Innate Defence Regulator Peptides as a Novel Anti-infective Strategy (94.2)
Bibliographic record
Abstract
Abstract The therapy of bacterial infections is under great threat as multiple antibiotic resistance increases and there is a paucity of new antibiotic discovery and development. Synthetic Innate Defence Regulator (IDR) peptides, which mimic natural host defence (antimicrobial) peptides, have been designed as a novel anti-infective strategy, working by selectively boosting innate immune protective mechanisms while dampening potentially harmful inflammation. These peptides can resolve serious infections in animal models. The optimal method of use of these peptides was evaluated here using an animal model infection by one of the highly resistant bacteria (Superbugs) afflicting our society, Staphylococcus aureus. A luminescent version of this bacterium was utilized to follow the kinetics of infection non-invasively using IVIS imaging. Protection was achieved by both prophylactic and therapeutic administration. Investigation of the mechanism of protection by examining cells infiltrating the infection site and cytokines/chemokines revealed remarkable parallels between in vitro action of the peptides in primary human cells and these animal models. System biology approaches such as Microarray and InnateDB are being utilized to decipher the complex. This has revealed several receptors, signaling pathways, transcription factors and effector proteins involved in the modulation by peptides of innate immunity.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".