Selective toxicity of a novel antimicrobial peptide Acidocin 4356 against Pseudomonas aeruginosa and Acinetobacter baumannii in human cell-based in vitro infection models
Bibliographic record
Abstract
Prior studies examined Acidocin 4356’s antibacterial and antivirulence effects against Pseudomonas aeruginosa , including cell membrane penetration abilities. Building on prior research, an in-vitro co-culture of human cells was established to evaluate the selectivity of Acidocin (ACD) by concurrently cultivating human cells and bacterial pathogens. This study evaluated the antibacterial effectiveness of ACD against Acinetobacter baumannii and Pseudomonas aeruginosa . Laser scanning confocal microscopy (LSCM) and scanning electron microscopy (SEM) revealed significant biofilm dispersion at ACD concentrations as low as 1/2 MIC. The cytotoxicity of ACD was evaluated on two human cell lines, Calu-6 and THP-1, using the MTT assay. The IC 50 values were 114 µg/mL and 24 µg/mL after a 12-hour treatment duration. In a co-culture model, the IC 50 increased to 118 µg/mL, showing greater resilience of THP-1 cells under these settings, mimicking in-vivo conditions. Fluorescent microscopy and flow cytometry analysis confirmed the MTT results, showing ACD’s potent antimicrobial effects and minimal toxicity to human cells, even after 12 h of treatment. Transmission electron microscopy (TEM) study revealed that normal Calu-6 cells included papillary outgrowths and microvilli, while infected cells displayed secretory vesicles, indicating an active response to P. aeruginosa infection. The present study thus serves as a critical step toward the development of an innovative therapeutic strategy targeting biofilm-associated infections.
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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) | 0.000 | 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".