Protein displacement dynamics on chromium oxide nanoparticles: Investigating the Vroman effect in a binary protein system
Bibliographic record
Abstract
Metal exposure through environmental and biomedical sources poses significant risks to human health. Biomedical implants made of metal alloys, such as stainless steel, degrade chemically and mechanically over time, resulting in surrounding tissues being exposed to chromium oxide (Cr₂O₃) nanoparticles. These nanoparticles interact dynamically with biological fluids and tissues, leading to protein adsorption and the formation of a protein corona, which influences material biocompatibility and the immune response. This study investigates the interaction of Cr₂O₃ nanoparticles with two plasma proteins—bovine serum albumin (BSA) and fibrinogen—with a particular emphasis on the displacement dynamics governed by the Vroman effect. Using a variety of spectroscopic, analytical, and imaging techniques, the study reveals that neither Cr₂O₃ nanoparticles nor the displacement of BSA induce fibrinogen unfolding or breakage of its disulfide bonds. Fibrinogen was found to rapidly (< 1 minute) replace BSA on the nanoparticle surface and also to deposit on top of BSA, suggesting the mechanism of protein exchange follows the transient complex exchange model. These findings highlight the complex dynamics that modulate protein adsorption and displacement and contribute to the growing body of work that aims to advance the understanding of nanoparticle-protein interactions. • Fibrinogen rapidly displaces BSA on the surface of chromium oxide nanoparticles. • Chromium oxide nanoparticles do not induce BSA or fibrinogen unfolding. • Fibrinogen was found to deposit on top of initially adsorbed BSA. • The mechanism of the Vroman effect follows the transient exchange model.
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 | 0.000 |
| 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 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".