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Record W2883784122 · doi:10.3390/vaccines6030044

Effects of LL-37 on Gingival Fibroblasts: A Role in Periodontal Tissue Remodeling?

2018· article· en· W2883784122 on OpenAlexfundno aff
Maelíosa McCrudden, Katherine O’Donnell, Chris R. Irwin, Fionnuala T. Lundy

Bibliographic record

VenueVaccines · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsnot available
FundersQueen's UniversityQueen's University Belfast
KeywordsHepatocyte growth factorFibroblastKeratinocyte growth factorMatrix metalloproteinaseWound healingInflammationSecretionGrowth factorBasic fibroblast growth factorChemistryCell biologyEndocrinologyInternal medicineBiologyMedicineImmunologyIn vitroBiochemistry

Abstract

fetched live from OpenAlex

Mounting evidence suggests that the host defence peptide, LL-37, plays a role in both inflammation and in wound healing; however, the role of this peptide in the remodeling and maintenance of oral tissues is not yet fully understood. Fibroblasts are the most abundant cell type within the periodontal tissues, and gingival fibroblasts play an important role in maintaining and repairing the gingival tissues which are constantly exposed to external insults. In this study we examined the direct effects of LL-37 treatment on gingival fibroblasts and found that LL-37 significantly increased secretion of both interleukin 8 (IL-8) and IL-6 from these cells. LL-37 tended to decrease matrix metalloproteinase (MMP) activity in gingival fibroblasts, but this decrease did not reach statistical significance. LL-37 significantly increased tissue inhibitor of metalloproteinase-1 (TIMP-1) production by gingival fibroblasts, but had no significant effect on TIMP-2 levels. LL-37 was also shown to significantly increase production of basic fibroblast growth factor (bFGF), hepatocyte growth factor (HGF), and keratinocyte growth factor (KGF) in gingival fibroblasts. Taken together, these results suggest an important role for the host defence peptide, LL-37, in modulating the fibroblast response to remodeling in periodontal tissues.

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)0.000
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 score0.009
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

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

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 head0.004
GPT teacher head0.218
Teacher spread0.214 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

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