Antimicrobial efficacy testing of antibiotic-containing biodegradable nanopolymers against biofilm and planktonic cells.
Bibliographic record
Abstract
Cystic fibrosis and rampant urogenital infections, caused by increasingly resistant microbial biofilms, call for more creative anti-infective systems. This study investigated the in vitro efficacy of levofloxacin (LEV)–loaded poly(D,L-lactide-co-glycolide) (PLGA) and poly-ε-caprolactone (PCL) nanopolymers against optimally-grown biofilms of Escherichia coli K12 W3110 and Pseudomonas aeruginosa PA01. High-throughput biofilm production and antimicrobial susceptibility testing were conducted in the Calgary Biofilm Device (CBD). Rich Luria-Bertani medium provided maximal accumulation of biofilm biomass, with optimum times (24 and 48 h, respectively) and temperatures (30 C and 21 C, respectively) found under dynamic culture conditions. For both pathogens, minimum inhibitory concentrations (MIC) of 2.8 µg/mL and 16.5 µg/mL total LEV load were found for LEV-PLGA and LEV-PCL, respectively. Minimum biofilm eradication concentrations (MBECs) improved at least 2-fold with increase in exposure time (48 hours) achieving LEV-PLGA MBECs of 1.4 and 0.2 µg/mL and LEV-PCL MBECs of 16.5 and 8.2 µg/mL for E. coli and P. aeruginosa, respectively. With efficient use of drug-encapsulated nanopolymers, bactericidal dosages are sufficiently lowered and dosing intervals can be extended due to the sustained drug release feature afforded by these efficacious nanocarriers.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".