Cancer-Nano-Interaction: From Cellular Uptake to Mechanobiological Responses
Bibliographic record
Abstract
With the advancement of nanotechnology, the nano-bio-interaction field has emerged. It is essential to enhance our understanding of nano-bio-interaction in different aspects to design nanomedicines and improve their efficacy for therapeutic and diagnostic applications. Many researchers have extensively studied the toxicological responses of cancer cells to nano-bio-interaction, while their mechanobiological responses have been less investigated. The mechanobiological properties of cells such as elasticity and adhesion play vital roles in cellular functions and cancer progression. Many studies have noticed the impacts of cellular uptake on the structural organization of cells and, in return, the mechanobiology of human cells. Mechanobiological changes induced by the interactions of nanomaterials and cells could alter cellular functions and influence cancer progression. Hence, in addition to biological responses, the possible mechanobiological responses of treated cells should be monitored as a standard methodology to evaluate the efficiency of nanomedicines. Studying the cancer-nano-interaction in the context of cell mechanics takes our knowledge one step closer to designing safe and intelligent nanomedicines. In this review, we briefly discuss how the characteristic properties of nanoparticles influence cellular uptake. Then, we provide insight into the mechanobiological responses that may occur during the nano-bio-interactions, and finally, the important measurement techniques for the mechanobiological characterizations of cells are summarized and compared. Understanding the unknown mechanobiological responses to nano-bio-interaction will help with developing the application of nanoparticles to modulate cell mechanics for controlling cancer progression.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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 teacher head, 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".