Influence of metabolic syndrome on post-stroke outcome, angiogenesis and vascular function in old rats determined by dynamic contrast enhanced MRI
Bibliographic record
Abstract
Stroke affects primarily aged and co-morbid people, aspects not properly considered to date. Since angiogenesis/vasculogenesis are key processes for stroke recovery, we purposed to determine how different co-morbidities affect the outcome and angiogenesis/vasculogenesis, using a rodent model of metabolic syndrome, and by dynamic enhanced-contrast imaging (DCE-MRI) to assess its non-invasive potential to determine these processes. Twenty/twenty-two month-old corpulent (JCR:LA-Cp/Cp), a model of metabolic syndrome and lean rats were used. After inducing the experimental ischemia by transient MCAO, angiogenesis was analyzed by histology, vasculogenesis by determination of endothelial progenitor cells in peripheral blood by flow cytometry and evaluating their pro-angiogenic properties in culture and the vascular function by DCE-MRI at 3, 7 and 28 days after tMCAO. Our results show an increased infarct volume, BBB damage and an impaired outcome in corpulent rats compared with their lean counterparts. Corpulent rats also displayed worse post-stroke angiogenesis/vasculogenesis, outcome that translated in an impaired vascular function determined by DCE-MRI. These data confirm that outcome and angiogenesis/vasculogenesis induced by stroke in old rats are negatively affected by the co-morbidities present in the corpulent genotype and also that DCE-MRI might be a technique useful for the non-invasive evaluation of vascular function and angiogenesis processes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".