A robust capillary liquid chromatography/tandem mass spectrometry method for quantitation of neuromodulatory endocannabinoids
Bibliographic record
Abstract
RATIONALE: Methods for quantifying anandamide (AEA) and 2-arachidonoyl glycerol (2-AG) are needed to support programs investigating molecular mechanisms of the central nervous system. Existing methods, while useful, are not well adapted to efficiently process large numbers of very small tissue samples. A unique challenge involves the disparity in endogenous levels of AEA (pmol/g tissue) and 2-AG (nmol/g tissue). METHODS: A simplified one-step solvent extraction procedure was developed for recovering endocannabinoids from rat brain tissues, and combined with capillary liquid chromatography/tandem mass spectrometry (LC/MS/MS). Various multiple reaction monitoring (MRM)-based methods were evaluated for limit of detection (LOD) and robustness. RESULTS: The optimized simultaneous quantitation method achieves an LOQ of 50 amol for AEA and 25 fmol for 2-AG, both with a linearity over 3 orders of magnitude, and elution times under 3 min. Accuracy, expressed as relative error (RE), is less than 12% for AEA and less than 6% for 2-AG. Precision, expressed as relative standard deviation (RSD), is less than 6% for AEA and less than 3% for 2-AG. Sample handling routines are sufficiently robust to support the automated analysis of thousands of samples from a range of tissue types. CONCLUSIONS: The microscale method is a sensitive, economical and robust alternative to the larger scale LC/MS methods currently implemented for quantitation of AEA and 2-AG.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".