Enzyme-responsive mannose-grafted magnetic nanoparticles for breast and liver cancer therapy and tumor-associated macrophage immunomodulation
Bibliographic record
Abstract
BACKGROUND: Chemo-immunotherapy modifies the tumor microenvironment to enhance the immune response and improve chemotherapy. This study introduces a dual-armed chemo-immunotherapy strategy combating breast tumor progression while re-polarizing Tumor-Associated Macrophage (TAM) using prodigiosin-loaded mannan-coated magnetic nanoparticles (PG@M-MNPs). METHODS: The physicochemical properties of one-step synthetized M-MNPs were analyzed, including X-ray diffraction, FTIR, DLS, VSM, TEM, zeta potential analysis, and drug loading content were carried out. Biocompatibility, cancer specificity, cellular uptake, and distribution of PG@M-MNPs were investigated using fluorescence and confocal laser scanning microscopy, and flow cytometry. Furthermore, the expression levels of IL-6 and ARG-1 after treatment with PG and PG@M-MNPs on M1 and M2 macrophage subsets were studied. RESULTS: The M-MNPs were successfully synthesized and characterized, demonstrating a size below 100 nm. The release kinetics of PG from M-MNPs showed sustained and controlled patterns, with enzyme-triggered release. Cytotoxicity assessments revealed an enhanced selectivity of PG@M-MNPs against cancer cells and minimal effects on normal cells. Additionally, immuno-modulatory activity demonstrates the potential of PG@M-MNPs to change the polarization dynamics of macrophages. CONCLUSION: These findings highlight the potential of a targeted approach to breast cancer treatment, offering new avenues for improved therapeutic outcomes and patient survival.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".