Monogalactosyldiacylglycerols, potent nitric oxide inhibitors from the marine microalga<i>Tetraselmis chui</i>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it