Polycationic Chitosan‐Conjugated Photosensitizer for Antibacterial Photodynamic Therapy<sup>†</sup>
Bibliographic record
Abstract
The complex nature of bacterial cell membrane and structure of biofilm has challenged the efficacy of antimicrobial photodynamic therapy. This study was aimed to synthesize a polycationic chitosan-conjugated rose bengal (CSRB) photosensitizer and test its antibiofilm efficacy on Enterococcus faecalis (gram positive) and Pseudomonas aeruginosa (gram negative) using photodynamic therapy. During experiments, CSRB was tested along with an anionic photosensitizer rose bengal (RB) and a cationic photosensitizer methylene blue (MB) for uptake and killing efficacy on 7-day-old E. faecalis and P. aeruginosa biofilms. Microbiological culture based analysis was used to analyze the cell viability, while laser scanning confocal microscopy (LSCM) was used to examine the structure of biofilm. The synthesized CSRB showed absorbance spectrum similar to the RB. The concentration of CSRB uptaken by both the bacterial biofilms was significantly higher than that of RB and MB (P < 0.05). Photoactivation resulted in significantly higher elimination of both bacterial biofilms sensitized with CSRB than RB and MB. The structure of biofilm under LSCM was found to be disrupted following CSRB treatment. The present study highlighted the importance of inherent cell membrane permeabilizing effect of chitosan and increased cell/biofilm uptake of conjugated photosensitizer to produce significant antibiofilm efficacy during photodynamic therapy.
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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".