TNF-α, IL-1B and IL-6 affect the differentiation ability of dental pulp stem cells
Bibliographic record
Abstract
BACKGROUND: This in vitro study examined the effect of the inflammatory cytokines (tumour necrosis factor-α (TNF-α), interleukin (IL)-1β, and IL-6) on osteogenic, chondrogenic, and adipogenic differentiation of dental pulp stem cells (DPSCs) which have significant relevance in future regenerative therapies. METHODS: DPSCs were isolated from the impacted third molar dental pulp and determined with flow cytometry analysis. DPSCs were divided into into 5 main groups with 3 subdivisions for each group making a total of 15 groups. Experimental groups were stimulated with TNF-α, IL-1β, IL-6, and a combination of all three to undergo osteogenic, chondrogenic, and adipogenic differentiation protocols. Next, the differentiation of each group was examined with different staining procedures under a light microscope. Histological analysis of osteogenic, chondrogenic, and adipogenic differentiated pellets was assessed using a modified Bern score. Statistical significance determined using one-way analysis of variance, and correlations were assessed using Pearson's test (two-tailed). RESULTS: Stimulation with inflammatory cytokines significantly inhibited the osteogenic, chondrogenic and adipogenic differentiation of DPSCs in terms of matrix and cell formation resulting in weak staining than the unstimulated groups with inflammatory cytokines. On contrary, the unstimulated groups of MSCs have shown to be highly proliferative ability in terms of osteogenic, chondrogenic, and adipogenic differentiation. CONCLUSIONS: DPSCs have high osteogenic, chondrogenic, and adipogenic differentiation capabilities. Pretreatment with inflammatory cytokines decreases the differentiation ability in vitro, thus inhibiting tissue formation.
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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 | 0.001 | 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".