Comparative analysis of the neuroprotective effects of ginsenosides Rg1 and Rb1 extracted from<i>Panax notoginseng</i>against cerebral ischemia
Bibliographic record
Abstract
Panax notoginseng, a traditional Chinese medicine, has been used for thousands of years to treat ischemic patients. More than 20 saponin components have been isolated from P. notoginseng root and identified chemically. However, these different chemical components have different roles. In this study we compared the neuroprotective mechanisms of ginsenosides Rg1, Rb1, Rg1/Rb1, and panax notoginsenoside (PNS) against injuries caused by cerebral ischemia-reperfusion (I/R). Our results show that all of these treatments significantly reduced infarction volume and alleviated neurological deficits caused by cerebral I/R. The increase in malondialdehyde (MDA) concentration was inhibited by these treatments in the hippocampus. The decreased expressions of thioredoxin-1 (Trx-1), copper-zinc superoxide dismutase (SOD-1), protein kinase B (PKB/Akt), and nuclear factor-kappa B (NF-κB) caused by cerebral I/R were restored by these treatments. The expression of heat shock protein 70 (HSP70) was enhanced in the middle cerebral artery occlusion (MCAO) group, as well as in all of the treatment groups. These results suggest that Rg1 and Rb1 have similar roles in protecting the brain from ischemic damage; however, neither Rg1/Rb1 nor PNS have synergistic effects, thus either Rg1 or the Rb1 monomer should be considered as a pharmacological neuroprotective strategy for use in the case of ischemic stroke.
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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 | 0.000 |
| 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".