Biocompatible EDOT–Pyrrole Conjugated Conductive Polymer Coating for Augmenting Cell Attachment, Activity, and Differentiation
Bibliographic record
Abstract
Developing scaffolds supporting functional cell attachment and tissue growth is critical in basic cell research, tissue engineering, and regenerative medicine approaches. Though poly(ethylene glycol) (PEG) and its derivatives are attractive for hydrogels and scaffold fabrication, they often require bioactive modifications due to their bioinert nature. In this work, biomimetic synthesized conductive polypyrrole-poly(3,4-ethylenedioxythiophene) copolymer doped with poly(styrenesulfonate) (PPy-PEDOT:PSS) was used as a biocompatible coating for poly(ethylene glycol) diacrylate (PEGDA) hydrogel to support neuronal and muscle cells' attachment, activity, and differentiation. The synthesized copolymer was characterized by Raman spectroscopy and dynamic light scattering. Its electrochemical properties were studied using galvanostatic charge-discharge (GCD) and voltammetry. PPy-PEDOT:PSS-coated hydrogels were characterized by Raman spectroscopy and atomic force microscopy, and protein adsorption was assessed using a quartz crystal microbalance with dissipation monitoring. Attachment and differentiation of the ND7/23 neuron hybrid cell line and C2C12 myoblasts were evaluated by cell cytoskeleton staining and quantification of morphological parameters. Viability was assessed by live/dead staining using flow cytometry. Cortex neural activity was studied by calcium ion influx that could be detected through the dynamic fluorescence changes of Fluo-4. The PPy-PEDOT:PSS coating supported cell attachment and differentiation and was nontoxic to cells. Primary neurons attached and remained responsive to electrical stimulation. Altogether, the biocompatible copolymer PPy-PEDOT:PSS is a simple yet effective alternative for hydrogel coating and presents great potential as an interface for nervous and other electrically excitable tissues.
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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".