Spotlight on Endocannabinoids in Healthy Individuals Using Volumetric Absorptive Microsampling Combined with LC-MS/MS Analysis
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Endocannabinoids (eCBs) are crucial to regulating physiological function in the human body. This study investigates the applicability of volumetric absorptive microsampling (VAMS) for eCB analysis in blood samples. First, a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed and validated to quantify a series of primary eCBs and eCB-like lipid mediators (i.e., AEA, OEA, SEA, PEA, LEA, 2-AG, and 2-OG). We then evaluated VAMS, considering several key parameters such as temperature, humidity, and hematocrit levels. This research systematically compared, for the first time, eCB levels in whole blood and plasma samples. We demonstrated an accuracy between 72.5% and 98.9%, with an interday and intraday precision below 8%, for all eCBs and eCB-like lipid mediators. The method exhibited minimal matrix effects, ranging from −15.0% to +9.0%. Most eCBs were stable under varying temperatures and storage conditions. However, drying time significantly affected the detected levels of 2-AG and 2-OG, suggesting that ex vivo biosynthesis may occur during drying. Interestingly, substantial differences in eCB levels were found between whole blood and plasma samples, emphasizing the importance of cellular components in eCB distribution. For example, 2-AG levels in whole blood ranged from 219–1,143 ng mL –1; plasma samples exhibited lower levels, ranging from 1.73–8.44 ng mL –1 . These results highlight the need for standardized sampling methods to ensure accurate and consistent measurements of eCBs in both blood and plasma samples. While this work has significant implications for clinical eCB research, further investigation is necessary to understand the underlying mechanisms and clinical impact of eCB and eCB-like lipid mediators research.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".