Fine tuning host responses in the face of infection: emerging roles and clinical applications of host defence peptides.
Bibliographic record
Abstract
Abstract Host defence peptides (HDPs) are powerful modulators of human innate immunity, and can modify the outcome of the endogenous host response to infection. The progressive development of pathogen resistance to conventional antimicrobial agents has lead to a new appreciation of HDPs for their ability to fight infection, enhance vaccine responses, limit infl ammation and promote wound healing, within the context of human disease. HDPs are a family of cationic, short, amphipathic peptides that include the classical mammalian antimicrobial peptides, cathelicidins and defensins, as well as non-antimicrobial peptides with similar immunomodulatory properties. This chapter reviews our current basic understanding of the anti-infective and immunomodulatory properties of both endogenous HDPs and synthetic derivatives (termed innate defence regulators) with regard to their ability to selectively fine tune the responses of host cells and physiology. The clinical application of these molecules is also discussed, with a focus on past and ongoing clinical trials of HDPs and innate defence regulators as novel therapeutics for infectious and infl ammatory diseases.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".