Drug delivery strategies for porphyrin-based photosensitizers in photodynamic antimicrobial chemotherapy
Bibliographic record
Abstract
In the face of increasing antimicrobial resistance (AMR) and the looming threat of a return to a pre-antibiotics era, the exploration of alternative antimicrobial strategies becomes imperative. Among these, Photodynamic Antimicrobial Chemotherapy (PACT) stands out as a promising solution due to its multi-target principle, diverging from the key-lock mechanism inherent in conventional antibiotics. However, the hydrophobic nature, non-targeted distribution, stability issues, and limited tissue penetration of photosensitizers (PSs), particularly the extensively studied porphyrin-based compounds, pose structural limitations impacting their bioavailability and effective delivery to target sites. Therefore, various delivery strategies are adopted to improve their effectiveness in PACT. This includes attaching the PS to a variety of delivery vehicles such as nanoparticles, and liposomes, or encapsulating them in nanostructures, such as micelles or dendrimers. This helps protect the PS from degradation, improve their solubility and bioavailability, and enhance their photophysical and photochemical properties, ultimately improving their effectiveness in PACT. In this paper, recent studies focusing on strategies used to improve the delivery of porphyrin-based PS in PACT are reviewed.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".