Avidin-Biotin functionalized self-assembled protein nanoparticles as EGFR targeted therapeutics for the treatment of lung cancer: characterization and cell viability
Bibliographic record
Abstract
Gefitinib targeting efficiency can be modified via enhancing its hydrophobicity and poor bioavailability by conjugating it with biotin-streptavidin conjugated Bovine serum albumin nanoparticles as a hotspot in the field of biological therapy for human cancer. The study aims to develop and characterize lyophilized Biotin-Streptavidin conjugated Bovine serum albumin nanoparticles containing Gefitinib prepared by desolvation method. As protein being the most amendable to surface functionalization were prepared by conjugating them with strongest interaction, that is, Biotin-Streptavidin. The Desolvation method is based on nanoprecipitation method that reduces the solubility of drug and polymer in aqueous solution by using desolvating agents in order to prepare protein nanoparticles that were further functionalized by biotinylation followed with streptavidin conjugation for enhancement in affinity and specificity. Protein NPs were found to be monodisperse with particle size in range of 130 ± 5.131 and 174 ± 3.055 with the approximate negatively surface charge zeta potential of −9 mv. The drug loading and the entrapment efficiency was found to be 16% and 74 ± 2.64% with 8 days of sustained drug release at the physiological pH. Cytotoxicity studies after 72 h shown better cell growth inhibition with biotinylated streptavidin conjugated bovine serum albumin nanoparticles containing gefitinib with reduced side effect and more apoptosis of cells as compared to gefitinib loaded bovine serum albumin nanoparticles and free gefitinib.
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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) | 0.000 | 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 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".