The pandemic of COVID-19 is continuously spreading, becoming a worldwide emergency. Early and fast identification of subjects with a current or past infection must be achieved to slow down the epidemiological widening. Here we report a Raman-based approach for the analysis of saliva, able to significantly discriminate the signal of patients with a current infection by COVID-19 from healthy subjects and/or subjects with a past infection. Our results demonstrated the differences in saliva biochemical composition of the three experimental groups, with modifications grouped in specific attributable spectral regions. The Raman-based classification model was able to discriminate the signal collected from COVID-19 patients with accuracy, precision, sensitivity and specificity of more than 95%. In order to translate this discrimination from the signal-level to the patient-level, we developed a Deep Learning model obtaining accuracy in the range 89–92%. These findings have implications for the creation of a potential Raman-based diagnostic tool, using saliva as minimal invasive and highly informative biofluid, demonstrating the efficacy of the classification model.

Mechanotransduction is the ability of cells to translate mechanical stimuli into biochemical signals that can ultimately influence gene expression, cell morphology and cell fate. Tenocytes are responsible for tendon mechanical adaptation converting mechanical stimuli imposed during mechanical loading, thus affecting extracellular matrix homeostasis. Since we previously demonstrated that MD-Tissue, an injectable collagen-based medical compound containing swine-derived collagen as the main component, is able to affect tenocyte properties, the aim of this study was to analyze whether the effects triggered by MD-Tissue were based on mechanotransduction-related mechanisms. For this purpose, MD-Tissue was used to coat Petri dishes and cytochalasin B was used to deprive tenocytes of mechanical stimulation mediated by the actin cytoskeleton. Cell morphology, migration, collagen turnover pathways and the expression of key mechanosensors were analyzed by morphological and molecular methods. Our findings confirm that MD-Tissue affects collagen turnover pathways and favors cell migration and show that the MD-Tissue-induced effect represents a mechanical input involving the mechanotransduction machinery. Overall, MD-Tissue, acting as a mechanical scaffold, could represent an effective medical device for a novel therapeutic, regenerative and rehabilitative approach to favor tendon healing in tendinopathies.

Corneal endothelial cells (CEnCs) play a fundamental role in maintaining the transparency of the cornea. CEnCs lose their full proliferating capacity when tissue damages occur. The loss in proliferation rate is associated with corneal edema and decrease in visual acuity, leading in severe cases, to blindness. In these situations, a corneal transplant is usually needed to restore the original tissue functions. Tissue engineering is an efficient alternative for the production of implantable films, which can regenerate the tissue functions regulating at the same time the immune-response. In this study, we proposed a stable and transparent film, composed of silk fibroin modified with glycerol (G/SF), as a potential substrate for corneal endothelial cells regeneration. Our results confirmed that G/SF films have a uniform structure, rougher surface and lower thickness respect to the SF film. In vitro tests show that G/SF films can induce a slight increase in CEnCs initial adhesion and proliferation rate if compared with the SF film. Morphology and gene expression evaluations demonstrated that the bioactive effects of silk fibroin were not affected by the presence of glycerol. For this reason, the G/SF films are suitable as CEnCs carrier and promising for the corneal damages treatments.