Algae-Synthesized Bismuth Nanoparticles for Drug Delivery in A549 Lung Cancer Cells
Bibliographic record
Abstract
The increasing prevalence of lung cancer, compounded by the limitations of conventional therapies, necessitates the exploration of innovative drug delivery systems.This study presents a novel approach to synthesizing bismuth nanoparticles (BiNPs) using Chlorella sp.extracts, aimed at enhancing targeted drug delivery for the human lung cancer cell line (A549).An extract of Chlorella sp. and bismuth nitrate was used to prepare BiNPs under optimized conditions.The nano-solution was characterized by various techniques.Gas chromatography-mass spectrometry (GC-MS) analysis was employed to identify the active algal phytocompounds.The cytotoxic activity of the BiNPs was tested against A549, while the normal human fibroblast cell line (NHF) was used to evaluate the biosafety of the nano-solution.Characterization using UV-vis spectroscopy and X-ray diffraction confirmed the successful synthesis of BiNPs, indicating a relative size of 26 nm.Cytotoxicity assay demonstrated that BiNPs exert a dose-dependent effect on A549 cells, showing significant selective toxicity with an IC50 of 5.797 µg/mL, while minimizing affecting NHF cells, which had an IC50 of 17.68 µg/mL.Furthermore, morphological assessments via microscopy indicated that BiNPs induced distinct apoptotic features in A549 cells.Gas chromatography-mass spectrometry analysis of the algal extract revealed the presence of bioactive compounds, including terpenoids and fatty acids, known for their antioxidant and anticancer properties, which may synergistically enhance the therapeutic efficacy of BiNPs.The study highlighted Chlorella-synthesized BiNPs as a promising targeted drug delivery system, advancing cancer nanomedicine and addressing challenges in traditional chemotherapy for lung cancer treatment.
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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".