Protective Effects of PPARγ Agonist on the Blood Brain Barrier (BBB) and Its Mechanisms after Cerebral Ischemia-Reperfusion
Bibliographic record
Abstract
Objective To investigate the protective effects of peroxisome proliferator-activated receptor gamma (PPARγ) agonist on the blood brain barrier (BBB) of the experimental ischemics brain tissue. Furthermore , to analyze its probable mechanism combining with its effect on the expression of the MM P-9 mRNA. Methods Adult male SD rats were divided into four groups:sham-operation+normal saline (NS), ischemia-reperfusion (I/R) +NS, I/R+ low-dose Pioglitazone (PGZ, PPARγ agonists, 10 mg/kg, once daily), and I/R+high-dose PGZ (15 mg/kg, once daily).The mode of transient middle cerebral artery occulision was made by using the suture of Longa. By intragastric administration ,all the rats were given PGZ daily for 3 days before operation, the low-dose group with 10mg·kg-1·d-1 and the high-dose group with 15mg·kg-1·d-1. The content of Evans Blue (EB) and the expression of MMP-9 mRNA were measured by Spectrophotometry and RT-PCR at 24 hrs after ischemia. Results The content of EB of the low-dose PGZ group (0.062±0.014A/g) and the high-dose PGZ group (0.043±0.011A/g) were signif icantly reduced compared with that of the I/R+NS group (0.081±0.015A/g) (P0.05), with significance difference during any two groups; the expression of MMP-9mRNA of the low-dose PGZ group (0.268±0.021) and the high-dose PGZ group (0.194±0.017) were signifi cantly reduced compared with that of the I/R+NS group (0.371±0.019) (P0.05), but no signifi cance difference between low-dose PGZ and high-dose PGZ groups.Conclusion PPARγagonists can improve the permeation of blood brain barrie in the ischemia brain tissue by down-regulate the expression of MMP-9mRNA, and it may be one of the mechanisms in the protective action of PPARγ on ischemia-reperfusion injury.
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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".