Use of Micellar Delivery Systems to Enhance Curcumin’s Stability and Microbial Photoinactivation Capacity
Bibliographic record
Abstract
Microbial photoinactivation using ultraviolet (UV) or visible light can be enhanced by photosensitizers. This study assessed the efficacy of encapsulating a food-grade photosensitizer (curcumin) in surfactant micelles on its water dispersibility, chemical stability, and antimicrobial activity. Stock curcumin-surfactant solutions were prepared with Surfynol 465 (S465) or Tween 80 (T80) (5 mM sodium citrate buffer). The antimicrobial activity of curcumin-loaded surfactant solutions was determined by monitoring the inactivation of Escherichia coli O157: H7 and Listeria innocua after 5-min irradiation with UV-A light (λ = 365 nm). The solutions mixed with the bacterial suspensions contained 1 µM curcumin and each surfactant below, near, and above their critical micelle concentrations (CMCs). The addition of surfactants at any level to the curcumin solution enhanced its dispersibility, stability, and efficacy as a photosensitizer, thereby enhancing its antimicrobial activity. Gram-positive bacteria were more susceptible than Gram-negative bacteria when curcumin-loaded micelles were used against them. The photoinactivation efficacy of curcumin-surfactant solutions depended on the pH of the solution (low > high), surfactant type (S465 > T80), and the amount of surfactant present (below CMC ≥ near CMC > above CMC = unencapsulated curcumin). This result suggests that excessive partitioning of curcumin into micelles reduced its ability to interact with microbial cells. Synergistic antimicrobial activity was observed when S465 was present below or near the CMC with curcumin at pH 3.5, which could be attributed to a more effective interaction of the photosensitizer with the cell membranes as supported by the fluorescence lifetime micrographs. The use of a micelle-based delivery system facilitates adsorption and generation of reactive oxygen species in the immediate environment of the microbial cell, enhancing photoinactivation.
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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 | 0.000 | 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 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".