Investigation of interaction between human hemoglobin A<sub>0</sub> and platinum anticancer drugs by capillary isoelectric focusing with whole column imaging detection
Bibliographic record
Abstract
CIEF with whole column imaging detection (WCID) was used to investigate the interaction of platinum-based anticancer drugs, cis-platinum(II) diamine dichloride (cisplatin) and [SP-4-2-{1R-trans)]-(1,2-cyclohexanediamine-N,N')[ethanedioata(2-)-O,O']platinum (oxaliplatin), with human hemoglobin A(0) (Hb). This technique facilitates the investigation and characterization of the formation of adducts between drugs and proteins. Cisplatin and oxaliplatin were mixed with the target protein at different concentrations (0:1, 1:1, 1:10, 1:50, and 1:100), and the reaction mixtures were incubated for 0, 0.5, 1, 12, 24, 48, and 72 h at 37 degrees C in a water-bath. The focused Hb-drug adduct profiles were imaged by WCID. At higher drug to protein molar ratios (for both oxaliplatin and cisplatin), the results exhibit significant changes in the peak shapes and heights, which may indicate the destabilization of the protein. However, the conformational change was less evident at lower molar ratios. In addition, a major pI shift was observed for the oxaliplatin reaction mixtures (for 1:10, 1:50, and 1:100 ratios). In comparison with previously reported findings obtained by other analytical methods, conclusions were drawn about the validity of CIEF as a simple and convenient method for the investigation of protein-drug interactions. These results may provide useful information for further understanding the activity and toxicity of these chemotherapeutic drugs and improving their clinical performance.
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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 | 0.001 |
| 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".