Biogenic silver nanomaterials synthesized from Ocimum sanctum leaf extract exhibiting robust antimicrobial and anticancer activities: Exploring the therapeutic potential
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
There is a surge in antibiotic consumption because of the emergence of resistance among microbial pathogens. In the escalating challenge of antibiotic resistance in microbial pathogens, silver nanoparticles (AgNPs)-mediated therapy has proven to be the most effective and alternative therapeutic strategy for bacterial infections and cancer treatment. This study aims to explore the potential of OsAgNPs derived from Ocimum sanctum's aqueous leaf extract as antimicrobial agents and anticancer drug delivery modalities. This study utilized a plant extract derived from Ocimum sanctum (Tulsi) leaves to synthesize silver nanoparticles (OsAgNPs), that were characterized by FTIR, TEM, SEM, and EDX. OsAgNPs were assessed for their antibacterial and anticancer potential. TEM analysis unveiled predominantly spherical or oval-shaped OsAgNPs, ranging in size from 4 to 98 nm. The (MICs) of OsAgNPs demonstrated a range from 0.350 to 19.53 μg/ml against clinical, multidrug-resistant (MDR), and standard bacterial isolates. Dual labelling with ethidium bromide and acridine orange demonstrated that OsAgNPs induced apoptosis in HeLa cells. The OsAgNPs-treated cells showed yellow-green fluorescence in early-stage apoptotic cells and orange fluorescence in late-stage cells. Furthermore, OsAgNPs exhibited a concentration-dependent decrease in HeLa cancer cell viability, with an IC50 value of 90 μg/ml noted. The study highlights the remarkable antibacterial efficacy of OsAgNPs against clinically significant bacterial isolates, including antibiotic-resistant strains. These results position the OsAgNPs as prospective therapeutic agents with the potential to address the growing challenges posed by antibiotic resistance and cervical cancer.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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