Comparative Study of Blue Light with Ultraviolet (UVC) Radiations on SARS-CoV-2 Virus
Bibliographic record
Abstract
The ongoing coronavirus pandemic requires more effective disinfection methods. The disinfection using ultraviolet light (UV), especially longer UVCs such as 254 nm, 270/280 nm have been proved to have virucidal properties, but its adverse effects on human skin and eyes limit its use to enclosed unoccupied spaces. Several studies conducted the in past have shown the effectiveness of blue light (405nm) against bacteria and fungi, but the virucidal property of 405nm has largely been unexplored. Based on previous studies, visible light mediates inactivation by absorbing the porphyrins and reacting with oxygen to produce reactive oxygen species (ROS). This causes oxidative damage to biomolecules such as protein, lipids, and nucleic acids, essential constituents of any virus. The virucidal potential of visible light has been speculated because the virus lacks porphyrins. This study demonstrated porphyrin independent viral inactivation and a comparative analysis of the effectiveness of 405nm against other UVC wavelengths. The beta coronavirus 1 (strain OC43) was treated against 405nm, 270/280nm, 254nm, and 222nm, and its efficacy was determined using median tissue culture infectious dose, i.e., TCID50. The results support the disinfection potential of visible light technology by providing a quantitative effect that can serve as a basic groundwork for future visible light inactivation technologies. In the future, blue light technology usage can be widened to hospitals, public places, aircraft cabins, and/or infectious laboratories to inactivate SARS-CoV-2.
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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 | 0.001 |
| 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) | 0.001 | 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".