Titanium dioxide nanoparticles (TiO₂NPs) suspensions (concentration 1.0 g/L) in synthetic sweat solution were applied on Franz cells for 24 h using intact and needle-abraded human skin. Titanium content into skin and receiving phases was determined. Cytotoxicity (MTT, AlamarBlue^® and propidium iodide, PI, uptake assays) was evaluated on HaCat keratinocytes after 24 h, 48 h, and seven days of exposure. After 24 h of exposure, no titanium was detectable in receiving solutions for both intact and damaged skin. Titanium was found in the epidermal layer after 24 h of exposure (0.47 ± 0.33 μg/cm²) while in the dermal layer, the concentration was below the limit of detection. Damaged skin, in its whole, has shown a similar concentration (0.53 ± 0.26 μg/cm²). Cytotoxicity studies on HaCaT cells demonstrated that TiO₂NPs induced cytotoxic effects only at very high concentrations, reducing cell viability after seven days of exposure with EC₅₀s of 8.8 × 10⁻⁴ M (MTT assay), 3.8 × 10⁻⁵ M (AlamarBlue^® assay), and 7.6 × 10⁻⁴ M (PI uptake, index of a necrotic cell death). Our study demonstrated that TiO₂NPs cannot permeate intact and damaged skin and can be found only in the stratum corneum and epidermis. Moreover, the low cytotoxic effect observed on human HaCaT keratinocytes suggests that these nano-compounds have a potential toxic effect at the skin level only after long-term exposure.

Background: High-throughput technologies enable the cost-effective collection and analysis of DNA methylation data throughout the human genome. This naturally entails missing values management that can complicate the analysis of the data. Several general and specific imputation methods are suitable for DNA methylation data. However, there are no detailed studies of their performances under different missing data mechanisms –(completely) at random or not- and different representations of DNA methylation levels (β and M-value). Results: We make an extensive analysis of the imputation performances of seven imputation methods on simulated missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR) methylation data. We further consider imputation performances on the popular β- and M-value representations of methylation levels. Overall, β-values enable better imputation performances than M-values. Imputation accuracy is lower for mid-range β-values, while it is generally more accurate for values at the extremes of the β-value range. The MAR values distribution is on the average more dense in the mid-range in comparison to the expected β-value distribution. As a consequence, MAR values are on average harder to impute. Conclusions: The results of the analysis provide guidelines for the most suitable imputation approaches for DNA methylation data under different representations of DNA methylation levels and different missing data mechanisms.