An anti-infective synthetic peptide with dual antimicrobial and immunomodulatory activities
Bibliographic record
Abstract
Antibiotic-resistant infections are predicted to kill 10 million people per year by 2050, costing the global economy $100 trillion. Therefore, there is an urgent need to develop alternative technologies. We have engineered a synthetic peptide called clavanin-MO, derived from a marine tunicate antimicrobial peptide, which exhibits potent antimicrobial and immunomodulatory properties both in vitro and in vivo. The peptide effectively killed a panel of representative bacterial strains, including multidrug-resistant hospital isolates. Antimicrobial activity of the peptide was demonstrated in animal models, reducing bacterial counts by six orders of magnitude, and contributing to infection clearance. In addition, clavanin-MO was capable of modulating innate immunity by stimulating leukocyte recruitment to the site of infection, and production of immune mediators GM-CSF, IFN-γ and MCP-1, while suppressing an excessive and potentially harmful inflammatory response by increasing synthesis of anti-inflammatory cytokines such as IL-10 and repressing the levels of pro-inflammatory cytokines IL-12 and TNF-α. Finally, treatment with the peptide protected mice against otherwise lethal infections caused by both Gram-negative and -positive drug-resistant strains. The peptide presented here directly kills bacteria and further helps resolve infections through its immune modulatory properties. Peptide anti-infective therapeutics with combined antimicrobial and immunomodulatory properties represent a new approach to treat antibiotic-resistant infections.
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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) | 0.001 | 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".