A liquid chromatography‐mass spectrometry method for nicotine and cotinine; utility in screening tobacco exposure in patients taking amiodarone
Bibliographic record
Abstract
A liquid chromatographic mass spectrometric (LC-MS) assay for the quantification of nicotine and cotinine in human specimens was developed. Human serum and urine (100 μL) were subjected to liquid-liquid extraction. For glucuronidated cotinine, serum was alkalinized and hydrolyzed before extraction. The dried samples were reconstituted and run using gradient flow reverse-phase liquid chromatography with MS detection. The ions utilized for quantification of nicotine, cotinine and milrinone (internal standard) were 162.8, 176.9 and 211.9 m/z, respectively. The mean recoveries were over 80% for cotinine and nicotine with excellent linearity between nominal concentrations and peak area ratios, over a wide concentration range. The percentage coefficient of variation and mean error of the inter- and intra-day validations were <15% for nicotine and cotinine. Analysis of serum from cardiac patients receiving amiodarone suggested that a number of patients were either active smokers or exposed to second-hand smoke. Significant concentrations of nicotine and cotinine were measured in the urine of a known smoking volunteer. The method was highly specific, sensitive and applicable as a tool in detecting and monitoring the passive exposure to tobacco smoke using small specimen volumes (0.1 mL).
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".