Machine Learning Analysis of Human Skin by Optoacoustic Mesoscopy for Automated Extraction of Psoriasis and Aging Biomarkers.

Ultra-wideband raster-scan optoacoustic mesoscopy (RSOM) is a novel modality that has demonstrated unprecedented ability to visualize epidermal and dermal structures in-vivo. However, an automatic and quantitative analysis of three-dimensional RSOM datasets remains unexplored. In this work we present our framework: Deep Learning RSOM Analysis Pipeline (DeepRAP), to analyze and quantify morphological skin features recorded by RSOM and extract imaging biomarkers for disease characterization. DeepRAP uses a multi-network segmentation strategy based on convolutional neural networks with transfer learning. This strategy enabled the automatic recognition of skin layers and subsequent segmentation of dermal microvasculature with an accuracy equivalent to human assessment. DeepRAP was validated against manual segmentation on 25 psoriasis patients under treatment and our biomarker extraction was shown to characterize disease severity and progression well with a strong correlation to physician evaluation and histology. In a unique validation experiment, we applied DeepRAP in a time series sequence of occlusion-induced hyperemia from 10 healthy volunteers. We observe how the biomarkers decrease and recover during the occlusion and release process, demonstrating accurate performance and reproducibility of DeepRAP. Furthermore, we analyzed a cohort of 75 volunteers and defined a relationship between aging and microvascular features in-vivo. More precisely, this study revealed that fine microvascular features in the dermal layer have the strongest correlation to age. The ability of our newly developed framework to enable the rapid study of human skin morphology and microvasculature in-vivo promises to replace biopsy studies, increasing the translational potential of RSOM.

Immunoassay screening in urine for synthetic cannabinoids - an evaluation of the diagnostic efficiency.

BACKGROUND: The abuse of synthetic cannabinoids (SCs) as presumed legal alternative to cannabis poses a great risk to public health. For economic reasons many laboratories use immunoassays (IAs) to screen for these substances in urine. However, the structural diversity and high potency of these designer drugs places high demands on IAs regarding cross-reactivity of the antibodies used and detection limits. METHODS: Two retrospective studies were carried out in order to evaluate the capability of two homogenous enzyme IAs for the detection of currently prevalent SCs in authentic urine samples. Urine samples were analyzed utilizing a 'JWH-018' kit and a 'UR-144' kit. The IA results were confirmed by an up-to-date liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) screening method covering metabolites of 45 SCs. RESULTS: The first study (n=549) showed an 8% prevalence of SCs use (LC-MS/MS analysis) among inpatients of forensic-psychiatric clinics, whereas all samples were tested negative by the IAs. In a second study (n=200) the combined application of both IAs led to a sensitivity of 2% and a diagnostic accuracy of 51% when applying the recommended IA cut-offs. Overall, 10 different currently prevalent SCs were detected in this population. The results can be explained by an insufficient cross-reactivity of the antibodies towards current SCs in combination with relatively high detection limits of the IAs. CONCLUSIONS: In light of the presented study data it is strongly recommended not to rely on the evaluated IA tests for SCs in clinical or forensic settings. For IA kits of other providers similar results can be expected.