Immunoassay analysis of proteins in gingival crevicular fluid samples from resorbing teeth
Bibliographic record
Abstract
OBJECTIVE: To carry out an immunoassay analysis of biomarkers expressed in gingival crevicular fluid (GCF) with the main goal of finding a useful diagnostic pattern to distinguish between resorbing deciduous teeth and nonresorbing controls. MATERIALS AND METHODS: A split-mouth design was used in this study with a total of 22 GCF samples collected from 11 patients in the mixed dentition. For each child, one deciduous molar with radiographic evidence of root resorption was used as the test tooth whereas the contralateral first permanent molar with formed roots was used as the control tooth. Samples were processed with immunoassays using a panel of selected biomarkers including interleukin-1 beta (IL-1b), interleukin-1 receptor antagonist (IL-1RA), nuclear factor kappa B ligand (RANKL), osteoprotegerin (OPG), matrix metalloproteinase-9 (MMP-9), and dentin sialoprotein (DSP). RESULTS: There were no statistically significant differences in levels of IL-1b, OPG, and MMP-9 between test and control sites (P > .05). IL-1RA was the only biomarker to show a significant down-regulation (P = .04) in GCF samples collected from resorbing teeth. RANKL data showed a heavily skewed distribution and was deemed unreliable. Only one deciduous GCF sample had detectable levels of DSP; therefore, no further statistical calculation was applicable because of the limited amount of data for this biomarker. CONCLUSIONS: This study indicated that IL1-RA is down-regulated in GCF from resorbing primary molars, thus suggesting this cytokine as a potential analyte to be included in a panel that can discriminate between resorbing and nonresorbing teeth.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".