Adsorption Dynamics of Uremic Toxins to Novel Modified Magnetic Nanoparticles
Bibliographic record
Abstract
Kidney dysfunction leads to the retention of metabolites in the blood compartment, some of which reach toxic levels. Uremic toxins are associated with the progression of kidney disease and other symptoms of kidney failure (i.e., nausea, itchiness, and hypertension). Toxin removal ameliorates symptoms and reduces further organ damage, but membrane-based methods are inadequate for this purpose. Engineered adsorbents may facilitate enhanced removal of retained toxins, especially those bound strongly by proteins. Poly 2-(methacryloyloxy)ethyl phosphorylcholine-co-β-cyclodextrin (p(MPC-co-PMβCD)) coated magnetic nanoparticles are synthesized, characterized for their physicochemical properties (Fourier-transform infrared spectroscopy (FTIR), nuclear magnetic resonance (NMR), thermogravimetric analysis(TGA), gel permeation chromatography (GPC), and transmission electron microscope (TEM), and evaluated toxin adsorption from a complex solution for the first time to quantify the effects of film chemistry and incubation time on the adsorbed toxinome (the collection of toxins). Uremic toxins are bound by even "low-fouling" polymer films themselves; providing further insight into how small molecule interactions with "low-fouling" films may affect protein-surface interactions. These results suggest a dynamic interaction between toxins and surfaces that is not driven by solution concentration alone. This knowledge will help advance the design of novel adsorbent films for clearing 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) | 0.001 | 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".