Self-Assembled Peptide Nanoparticle-Mediated Macrophage Polarization Enhances Anticancer Efficacy of Chemotherapeutics in Triple-Negative Breast Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Peptide-based self-assembled materials are being investigated as a biodegradable option with various biomedical applications such as drug-delivery carriers and as biosensors. In this study, we report the mechanism of action of a synthetic β-sheet and turn rich macrocyclic host defense peptide that self-assemble in the form of nanoparticles and stimulate naïve and tumor-associated macrophages, resulting in the production of pro-inflammatory mediators in macrophage monoculture and in a macrophage/triple-negative breast cancer (TNBC) coculture model. Our results show that macrocyclic peptide-based nanomaterials termed as mCA4 engage with toll-like receptors of macrophages, activating downstream pathways in both naïve and IL-4-pretreated macrophages, and result in the production of pro-inflammatory mediators by the immune cells. The immunomodulatory potential of mCA4 is highly sequence-specific, and while an inactive analogue prepared by swapping a single amino acid in the peptide chain does not diminish the self-assembly properties of the parent peptide, its impact on the immunomodulatory potential is detrimental. The immunomodulatory potential of mCA4 is further confirmed in macrophage monoculture and in macrophage/TNBC coculture by proteomics and by Western blot analysis. The treatment of macrophage/TNBC coculture with mCA4 and chemotherapeutics enhanced anticancer efficacies of chemotherapeutics, suggesting immuno-adjuvant-like properties of materials for TNBC treatment.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it