Albumin coatings for counteracting uremic metabolites effects on host responses to biomaterials
Bibliographic record
Abstract
Chronic kidney disease is a progressive condition characterized by a decline in kidney function that is the impetus for an increased retention of uremic metabolites (UMs) in the blood compartment which is correlated with adverse patient outcomes. The inefficient clearance of some UMs using membrane hemodialysis is a significant problem, and adsorptive materials are actively being researched to overcome this issue. Albumin is an abundant serum protein known to bind UMs and minimize non-specific protein adsorption at albumin-modified surfaces: two important aspects for designing modified surfaces for clearing UMs from blood. Herein, we anchored albumin to nanoparticles to understand if UM effects on protein-driven host responses to nanoparticles could be minimized. UM doped platelet-poor plasma was used to characterize protein-initiated clotting kinetics and immunoblot analysis of important protein mediators of the immune, fibrinolytic, and coagulation cascades. The amount of particles and density of adsorbed BSA could return clot formation to that seen for uremic plasma controls, but were unable to return these conditions to that seen for normal plasma. These surfaces significantly lower amounts of adsorbed C3, α 1 -antrypsin, Protein S, cleavage fragments of fibrinogen, prothrombin, factors XI and XII, and antithrombin compared to bare controls with UMs. However, in almost all cases the effect of UMs still led to dramatic increases in adsorbed proteins, and BSA films only reduced adsorption of IgG, vitronectin, prothrombin and antithrombin compared to normal plasma results. BSA films did ameliorate the effect of nanoparticles in uremic plasma. At these concentrations, these films were unable to adsorb enough UMs to negate their effects. This is an important milestone in the design of hemocompatible surfaces for clearing UMs from the blood.
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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".