Elucidation of the Chemical Structure and Determination of the Production Conditions for a Bioactive Maillard Reaction Product, [5-(5,6-Dihydro-4<i>H</i>-pyridin-3-ylidenemethyl)furan-2-yl]methanol, Isolated from a Glucose–Lysine Heated Mixture
Bibliographic record
Abstract
We previously isolated a bioactive molecule, named F3-A, from an aqueous glucose (Glc) and lysine (Lys) Maillard reaction (MR) model system. Herein, F3-A was verified as [5-(5,6-dihydro-4H-pyridin-3-ylidenemethyl)furan-2-yl]methanol (5) and was subsequently synthesized for confirmation of bioactivity. Using Taguchi and factorial designs, we determined that the conditions which best increased the yield of F3-A were at pH 6 with a sugar:amino acid ratio of 2:1 and heating time of 12 h at 100 °C. The MR mixtures containing glucose produced highest yield, compared to fructose, lactose, and sucrose. Both the F3-A recovered from Glc-Lys MR mixture and the synthesized product exhibited significant (P < 0.05), dose dependent, nitric oxide (NO) inhibitory activity in Caco-2 cells that was comparable to aminoguanidine (AG) and pyrrolidine dithiocarbamate (PDTC), respectively. Finally, an additional inhibitory effect of F3-A was determined when coincubated with AG in cytokine-induced Caco-2 cells. This bioactivity points to a potential role in preventing intestinal inflammation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".