Enhancing Breast Cancer Therapy: Nanocarrier-Based Targeted Drug Delivery
Bibliographic record
Abstract
Breast cancer (BC) is one of the most common types of cancer in women. Triple-negative breast cancer (TNBC), characterized by the absence of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), exhibits a highly aggressive phenotype with increased metastasis and resistance to conventional treatments. Nanocarrier technology is consistently employed to overcome limitations associated with traditional breast cancer therapy. The targeted drug delivery approach using nanocarriers enhances bioavailability, prolongs circulation, and facilitates effective drug accumulation at the tumor site through active or passive targeting. Currently, the FDA has approved a few nanocarrier systems, and numerous nano formulations are undergoing preclinical and clinical development for breast cancer targeting. Common nanocarrier types include polymeric micelles, microemulsions, magnetic microemulsions, liposomes, dendrimers, carbon nanotubes, and magnetic nanoparticles (NPs). This review extensively explores the targeting potential of nanocarriers in breast cancer. This study will provide a concise summary of current advances in treatment of breast cancer and diagnostics.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".