Comparison of the response of cultured osteoblasts and osteoblasts outgrown from rat calvarial bone chips to nonfouling KRSR and FHRRIKA‐peptide modified rough titanium surfaces
Bibliographic record
Abstract
Mimicking proteins found in the extracellular matrix (ECM) using specific peptide sequences is a well-known strategy for the design of biomimetic surfaces, but has not yet been widely exploited in the field of biomedical implants. This study investigated osteoblast and, as a control, fibroblast proliferation to novel consensus heparin-binding peptides sequences KRSR and FHRIKKA that were immobilized onto rough (particle-blasted and chemically etched) commercially pure titanium surfaces using a poly(L-lysine)-graft-poly(ethylene glycol) (PLL-g-PEG) molecular assembly system. This platform enabled a detailed study of specific cell-peptide interactions even in the presence of serum in the culture medium; thanks to the excellent nonfouling properties of the PLL-g-PEG surface. Cell-binding peptide sequence RGD in combination with KRSR or FHRRIKA was used to examine a potentially-enhanced or synergistic effect on osteoblast proliferation. Bare titanium and bioinactive surfaces (i.e., unfunctionalized PLL-g-PEG and scrambled KSSR, RFHARIK, and RDG) were used as control substrates. Additionally, in a newly developed experimental setup, freshly harvested bone chips from newborn rat calvariae were placed onto the same type of surfaces investigating size and pattern of osteoblast outgrowths. The findings of the current study demonstrated that the difference in osteoblast and fibroblast proliferation was influenced by surface topography more so than by the presence of surface-bound KRSR and FHRRIKA. On the other hand, in comparison with the control surfaces, osteoblast outgrowths from rat calvarial bone chips covered a significantly larger area on RGD, KRSR, and FHRRIKA surfaces after 8 days and also migrated in an isotropic way unlike cells on the bioinactive substrates. Furthermore, the stimulatory effect of 0.75 pmol cm(-2) RGD on osteoblast migration pattern could be enhanced when applied in combination with 2.25 pmol cm(-2) KRSR.
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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".