Matrix metalloproteinases gene polymorphism haplotype is a risk factor to implant loss: A case‐control study
Bibliographic record
Abstract
BACKGROUND: Dental implants consist in the treatment of choice to replace tooth loss. The knowledge that implant loss tends to cluster in subsets of individuals may indicate that host response is influenced by genetic factors. Matrix metalloproteinases (MMPs) are enzymes that contribute to degradation and removal of collagen from extracellular matrix. PURPOSE: This case-control study aimed to investigate the haplotypic combination of MMP polymorphism (rs1144393, rs1799750, rs3025058, and rs11225395) and implant loss. MATERIALS AND METHODS: Two hundred nonsmokers subjects were matched by gender, age, implant number and position and divided in control group, 100 patients with one or more healthy implants, and test group, and 100 patients with one or more implant failures. Genomic DNA was extracted from saliva and genotypes were obtained by PCR-RFLP. RESULTS: A significant association of rs1799750 (MMP-1) and rs11225395 (MMP-8) polymorphism on early implant loss was demonstrated (P ≤ 0.001). Global haplotype analysis indicated a significant difference between both groups (P < 0.0001). Haplotype T-A-GG-5A-C had a statistically significant risk effect, while haplotype C-A-G-6A-C andT-G-2G-5A-C had a protective effect in implant loss. CONCLUSIONS: The results of this study showed that MMPs haplotype are a risk factor to early implant loss.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".