BCC and Immunocryosurgery scar differentiation through computational resolution-enhanced OCT images and skin optical attenuation: A proof-of-concept study.

Monitoring medical therapy remains a challenging task across all non-surgical skin cancer treatment modalities. In addition, confirmation of residual tumours after treatment is essential for the early detection of potential relapses. Optical coherence tomography (OCT), a non-invasive method for real-time cross-sectional imaging of living tissue, is a promising imaging approach for assessing relatively flat, near-surface skin lesions, such as those that occur in most basal cell carcinomas (BCCs), at the time of diagnosis. However, the skin's inherent property of strong light scattering impedes the implementation of OCT in these cases due to the poor image quality. Furthermore, translating OCT's optical parameters into practical use in routine clinical settings is complicated due to substantial observer subjectivity. In this retrospective pilot study, we developed a workflow based on the upscale of the OCT images resolution using a deep generative adversarial network and the estimation of the skin optical attenuation coefficient. At the site of immunocryosurgery-treated BCC, the proposed methodology can extract optical parameters and discriminate objectively between tumour foci and scar tissue.

Towards non-invasive monitoring of non-melanoma skin cancer using spatially offset Raman spectroscopy.

BCC (basal cell carcinoma) and SCC (squamous cell carcinoma) account for the vast majority of cases of non-melanoma skin cancer (NMSC). The gold standard for the diagnosis remains biopsy, which, however, is an invasive and time-consuming procedure. In this study, we employed spatially offset Raman spectroscopy (SORS), a non-invasive approach, allowing the assessment of deeper skin tissue levels and collection of Raman photons with a bias towards the different layers of epidermis, where the non-melanoma cancers are initially formed and expand. Ex vivo Raman measurements were acquired from 22 skin biopsies using conventional back-scattering and a defocused modality (with and without a spatial offset). The spectral data were assessed against corresponding histopathological data to determine potential prognostic factors for lesion detection. The results revealed a positive correlation of protein and lipid content with the SCC and BCC types, respectively. By further correlating with patient data, multiple factor analysis (MFA) demonstrated a strong clustering of variables based on sex and age in all modalities. Specifically for the defocused modality (zero and 2 mm offset), further clustering occurred based on pathology. This study demonstrates the utility of the SORS technology in NMSC diagnosis prior to histopathological examination on the same tissue.

Microbial profiles of Greek PDO cheeses assessed with amplicon metabarcoding.

Greece is a country possessing many cheese products granted with a PDO (Protected Designation of Origin) certificate, with high exporting activities. In this study, we analyzed six popular cheese PDO products purchased from different industries to assess their microbial communities using amplicon metabarcoding analysis. To this end, using Next Generation Sequencing technology, we sequenced the 16S rRNA gene and the ITS spacer for prokaryotes and fungi, respectively. Alpha diversity indices revealed higher bacterial species richness for some cheeses (Kopanisti, Batzos) and poor for others (Feta, Galotiri). Kopanisti, together with Kalathaki and Anevato, also presented increased species diversity concerning fungal populations. Results showed that lactic acid bacteria (LAB) prevailed the bacterial populations in all samples (Lactococcus, Lactobacillus, Streptococcus, Leuconostoc), whereas for fungi, members of the Saccharomycetaceae, Dipodascaceae and Debaryomycetaceae families prevailed the fungal populations. Several other genera were identified that make up each product's microbiome leading to the creation of the unique organoleptic attributes of Greek PDO cheeses. However, the identified species could not be directly linked to certain cheese types, assuming that starter and adjunct cultures, combined with the raw material used during production greatly impact the microbial communities in cheeses. Our data, produced for the first time for six Greek PDO cheeses, can be exploited in the process of creating a core microbial signature within each cheese type, supporting the Greek brand name and valorizing cheese products.

The lipid profile of three Malassezia species assessed by Raman spectroscopy and discriminant analysis.

Malassezia yeasts constitute the major eukaryotic cutaneous flora of homoeothermic vertebrates. These lipophilic yeasts are able to cause, trigger, or aggravate common skin diseases under favorable conditions. Species identification and subspecies differentiation is currently based on morphological characteristics, lipid assimilation profile, and molecular tests. Mass spectrometry has been also reported as a reliable, yet costly and labor-intensive, method to classify Malassezia yeasts. Here, we introduce Raman spectroscopy as a new molecular technique able to differentiate three phylogenetically close Malassezia species (M.globosa, M.pachydermatis, and M.sympodialis) by examining their lipid metabolic profile. Using Raman spectroscopy, lipid fingerprints of Malassezia cultures on Leeming-Notman agar, were analyzed by spectral bands assignment and partial least squares discriminant analysis. Our results demonstrate differential utilization of lipid supplements among these three species and the ability of Raman spectroscopy to rapidly and accurately discriminate them by predictive modelling.