Algae-Synthesized Bismuth Nanoparticles for Drug Delivery in A549 Lung Cancer Cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 g/mL, while minimizing affecting NHF cells, which had an IC50 of 17.68 g/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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it