Synthetic Cationic Peptide IDR-1018 Modulates Human Macrophage Differentiation
Bibliographic record
Abstract
Macrophages play a critical role in the innate immune response. To respond in a rapid and efficient manner to challenges in the micro-environment, macrophages are able to differentiate towards classically (M1) or alternatively (M2) activated phenotypes. Synthetic, innate defense regulators (IDR) peptides, designed based on natural host defence peptides, have enhanced immunomodulatory activities and reduced toxicity leading to protection in infection and inflammation models that is dependent on innate immune cells like monocytes/macrophages. Here we tested the effect of IDR-1018 on macrophage differentiation, a process essential to macrophage function and the immune response. Using transcriptional, protein and systems biology analysis, we observed that differentiation in the presence of IDR-1018 induced a unique signature of immune responses including the production of specific pro and anti-inflammatory mediators, expression of wound healing associated genes, and increased phagocytosis of apoptotic cells. Transcription factor IRF4 appeared to play an important role in promoting this IDR-1018-induced phenotype. The data suggests that IDR-1018 drives macrophage differentiation towards an intermediate M1-M2 state, enhancing anti-inflammatory functions while maintaining certain pro-inflammatory activities important to the resolution of infection. Synthetic peptides like IDR-1018, which act by modulating the immune system, could represent a powerful new class of therapeutics capable of treating the rising number of multidrug resistant infections as well as disorders associated with dysregulated immune responses.
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) | 0.001 | 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".