Monogalactosyldiacylglycerols, potent nitric oxide inhibitors from the marine microalga<i>Tetraselmis chui</i>
Bibliographic record
Abstract
Methanolic extracts of some marine and freshwater microalgae were tested for their nitric oxide (NO) inhibitory activity on lipopolysaccharide-induced NO production in RAW264.7 macrophage cells. Among the tested extracts, Tetraselmis chui extract showed the strongest NO inhibitory activity, thus selected for further study. NO inhibitory activity guided isolation led to identification of two monogalactosyldiacylglycerols (MGDGs) (2S)-1-O-(6Z,9Z,12Z,15Z-octadecatetranoyl)-2-O-(4Z,7Z,10Z,13Z-hexadecatetranoyl)-3-O-β-D-galactopyranosylglycerol (1) and (2S)-1-O-(9Z,12Z,15Z-octadecatrinoyl)-2-O-(4Z,7Z,10Z,13Z-hexadecatetranoyl)-3-O-β-D-galactopyranosylglycerol (2) from the MeOH extract of T. chui. The stereo-chemistry of 1 was elucidated by classical degradation method. MGDGs 1 and 2 showed strong NO inhibitory activity compared to N(G)-methyl-L-arginine acetate salt, a well known NO inhibitor used as a positive control. Isolated MGDGs suppressed NO production through down-regulation of inducible NO synthase protein. A structure activity relationship study suggested that the polyunsaturated fatty acids of the MGDGs are responsible for NO inhibition. Moreover, increasing unsaturation on the fatty acid side chains enhanced the NO inhibitory potency of the MGDGs.
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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) | 0.000 | 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".