Antimicrobial peptides and nitric oxide production by neutrophils from periodontitis subjects
Bibliographic record
Abstract
Neutrophils play an important role in periodontitis by producing nitric oxide (NO) and antimicrobial peptides, molecules with microbicidal activity via oxygen-dependent and -independent mechanisms, respectively. It is unknown whether variation in the production of antimicrobial peptides such as LL-37, human neutrophil peptides (HNP) 1-3, and NO by neutrophils influences the pathogenesis of periodontal diseases. We compared the production of these peptides and NO by lipopolysaccharide (LPS)-stimulated neutrophils isolated from healthy subjects and from patients with periodontitis. Peripheral blood neutrophils were cultured with or without Aggregatibacter actinomycetemcomitans-LPS (Aa-LPS), Porphyromonas gingivalis-LPS (Pg-LPS) and Escherichia coli-LPS (Ec-LPS). qRT-PCR was used to determine quantities of HNP 1-3 and LL-37 mRNA in neutrophils. Amounts of HNP 1-3 and LL-37 proteins in the cell culture supernatants were also determined by ELISA. In addition, NO levels in neutrophil culture supernatants were quantitated by the Griess reaction. Neutrophils from periodontitis patients cultured with Aa-LPS, Pg-LPS and Ec-LPS expressed higher HNP 1-3 mRNA than neutrophils from healthy subjects. LL-37 mRNA expression was higher in neutrophils from patients stimulated with Aa-LPS. Neutrophils from periodontitis patients produced significantly higher LL-37 protein levels than neutrophils from healthy subjects when stimulated with Pg-LPS and Ec-LPS, but no difference was observed in HNP 1-3 production. Neutrophils from periodontitis patients cultured or not with Pg-LPS and Ec-LPS produced significantly lower NO levels than neutrophils from healthy subjects. The significant differences in the production of LL-37 and NO between neutrophils from healthy and periodontitis subjects indicate that production of these molecules might influence individual susceptibility to important periodontal pathogens.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".