Off to the Organelles - Killing Cancer Cells with Targeted Gold Nanoparticles
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
Gold nanoparticles (AuNPs) are excellent tools for cancer cell imaging and basic research. However, they have yet to reach their full potential in the clinic. At present, we are only beginning to understand the molecular mechanisms that underlie the biological effects of AuNPs, including the structural and functional changes of cancer cells. This knowledge is critical for two aspects of nanomedicine. First, it will define the AuNP-induced events at the subcellular and molecular level, thereby possibly identifying new targets for cancer treatment. Second, it could provide new strategies to improve AuNP-dependent cancer diagnosis and treatment. Our review summarizes the impact of AuNPs on selected subcellular organelles that are relevant to cancer therapy. We focus on the nucleus, its subcompartments, and mitochondria, because they are intimately linked to cancer cell survival, growth, proliferation and death. While non-targeted AuNPs can damage tumor cells, concentrating AuNPs in particular subcellular locations will likely improve tumor cell killing. Thus, it will increase cancer cell damage by photothermal ablation, mechanical injury or localized drug delivery. This concept is promising, but AuNPs have to overcome multiple hurdles to perform these tasks. AuNP size, morphology and surface modification are critical parameters for their delivery to organelles. Recent strategies explored all of these variables, and surface functionalization has become crucial to concentrate AuNPs in subcellular compartments. Here, we highlight the use of AuNPs to damage cancer cells and their organelles. We discuss current limitations of AuNP-based cancer research and conclude with future directions for AuNP-dependent cancer 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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