Analysis of insulin and insulin analogs from dried blood spots by means of liquid chromatography–high resolution mass spectrometry
Bibliographic record
Abstract
While dried blood spot (DBS) analysis concerning low molecular mass molecules has become more and more established in various fields of analytical chemistry, the utility of DBS in determining peptides and proteins from DBS is yet comparably limited. In consideration of the fact that the apparent benefits of DBS sampling are similar for analytes of lower and higher molecular mass, dedicated (non-generic) sample preparation procedures are required that meet the needs for detecting peptidic drugs and hormones in DBS. The analysis of insulin and its synthetic analogs by mass spectrometry has received increased attention in several fields such as doping controls, forensics, and drug metabolism and pharmacokinetics studies. Hence, a strategy facilitating the analysis of insulin and its synthetic or animal analogs (human, Lispro, Aspart, Glulisine, Glargine, Detemir, Tresiba, and porcine and bovine insulin) from DBS was developed. The successful analysis of these substances at physiologically relevant concentrations was realized after ultrasonication-assisted extraction, immunoaffinity purification, and liquid chromatographic separation followed by high resolution mass spectrometric detection (with or without ion mobility). Assay validation demonstrated adequate sensitivity (LOD 0.5 ng/mL for most insulins), as well as precise (< 25%) and reproducible results for all included target insulins. Additionally, proof-of-principle data were obtained by the analysis of DBS samples obtained from healthy volunteers in non-fasting state as well as a sample from a diabetic volunteer treated with the fast acting analog insulin Aspart.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".