Green synthesis, characterization, and antimicrobial activity of silver nanoparticles from water-soluble fractions of Brazilian Kefir
Bibliographic record
Abstract
Nanotechnology offers promising new avenues for combating drug-resistant pathogens. Given its antioxidant capacity, the water-soluble fraction of Brazilian kefir was hypothesized to serve as an effective reducing agent for the green synthesis of silver nanoparticles (AgNPs). It was further hypothesized that both the entire fraction (WSF) and the < 10 kDa fraction AgNPs would augment the therapeutic effects of kefir, particularly its antimicrobial activity. The successful synthesis was confirmed through the use of UV-Visible spectroscopy and Fourier-transform infrared analyses. WSF-AgNPs demonstrated potent antimicrobial activity, with minimum inhibitory concentrations of 25 µg/mL against A. baumannii (p < 0.0001) and 50 µg/mL against K. pneumoniae (p < 0.0001). Although no toxicity was observed in long-term tests on adult Drosophila melanogaster, AgNPs intake impaired larvae development. Oxidative stress analysis showed mild oxidative imbalance on advanced oxidation protein products (AOPP), sulfhydryl, and reduced glutathione (GSH) contents, with no alterations observed in reactive oxygen species (ROS) quantities, ferric reducing antioxidant power (FRAP), and catalase (CAT) activity. These findings suggest that kefir-derived AgNPs may have potential for combating drug-resistant infections. Future studies should focus on enhancing specificity through compound conjugation and investigating broader applications, including disinfectants, wound healing, and antibiotic development.
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) | 0.000 | 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 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".