Porphyrin nanoemulsion for antimicrobial photodynamic therapy: effective delivery to inactivate biofilm-related infections
Bibliographic record
Abstract
The management of biofilm-related infections is a challenge in healthcare, and antimicrobial photodynamic therapy (aPDT) is a powerful tool that has demonstrated a broad-spectrum activity. Nanotechnology has been used to increase the aPDT effectiveness by improving the photosensitizer’s delivery properties. NewPS is a simple, versatile, and safe surfactant-free nanoemulsion with a porphyrin salt shell encapsulating a food-grade oil core with promising photodynamic action. This study evaluated the use of NewPS for aPDT against microorganisms in planktonic, biofilm, and in vivo models of infected wounds. First, the potential of NewPS-mediated aPDT to inactivate Streptococcus pneumoniae and Staphylococcus aureus suspensions was evaluated. Then, a series of protocols were assessed against S. aureus biofilms by means of cell viability and confocal microscopy. Finally, the best biofilm protocol was used for the treatment of S. aureus in a murine-infected wound model. A high NewPS-bacteria cell interaction was achieved since 0.5 nM and 30 J/cm 2 was able to kill S. pneumoniae suspension. In the S. aureus biofilm, enhanced efficacy of NewPS-aPDT was achieved when 100 µM of NewPS was applied with longer periods of incubation at the light dose of 60 J/cm 2 . The best single and double-session protocol reduced 5.56 logs and 6.03 logs, respectively, homogeneous NewPS distribution, resulting in a high number of dead cells after aPDT. The in vivo model showed that one aPDT session enabled a reduction of 6 logs and faster tissue healing than the other groups. In conclusion, NewPS-aPDT may be considered a safe and effective anti-biofilm antimicrobial photosensitizer.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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 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".