Fetuin‐<scp>A</scp> adsorption to tunable polydimethylsiloxane and subsequent macrophage response
Bibliographic record
Abstract
Surface modifications can be applied to biomaterials to alter the various surface properties that influence protein-material interactions and the cellular response. The plasma protein fetuin-A has been found to adsorb to many biomaterials but details of its interactions with polydimethylsiloxane (PDMS) and roles in regulating the immune response are not clear. Here, PDMS modifications are achieved by altering the ratio of PDMS formulations to control elastic modulus, and by coating PDMS with polydopamine (PDA) to attach fetuin-A. Surface characterization confirmed that altering the PDMS formulation changed the elastic modulus without affecting surface wetting properties. Surface roughness was measured using atomic force microscopy and surface chemistry was determined using X-ray photoelectron spectroscopy, with only minor changes detected on the softest samples. PDA deposition on PDMS was confirmed and contact angle measurements demonstrated an increase in hydrophilicity. Fetuin-A adsorption was influenced by the PDMS formulations, adsorption changed in a competitive plasma environment, and PDA was able to immobilize the greatest amount of fetuin-A. The inflammatory effects of fetuin-A were investigated, and data suggests that the elastic modulus influences cytokine secretion from macrophages at certain timepoints, a result likely due to varied protein amounts and orientations/conformations in response to material stiffness. The addition of a PDA layer demonstrated the potentially cytokine mitigating effect upon fetuin-A immobilization when compared to unmodified PDMS samples. The results provide new insight into the interactions of fetuin-A with PDMS and PDA, and the potential immune regulatory properties of fetuin-A modified materials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".