Nanostructured, Self-Assembling Peptide K5 Blocks TNF-<i>α</i>and PGE<sub>2</sub>Production by Suppression of the AP-1/p38 Pathway
Bibliographic record
Abstract
Nanostructured, self-assembling peptides hold promise for a variety of regenerative medical applications such as 3D cell culture systems, accelerated wound healing, and nerve repair. The aim of this study was to determine whether the self-assembling peptide K5 can be applied as a carrier of anti-inflammatory drugs. First, we examined whether the K5 self-assembling peptide itself can modulate various cellular inflammatory responses. We found that peptide K5 significantly suppressed the release of tumor-necrosis-factor- (TNF-) α and prostaglandin E₂ (PGE₂) from RAW264.7 cells and peritoneal macrophages stimulated by lipopolysaccharide (LPS). Similarly, there was inhibition of cyclooxygenase- (COX-) 2 mRNA expression assessed by real-time PCR, indicating that the inhibition is at the transcriptional level. In agreement with this finding, peptide K5 suppressed the translocation of the transcription factors activator protein (AP-1) and c-Jun and inhibited upstream inflammatory effectors including mitogen activated protein kinase (MAPK), p38, and mitogen-activated protein kinase kinase 3/6 (MKK 3/6). Whether this peptide exerts its effects via a transmembrane or cytoplasmic receptor is not clear. However, our data strongly suggest that the nanostructured, self-assembling peptide K5 may possess significant anti-inflammatory activity via suppression of the p38/AP-1 pathway.
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".