Quantitative UV-C dose validation with photochromic indicators for informed N95 emergency decontamination
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Abstract
With COVID-19 N95 shortages, frontline medical personnel are forced to reuse this disposable–but sophisticated–multilayer respirator. Widely used to decontaminate nonporous surfaces, UV-C light has demonstrated germicidal efficacy on porous, non-planar N95 respirators when all surfaces receive ≥1.0 J/cm 2 dose. Of utmost importance across disciplines, translation of empirical evidence to implementation relies upon UV-C measurements frequently confounded by radiometer complexities. To enable rigorous on-respirator measurements, we introduce a photochromic indicator dose quantification technique for: (1) UV-C treatment design and (2) in-process UV-C dose validation. While addressing outstanding indicator limitations of qualitative readout and insufficient dynamic range, our methodology establishes that color-changing dosimetry can achieve the necessary accuracy (>90%), uncertainty (<10%), and UV-C specificity (>95%) required for UV-C dose measurements. In a measurement infeasible with radiometers, we observe a striking ~20× dose variation over N95s within one decontamination system. Furthermore, we adapt consumer electronics for accessible quantitative readout and use optical attenuators to extend indicator dynamic range >10× to quantify doses relevant for N95 decontamination. By transforming photochromic indicators into quantitative dosimeters, we illuminate critical considerations for both photochromic indicators themselves and UV-C decontamination processes.
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SciScore for 10.1101/2020.07.17.20156497: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Similarly, the L*a*b* colors were converted to the RGB color space using built-in MATLAB functions and the Euclidean RGB color difference was computed as: Similar as in the Lab* color space, ΔR, ΔG, and ΔB are the differences between the exposed and reference RGB coordinates.
MATLABsuggested: (MATLAB, RRID:SCR_001622)Measured doses and their uncertainties (one standard deviation, utotal and unorm) were plotted as heatmaps and histograms using the ‘inferno’ perceptually uniform, colorblind-friendly colormap, which was created by Stéfan van der Walt and Nathaniel Smith and adapted from … SciScore for 10.1101/2020.07.17.20156497: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Similarly, the L*a*b* colors were converted to the RGB color space using built-in MATLAB functions and the Euclidean RGB color difference was computed as: Similar as in the Lab* color space, ΔR, ΔG, and ΔB are the differences between the exposed and reference RGB coordinates.
MATLABsuggested: (MATLAB, RRID:SCR_001622)Measured doses and their uncertainties (one standard deviation, utotal and unorm) were plotted as heatmaps and histograms using the ‘inferno’ perceptually uniform, colorblind-friendly colormap, which was created by Stéfan van der Walt and Nathaniel Smith and adapted from Python’s matplotlib for use in MATLAB® by Ander Biguri39 Python’ssuggested: (PyMVPA, RRID:SCR_006099)matplotlibsuggested: (MatPlotLib, RRID:SCR_008624)txt files using a custom Python script and read into MATLAB®. Pythonsuggested: (IPython, RRID:SCR_001658)MATLAB®suggested: (MATLAB, RRID:SCR_001622)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Device-specific calibration facilitates quantification using widely-available imaging tools: To overcome spectrocolorimeter limitations (e.g., cost, availability, and throughput) as well as capture spatial information already recorded in the PCIs, we generated and assessed device-specific calibration curves using widely-available imaging tools under controlled lighting conditions. The calibration curves were generated from raw images of PCIs acquired using a flatbed scanner (Canon LiDE-400), digital SLR camera (DSLR, Nikon D5500), and smartphone (iPhone X) (DSLR and iPhone images were acquired in a light box to provide isolation from ambient illumination). All tools captured the entire surface of both the exposed PCI as well as an unexposed reference. The resulting calibration curves were then compared to those generated with data from the RM200QC (Figure 4(a)). We observe the highest CIEDE2000 ΔE values from measurement with the cameras. Though the flatbed scanner measures the lowest ΔE values, its measurements correspond with those of the RM200QC and conveniently do not require a light box. We further assessed the squared sums of the residuals (SSE) for all fits as a measure of calibration accuracy (Figure 4(b)) and include quantification of the dose measurement relative uncertainty in Supplementary Figure S11. We observe the lowest SSE for the RM200QC, but almost all imaging approaches yield PCI1 quantification meeting the 10% ideal target relative uncertainty for a ∼0.10 ...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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