Defensin-Mediated Oral Immunity: A Systematic Review and Meta-Analysis of DEFB1 Expression in Preventive Dentistry Strategies
Bibliographic record
Abstract
Background: Human beta-defensin 1 (DEFB1), together with other defensins, serves as a fundamental immune protector for oral tissues since it suppresses harmful microbial growth and fosters healthy oral conditions. The research included two main purposes assessing the relationship between DEFB1 expression levels and genetic polymorphism with oral disease risks, and evaluating DEFB1's potential for oral disease prevention biomarker use. Methods: This research performed a systematic review and meta-analysis on 11 observational studies using PubMed, Google Scholar, and Web of Science databases. The data extraction process concentrated on recording DEFB1 expression data together with clinical results and population characteristics. The risk-of-bias tools were done using the Newcastle Ottawa tool, while a random-effects model was applied for meta-analysis due to heterogeneity in the studies. Results: Eleven studies that contained DEFB1 gene polymorphisms and expression were considered. Rs11362 and rs1799946 polymorphisms and low levels of DEFB1 gene/protein were linked to increased risks of oral diseases, such as caries and periodontitis. The effect sizes were noted as 2.26 (95% CI: 0.90 -5.67) and -0.59 (95% CI: -1.28 to 0.09). Subgroup and sensitivity analyses showed the same direction of effect. Nonetheless, the significant heterogeneity was recorded (I2 > 90 percent), and the overall certainty of evidence was low because of the study design, inconsistency, and imprecision. Discussion: Research findings show that DEFB1 demonstrates potential as an indicator of susceptibility to diseases, although specific studies exhibit some variation in their results. Additional research needs standardized approaches to advance knowledge.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".