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 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) | 0.000 | 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 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".