Hemocompatibility of cyclodextrin-coated magnetic nanoparticles under uremic conditions
Bibliographic record
Abstract
The accumulation of uremic toxins, a hallmark of kidney failure in hemodialysis-dependent patients, highlights hemodialysis's limits to effectively removing a broad range of toxins. Adsorption-based strategies have emerged as a promising solution. However, a critical gap remains in understanding the hemocompatibility of these materials in the context of kidney failure. In this study we investigated the hemocompatibility and protein adsorption behavior of α-, β-, and γ-cyclodextrin-coated magnetic nanoparticles in modeled uremic plasma and blood. Cyclodextrin-coated nanoparticles exhibited greater protein adsorption compared to untreated plasma, while cyclodextrin-modified particles showed significantly reduced protein adsorption per unit surface area compared to bare nanoparticles. Immunoblotting revealed distinct protein adsorption profiles compared to untreated plasma, where an increased trend in surface adsorption was observed for most plasma proteins in uremic plasma, with substantial changes in the binding of complement proteins, fibrinogen, α 2 macroglobulin, fibronectin, protein S, and immunoglobulins. We did not observe any clear indications of hemo-incompatibility of the MNPs under uremic conditions in whole blood. Interestingly, we also found some evidence of improved platelet responsiveness and clot formation time with MNP treatment, under uremic conditions. These findings underscore the potential of cyclodextrin-coated magnetic nanoparticles for safer, more efficient blood detoxification strategies in kidney failure.
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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) | 0.001 | 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".