Novel Lignin-Capped Silver Nanoparticles against Multidrug-Resistant Bacteria
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The emergence of bacteria resistant to antibiotics and the resulting infections are increasingly becoming a public health issue. Multidrug-resistant (MDR) bacteria are responsible for infections leading to increased morbidity and mortality in hospitals, prolonged time of hospitalization, and additional burden to financial costs. Therefore, there is an urgent need for novel antibacterial agents that will both treat MDR infections and outsmart the bacterial evolutionary mechanisms, preventing further resistance development. In this study, a green synthesis employing nontoxic lignin as both reducing and capping agents was adopted to formulate stable and biocompatible silver–lignin nanoparticles (NPs) exhibiting antibacterial activity. The resulting silver–lignin NPs were approximately 20 nm in diameter and did not agglomerate after one year of storage at 4 °C. They were able to inhibit the growth of a panel of MDR clinical isolates, including Staphylococcus aureus, Staphylococcus epidermidis, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Acinetobacter baumannii, at concentrations that did not affect the viability of a monocyte-derived THP-1 human cell line. Furthermore, the exposure of silver–lignin NPs to the THP-1 cells led to a significant increase in the secretion of the anti-inflammatory cytokine IL-10, demonstrating the potential of these particles to act as an antimicrobial and anti-inflammatory agent simultaneously. P. aeruginosa genes linked with efflux, heavy metal resistance, capsular biosynthesis, and quorum sensing were investigated for changes in gene expression upon sublethal exposure to the silver–lignin NPs. Genes encoding for membrane proteins with an efflux function were upregulated. However, all other genes were membrane proteins that did not efflux metals and were downregulated.
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".