High-Sensitivity NanoLC−MS/MS Analysis of Urinary Desmosine and Isodesmosine
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) is characterized by the degradation of elastin, the major insoluble protein of lung tissues. The degradation of elastin gives rise to desmosine (DES) and isodesmosine (IDES), two major urinary products typified by a hydrophilic pyridinium-based cross-linker structure. A high sensitivity method based on nanoflow liquid chromatography tandem mass spectrometry with multiple reaction monitoring was developed for the analysis of urinary DES and IDES. The analytes were derivatized with propionic anhydride and deuterated DES (D(4)-DES) was used as an internal standard. This method enables the quantification of DES and IDES in as little as 50 microL of urine and provides a detection limit of 0.10 ng/mL (0.95 fmol on-column). We report the analysis of DES and IDES in a cohort of 40 urine specimens from four groups of individuals: (a) COPD rapid decliners (11.8 +/- 3.7 ng/mg creatine (crea)), (b) COPD slow decliners (16.0 +/- 3.1 ng/mg crea), (c) healthy smokers (13.2 +/- 1.9 ng/mg crea), and (d) healthy nonsmokers (14.9 +/- 2.9 ng/mg crea). Our analysis reveals a statistically significant decrease in the level of urinary DES and IDES in COPD rapid decliner patients compared to healthy nonsmoker controls and COPD slow decliner patients. This methodology may be useful for monitoring DES and IDES levels in well controlled animal models for COPD or for longitudinal studies in COPD patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 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".