Cathelicidin LL-37 in Severe <i>Streptococcus pyogenes</i> Soft Tissue Infections in Humans
Bibliographic record
Abstract
Severe soft tissue infections, such as necrotizing fasciitis and severe cellulitis, caused by group A streptococci (GAS) are rapidly progressing life-threatening infections characterized by massive bacterial loads in the tissue even late after the onset of infection. Antimicrobial peptides are important components of the innate host defense, and cathelicidins have been shown to protect against murine necrotic skin infections caused by GAS. However, it has been demonstrated that the streptococcal cysteine protease SpeB proteolytically inactivates the human cathelicidin LL-37 in vitro. Here we have investigated the expression of LL-37 and its interaction with GAS and SpeB during acute severe soft tissue infections by analyses of patient tissue biopsy specimens. The results showed large amounts of LL-37, both the proform (hCAP18) and the mature peptide, in the tissue. Confocal microscopy identified neutrophils as the main source of the peptide. A distinct colocalization between the bacteria and LL-37 could be noted, and bacterial loads showed positive correlation to the LL-37 levels. Areas with high LL-37 levels coincided with areas with large amounts of SpeB. Confocal microscopy confirmed strong colocalization of GAS, SpeB, and LL-37 at the bacterial surface. Taken together, the findings of this study provide in vivo support of the hypothesis that SpeB-mediated inactivation of LL-37 at the streptococcal surface represents a bacterial resistance mechanism at the infected tissue site in patients with severe GAS tissue infections.
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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 | 0.000 | 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 | 0.001 | 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) | 0.000 | 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".