Green synthesis of <i>Kickxia elatine</i> -induced silver nanoparticles and their role as anti-acetylcholinesterase in the treatment of Alzheimer’s disease
Bibliographic record
Abstract
Abstract The synthesis of silver nanoparticles (AgNPs) by the green method is favored as compared to chemical synthesis due to their appreciable properties of less toxicity and simple synthesis. The current study designed the biosynthesis of AgNPs in one step by using the plant Kickxia elatine (KE) extract and then investigated its inhibiting activity against rat’s brain acetylcholinesterase (AChE) ex vivo. Ultraviolet spectrum at 416 nm confirmed the formation of AgNPs. X-ray diffractometer calculated size was reported to be 42.47 nm. The SEM analysis confirmed spherical-shaped AgNPs. FT-IR suggested that the phytochemical groups present in the KE extract and their nanoparticles (NPs) are responsible for the biosynthesized of NPs. EDX analysis presented that Ag was the chief element with 61.67%. Both KE extract and AgNPs showed significant anti-AChE activity at 175 µg·mL −1 . Statistical analysis showed that both KE and AgNPs exhibited non-competitive type inhibition against AChE, i.e. V max decreased (34.17–68.64% and 22.29–62.10%), while K m values remained constant. It is concluded that KE and AgNPs can be considered an inhibitor of rats’ brain AChE. Furthermore, the synthesis of AgNP-based drugs can be used as a cheaper and alternative option against diseases such as Alzheimer’s disease.
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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 | 0.001 | 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".