Lnc‐NTF3‐5 promotes osteogenic differentiation of maxillary sinus membrane stem cells via sponging miR‐93‐3p
Bibliographic record
Abstract
BACKGROUND: The function and the mechanism of long non-coding RNAs (lncRNAs) on the osteogenic differentiation of maxillary sinus membrane stem cells (MSMSCs) remain largely unknown. MATERIALS AND METHODS: The expression of lnc-NTF3-5 and Runt-related transcription factor 2 (RUNX2), Osterix (OSX), and Alkaline Phosphatase (ALP) was examined by quantitative real-time PCR (qRT-PCR) in MSMSCs during the process osteogenic differentiation. Then the function of lnc-NTF3-5 was evaluated by loss- and gain-of-function techniques, as well as qRT-PCR, western blot, and Alizarin Red staining. In addition, the microRNAs (miRNAs) sponge potential of lnc-NTF3-5 was assessed through RNA immunoprecipitation, dual luciferase reporter assay, and in vivo ectopic bone formation. RESULTS: Lnc-NTF3-5, RUNX2, OSX, and ALP increased alone with the differentiation. Inhibition of lnc-NTF3-5 decreased the expression of RUNX2, OSX, and ALP both at mRNA and protein levels. Alizarin red staining showed similar trend. In contrast, overexpression of lnc-NTF3-5 presented totally opposite effects. Besides, overexpression of lnc-NTF3-5 could decrease the expression of microRNA-93-3p (miR-93-3p). Enhance miR-93-3p could also inhibit the expression level of lnc-NTF3-5. RNA immunoprecipitation demonstrated that lnc-NTF3-5 is directly bound to miR-93-3p and dual luciferase reporter assay proved that miR-93-3p targets 3' UTR of RUNX2 to regulate its expression. Ultimately, in vivo bone formation study showed that lnc-NTF3-5 and miR-93-3p inhibitor co-transfection group displayed the strongest bone formation. CONCLUSIONS: The novel pathway lnc-NTF3-5/miR-93-3p/RUNX2 could regulate osteogenic differentiation of MSMSCs and might serve as a therapeutic target for bone regeneration in the posterior maxilla.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".