Search

Far-Infrared Radiation (FIR) is emerging as a novel non-invasive tool for mitigating inflammation and oxidative stress, offering potential benefits for certain medical conditions such as cardiovascular disease and chronic inflammatory disorders. We previously demonstrated that the application of patch-based FIR therapy on human umbilical vein endothelial cells (HUVECs) reduced the expression of inflammatory biomarkers and the levels of reactive oxygen species (ROS). Several in vitro studies have shown the inhibitory effects of FIR therapy on cell growth in different cancer cells (including murine melanoma cells), mainly using the wound healing assay, without direct cell motility or tracking analysis. The main objective of the present study was to conduct an in-depth analysis of single-cell motility and tracking during the wound healing assay, using an innovative high-throughput technique in the human melanoma cell line M14/C2. This technique evaluates various motility descriptors, such as average velocity, average curvature, average turning angle, and diffusion coefficient. Our results demonstrated that patch-based FIR therapy did not impact cell proliferation and viability or the activation of mitogen-activated protein kinases (MAPKs) in the human melanoma cell line M14/C2. Moreover, no significant differences in cell motility and tracking were observed between control cells and patch-treated cells. Altogether, these findings confirm the beneficial effects of the in vitro application of patch-based FIR therapy in human melanoma cell lines, although such effects need to be confirmed in future in vivo studies.

This study evaluates the feasibility of a local action program for HCV micro-elimination in highly endemic areas. Retrospective analysis: administrative and laboratory data (Local Health Unit, southern Italy) were integrated to quantize the anti-HCV-positive subjects not RNA tested and untreated HCV-infected subjects (2018–2022). Prospective analysis: all subjects admitted to a division of the LHU largest hospital (2021–2022) were tested for HCV, with linkage of active-infected patients to care. Overall, 49287 subjects were HCV-Ab tested: 1071 (2.2%) resulted positive without information for an HCV RNA test and 230 (0.5%) had an active infection not yet cured. Among 856 admitted subjects, 54 (6.3%) were HCV-Ab+ and 27 (3.0%) HCV RNA+. Of HCV-infected patients, 22.2% had advanced liver disease, highlighting the need for earlier diagnosis; 27.7% were unaware of HCV infection; and 20.4% were previously aware but never referred to a clinical center. Of these, 26% died and 74% received treatment. Our study emphasizes the value of an active HCV hospital case-finding program to enhance diagnosis in patients with several comorbidities and to easily link them to care. Our data strongly suggest extending this program to all hospital wards/access as a standard of care, particularly in highly endemic areas, to help HCV disease control and take steps in achieving the elimination goals.

Cell motility is the brilliant result of cell status and its interaction with close environments. Its detection is now possible, thanks to the synergy of high-resolution camera sensors, time-lapse microscopy devices, and dedicated software tools for video and data analysis. In this scenario, we formulated a novel paradigm in which we considered the individual cells as a sort of sensitive element of a sensor, which exploits the camera as a transducer returning the movement of the cell as an output signal. In this way, cell movement allows us to retrieve information about the chemical composition of the close environment. To optimally exploit this information, in this work, we introduce a new setting, in which a cell trajectory is divided into sub-tracks, each one characterized by a specific motion kind. Hence, we considered all the sub-tracks of the single-cell trajectory as the signals of a virtual array of cell motility-based sensors. The kinematics of each sub-track is quantified and used for a classification task. To investigate the potential of the proposed approach, we have compared the achieved performances with those obtained by using a single-trajectory paradigm with the scope to evaluate the chemotherapy treatment effects on prostate cancer cells. Novel pattern recognition algorithms have been applied to the descriptors extracted at a sub-track level by implementing features, as well as samples selection (a good teacher learning approach) for model construction. The experimental results have put in evidence that the performances are higher when a further cluster majority role has been considered, by emulating a sort of sensor fusion procedure. All of these results highlighted the high strength of the proposed approach, and straightforwardly prefigure its use in lab-on-chip or organ-on-chip applications, where the cell motility analysis can be massively applied using time-lapse microscopy images.