Restoration of miR‐1305 relieves the inhibitory effect of nicotine on periodontal ligament‐derived stem cell proliferation, migration, and osteogenic differentiation
Bibliographic record
Abstract
BACKGROUND: Nicotine hinders the regenerative potentials of human periodontal ligament-derived stem cells (PDLSCs) and delays the healing process of periodontal diseases, but the underlying mechanism remains unclear. miR-1305 upregulation and its potential target RUNX2 downregulation exist in the PDLSCs exposed to nicotine. In this study, we aimed to investigate whether nicotine inhibits PDLSC proliferation, migration, and osteogenic differentiation by increasing miR-1305 level and decreasing RUNX2 level. METHODS: Quantitative real-time PCR (qRT-PCR) and Western blot assays were performed to detect the expression levels of miR-1305 and RUNX2 in the PDLSCs exposed to nicotine, respectively. PDLSCs with miR-1305 overexpression, low expression, or RUNX2 overexpression were constructed by lipofectin transfection. MTT, migration, and Western blot assays were applied to assess the effect of miR-1305 on PDLSC proliferation, migration, and osteogenic differentiation, respectively. Target prediction and luciferase reporter assays were performed to investigate the targets of miR-1305. RESULTS: Nicotine promoted miR-1305 expression and inhibited RUNX2 expression in PDLSCs. Cell proliferation, migration, and differentiation detection showed that nicotine suppressed proliferation, migration, and osteogenic differentiation of PDLSCs, and restoration of miR-1305 relieved the inhibitory effect of nicotine on PDLSCs. Moreover, we identified and validated that RUNX2 was a direct target of miR-1305, and upregulation of RUNX2 had similar effects with the downregulation of miR-1305 on relieving the inhibitory effect of nicotine on PDLSCs. CONCLUSION: Nicotine suppresses proliferation, migration, and osteogenic differentiation of PDLSCs, and restoration of miR-1305 relieves the inhibitory effect of nicotine on PDLSCs depending on its target RUNX2.
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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 | 0.001 | 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".