Effect of <i>Crotalaria medicaginea</i>-based silver nanoparticles on morphological and molecular inflammatory markers
Bibliographic record
Abstract
There is a growing need for novel natural anti-inflammatory agents due to the side effects posed by existing anti-inflammatory compounds. Therefore, the present study aimed to prepare silver nanoparticles (AgNPs) from Crotalaria medicaginea and check their antioxidant potential. After characterizing these AgNPs (using a particle size analyzer, scanning electron microscopy-SEM, and Fourier transform infrared-FTIR analysis), their antioxidant and anti-inflammatory potential was assessed with the help of standard methods, both i n vivo and in vitro . Furthermore, mRNA expression for inflammatory markers was determined using qPCR. Results showed that Crotalaria- fabricated silver nanoparticles (C-AgNPs) had strong in vitro antioxidant and anti-inflammatory activities. In vivo studies corroborated the results, demonstrating significant activity as compared with disease and drug control groups. C-AgNPs showed maximum inhibition of inflammation in rats as 69.46, 29.65, and 57.58% in carrageenan-induced paw edema (CPE), cotton pellet-induced granuloma (CPG), and xylene-induced ear edema (XEE), respectively, as compared with other treatments and control. C-AgNPs also upregulated anti-inflammatory markers such as IL-4 (64.22%, 54.41%, and 68.44%) and IL-10 (74.36%, 92.19%, and 92.56%) in all three groups of inflammation studies, respectively. Therefore, the present study concludes that C-AgNPs have potent antioxidant and anti-inflammatory potential. They can also upregulate the antioxidant system and inflammation markers of the living body to boost the immune system.
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.
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 | 0.000 |
| 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) | 0.001 | 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".