The Potential of Human Peptide LL-37 as an Antimicrobial and Anti-Biofilm Agent
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rise in antimicrobial resistant bacteria threatens the current methods utilized to treat bacterial infections. The development of novel therapeutic agents is crucial in avoiding a post-antibiotic era and the associated deaths from antibiotic resistant pathogens. The human antimicrobial peptide LL-37 has been considered as a potential alternative to conventional antibiotics as it displays broad spectrum antibacterial and anti-biofilm activities as well as immunomodulatory functions. While LL-37 has shown promising results, it has yet to receive regulatory approval as a peptide antibiotic. Despite the strong antimicrobial properties, LL-37 has several limitations including high cost, lower activity in physiological environments, susceptibility to proteolytic degradation, and high toxicity to human cells. This review will discuss the challenges associated with making LL-37 into a viable antibiotic treatment option, with a focus on antimicrobial resistance and cross-resistance as well as adaptive responses to sub-inhibitory concentrations of the peptide. The possible methods to overcome these challenges, including immobilization techniques, LL-37 delivery systems, the development of LL-37 derivatives, and synergistic combinations will also be considered. Herein, we describe how combination therapy and structural modifications to the sequence, helicity, hydrophobicity, charge, and configuration of LL-37 could optimize the antimicrobial and anti-biofilm activities of LL-37 for future clinical use.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it