Patch-Based Far-Infrared Radiation (FIR) Therapy Does Not Impact Cell Tracking or Motility of Human Melanoma Cells In Vitro.

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.

Integrating machine learning and biosensors in microfluidic devices: A review.

Microfluidic devices are increasingly widespread in the literature, being applied to numerous exciting applications, from chemical research to Point-of-Care devices, passing through drug development and clinical scenarios. Setting up these microenvironments, however, introduces the necessity of locally controlling the variables involved in the phenomena under investigation. For this reason, the literature has deeply explored the possibility of introducing sensing elements to investigate the physical quantities and the biochemical concentration inside microfluidic devices. Biosensors, particularly, are well known for their high accuracy, selectivity, and responsiveness. However, their signals could be challenging to interpret and must be carefully analysed to carry out the correct information. In addition, proper data analysis has been demonstrated even to increase biosensors' mentioned qualities. To this regard, machine learning algorithms are undoubtedly among the most suitable approaches to undertake this job, automatically learning from data and highlighting biosensor signals' characteristics at best. Interestingly, it was also demonstrated to benefit microfluidic devices themselves, in a new paradigm that the literature is starting to name "intelligent microfluidics", ideally closing this benefic interaction among these disciplines. This review aims to demonstrate the advantages of the triad paradigm microfluidics-biosensors-machine learning, which is still little used but has a great perspective. After briefly describing the single entities, the different sections will demonstrate the benefits of the dual interactions, highlighting the applications where the reviewed triad paradigm was employed.

Cell Electrokinetic Fingerprint: A Novel Approach Based on Optically Induced Dielectrophoresis (ODEP) for In-Flow Identification of Single Cells.

A novel optically induced dielectrophoresis (ODEP) system that can operate under flow conditions is designed for automatic trapping of cells and subsequent induction of 2D multi-frequency cell trajectories. Like in a "ping-pong" match, two virtual electrode barriers operate in an alternate mode with varying frequencies of the input voltage. The so-derived cell motions are characterized via time-lapse microscopy, cell tracking, and state-of-the-art machine learning algorithms, like the wavelet scattering transform (WST). As a cell-electrokinetic fingerprint, the dynamic of variation of the cell displacements happening, over time, is quantified in response to different frequency values of the induced electric field. When tested on two biological scenarios in the cancer domain, the proposed approach discriminates cellular dielectric phenotypes obtained, respectively, at different early phases of drug-induced apoptosis in prostate cancer (PC3) cells and for differential expression of the lectine-like oxidized low-density lipoprotein receptor-1 (LOX-1) transcript levels in human colorectal adenocarcinoma (DLD-1) cells. The results demonstrate increased discrimination of the proposed system and pose an additional basis for making ODEP-based assays addressing cancer heterogeneity for precision medicine and pharmacological research.

Hepatitis C Virus Micro-Elimination Plan in Southern Italy: The "HCV ICEberg" Project.

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.

NeuriTES. Monitoring neurite changes through transfer entropy and semantic segmentation in bright-field time-lapse microscopy.

One of the most challenging frontiers in biological systems understanding is fluorescent label-free imaging. We present here the NeuriTES platform that revisits the standard paradigms of video analysis to detect unlabeled objects and adapt to the dynamic evolution of the phenomenon under observation. Object segmentation is reformulated using robust algorithms to assure regular cell detection and transfer entropy measures are used to study the inter-relationship among the parameters related to the evolving system. We applied the NeuriTES platform to the automatic analysis of neurites degeneration in presence of amyotrophic lateral sclerosis (ALS) and to the study of the effects of a chemotherapy drug on living prostate cancer cells (PC3) cultures. Control cells have been considered in both the two cases study. Accuracy values of 93% and of 92% are achieved, respectively. NeuriTES not only represents a tool for investigation in fluorescent label-free images but demonstrates to be adaptable to individual needs.

Deciphering Cancer Cell Behavior From Motility and Shape Features: Peer Prediction and Dynamic Selection to Support Cancer Diagnosis and Therapy.

Cell motility varies according to intrinsic features and microenvironmental stimuli, being a signature of underlying biological phenomena. The heterogeneity in cell response, due to multilevel cell diversity especially relevant in cancer, poses a challenge in identifying the biological scenario from cell trajectories. We propose here a novel peer prediction strategy among cell trajectories, deciphering cell state (tumor vs. nontumor), tumor stage, and response to the anticancer drug etoposide, based on morphology and motility features, solving the strong heterogeneity of individual cell properties. The proposed approach first barcodes cell trajectories, then automatically selects the good ones for optimal model construction (good teacher and test sample selection), and finally extracts a collective response from the heterogeneous populations via cooperative learning approaches, discriminating with high accuracy prostate noncancer vs. cancer cells of high vs. low malignancy. Comparison with standard classification methods validates our approach, which therefore represents a promising tool for addressing clinically relevant issues in cancer diagnosis and therapy, e.g., detection of potentially metastatic cells and anticancer drug screening.

A Camera Sensors-Based System to Study Drug Effects On In Vitro Motility: The Case of PC-3 Prostate Cancer Cells.

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.

Infection with hepatitis viruses, FIB-4 index and risk of hepatocellular carcinoma in southern Italy: a population-based cohort study.

BACKGROUND: The incidence of hepatocellular carcinoma (HCC) and its association with hepatitis C (HCV) and hepatitis B virus (HBV) infections, FIB-4 index and liver enzymes was assessed in an area of the province of Naples covered by a population-based cancer registry. METHODS: We conducted a cohort investigation on 4492 individuals previously enrolled in a population-based seroprevalent survey on HCV and HBV infections. The diagnosis of HCC was assessed through a record linkage with the cancer registry. Hepatic metabolic activity was measured through serum alanine transaminase, aspartate aminotransferase, gamma-glutamyl-transferase, and platelet. The FIB-4 index was used as a marker of fibrosis. We computed HCC incidence rates (IR) for 100,000 (105) person-years of observation, and multivariable hazard ratios (HR) with 95 % confidence intervals (CI) to assess risk factors for HCC. RESULTS: Twenty two cases of HCC were diagnosed during follow-up (IR = 63.3 cases/105). Significantly increased HCC risks were documented in individuals with higher than normal liver enzymes and low platelet count; in the 239 HCV RNA-positives (HR = 61.8, 95 % CI:13.3-286); and in the 95 HBsAg-positives (HR = 75.0) -as compared to uninfected individuals. The highest FIB-4 score was associated with a 17.6-fold increased HCC risk. CONCLUSIONS: An elevated FIB-4 index turned out to be an important predictor of HCC occurrence. Although the standard method to assess hepatic fibrosis in chronic hepatitis remains the histologic staging of liver biopsy specimen, the assessment of FIB-4 in HCV RNA-positive individuals may help in identifying the highest HCC-risk individuals who need anti-HCV treatment most urgently.