Characterization of Intact Hemoglobin and Oxaliplatin Interaction by Nanoelectrospray Ionization Tandem Mass Spectrometry
Bibliographic record
Abstract
BACKGROUND: Mass spectrometric (MS) detection of intact hemoglobin (Hb) adducts presents considerable analytical challenges because of the noncovalent association of the 4 subunits of Hb, and MS characterization of the interaction of intact Hb with platinum drugs has not been reported. We developed a technique for detecting intact Hb and its drug adduct and studied the interactions between intact Hb and oxaliplatin. METHODS: We incubated a series of mixtures of Hb and oxaliplatin at 37 degrees C for 24 h or 5 days to investigate adduct formation. Blood samples from colorectal cancer patients undergoing oxaliplatin treatment were analyzed for novel adducts of intact Hb-oxaliplatin, which were characterized with nanoelectrospray ionization quadrupole time-of-flight MS. RESULTS: Two intact Hb adducts, one with the whole oxaliplatin molecule and the other with oxaliplatin losing the oxalate ligand, were identified. Analysis of erythrocytes from the cancer patients provided direct evidence that oxaliplatin accumulated as Hb adducts in erythrocytes. A higher fraction (approximately 70%) of Hb was bound to oxaliplatin in erythrocytes from a patient who could not tolerate oxaliplatin treatment than in erythrocytes from another patient who benefited from this treatment. CONCLUSIONS: The nanoelectrospray tandem MS technique enabled determination of the intact Hb tetramer and its association with oxaliplatin. Hb-oxaliplatin adducts in erythrocytes may serve as a clinical biomarker for toxic response and treatment efficacy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".