Application of Solid Phase Microextraction for Quantitation of Polyunsaturated Fatty Acids in Biological Fluids
Bibliographic record
Abstract
Development of a straightforward strategy for simultaneous quantitative analysis of nonesterified fatty acids (NEFA) species in biofluids is a challenging task because of the extreme complexity of fatty acid distribution in biological matrices. In this study, we present a direct immersion solid phase microextraction method coupled to a liquid chromatography-mass spectrometry platform (DI-SPME- HPLC-ESI -MS) for determination of unconjugated fatty acids (FA) in fish and human plasma. The proposed method was fully validated according to bioanalytical method validation guidelines. The LOD and LOQ were in the range of 0.5-2 and 5-12 ng/mL, respectively, with a linear dynamic range of 100 fold for each compound. Absolute and relative matrix effects were comprehensively evaluated and found to be in the acceptable range of 91-116%. The affinity constant (Ka) of individual FAs to protein albumin was determined to be 9.2 × 10(4) to 4.3 × 10(5) M(-1). The plasma protein binding (PPB%) was calculated and found to be in the range of 98.0-99.7% for different polyunsaturated fatty acids (PUFAs). The PUFAs under study were found at a high concentration range in fish plasma, whereas only a few were within quantification range in control human plasma. The method was successfully applied for monitoring PUFA changes during the operation in plasma samples obtained from patients undergoing cardiac surgery with the use of cardiopulmonary bypass (CPB). The most significant contribution induced by surgery was noticed in the concentration level of α-linolenic acid (18:3, ALA), arachidonic acid (20:4, AA), and docosahexanoic acid (22:6, DHA) soon after administration of CPB in all cases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".