Human monocyte adhesion onto RGD and PHSRN peptides delivered to the surface of a polycarbonate polyurethane using bioactive fluorinated surface modifiers
Bibliographic record
Abstract
Fluorinated oligomers, when blended into polyurethane, have been shown to migrate to the surface and generate an interface that minimizes protein denaturation and reduces cell activation. This type of surface modification can be achieved with ppm quantities of a bioactive fluorinated surface modifier (BFSM), enabling the introduction of bioactive agents onto a surface in one manufacturing step. In the current study, two BFSMs were synthesized with covalently conjugated RGD and PHSRN peptides near the fluorine terminal groups, and were shown to be surface active in polyurethane blends. CyQuant cell enumeration, scanning electron microscopy, and cell viability assays all indicated that the bioactive (and fluorinated) substrates supported enhanced monocyte interaction. The simplicity of the surface modification technique and the demonstrated ability of the peptide BFSMs to influence cell attachment and spreading indicate the potential benefits and practical value of the BFSM technology in tailoring surfaces for biomaterial applications. This was specifically highlighted for human blood monocytes, a key cell involved in the early stages of wound healing.
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 | 0.000 |
| 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".