Modulation of the Local Neutrophil Response by a Novel Hyaluronic Acid-Binding Peptide Reduces Bacterial Burden during Staphylococcal Wound Infection
Bibliographic record
Abstract
Novel approaches targeting the host's immune response to treat Staphylococcus aureus infections have significant potential to improve clinical outcomes, in particular during infection with antibiotic-resistant strains. The hyaluronic acid-binding peptide (HABP) PEP35 was assessed for its ability to treat S. aureus infections using a clinically relevant murine model of surgical wound infection. PEP35 demonstrated no direct antimicrobial activity against a range of antibiotic-susceptible and antibiotic-resistant clinical isolates of Staphylococcus aureus. However, when this peptide was administered at the onset of infection and up to 4 h postchallenge with a methicillin-susceptible (MSSA) or a methicillin-resistant (MRSA) strain of S. aureus, it significantly reduced the bacterial burden at the wound infection site. PEP35 reduced the tissue bacterial burden by exclusively modulating the local neutrophil response. PEP35 administration resulted in a significant early increase in local CXCL1 and CXCL2 production, which resulted in a more rapid influx of neutrophils to the infection site. Importantly, neutrophil influx was not sustained after treatment with PEP35, and administration of PEP35 alone did not induce a local inflammatory response. The immunomodulatory effects of PEP35 on CXC chemokine production were TLR2 and NF-κB dependent. We propose a novel role for a HABP as an innate immunomodulator in the treatment of MSSA and MRSA surgical wound infection through enhancement of the local CXC chemokine-driven neutrophil response.
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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 | 0.000 |
| 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 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".