Precise detection of early melanomas is essential as the stage of disease guides treatment options. One growing field that may facilitate the advancement of early melanoma detection, is achieved through profiling serum extracellular vesicles (EVs) using sensitive nanotechnology. As a proof of principle, using a detection platform that combines a microfluidic device and surface‐enhanced Raman spectroscopy (SERS), the expression profiles of 4 protein biomarkers in serum EVs (termed as “EV SERS signatures”) derived from 20 early stage melanoma patients (including in situ melanoma) and 21 healthy participants are multiplexed. Significantly higher signal intensities of selected protein biomarkers are observed in serum EVs from melanoma patients compared with healthy participants, with mean fold‐changes ranging from 3.7 to 4.2. It is demonstrated that the EV SERS signatures can accurately separate melanoma patients and healthy individuals, with an area under the curve of 0.95. Thus, with further development, this ultra‐sensitive detection platform, combined with the panel of melanoma‐associated biomarkers, has the ability to differentiate early stage melanoma patients from healthy participants.

Early detection of skin pathologies with current clinical diagnostic tools is challenging, particularly when there are no visible colour changes or morphological cues present on the skin. In this study, we present a terahertz (THz) imaging technology based on a narrow band quantum cascade laser (QCL) at 2.8 THz for human skin pathology detection with diffraction limited spatial resolution. THz imaging was conducted for three different groups of unstained human skin samples (benign naevus, dysplastic naevus, and melanoma) and compared to the corresponding traditional histopathologic stained images. The minimum thickness of dehydrated human skin that can provide THz contrast was determined to be 50 µm, which is approximately one half-wavelength of the THz wave used. The THz images from different types of 50 µm-thick skin samples were well correlated with the histological findings. The per-sample locations of pathology vs healthy skin can be separated from the density distribution of the corresponding pixels in the THz amplitude–phase map. The possible THz contrast mechanisms relating to the origin of image contrast in addition to water content were analyzed from these dehydrated samples. Our findings suggest that THz imaging could provide a feasible imaging modality for skin cancer detection that is beyond the visible.

Inflammatory skin conditions are the 4th leading cause of non-fatal health burden in the general population worldwide. The diagnosis of skin lesions due to systemic drug reactions, viral or bacterial exanthems, or in patients with psoriasis, atopic dermatitis or contact dermatitis is often difficult and relies heavily upon conventional histopathologic examination. Conversely, it is widely accepted that the cutaneous profile of inflammatory markers, or ‘inflammatory signature’, is differentially expressed in various skin conditions. In this pilot study, we investigated the possibility of inflammatory skin disease diagnosis from an immunological perspective in small punch biopsies. We collected lesional and perilesional punch biopsies from 139 patients suffering from a variety of inflammatory skin conditions and attending the Dermatology Department at the Princess Alexandra Hospital in Brisbane, Australia. Using bead-based immunoassays we were able to measure 13 out of 17 inflammatory markers from a pre-selected multi-analyte panel and to detect significant differences between lesional and perilesional biopsies from each individual patient. Hierarchical and unbiased clustering methods based on inflammatory signatures grouped psoriasis and atopic dermatitis lesions into individual clusters in contrast to other skin conditions, highlighting the potential of inflammatory signatures to be used as diagnostic differentiators and to inform alternative targets in anti-inflammatory treatment strategies.

Applying spatial transcriptomics (ST) to explore a vast amount of formalin-fixed paraffin-embedded (FFPE) archival cancer tissues has been highly challenging due to several critical technical issues. In this work, we optimised ST protocols to generate unprecedented spatial gene expression data for FFPE skin cancer. Skin is among the most challenging tissue types for ST due to its fibrous structure and a high risk of RNAse contamination. We evaluated tissues collected from ten years to two years ago, spanning a range of tissue qualities and complexity. Technical replicates and multiple patient samples were assessed. Further, we integrated gene expression profiles with pathological information, revealing a new layer of molecular information. Such integration is powerful in cancer research and clinical applications. The data allowed us to detect the spatial expression of non-coding RNAs. Together, this work provides important technical perspectives to enable the applications of ST on archived cancer tissues.

Comparison of three methods (Consensual Expert Judgement, Algorithmic and Probabilistic Approaches) of causality assessment of adverse drug reactions: an assessment using reports made to a French pharmacovigilance centre.