Total serum pentosidine quantification using liquid chromatography-tandem mass spectrometry
Bibliographic record
Abstract
Pentosidine (PEN) is an Advanced Glycation End-product (AGE) that is known to accumulate in bone collagen with aging and contribute to fracture risk. The PEN content in bone is correlated with serum PEN, making it an attractive, potential osteoporosis biomarker. We sought to develop a method for quantifying PEN in stored serum. After conducting a systematic narrative review of PEN quantification methodologies, we developed a liquid chromatography tandem mass spectrometry (LC-MS/MS) method for quantifying total serum PEN. Our method is both sensitive and precise (LOD 2 nM, LOQ 5 nM, %CV < 6.5 % and recovery 91.2-100.7 %). Our method is also equivalent or better than other methods identified in our review. Additionally, LC-MS/MS avoids the pitfalls and limitations of using fluorescence as a means of detection and could be adapted to investigate a broad range of AGE compounds.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".