Immunomodulatory activity of synthetic innate defence regulators (IDRs) (134.45)
Bibliographic record
Abstract
Abstract Cationic host defence (antimicrobial) peptides, e.g. cathelicidin LL-37, have a variety of immunomodulatory activities that favour the safe resolution of infections. We have studied novel synthetic cationic innate defence regulator peptides that are not directly antimicrobial but are anti-infective in vivo, due to modulation of innate immunity. A range of peptides derived from the small bovine peptide bactenecin were screened for immunomodulatory activities in vitro; e.g. promotion of chemokine production and suppression of pro-inflammatory cytokines. Since innate immunity is complex, involving >1,500 gene products, a systems biology approach was utilized to characterize peptide modulation of innate immunity, including analysis of receptors, signalling pathways, transcription factors and downstream genes. To permit visualization and bioinformatic analysis of complex events, an innate immunity database (www.innatedb.ca), a network visualization tool (Cerebral) and downstream analysis tools (e.g. pathway overrepresentation analysis) were developed and provided insight into how this selective modulation occurs. In vivo data indicate that these activities provide protection in animal model infections of Gram positive and Gram negative bacterial infections as well as severe malaria. Thus IDRs have great potential for use as novel anti-infective agents. Supported by Genome BC, FNIH and CIHR.
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) | 0.000 | 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) | 0.002 | 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".