Synthesis of Silver Nanoparticles Using <i>Camellia sinensis</i> Leaf Extract: Promising Particles for the Treatment of Cancer and Diabetes
Bibliographic record
Abstract
Both diabetes and cancer pose significant threats to public health. To overcome these challenges, nanobiotechnology offers innovative solutions for the treatment of these diseases. However, the synthesis of nanoparticles can be complex, costly and environmentally toxic. Therefore, in this study, we successfully synthesized Camellia sinensis silver nanoparticles (CS-AgNPs) biologically from methanolic leaf extract of C. sinensis and as confirmed by the visual appearance which exhibited strong absorption at 456 nm in UV-visible spectroscopy. The fourier transform infrared spectroscopy (FTIR) analysis revealed that phytochemicals of C. sinensis were coated with AgNPs. Scanning electron microscopy (SEM) analysis showed the spherical shape of CS-AgNPs, with a size of 15.954 nm, while X-ray diffraction spectrometry (XRD) analysis detected a size of 20.32 nm. Thermogravimetric analysis (TGA) indicated the thermal stability of CS-AgNPs. The synthesized CS-AgNPs significantly inhibited the ehrlich ascites carcinoma (EAC) cell growth with 53.42±1.101 %. The EAC cell line induced mice exhibited increased level of the serum aspartate aminotransferase (AST), alanine transaminase (ALT), and alkaline phosphatase (ALP), however this elevated serum parameter significantly reduced and controlled by the treatment with CS-AgNPs. Moreover, in a streptozotocin-induced diabetic mice model, CS-AgNPs greatly reduced blood glucose, total cholesterol, triglyceride, low-density lipoprotein (LDL) and creatinine levels. These findings highlight that the synthesized CS-AgNPs have significant anticancer and antidiabetic activities that could be used as promising particles for the treatment of these major diseases. However, pre-clinical and clinical trial should be addressed before use this particles as therapeutics agents.
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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 | 0.001 |
| 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) | 0.001 | 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 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".