Retinal Ischemia: MMP-9; Its Relation to Resveratrol, Baicalein, S-allyl L-cysteine and Chi Ju Di Huang Wan
Bibliographic record
Abstract
Ischemia has been reported to be related to matrix metalloproteinase-9 (MMP-9) level upregulation. Neovascular glaucoma (NVG) (in proliferative diabetic retinopathy and central/branch retinal vein occlusion) and age-related macular degeneration are known vision-threatening retinal ischemic disorders. In this review, an extensive discussion has been made into how MMP-9 is related to retinal ischemia and what the underlying mechanisms of various traditional Chinese medicine (TCM) compounds and combinations are. High intraocular pressure-induced retinal ischemic changes were characterized by decreased electroretinogram (ERG) b-wave amplitudes, a loss of choline acetyltransferase (ChAT) immunopositive amacrine neurons/processes, increased Müller’s vimentin immunoreactivity, and profound retinal ganglion cell (RGC) death. It has also been observed hypoxia inducible factor-1α (HIF-1α), MMP-9 and vascular endothelium growth factor (VEGF) upregulation at the protein/mRNA levels. After ischemia, both the p38 mitogen-activated protein kinases (MAPKs)-stimulated MMP-9 upregulation and HIF-1α-triggered VEGF overexpression might result in neovascularization. Conclusively, baicalein seems to have neuroprotection via antioxidation, antiapoptosis, HO-1 upregulation and HIF-1α, VEGF, and/or MMP-9 downregulation. Furthermore, baicalein inhibition on retinal ischemia-induced MMP-9 upregulation seems to be “partly” associated with its antioxidation. Additionally, retinal ischemia, oxidative stress, and/or kainate excitotoxicity could be protected by resveratrol: “via MMP-9 and inducible nitric oxide synthetase (iNOS) downregulation, and heme oxygenase-1 (HO-1) upregulation,” S-allyl L-cysteine (SAC): “through iNOS, HIF-1α, VEGF inhibition and/or MMP-9 downregulation, and antiapoptosis,” Chi Ju Di Huang Wan: “by means of antiapoptosis, antioxidation, MMP-9 downregulation and p38 MAPK inhibition.”
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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 | 0.001 | 0.001 |
| 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".