Evaluation of the direct effects of poly(dopamine) on the in vitro response of human osteoblastic cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Functional polymeric coatings have rapidly become one of the most efficient strategies to endow biomaterials with enhanced bioactive properties. Among the bio-inspired polymers used for biomedical applications, mussel-derived poly(dopamine) (PDA) has increasingly attracted considerable interest because of its unique characteristics. In this work, we carried out detailed physicochemical characterization of a PDA film deposited on nanoporous titanium. In particular, we employed spectroscopic techniques (Raman and ATR-FTIR) and Digital Pulsed Force Mode Atomic Force microscopy (DPFM-AFM) to probe the chemical makeup and the nanomechanical properties of PDA-coated surfaces. In addition, we investigated protein adsorption by ATR-FTIR and quantified it with ten different serum proteins by Liquid Chromatography Mass spectroscopy (LC-MS), aiming at elucidating their potential contribution to the subsequent cell colonization. Successively, we assessed the response of MG-63 human osteoblastic cells to PDA-coated titanium both the multiple- and single-cell levels. Results for this study demonstrate that, compared to bare and nanoporous titanium, the PDA coating positively influences the adhesion and proliferation of MG-63 cells. In addition, we focus on how the three different substrates influence cell morphology (i.e. aspect ratio and form factor), the establishment of focal adhesions and the expression of RhoA, a protein involved in cell contractility. In conclusion, our work provides a deeper insight on the in vitro response of human osteoblastic cells to poly(dopamine) by closing in on specific aspects of cell-PDA interactions, ultimately reaffirming the potential of this bio-inspired polymer as a functional coating for bone tissue engineering applications.
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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 | 0.004 | 0.003 |
| 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) | 0.000 | 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 it