Galectin-3 Plays an Important Role in Preterm Birth Caused by Dental Infection of Porphyromonas gingivalis
Bibliographic record
Abstract
Dental infection is risk for preterm birth (PTB) through unclear mechanisms. We established a dental infection-induced PTB mouse model, in which Porphyromonas gingivalis (P.g.) induced PTB by 2 days. We analysed pathogenic factors contributing to PTB and their effects on trophoblasts in vitro. TNF-α, IL-8, and COX-2 were upregulated in P.g.-infected placenta. Galectin-3 (Gal-3), an immune regulator, was significantly upregulated in placenta, amniotic fluid, and serum. In vitro, P.g.-lipopolysaccharide (P.g.-LPS) increased TNF-α and Gal-3 in trophoblasts via NF-κB/MAPK signalling. Gal-3 inhibition significantly downregulated P.g.-LPS-induced TNF-α production. TNF-α upregulated Gal-3. Gal-3 also increased cytokines and Gal-3 through NF-κB/MAPK signalling. Moreover, Gal-3 suppressed CD-66a expression at the maternal-foetal interface. Co-stimulation with Gal-3 and P.g.-LPS upregulated cytokine levels, while Gal-3 plus Aggregatibacter actinomycetemcomitans (A.a.)- or Escherichia coli (E. coli)-LPS treatment downregulated them, indicating the critical role of Gal-3 especially in P.g. dental infection-induced PTB. P.g.-dental infection induced PTB, which was associated with Gal-3-dependent cytokine production. New therapies and/or diagnostic systems targeting Gal-3 may reduce PTB.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".