Human mesenchymal stromal cell adhesion and expansion on fluoropolymer surfaces modified with oxygen and nitrogen-rich plasma polymers
Bibliographic record
Abstract
Fluorinated ethylene propylene (FEP) vessels are of significant interest for therapeutic cell biomanufacturing applications due to their chemical inertness, hydrophobic surface, and high oxygen permeability properties.However, these properties also limit the adhesion and survival of anchorage-dependent cells.Here, we develop novel plasma polymer coatings to modify FEP surfaces, enhancing the adhesion and expansion of human mesenchymal stromal cells.Similar to commercially available tissue culture polystyrene vessels, oxygen-rich or nitrogen-rich surface chemistries can be achieved using this approach.While steam sterilization increased the roughness of the coatings and altered the surface chemistry, the overall wettability and oxygen or nitrogenrich nature of the coatings were maintained.In the absence of proteins during initial cell attachment, cells adhered to surfaces even in the presence of chelators, whereas adhesion was abrogated with chelator in a protein-containing medium, suggesting that integrin-mediated adhesion predominates over physicochemical tethering in normal protein-containing cell seeding conditions.Albumin adsorption was more elevated on nitrogen-rich coatings compared to the oxygen-rich coatings, which was correlated with a higher extent of hMSC expansion after 3 days.Both the oxygen and nitrogen-rich coatings significantly improved hMSC adhesion and expansion compared to untreated FEP.FEP surfaces with nitrogen-rich coatings were practically equivalent to commercially available standard tissue culture-treated polystyrene surfaces in terms of hMSC yields.Plasma polymer coatings show significant promise in expanding the potential usage of FEP-based culture vessels for cell therapy applications.
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) | 0.000 | 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".