Inhibition of non‐enzymatic glycation by silk extracts from a Mexican land race and modern inbred lines of maize (<i>Zea mays</i>)
Bibliographic record
Abstract
Non-enzymatic glycation and the accumulation of advanced glycation end products (AGEs) are associated with various disease states, including complications of diabetes and aging. Secondary metabolites from several plant species are known to inhibit non-enzymatic glycation and the formation of AGEs, including flavonoids found in the style (silk) of Zea mays (maize). Thirteen modern maize inbreds and one land race were tested for in vitro inhibition of non-enzymatic glycation of bovine serum albumin. Many of the tested extracts exhibited inhibitory activity, in particular the newest inbreds, which were bred for resistance to gibberella ear rot (Fusarium graminearum) and common smut (Ustilago maydis). The most active maize genotype (CO441), displaying an IC50 of 9.5 microg/mL, was more effective than aminoguanidine, a known inhibitor of glycation. Zapalote chico, a land race with high maysin content, showed only moderate inhibitory activity compared with the modern maize genotypes. Antiglycation activity was highly correlated with the total phenolic content of silk extracts and mildly correlated with resistance to certain fungal infections. The results identify modern resistant and high phenolic maize inbreds as promising candidates for the development of natural AGE inhibitors for the prevention and treatment of diabetic complications and the degenerative effects of aging.
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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".