Efficacy of bioactive nanoparticles on tissue‐endotoxin induced suppression of stem cell viability, migration and differentiation
Bibliographic record
Abstract
AIM: To characterize a lipopolysaccharide (LPS)-treated dentine tissue model (LPS dentine) to analyse the efficacy of polycationic chitosan nanoparticles (CSnp) and/or dexamethasone conjugate chitosan nanoparticles (Dex-CSnp) on the viability/differentiation potential of stem cells from apical papilla (SCAP) when exposed to LPS dentine. A further aim was to understand the effect of macrophage-dependent inflammation on SCAP migration in the presence of LPS dentine. METHODOLOGY: A total of 88 dentine slabs were used. TOF-SIMS analysis was performed amongst the LPS-treated and untreated dentine groups (n = 2/group). The study was conducted using four dentine groups: no treatment (control); LPS treatment only; LPS treatment followed by CSnp conditioning; and LPS treatment followed by Dex-CSnp conditioning groups. SCAP adherence, viability, differentiation and biomineralization potential on dentine from different groups were studied using fluorescent and scanning electron microscopy. Inflammation by macrophages in response to LPS dentine was quantified, and effect on SCAP migration was analysed. Statistical analysis was performed using Student's t-test with a significance level of P < 0.05. RESULT: TOF-SIMS analysis confirmed LPS contamination. LPS dentine affected SCAP viability but not adherence to dentine (P < 0.001). Conditioning of LPS dentine with either nanoparticles improved SCAP viability (P < 0.01) and rescued other LPS related adverse effects on SCAPs, such as F-actin disruption, decrease in differentiation/biomineralization potential. IL-6 produced by macrophages in response to LPS-treated dentine impeded SCAP migration (P < 0.001), diminished on CSnp and Dex-CSnp conditioning groups (P < 0.01). CONCLUSION: This study developed an LPS-dentine model and highlighted the ability of CSnp and Dex-CSnp to promote stem cell viability, migration, differentiation potential and reduce inflammation, providing an environment conducive for tissue regeneration/repair.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".