Characterization and in vitro antitumor, antibacterial and antifungal activities of green synthesized silver nanoparticles using cell extract of Nostoc sp. strain HKAR-2
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Bibliographic record
Abstract
In the present study we have made an attempt to develop an eco-friendly, cheap and convenient biological (green) method for the synthesis of silver nanoparticles (AgNPs) using the cell extract of the cyanobacterium Nostoc sp. strain HKAR-2. Their anticancerous, antifungal and antibacterial properties were also studied against MCF-7 cells, two fungal strains (Aspergillus niger and Trichoderma harzianum) and two plant bacterial strains (Ralstonia solanacearum and Xanthomonas campestris), respectively. The structural, morphological and optical properties of green synthesized AgNPs were determined by UV-VIS spectroscopy, Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction, transmission electron microscopy selected area electron diffraction (TEM-SAED) and scanning electron microscopy (SEM). Spectroscopic analysis showed the peak at 419 nm due to the reduction of AgNO3 into silver ion by cyanobacterial extract indicating surface plasmon resonance (SPR) of the synthesized AgNPs. The XRD pattern of AgNPs showed the characteristic Bragg peaks at (111), ( FTIR analysis revealed that proteins and amino acids are responsible for the reduction of AgNO3 into Ag + as well as for the stability of nanoparticles. Zeta potential confirmed that the charge on the nanoparticles is 1.80 mV which indicates the presence of stable nanoparticles. The results of SEM and TEM confirmed the large agglomerated shape of AgNPs with size ranging between 51-100 nm. The AgNPs showed a dose-dependent cytotoxic activity against human breast cancer MCF-7 cells with IC50 of 27.5 g/ml. They also exhibited excellent antibacterial and antifungal activities.
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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 | 0.001 |
| 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