Hemodynamic Effects of Combined Focal Cerebral Ischemia and Amyloid Protein Toxicity in a Rat Model: A Functional CT Study
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Clinical evidence indicates that cerebral ischemia (CI) and a pathological factor of Alzheimer's disease, the β-amyloid (Aβ) protein, can increase the rate of cognitive impairment in the ageing population. Using the CT Perfusion (CTP) functional imaging, we sought to investigate the interaction between CI and the Aβ protein on cerebral hemodynamics. METHODS: A previously established rat model of CI and Aβ was used for the CTP study. Iodinated contrast was given intravenously, while serial CT images of sixteen axial slices were acquired. Cerebral blood flow (CBF) and blood volume (CBV) parametric maps were co-registered to a rat brain atlas and regions of interest were drawn on the maps. Microvascular alteration was investigated with histopathology. RESULTS: CTP results revealed that ipsilateral striatum of Aβ+CI and CI groups showed significantly lower CBF and CBV than control at the acute phase. Striatal CBF and CBV increased significantly at week 1 in the CI and Aβ+CI groups, but not in the Aβ alone or control group. Histopathology showed that average density of dilated microvessels in the ipsilateral striatum in CI and Aβ+CI groups was significantly higher than control at week 1, indicating this could be associated with hyperperfusion and hypervolemia observed from CTP results. CONCLUSION: These results demonstrate that CTP can quantitatively measure the hemodynamic disturbance on CBF and CBV functional maps in a rat model of CI interacting with Aβ.
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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) | 0.002 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".