Photodynamic therapy with nanoparticles to combat microbial infection and resistance
Bibliographic record
Abstract
Infections caused by drug-resistant pathogens are rapidly increasing in incidence and pose an urgent global health concern. New treatments are needed to address this critical situation while preventing further resistance acquired by the pathogens. One promising approach is antimicrobial photodynamic therapy (PDT), a technique that selectively damages pathogenic cells through reactive oxygen species (ROS) that have been deliberately produced by light-activated chemical reactions via a photosensitiser. There are currently some limitations to its wider deployment, including aggregation, hydrophobicity, and sub-optimal penetration capabilities of the photosensitiser, all of which decrease the production of ROS and lead to reduced therapeutic performance. In combination with nanoparticles, however, these challenges may be overcome. Their small size, functionalisable structure, and large contact surface allow a high degree of internalization by cellular membranes and tissue barriers. In this review, we first summarise the mechanism of PDT action and the interaction between nanoparticles and the cell membrane. We then introduce the categorisation of nanoparticles in PDT, acting as nanocarriers, photosensitising molecules, and transducers, in which we highlight their use against a range of bacterial and fungal pathogens. We also compare the antimicrobial efficiency of nanoparticles to unbound photosensitisers and examine the relevant safety considerations. Finally, we discuss the use of nanoparticulate drug delivery systems in clinical applications of antimicrobial PDT.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".