Cancer Nanotechnology: Use of Smart Nanomaterials for Improved Outcome in Cancer Therapy
Bibliographic record
Abstract
In the battle against cancer, surgery, radiation and chemotherapy remain as the most widely used treatment options. Despite recent progress in conventional methods, there is still an immense need for new treatments that can eradicate cancerous cells while causing much less damage to the healthy tissue. Introduction of high atomic number materials such gold nanoparticles (GNPs) within the tumor could enhance the local radiation dose while minimizing the damage to surrounding tissue. In addition to this, NP-based technology has the capability to develop novel multiplex systems to combine more than one treatment modality for creating a more aggressive and effective approach in eradicating cancer. However, consideration of all three interfaces: in vivo delivery, tissue penetration, and successful delivery to individual tumor cells can play a bigger role in their future success. It is known that in vitro data cannot be extrapolated directly to in vivo or clinical settings. However, three dimensional tissue models can be used to create certain tumor microenvironment conditions for testing the potential of Nanotherapeutics before using them in vivo models. Such optimizations could lead to better results and would accelerate clinical use of Nanotherapeutics. I will discuss how we exploited three dimensional tissue models along with gold nanoparticle as a model nanoparticle system to improve the bio-nano interface for improved outcome in cancer therapy at this conference.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".