Detection and Measurements of Soluble Intercellular Adhesion Molecules at Implants and Teeth: A Comparative Study
Bibliographic record
Abstract
BACKGROUND: Adhesion molecules on endothelial cells and in the periodontal tissues control the immigration and retention of cells. The level of soluble intercellular adhesion molecules (sICAMs) has been used as a marker of the severity and/or extent of the inflammatory process in a wide range of pathologies, including periodontitis. PURPOSE: This study was designed to detect and compare sICAM-1 at teeth and implants in relation to clinical periodontal and periimplant parameters. METHOD: Regular recall patients with (1) implants and teeth, (2) implants, and (3) teeth were examined. Samples of sulcus fluid were collected from teeth and implants. The concentration of sICAM-1 was measured by enzyme-linked immunoabsorbent assay. Periodontal parameters were recorded after sampling. RESULTS: The range of measured sICAM-1 was large (from below 100 to 1,200 ng/mL). The concentration of sICAM-1 was not different for teeth and implants but was significantly elevated in sites with positive bleeding on probing (BoP), namely, 571 ng/mL at teeth and 529 ng/mL at implants compared with 150 ng/mL and 169 ng/mL, respectively, with negative BoP. The regression analysis showed that the concentration of sICAM-1 was highly associated with positive BoP but was not dependent on the fluid volume. CONCLUSIONS: A similarity of the sulcus fluid at teeth and implants was observed with regard to the detection of sICAM-1.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".