Innate Defense Regulator Peptide 1018 in Wound Healing and Wound Infection
Bibliographic record
Abstract
Innate defense regulators (IDRs) are synthetic immunomodulatory versions of natural host defense peptides (HDP). IDRs mediate protection against bacterial challenge in the absence of direct antimicrobial activity, representing a novel approach to anti-infective and anti-inflammatory therapy. Previously, we reported that IDR-1018 selectively induced chemokine responses and suppressed pro-inflammatory responses. As there has been an increasing appreciation for the ability of HDPs to modulate complex immune processes, including wound healing, we characterized the wound healing activities of IDR-1018 in vitro. Further, we investigated the efficacy of IDR-1018 in diabetic and non-diabetic wound healing models. In all experiments, IDR-1018 was compared to the human HDP LL-37 and HDP-derived wound healing peptide HB-107. IDR-1018 was significantly less cytotoxic in vitro as compared to either LL-37 or HB-107. Furthermore, administration of IDR-1018 resulted in a dose-dependent increase in fibroblast cellular respiration. In vivo, IDR-1018 demonstrated significantly accelerated wound healing in S. aureus infected porcine and non-diabetic but not in diabetic murine wounds. However, no significant differences in bacterial colonization were observed. Our investigation demonstrates that in addition to previously reported immunomodulatory activities IDR-1018 promotes wound healing independent of direct antibacterial activity. Interestingly, these effects were not observed in diabetic wounds. It is anticipated that the wound healing activities of IDR-1018 can be attributed to modulation of host immune pathways that are suppressed in diabetic wounds and provide further evidence of the multiple immunomodulatory activities of IDR-1018.
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 | 0.001 |
| 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".