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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".