Non-Thermal Atmospheric Pressure Plasma-Conditioned Root Dentin Promotes Attraction and Attachment of Primary Human Dental Pulp Stem Cells in Real-Time Ex Vivo
Bibliographic record
Abstract
This study investigated if non-thermal atmospheric pressure plasma (NTAPP) treatment of root dentin surfaces promotes human dental pulp stem cell (hDPSCs) adhesion. Freshly extracted human single-rooted teeth (n = 36) were decoronated and cut (first vertically, then horizontally) into root dentin slices (3 mm thick). Primary hDPSCs cultures were seeded onto slices randomly assigned to pretreatment groups (n = 9/group): NaOCl (1.5%), EDTA (17%) then NTAPP (Group I); NaOCl then NTAPP (Group II); NaOCl then EDTA (Group III); and NaOCl alone (Group IV). Cell viability and proliferation were measured using MTT assay with log-linear statistical analysis. Cell attachment and spreading morphologies on dentin slices (n = 3/group) were examined through scanning electron microscopy. Early cell adhesion events and subcellular activities were observed in real time by live-cell imaging through holotomographic microscopy. Cell viability and proliferation were significantly higher on NTAPP-treated dentin (p < 0.05), without interactions with EDTA (p > 0.05). The attachment, spreading, extensions and multiple layers of hDPSCs were heightened on NTAPP-treated dentin. Cell adhesion, spreading, and dentinal tubule penetration were hastened on NTAPP-treated dentin surfaces in real-time, with elevated subcellular activities and intracellular lipid droplet formation. NTAPP-treated root dentin surfaces support enhanced cellular responses, potentially promoting pulp-dentin regeneration.
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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) | 0.002 | 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".