Elucidating the fate of nanoparticles among key cell components of the tumor microenvironment for promoting cancer nanotechnology
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Successful integration of nanotechnology into the current paradigm of cancer therapy requires proper understanding of the interface between nanoparticles (NPs) and cancer cells, as well as other key components within the tumor microenvironment (TME), such as normal fibroblasts (FBs) and cancer-associated FBs (CAFs). So far, much focus has been on cancer cells, but FBs and CAFs also play a critical role: FBs suppress the tumor growth while CAFs promote it. It is not yet known how NPs interact with FBs and CAFs compared to cancer cells. Hence, our goal was to elucidate the extent of NP uptake, retention, and toxicity in cancer cells, FBs, and CAFs to further understand the fate of NPs in a real tumor-like environment. The outcome of this would guide designing of NP-based delivery systems to fully exploit the TME for a better therapeutic outcome. We used gold nanoparticles as our model NP system due to their numerous applications in cancer therapy, including radiotherapy and chemotherapy. A cervical cancer cell line, HeLa, and a triple-negative breast cancer cell line, MDA-MB-231 were chosen as cancer cell lines. For this study, a clinically feasible 0.2 nM concentration of GNPs was employed. According to our results, the cancer cells and CAFs had over 25- and 10-fold higher NP uptake per unit cell volume compared to FBs, respectively. Further, the cancer cells and CAFs had over 30% higher NP retention compared to FBs. There was no observed significant toxicity due to GNPs in all the cell lines studied. Higher uptake and retention of NPs in cancer cells and CAFs vs FBs is very important in promoting NP-based applications in cancer therapy. Our results show potential in modulating uptake and retention of GNPs among key components of TME, in an effort to develop NP-based strategies to suppress the tumor growth. An ideal NP-based platform would eradicate tumor cells, protect FBs, and deactivate CAFs. Therefore, this study lays a road map to exploit the TME for the advancement of “smart” nanomedicines that would constitute the next generation of cancer therapeutics.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it