Regeneration of axotomized retinal ganglion cells is promoted by a mixture of american ginseng extract, ginkgo biloba extract and St. John's wort extract
Bibliographic record
Abstract
It has been shown that attachment of a peripheral nerve (PN) graft to the transected optic nerve (ON) stump can markedly increase both the number of surviving and regenerating retinal ganglion cells (RGCs). We recently observed that a mixture of American ginseng (AG), ginkgo biloba (GB) and St. John’s wort (SJW) extracts, can increase viability of axotomized RGCs. We therefore examined the effects of different mixtures of AG, GB and SJW extracts on long distance regeneration of RGCs into a PN graft. ON was transected at 0.5 mm from the optic disc. A 1-cm segment of an autologous sciatic nerve was sutured onto the proximal ocular stump. Animals then received daily oral administration of: (1) vehicle (0.01M PBS); (2) 30mg of AG extract; (3) 30mg of AD-FX, a mixture of 80% AG and 20% GB extracts by weight or (4) 30mg of Menta-FX, a mixture of 30.8% AG, 7.7% GB and 61.5% SJW extracts by weight, for 21 days starting on the day of operation. Standardized batches of AD-FX and Menta-FX were purchased from CV Technologies, Canada. The number of regenerating RGCs 21 days after grafting was determined by injecting 6% FluoroGold into the PN graft 3 days before the animals were killed. The retinae were dissected and the number of fluorescent labeled RGCs was counted. We found that only treatment with Menta-FX significantly promoted regeneration of axotomized RGCs, inducing an 87% increase in the number of regenerating RGCs (p<0.05, one way ANOVA). We therefore showed for the first time that a mixture of AG, GB and SJW extracts can significantly augment regeneration of axotomized RGCs. \nSupported by Research grants from the University of Hong Kong
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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 | 0.000 | 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".