An Efficient Sample Preparation Method for High-Throughput Analysis of 15(S)-8-iso-PGF2α in Plasma and Urine by Enzyme Immunoassay
Bibliographic record
Abstract
Although several methods have been reported on the analysis of the oxidative stress marker 15(S)-8-iso-prostaglandin-F2alpha (8-iso-PGF2α) in biological fluids, they either involve extensive sample preparation and costly technology or require high sample volume. This study presents a sample preparation method that utilizes low sample volume for 8-iso-PGF2α analysis in plasma and urine by an enzyme immunoassay (EIA). In brief, 8-iso-PGF2α in deproteinized plasma or native urine sample is complexed with an antibody and then captured by molecular weight cut-off filtration. This method was compared with two other sample preparation methods that are typically used in the analysis of 8-iso-PGF2α by EIA: Cayman's affinity column purification method and solid-phase extraction on C-18. The immunoaffinity purification method described here was superior to the other two sample preparation methods and yielded recovery values of 99.8 and 54.1% for 8-iso-PGF2α in plasma and urine, respectively. Analytical precision (relative standard deviation) was ±5% for plasma and ±15% for urine. The analysis of healthy human plasma and urine resulted in basal 8-iso-PGF2α levels of 31.8 ± 5.5 pg/mL and 2.9 ± 2.0 ng/mg creatinine, respectively. The robustness and analytical performance of this method makes it a promising tool for high-throughput screening of biological samples for 8-iso-PGF2α.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".