Nano-pharmaceutical Formulations for Targeted Drug Delivery against HER2 in Breast Cancer
Bibliographic record
Abstract
Nanotechnology has revolutionized fundamental opportunities for higher specific drug delivery with minimum side effects. Since its inception, the goal of nanotechnology has been to advance effective and reliable systems for precise anti-cancer therapy and diagnosis. To accomplish this goal, bio-conjugation strategies of therapeutic agents loaded nanoparticles with monoclonal antibodies or their analogues have demonstrated a targeted approach both in vitro and in vivo. In this review, we primarily focus on the specific recognition of HER2 receptors of HER2 overexpressed tumor cells, and evaluate anti-HER2 monoclonal antibody as an effective tool for active targeting. Currently, a variety of nanoparticle systems are under both preclinical and clinical trials for targeting to HER2 positive breast cancer. Different nanotechnology scaffolds including liposomes, dendrimers, micelles, polymeric and inorganic nanoparticles that have higher flexibility for macromolecular synthesis and versatile functionalizing properties have been reviewed in this study. Continuing advances in anti-HER2 functionalized nanoparticles have good potential to lead to the development of nano-therapy against HER2 positive breast cancer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".