Simple Identification of A Cross-Linked Hemoglobin by Tandem Mass Spectrometry in Human Serum
Bibliographic record
Abstract
Hemoglobin-based oxygen therapeutics are prepared by reaction of hemoglobin with cross-linking molecules and are utilized as blood substitutes. They can be used as doping agents to increase the oxygen-carrying capacity of hemoglobin. We have compared a glutaraldehyde-polymerized bovine hemoglobin (Oxyglobin, Biopure Corp.) with natural bovine hemoglobin by mass spectrometry in order to detect specific fragment ions of the cross-linked protein for further potential applications in doping control of human blood samples. HCl acid (6 N) hydrolysis was performed in parallel on both proteins. Hydrolysates were then analyzed by direct infusion electrospray mass spectrometry (ESIMS) using a triple quadrupole mass spectrometer. Confirmation and precision were obtained by LC-ESIMS(n) experiments performed on an ion trap mass spectrometer. Chromatographic and mass spectrometry data allowed detection of two potential Oxyglobin-specific ions--m/z 299 and 399--that were shown to lose a 159 u neutral fragment under collision-induced dissociation conditions. Thus, monitoring of constant neutral loss of 159 u on acid hydrolysates of human serum samples spiked with different amounts of Oxyglobin has proved to be an efficient screening method to specifically detect and identify Oxyglobin. LC-MS of the spiked serum sample hydrolysates enabled detection of Oxyglobin at a detection limit of 4 g x L(-1).
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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 | 0.001 | 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".