Paradigm of Vanadium pentoxide nanoparticle-induced autophagy and apoptosis in triple-negative breast cancer cells
Bibliographic record
Abstract
Abstract Chemo-resistance remains the main hurdle to cancer therapy, challenging the improvement of clinical outcomes in cancer patients. Therefore, exploratory studies to address chemo-resistance through various approaches are highly rewarding. Nanomedicine is a promising recent advancement in this direction. Comprehensive studies to understand the precise molecular interactions of nanomaterials is necessary to validate their specific “nano induced” effects. Here, we illustrate in detail the specific biological interactions of vanadium pentoxide nanoparticles (VnNp) on triple-negative breast cancer cells and provide initial insights towards its potential in breast cancer management at the cellular level. VnNp shows a time-dependent anti-oxidant and pro-oxidant property in vitro . These nanoparticles specifically accumulate in the lysosomes and mitochondria, modulate various cellular processes including impaired lysosomal function, mitochondrial damage, and induce autophagy. At more extended periods, VnNp influences cell cycle arrest and inhibits cell migration potentiating the onset of apoptosis. Preliminary in vivo studies, on exposing healthy Swiss albino mice to VnNp demonstrated normal blood parameters, organ distribution, and tissue redox balance which further indicated the absence of any adverse organ toxicity. Hence, we foresee tumor-targeting VnNp as a potential drug molecule for future cancer management.
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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".