[Expression of SFRP1 and MIF in elderly patients with severe periodontitis and its correlation with cognitive function].
Bibliographic record
Abstract
PURPOSE: To investigate the expression of secreted frizzle-related protein 1 (SFRP1) and macrophage migration inhibitory factor (MIF) in elderly patients with severe periodontitis and its correlation with cognitive function. METHODS: Thirty-two elderly patients with periodontitis in Qingdao Stomatological Hospital from February 2018 to February 2019 were enrolled, and divided into two groups according to the severity: mild group and severe group. All selected subjects received periodontal examination and Montreal cognitive assessment (MoCA).The expression of SFRP1 and MIF in serum was also determined. Then the correlations among SFRP1 and MIF periodontal index and cognitive function was analyzed. The data were processed by SPSS 20.0 software package. RESULTS: The probing depth (PD), clinical attachment level (CAL), sulcus bleeding index (SBI) and gingival crevicular fluid (GCF) showed significant difference between the two groups (P<0.05). The serum levels of SFRP1 and MIF in the severe group were significantly higher than those in the mild group (P<0.05). Serum SFRP1 level was positively correlated with MIF (P<0.05). Serum SFRP1 and MIF levels were positively correlated with periodontal index (P<0.05). The MoCA score of the severe group was significantly lower than that of the mild group (P<0.05). Serum SFRP1 and MIF levels were negatively correlated with MoCA score (P<0.05). CONCLUSIONS: SFRP1 and MIF are highly expressed in serum and gingival tissues of elderly patients with severe periodontitis, and are closely related to the degree of periodontal damage. Meanwhile, patients with periodontitis may have some degree of cognitive dysfunction, and SFRP1 and MIF may affect the periodontal tissue structure through Wnt/β-catenin signal pathway and participate in the occurrence and development of cognitive dysfunction.
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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".