Sphingosine kinase activity and sphingosine‐1‐phosphate in the inflamed human periodontium
Bibliographic record
Abstract
OBJECTIVES: This study evaluated changes in the levels of Sphingosine-1-Phosphate (S1P) and Sphingosine Kinase (SPHK) activity in response to non-surgical periodontal treatment in humans. METHODS: Diseased (n = 65) and healthy sites (n = 72) were screened in 18 patients with localized periodontitis stage II or III. Periodontal clinical parameters were recorded, and the gingival crevicular fluid (GCF) collected at baseline, 30 and 90 days of non-surgical treatment. Internal control sites without attachment loss/bleeding were sampled at baseline and after 90 days of treatment. SPHK activity and S1P levels and SPHK 1/2 isoforms were determined in the GCF at different time points using ELISA. RESULTS: Non-surgical treatment caused significant improvement in all periodontal clinical parameters (p < 0.01). Activity of SPHK and S1P levels was decreased (p < 0.05) 30 days after treatment and continued up to 90 days (p < 0.01); control sites remained unchanged throughout the study and resembled treated sites at 3 months (p > 0.05). SPHK1 levels presented decrease after periodontal treatment (p < 0.001). SPHK2 levels were lower than SPHK1 (p < 0.001) and remained unchanged. CONCLUSIONS: S1P levels and SPHK activity decreased within 3 months of non-surgical periodontal treatment, which were correlated with improvements in periodontal parameters. Only SPHK1 levels varied significantly in the states of health and disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".