A Systematic Review of Clinical and Experimental Periodontitis Studies Demonstrating the Expression of PPAR-Gamma: A Meta-Analysis and Bioinformatics Approach
Bibliographic record
Abstract
Aim: Peroxisome Proliferator-Activated Receptor Gamma (PPAR-γ) plays important anti-inflammatory roles, including in periodontitis. This systematic review with a meta-analysis compiles evidence on the transcriptional and translational levels of PPAR-γ in clinical and experimental periodontitis studies, alongside functional enrichment and PPAR-γ interaction network analyses. Method: Electronic searches were conducted in six databases for publications up to February 2024. For the meta-analysis of experimental studies of periodontitis, PPAR-γ levels in the periodontal tissues were assessed through gene expression (RT-qPCR) or protein expression (Western blotting). In the clinical periodontitis studies, PPAR-γ levels in the gingival tissues were evaluated through protein expression (immunohistochemistry). A risk of bias (RoB) assessment was performed using the Systematic Review Centre for Laboratory Animal Experimentation (SYRCLE) and Newcastle Ottawa Scale (NOS) tools for experimental and clinical studies, respectively. The enrichment analysis was performed using the g:Profiler tool, and gene interaction networks were analyzed using GeneMANIA. Results: The meta-analysis demonstrated significantly lower PPAR-γ protein levels in the periodontal tissues from animals with periodontitis. PPARG mRNA and PPAR-γ quantification through immunohistochemistry remained inconclusive. The bioinformatics analyses indicated direct or indirect PPAR-γ-associated molecules involved in the immune response to periodontitis. The PPAR-γ protein expression was higher in periodontal tissues from healthy animals compared to that from those with periodontitis. Conclusions: Given the inconclusive findings of RT-qPCR and immunohistochemistry, further PPARG mRNA and PPAR-γ protein evaluations are needed to clarify their levels in the periodontal tissues.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".