Analysis of diastereomeric DAG naphthylethylurethanes by normal‐phase HPLC with on‐line electrospray MS
Bibliographic record
Abstract
Normal-phase HPLC resolution of sn-1,2(2,3)- and x-1,3-DAG generated by partial Grignard degradation from natural TAG was carried out with both (R)-(-) and (S)-(+)-1-(1-naphthyl)ethylurethane derivatives. The diastereomeric sn-1,2- and sn-2,3-DAG derivatives were resolved using two Supelcosil LC-Si (5 microm, 25 cm x 4.6 mm i.d.) columns in series and an isocratic elution with 0.37% isopropanol in hexane at a flow rate of 0.7 mL/min. The DAG were detected by UV absorption at 280 nm and were identified by electrospray ionization MS in the positive ion mode following postcolumn addition of chloroform/methanol/30% ammonium hydroxide (75:24.5:0.5, by vol) at 0.6 mL/min. Application of the method to a stereospecific analysis of the molecular species of TAG of rat VLDL showed that the TAG composition of VLDL circulating under basal conditions differs markedly from that of VLDL secreted by the liver during inhibition of serum lipases. The inhibition of serum lipases resulted in a significant proportional decrease in 16:0 and PUFA and an increase in 18:0 and oligoenoic FA in the sn-1-position, whereas the FA compositions in the sn-2- and sn-3-positions were much less affected.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".