Hemocompatibility of Albumin-Modified Magnetic Nanoparticles
Bibliographic record
Abstract
Kidney failure leads to the accumulation of metabolites in the blood compartment. This build-up of metabolites has been associated with increased mortality and morbidity in these patients; thus, these metabolites are commonly called uremic toxins. The retention of some uremic toxins in the blood results from a strong interaction with serum albumin, preventing their clearance using standard hemodialysis techniques. Adsorbents are considered the next-generation technology for clearing uremic toxins from the blood, and iron oxide magnetic nanoparticles are a promising material due to a high surface area that is easily modified and the ability to remove them from blood with an external magnetic field. Plasma protein adsorption and clot formation kinetics were determined for unmodified and albumin-modified iron oxide magnetic nanoparticles. Albumin was selected because it can bind uremic toxins, and it is commonly used to passivate surfaces. Coatings were formed and characterized using transmission electron microscopy, thermogravimetric analysis, and zeta-potential analysis. Clotting kinetics, total protein assays, and immunoblots were used to analyze the effect surface modification has on protein adsorption events. Unmodified nanoparticles showed rapid clotting and more adsorbed protein compared to albumin-coated iron oxide nanoparticles. Immunoblots show that modified particles showed changes in albumin, protein C, Immunoglobulin G, transferrin, fibrinogen, α1-antitrypsin, vitronectin, plasminogen, prothrombin, and antithrombin levels compared to unmodified controls. The hemocompatibility of adsorbent materials is essential to their clinical application in clearing the blood of uremic toxins.
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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".