CD) or folic acid deficient diet (FADD), and later these animals either had microcoils implanted around their common carotid arteries to model VCI or a sham, control surgery. For hippocampal morphological analysis, the thickness of the granular cell layer of the dentate gyrus within the hippocampus was measured. It was hypothesized that a mouse model of VCI with microcoil treatment and reduced levels of folic acid would reduce blood flow, and increase cell death resulting in a reduced thickness of the granular cell layer within the dentate gyrus of the hippocampus. Our results show that thickness had an increasing trend in the in the FADD microcoil group. These data suggest that there may be an additional factor coming into play such as compensation by neural stem cells that needs to be further researched.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".