Cathelicidins and functional analogues as antisepsis molecules
Bibliographic record
Abstract
The emergence of antibiotic-resistant bacteria together with the limited success of sepsis therapeutics has lead to an urgent need for the development of alternative strategies for the treatment of systemic inflammatory response syndrome and related disorders. Immunomodulatory compounds that do not target the pathogen directly (therefore limiting the development of pathogen resistance), and target multiple inflammatory mediators, are attractive candidates as novel therapeutics. Cationic host defence peptides such as cathelicidins have been demonstrated to be selectively immunomodulatory in that they can confer anti-infective immunity and modulate the inflammatory cascade through multiple points of intervention. The human cathelicidin LL-37, for example, has modest direct antimicrobial activity under physiological conditions, but has been demonstrated to have potent antiendotoxin activity in animal models, as well as the ability to resolve certain bacterial infections. A novel synthetic immunomodulatory peptide, IDR-1, built on this same theme has no direct antimicrobial activity, but is effective in restricting many types of infection, while limiting pro-inflammatory responses. The ability of these peptides to selectively suppress harmful pro-inflammatory responses, while maintaining beneficial infection-fighting components of host innate defences makes them a good model for antisepsis therapies that merit further investigation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".