Translational feasibility and efficacy of nasal photodynamic disinfection of SARS-CoV-2
Bibliographic record
Abstract
The lack of therapeutic options to fight Covid-19 has contributed to the current global pandemic. Despite the emergence of effective vaccines, development of broad-spectrum antiviral treatment remains a significant challenge, in which antimicrobial photodynamic therapy (aPDT) may play a role, especially at early stages of infection. aPDT of the nares with methylene blue (MB) and non-thermal light has been successfully utilized to inactivate both bacterial and viral pathogens in the perioperative setting. Here, we investigated the effect of MB-aPDT to inactivate human betacoronavirus OC43 and SARS-CoV-2 in vitro and in a proof-of-principle COVID-19 clinical trial to test, in a variety of settings, the practicality, technical feasibility, and short-term efficacy of the method. aPDT yielded inactivation of up to 6-Logs in vitro, as measured by RT-qPCR and infectivity assay. From a photo-physics perspective, the in vitro results suggest that the response is not dependent on the virus itself, motivating potential use of aPDT for local destruction of SARS-CoV-2 and its variants. In the clinical trial we observed variable effects on viral RNA in nasal-swab samples as assessed by RT-qPCR attributed to aPDT-induced RNA fragmentation causing falsely-elevated counts. However, the viral infectivity in clinical nares swabs was reduced in 90% of samples and undetectable in 70% of samples. This is the first demonstration based on quantitative clinical viral infectivity measurements that MB-aPDT is a safe, easily delivered and effective front-line technique that can reduce local SARS-CoV-2 viral load.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".