Light-based therapy of infected wounds: a review of dose considerations for photodynamic microbial inactivation and photobiomodulation
Bibliographic record
Abstract
Significance: Chronic or surgical wound infections in healthcare remain a worldwide problem without satisfying options. Systemic or topical antibiotic use is an inadequate solution, given the increase in antimicrobial-resistant microbes. Hence, antibiotic-free alternatives are needed. Antimicrobial photodynamic inactivation (aPDI) has been shown to be effective in wound disinfection. Among the impediments to the wide utility of aPDI for wounds is the high variability in reported photosensitizer and light dose to be effective and unintentional detrimental impact on the wound closure rates. Additionally, the time required by the healthcare professional to deliver this therapy is excessive in the present form of delivery. Aim: We reviewed the dose ranges for various photosensitizers required to achieve wound disinfection or sterilization while not unintentionally inhibiting wound closure through concomitant photobiomodulation (PBM) processes. Approach: To allow comparison of aPDI or PBM administered doses, we employ a unified dose concept based on the number of absorbed photons per unit volume by the photosensitizer or cytochrome C oxidase for aPDI and PBM, respectively. Results: One notes that for current aPDI protocols, the absorbed photons per unit volume for wound disinfection or sterilization can lead to inhibiting normal wound closure through PBM processes. Conclusion: Options to reduce the dose discrepancy between effective aPDI and PBM are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".