Is Pycnogenol a Double-edged Sword? Cataractogenic<i>In Vitro</i>, but Reduces Cataract Risk in Diabetic Rats
Bibliographic record
Abstract
PURPOSE: Pycnogenol was used (a) to study its antioxidant activity, (b) to study its effects on lens integrity in organ culture and (c) in vivo to determine whether it could reduce the damage in model diabetic cataract. METHODS: For (a) our luminescent antioxidant assay was used, (b) lenses were incubated in medium 199, with 55.6 mM glucose. Lenses were stained with 0.014 mM rhodamine 123 for 15 min to stain mitochondria, immobilized in 1% agarose in M199, and the equatorial region examined by a Zeiss confocal microscope. For (c) cataract grades of streptozotocin diabetic rats fed 1% pycnogenol were followed for 12 weeks. RESULTS: (a) Pycnogenol in vitro was an antioxidant when challenged with peroxide. (b) In vitro, when [570 mg/L] pycnogenol in dimethyl sulfoxide (DMSO) was used, lenses turned opaque after 3 d of incubation, in both pycnogenol controls and glucose + pycnogenol. Normal controls (DMSO, n = 4) and controls (n = 4) remained clear after 8 d of incubation. After 3 d of incubation with pycnogenol, cumulative protein leakage was greater than 0.28 mg/mL versus 8 d controls (0.018 mg/mL). Similar damage occurred at pycnogenol concentrations as low as 20 mg/L. The 20 mg/L pycnogenol control showed mitochondrial death, and calcium concentration in the lens equatorial differentiating fiber cells increased. (c) In vivo feeding pycnogenol resulted in similar growth and body condition for diabetic rats, and lower cataract grades at 9 and 11 weeks: final serum glucose levels were not significantly different, but glycohemoglobin A1 levels were significantly lower (83.9% of normal, p < 0.05) in pycnogenol-fed diabetic rats. CONCLUSIONS: Although it appears that pycnogenol has a potential toxic effect on incubated lenses, it appears in vivo to have a marginal protective effect, and also significantly reduces glycation of proteins. Supported by Cognis US (formerly Henkel Chemical Co.) and Horphag Research.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".