COMPARATIVE ANALYSIS OF Adansonia digitata NANOPARTICLE AND ENCAPSULATION: SYNTHESIS, CHARACTERIZATION, ANTIMICROBIAL, AND ANTICANCER ASSESSMENT
Bibliographic record
Abstract
This study aims to further explore the synthesis, characterization, encapsulation, and biomedical applications of Adansonia digitata Baobab nanoparticles. Using a nano-precipitation technique, Gum Arabic and Polyvinyl alcohol were added to the nanoparticles that had been synthesized using the sonochemical process. Transmission electron microscopy was used to determine the physico-chemical properties of the synthesized and encapsulated nanoparticles, providing information about their morphology. Fourier Transform Infrared (FTIR) spectroscopy was employed to examine the chemical functional groups present in the samples. The particle sizes of ADNPs and Cap-ADNPs were verified by dynamic light scattering (DLS) analysis. While encapsulated Cap-ADNPs had a greater average size of around 230 nm with a PDI of 0.311, the average particle size for ADNPs was approximately 94 nm with a PDI of 0.208. Tests were conducted on the antibacterial activity of ADNPs and Cap-ADNPs against a range of specific Gram-positive and Gram-negative bacteria as well as certain fungi. Additionally, the nanoparticles' cytotoxicity toward human colon cancer cells (HCT-116) and human breast cancer cells (MCF-7) was assessed. With an IC50 of 73.6 mg/ml, ADNPs showed modest inhibitory action against HCT-116 cells; in contrast, Cap-ADNPs had a significantly greater impact, with an IC50 of 34.1 mg/ml. With an IC50 of 18.3 mg/ml, Cap-ADNPs have shown exceptional potency against MCF7 cells, whereas ADNPs had moderate inhibitory effects, with an IC50 of 64.7 mg/ml. According to preliminary findings, ADNPs and Cap-ADNPs have a great deal of promise to be effective therapeutic options in upgraded forms for use in bio-nanomedicine. Keywords: Adansonia digitata nanoparticles, nano-encapsulation, antimicrobial activity, cytotoxicity
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".