Detection of periodontal disease activity based on histatin degradation in individuals with cerebral palsy
Bibliographic record
Abstract
Objectives: This proof-of-concept study aimed at evaluating the proteolytic profile of histatin 1 and 5 in saliva of adolescents with spastic cerebral palsy (CP) with gingivitis. Methods: This cross-sectional study included 24 individuals allocated into three groups: G1 (CP with gingivitis; n = 8), G2 (without CP and without gingivitis; n = 8), and G3 (without CP and with gingivitis; n = 8). The gingival index (GI) and simplified oral hygiene index (OHI-S) were evaluated. Whole saliva was collected and used to assess the rate and mode of histatin 1 and 5 at different times. The degradation products were visualized after cationic PAGE and the protein band densities (BDs) were compared with a protein standard. Fragmentation products were collected from the gel, pooled by group and characterized by mass spectrometry. BDs and gingival health parameters were analyzed by One-Way ANOVA or Kruskal Wallis tests, whereas poisson multilevel regression was used to the factors that influenced histatin degradation (α = 5%). Results: Groups G1 and G3 differed significantly on OHI-S, visible biofilm, oral calculus and GI (p < 0.001). Poisson Regression showed that: 1) CP and gingivitis influenced the degradation of histatin 1 and 5 (p < 0.05); 2) The degradation of histatin 5 was influenced by age and male sex (p < 0.05); and 3) GI influenced significantly the degradation of histatin 1 (p < 0.001). Unique histatin degradation peptides were identified in individuals with gingivitis. Conslusions: These data demonstrated that both the kinetics and pattern of histatins degradation differ according to the gingival health or disease conditions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".