Alkylated EDTA potentiates antibacterial photodynamic activity of protoporphyrin
Bibliographic record
Abstract
Abstract Antibiotic resistance has garnered significant attention due to the scarcity of new antibiotics in development. Protoporphyrin IX (PpIX)-mediated photodynamic therapy shows promise as a novel antibacterial strategy, serving as an alternative to antibiotics. However, the poor solubility of PpIX and its tendency to aggregate greatly hinder its photodynamic efficacy. In this study, we demonstrate that alkylated EDTA derivatives (aEDTA), particularly C14-EDTA, can enhance the solubility of PpIX by facilitating its dispersion in aqueous solutions. The combination of C14-EDTA and PpIX exhibits potent antibacterial activity against Staphylococcus aureus (S. aureus) when exposed to LED light irradiation. Furthermore, this combination effectively eradicates S. aureus biofilms, which are known to be strongly resistant to antibiotics, and demonstrates high therapeutic efficacy in an animal model of infected ulcers. Mechanistic studies reveal that C14-EDTA can disrupt PpIX crystallization, increase bacterial membrane permeability and sequester divalent cations, thereby improving the accumulation of PpIX in bacteria. This, in turn, enhances reactive oxygen species (ROS) production and the antibacterial photodynamic activity. Overall, this effective strategy holds great promise in combating antibiotic-resistant strains. Graphical Abstract
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".