Serum protein albumin and chromium: Mechanistic insights into the interaction between ions, nanoparticles, and protein
Bibliographic record
Abstract
The interaction of human proteins and metal species, both ions and nanoparticles, is poorly understood despite their growing importance. These materials are the by-products of corrosion processes and are of relevance for food and drug manufacturing, nanomedicine, and biomedical implant corrosion. Here, we study the interaction of Cr(III) ions and chromium oxide nanoparticles with bovine serum albumin in physiological conditions. This study combined electrophoretic mobility measurements, spectroscopy, and time-of-flight secondary ion mass spectrometry with principal component analysis. It was determined that neither metal species resulted in global albumin unfolding. The Cr(III) ions interacted strongly with amino acids found in previously discovered metal binding sites, but also were most strongly implicated in the interaction with negatively charged acid residues, suggesting an electrostatic interaction. Bovine serum albumin (BSA) was found to bind to the Cr 2 O 3 nanoparticles in a preferential orientation, due to electrostatic interactions between the positive amino acid residues and the negative chromium oxide nanoparticle surface. These findings ameliorate our understanding of the interaction between trivalent chromium ions and nanoparticles, and biological macromolecules. • Corrosion of implants can release both chromium ions and nanoparticles. • Chromium ions and chromium oxide nanoparticles interact with albumin differently. • Trivalent chromium ions induce albumin aggregation through salt-bridging. • Chromium oxide nanoparticles also cause serum albumin aggregation. • Chromium oxide nanoparticles develop an albumin corona through electrostatic forces.
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.
How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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 teacher head, 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".