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Ultraviolet C (UVC) light has long been used as a sterilizing agent, primarily through devices that emit at 254 nm. Depending on the dose and duration of exposure, UV 254 nm can cause erythema and photokeratitis and potentially cause skin cancer since it directly modifies nitrogenated nucleic acid bases. Filtered KrCl excimer lamps (emitting mainly at 222 nm) have emerged as safer germicidal tools and have even been proposed as devices to sterilize surgical wounds. All the studies that showed the safety of 222 nm analyzed cell number and viability, erythema generation, epidermal thickening, the formation of genetic lesions such as cyclobutane pyrimidine dimers (CPDs) and pyrimidine-(6-4)-pyrimidone photoproducts (6-4PPs) and cancer-inducing potential. Although nucleic acids can absorb and be modified by both UV 254 nm and UV 222 nm equally, compared to UV 254 nm, UV 222 nm is more intensely absorbed by proteins (especially aromatic side chains), causing photooxidation and cross-linking. Here, in addition to analyzing DNA lesion formation, for the first time, we evaluated changes in the proteome and cellular pathways, reactive oxygen species formation, and metalloproteinase (MMP) levels and activity in full-thicknessin vitroreconstructed human skin (RHS) exposed to UV 222 nm. We also performed the longest (40 days)in vivostudy of UV 222 nm exposure in the HRS/J mouse model at the occupational threshold limit value (TLV) for indirect exposure (25 mJ/cm2) and evaluated overall skin morphology, cellular pathological alterations, CPD and 6-4PP formation and MMP-9 activity. Our study showed that processes related to reactive oxygen species and inflammatory responses were more altered by UV 254 nm than by UV 222 nm. Our chronicin vivoexposure assay using the TLV confirmed that UV 222 nm causes minor damage to the skin. However, alterations in pathways related to skin regeneration raise concerns about direct exposure to UV 222 nm.

The effect of image enhancement methods on the final result of image analysis workflows is often left out of discussions in scientific papers. In fact, before reaching a definitive enhancement workflow and its settings, there often is a great amount of pre-testing and parameter tweaking. In this work, we take the biofilament tracing problem and propose a systematic approach to testing and evaluating major image enhancement methods that are applied prior to execution of six filament tracing methods (APP, APP2, FarSIGHT Snake, NeuronStudio, Neutube and Rivulet2). We used a full factorial design of experiments to analyse five enhancement methods (deconvolution, background subtraction, pixel intensity normalization, Frangi vessel enhancement and smoothing) and the order in which they are applied, evaluating their effect on the signal-to-noise ratio, structural similarity index and geometric tracing scores of 3D images of a fungal mycelium and a synthetic neuronal tree. Our approach proved valuable as a tool to support the choice of enhancement and filament tracing workflow. For example, the use of deconvolution followed by median filtering gives the best geometric tracing scores if Neutube is used in the image of the fungal mycelium. Also, we show that FarSIGHT Snake and Neutube are the most robust filament tracing methods to changes in image quality. In addition, we reinforce the importance of extensive testing of new filament tracing methods against a broad range of image qualities and filament characteristics.