Effect of Uremic Toxins and Methoxy-PEO Chain Density on Plasma Protein Adsorption
Bibliographic record
Abstract
Protein adsorption can direct the host response to blood-contacting biomaterials. Poly(ethylene oxide) (PEO) is commonly employed to minimize nonspecific protein adsorption. Although chain density has been observed to play a role in the inherent resistance of protein adsorption by end-tethered films of PEO, only a few papers correlate the change in PEO chain densities with the adsorbed plasma protein composition. Almost all studies rely upon blood from healthy patients for these studies even though they are applied to the unhealthy. In the case of patients with kidney failure, there is a remarkable change in the blood composition due to retained metabolites. In the pursuit of personalized dialysis, we must address this dearth in the literature regarding the effect of metabolite accumulation in the blood compartment on the adsorption of protein to blood-contacting biomaterials. To this end, surface films of different methoxy-PEO (mPEO) chain densities were used to evaluate the changes in adsorbed proteins in the presence of uremic metabolites (i.e., uremic toxins). End-tethered mPEO films were characterized using contact angles, ellipsometry, and X-ray photoelectron spectroscopy. Plasma protein adsorption was conducted with and without uremic toxins commonly found in patients with end stage kidney disease, and the adsorbed protein profile was identified using immunoblots. It was found that the presence of uremic toxins led to a notable increase in the adsorption of almost all of the proteins. It was evident that while chain density plays a role in overall protein resistance, the effect of uremic toxins led to substantial increases in adsorbed proteins and needs to be considered when designing next-generation blood-contacting materials.
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 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) | 0.001 | 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".