Investigation of the binding of Cr(III) complexes to bovine and human serum proteins: A proteomic approach
Bibliographic record
Abstract
Chromium (Cr) compounds are widely used in alloys manufacturing and forming processes. One of the main concerns in the use of cobalt-chromium (Co-Cr) alloy-based implants is the long-term fate of Co and Cr ions in the blood, organs, and urine of patients. Our previous studies have shown that Cr(III) forms complexes in different cell culture media, whereas Cr(VI) does not form any detectable structure under the same conditions. Because Cr(VI) is known to be more toxic than Cr(III), we hypothesized that the presence of serum proteins in the molecular structure of Cr(III) may be responsible for the difference in toxicity. We investigated the interaction of the Cr(III) complexes with serum proteins and their internalization by U937 macrophage-like cells. By using a proteomic approach, we showed that in the presence of fetal bovine serum, Cr(III) complexes interacted only with albumin, whereas they interacted mainly with albumin, transferrin, and immunoglobulins (Ig) in the presence of human serum (HS). Cr(III) complexes were more easily engulfed by U937 macrophage-like cells when they were formed with HS. To the best of our knowledge, this is the first report on the formation of Cr(III) complexes in the presence of serum proteins and the interaction of these complexes with U937 macrophage-like cells. (c) 2010 Wiley Periodicals, Inc. J Biomed Mater Res, 2010.
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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) | 0.001 | 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 | 0.001 |
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".