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Record W2807263881 · doi:10.1038/s41598-018-21072-y

Galectin-3 Plays an Important Role in Preterm Birth Caused by Dental Infection of Porphyromonas gingivalis

2018· article· en· W2807263881 on OpenAlexfundno aff
Mutsumi Miyauchi, Min Ao, Hisako Furusho, Chanbora Chea, Atsuhiro Nagasaki, Shinnichi Sakamoto, Toshinori Ando, Toshihiro Inubushi, Katsuyuki Kozai, Takashi Takata

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicGalectins and Cancer Biology
Canadian institutionsnot available
FundersQueen's University
KeywordsPorphyromonas gingivalisGalectin-3MedicineGalectinGalectin-1MicrobiologyPeriodontitisImmunologyBiologyDentistry

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.241
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations34
Published2018
Admission routes1
Has abstractyes

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