Effect of Engineered Nanoparticles-treated Root Canal Biofilm on Macrophage Plasticity and Interactions with Human Periodontal Ligament Fibroblast
Bibliographic record
Abstract
The intracanal microbes, particularly in proximity to the apical foramen, often result in chronic inflammation of the periapical tissues that might lead to compromised post-treatment healing. Macrophages (MQ), which are major constituents of periapical lesions, respond to microbial factors and exhibit a key role in the pathogenesis as well as the healing of apical periodontitis owing to their functional plasticity. Depending on the microenvironmental cues, they polarize into pro-inflammatory (M1) or anti-inflammatory/pro-wound healing (M2) phenotype. MQ crosstalk with other immune cells and periradicular multipotent cells, such as periodontal ligament fibroblasts (PdLF), via soluble inflammatory signals, to regulate the disease/healing outcomes. Engineered chitosan-based nanoparticles (CSnp) that possess effective antibacterial activities could favorably interact with host cells by virtue of their inherent bioactivity and customized physicochemical properties. Herein, we aimed to (1) understand the influence of residual root canal biofilm on MQ-PdLF interaction, and (2) evaluate the effect of engineered CSnp on modulating residual biofilm-mediated inflammatory response of MQ and their interaction with PdLF. An ex-vivo model of Enterococcus faecalis biofilm in root canals was characterized to evaluate the inflammatory response to untreated versus treated biofilms. Engineered CSnp dispersed in carboxymethyl chitosan (CMCS) resulted in significant reduction of 6-week-old biofilm compared to conventional disinfection (P
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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".