Elucidating structure–function relationships governing the interfacial response of human mesenchymal stem cells to polydopamine coatings
Bibliographic record
Abstract
Deposition of mussel-inspired polydopamine (PDA) has rapidly emerged as a simple yet effective strategy to functionalize the surface of biomaterials. The experimental simplicity of the deposition process, combined with native bioactivity and bioadhesive properties, make PDA an attractive solution for biomedical applications, ranging from functional biomaterials for tissue engineering to antibacterial surfaces. Unveiling the interplay among deposition parameters, physicochemical properties of the resulting structures and their functions, is a fundamental aspect to unlock a more sophisticated knowledge of PDA biofunctionalization and its role in controlling key biological events, such as stem cell response. Although the mechanism for the bioinductive capacity of PDA is not fully understood, surface topography, chemistry and adhesive properties are believed to play a critical role, either individually or in combination. This work addresses the differential roles of such surface properties on PDA bioactivity. We achieved novel insights on the physicochemical makeup of two PDA coatings obtained by varying one critical parameter (i.e., solution agitation) during the deposition. Successively, we focused on the effects on human mesenchymal stem cells (hMSCs) in both normal and serum-free culturing conditions. This study reveals both the serum-dependent and independent cueing involved in bioactive induction caused by PDA.
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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) | 0.000 | 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 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".