Analysis of insulin and insulin analogs from dried blood spots by means of liquid chromatography–high resolution mass spectrometry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it