Preparation and in vitro characterization of modified bio-degradable albumin-based nanoparticles for the efficient delivery of therapeutic drugs and genes in breast cancer applications
Bibliographic record
Abstract
Breast cancer is considered the second most commonly diagnosed type of cancer across the world. The common modes of treatment are limited by severe side-effects that hinder the efficacy of the drugs, compromise the patients' quality of life and often lead to other disorders. One of the main focuses of nanobiotechnology research is to develop novel anti-cancer drug delivery systems that improve the drug efficacy, limit harmful side effects and also allow for the delivery of developing therapeutics that are rapidly degraded in circulation, such as small interfering RNA (siRNA). Nano-carriers are helpful particularly in anti-cancer drug delivery due to the Enhanced Permeability and Retention (EPR) effect. In the current research study, we developed and investigated the use of surface modified HSA nanoparticles for the delivery of anti-cancer therapeutics in breast cancer applications. Results showed formation of modified HSA nanoparticles of sizes below 150 nm and contained a positive surface charge. The cellular uptake of the nanoparticles was higher in coated particles (average: ~70%) than uncoated particles. Furthermore, the cytotoxicity assessment of modified HSA nanoparticles suggested that empty particles are biocompatible and non-toxic to cells. Therefore, the presented PEI-enhanced and TAT-coated HSA nanoparticles form an appealing delivery system for anti-cancer therapeutics with a potential for clinical application.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".