Advanced biomaterial agent from chitosan/poloxamer 407-based thermosensitive hydrogen containing biosynthesized silver nanoparticles using Eucalyptus camaldulensis leaf extract
Bibliographic record
Abstract
CONTEXT: The emergence of multidrug-resistant (MDR) pathogens poses a significant challenge for global public health systems, increasing hospital morbidity and mortality and prolonged hospitalization. OBJECTIVE: We evaluated the antimicrobial activity of a thermosensitive hydrogel containing bio-synthesized silver nanoparticles (bio-AgNPs) based on chitosan/poloxamer 407 using a leaf extract of Eucalyptus calmadulensis. RESULTS: The thermosensitive hydrogel was prepared by a cold method after mixing the ingredients and left at 4°C overnight to ensure the complete solubilization of poloxamer 407. The stability of the hydrogel formulation was evaluated at room temperature for 3 months, and the absorption peak (420 nm) of the NPs remained unchanged. The hydrogel formulation demonstrated rapid gelation under physiological conditions, excellent water retention (85%), and broad-spectrum antimicrobial activity against MDR clinical isolates and ATCC strains. In this regard, minimum inhibitory concentration and minimum microbial concentration values of the bio-AgNPs ranged from 2-8 μg/mL to 8-128 μg/mL, respectively. Formulation at concentrations <64 μg/mL showed no cytotoxic effect on human-derived macrophages (THP-1 cells) with no induction of inflammation. CONCLUSIONS: The formulated hydrogel could be used in biomedical applications as it possesses a broad antimicrobial spectrum and anti-inflammatory properties without toxic effects on human cells.
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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 | 0.000 |
| 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".