Noble Hybrid Nanostructures as Efficient Anti-Proliferative Platforms for Human Breast Cancer Cell
Bibliographic record
Abstract
Nanomaterials have proven to possess great potential in biomaterials research. Recently, they have suggested considerable promise in cancer diagnosis and therapy. Among others, silicon (Si) nanomaterials have been extensively employed for various biomedical applications; however, the utilization of Si for cancer therapy has been limited to nanoparticles, and its potential as anticancer substrates has not been fully explored. Noble nanoparticles have also received considerable attention owing to unique anticancer properties to improve the efficiency of biomaterials for numerous biological applications. Nevertheless, immobilization and control over delivery of the nanoparticles have been challenge. Here, we develop hybrid nanoplatforms to efficiently hamper breast cancer cell adhesion and proliferation. Platforms are synthesized by femtosecond laser processing of Si into multiphase nanostructures, followed by sputter-coating with gold (Au)/gold-palladium (Au-Pd) nanoparticles. The performance of the developed platforms was then examined by exploring the response of normal fibroblast and metastatic breast cancer cells. Our results from the quantitative and qualitative analyses show a dramatic decrease in the number of breast cancer cells on the hybrid platform compared to untreated substrates. Whereas, fibroblast cells form stable adhesion with stretched and elongated cytoskeleton and actin filaments. The hybrid platforms perform as dual-acting cytophobic/cytostatic stages where Si nanostructures depress breast cancer cell adhesion while immobilized Au/Au-Pd nanoparticles are gradually released to affect any surviving cell on the nanostructures. The nanoparticles are believed to be taken up by breast cancer cells via endocytosis, which subsequently alter the cell nucleus and may cause cell death. The findings suggest that the density of nanostructures and concentration of coated nanoparticles play critical roles on cytophobic/cytostatic properties of the platforms on human breast cancer cells while having no or even cytophilic effects on fibroblast cells. Because of the remarkable contrary responses of normal and cancer cells to the proposed platform, we envision that it will provide novel applications in cancer research.
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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) | 0.001 | 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".