Determination of human insulin and its analogues in human blood using liquid chromatography coupled to ion mobility mass spectrometry (LC‐IM‐MS)
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
The qualitative and quantitative determination of insulin from human blood samples is an emerging topic in doping controls as well as in other related disciplines (e.g. forensics). Beside the therapeutic use, insulin represents a prohibited, performance enhancing substance in sports drug testing. In both cases accurate, sensitive, specific, and unambiguous determination of the target peptide is of the utmost importance. The challenges concerning identifying insulins in blood by liquid chromatography coupled to ion mobility mass spectrometry (LC-IM-MS) are detecting the basal concentrations of approximately 0.2 ng/mL and covering the hyperinsulinaemic clamps at > 3 ng/mL simultaneously using up to 200 μL of plasma or serum. This is achieved by immunoaffinity purification of the insulins with magnetic beads and subsequent separation by micro-scale liquid chromatography coupled to ion mobility / high resolution mass spectrometry. The method includes human insulin as well as the synthetic or animal analogues insulin aspart, glulisine, glargine, detemir, lispro, bovine, and porcine insulin. The method validation shows reliable results considering specificity, limit of detection (0.2 ng/mL except for detemir: 0.8 ng/mL), limit of quantification (0.5 ng/mL for human insulin), precision (CV < 20%), linearity (r > 0.99), recovery, accuracy (>90%), robustness (plasma/serum), and ion suppression. For quantification of human insulin a labelled internal standard ([[(2) H10 ]-Leu(B6,B11,B15,B17) ] - human Insulin) is introduced. By means of the additional ion mobility separation of the different analogues, the chromatographic run time is shortened to 8 min without losing specificity. As proof-of-concept, the procedure was successfully applied to different blood specimens from diabetic patients receiving recombinant synthetic analogues.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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