Aged garlic extract and S-allylcysteine increase the GLUT3 and GCLC expression levels in cerebral ischemia
Bibliographic record
Abstract
BACKGROUND: During cerebral ischemia, energy restoration through the regulation of glucose transporters and antioxidant defense mechanisms is essential to maintain cell viability. Antioxidant therapy has been considered effective to attenuate brain damage; moreover, the regulation of transcription factors that positively regulate the expression of glucose transporters is associated with this therapy. Recently, it has been reported that the use of antioxidants such as S-allylcysteine (SAC), a component of aged garlic extract (AGE), improves survival in experimental models of cerebral ischemia. OBJECTIVES: The aim of this study was to determine the effect of AGE and SAC on the level of mRNA expression of the main neuronal glucose transporter (GLUT3) and the glutamate cysteine ligase catalytic subunit (GCLC) in rats with transient focal cerebral ischemia. MATERIAL AND METHODS: Cerebral ischemia was induced in male Wistar rats by middle cerebral artery occlusion (MCAO) for 2 h. The animals were sacrificed after different reperfusion times (0-48 h). Animals injected with AGE (360 mg/kg, intraperitoneally (i.p.)) and SAC (300 mg/kg, i.p.) at the beginning of reperfusion were sacrificed after 2 h. The mRNA expression level was analyzed in the fronto-parietal cortex using quantitative polymerase chain reaction (qPCR). RESULTS: Two major increases in GLUT3 expression at 1 h and 24 h of reperfusion were found. Both treatments increased GLUT3 and GCLC mRNA levels in control and under ischemic/reperfusion injury animals. CONCLUSIONS: This data suggests that SAC and AGE might induce neuroprotection, while controlling reactive oxygen species (ROS) levels, as indicated by the increase in GCLC expression, and regulating the energy content of the cell by increasing glucose transport mediated by GLUT3.
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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 | 0.000 |
| 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".