Binding Capacity and Adsorption Stability of Uremic Metabolites to Albumin-Modified Magnetic Nanoparticles
Bibliographic record
Abstract
Kidney disease causes the retention of uremic metabolites in blood, which is associated with many comorbidities. Hemodialysis does not properly clear many metabolites, including large, middle-sized, and small protein-bound uremic toxins (PBUTs). Adsorption strategies for metabolite removal require the development of engineered adsorbents with tailored surfaces to increase the binding of desired metabolites. Albumin is uniquely positioned for modifying blood-contacting surfaces to absorb uremic metabolites, as it (i) minimizes non-specific protein adsorption and (ii) binds a range of molecules at Sudlow Sites I and II with different affinities. It is unknown if albumin-modified surfaces retain the adsorption qualities of solution-free albumin, namely, adsorption stability or specificity. Herein, albumin was covalently attached to iron oxide nanoparticles and characterized using multiple methods. Metabolite adsorption was conducted by incubating particles in a model solution of thirty-three uremic metabolites associated with kidney failure. Adsorption efficiency, selectivity, and stability were affected by albumin concentration and incubation time. Metabolite adsorption was found to change with time, and it was more effective on albumin-modified particles than unmodified controls. The findings outlined in this paper are crucial for the design of next-generation advanced blood-contacting materials to enhance dialysis and blood purification for patients with kidney disease.
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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 | 0.001 |
| 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 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".