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.

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.

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).

Investigating the impact of greenery elements in office environments on cognitive performance, visual attention and distraction: An eye-tracking pilot-study in virtual reality.

The human-nature connection is one of the main aspects determining supportive and comfortable office environments. In this context, the application of eye-tracking-equipped Virtual Reality (VR) devices to support an evaluation on the effect of greenery elements indoors on individuals' efficiency and engagement is limited. A new approach to investigate visual attention, distraction, cognitive load and performance in this field is carried out via a pilot-study comparing three virtual office layouts (Indoor Green, Outdoor Green and Non-Biophilic).

Focus enhancement in long schlieren imaging systems using corrector lenses.

Long schlieren imaging systems, where the parallel light test section is longer than the focal length of the focusing schlieren optics, have a limited region in the test section in which any occluding object, such as a wind tunnel model, is in focus in the schlieren image. Corrector lenses are introduced here to alter the location in the test section where image focus is achieved. Corrector lenses with focal lengths varying from -1000 to -100 were studied.

Deep-Manager: a versatile tool for optimal feature selection in live-cell imaging analysis.

One of the major problems in bioimaging, often highly underestimated, is whether features extracted for a discrimination or regression task will remain valid for a broader set of similar experiments or in the presence of unpredictable perturbations during the image acquisition process. Such an issue is even more important when it is addressed in the context of deep learning features due to the lack of a priori known relationship between the black-box descriptors (deep features) and the phenotypic properties of the biological entities under study. In this regard, the widespread use of descriptors, such as those coming from pre-trained Convolutional Neural Networks (CNNs), is hindered by the fact that they are devoid of apparent physical meaning and strongly subjected to unspecific biases, i.

Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response.

High-throughput phenotyping is becoming increasingly available thanks to analytical and bioinformatics approaches that enable the use of very high-dimensional data and to the availability of dynamic models that link phenomena across levels: from genes to cells, from cells to organs, and through the whole organism. The combination of phenomics, deep learning, and machine learning represents a strong potential for the phenotypical investigation, leading the way to a more embracing approach, called machine learning phenomics (MLP). In particular, in this work we present a novel MLP platform for phenomics investigation of cancer-cells response to therapy, exploiting and combining the potential of time-lapse microscopy for cell behavior data acquisition and robust deep learning software architectures for the latent phenotypes extraction.

Sustainable and resilient strategies for touristic cities against COVID-19: An agent-based approach.

Touristic cities will suffer from COVID-19 emergency because of its economic impact on their communities. The first emergency phases involved a wide closure of such areas to support "social distancing" measures (i.e.

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.

A probabilistic model to evaluate the effectiveness of main solutions to COVID-19 spreading in university buildings according to proximity and time-based consolidated criteria.

Unlabelled: University buildings are one of the most relevant closed environments in which the COVID-19 event clearly pointed out stakeholders' needs toward safety issues, especially because of the possibility of day-to-day presences of the same users (i.e. students, teachers) and overcrowding causing long-lasting contacts with possible "infectors".

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.

Accelerating the experimental responses on cell behaviors: a long-term prediction of cell trajectories using Social Generative Adversarial Network.

The incremented uptake provided by time-lapse microscopy in Organ-on-a-Chip (OoC) devices allowed increased attention to the dynamics of the co-cultured systems. However, the amount of information stored in long-time experiments may constitute a serious bottleneck of the experimental pipeline. Forward long-term prediction of cell trajectories may reduce the spatial-temporal burden of video sequences storage.

Machine Learning (ML) based-method applied in recurrent pregnancy loss (RPL) patients diagnostic work-up: a potential innovation in common clinical practice.

RPL is a very debated condition, in which many issues concerning definition, etiological factors to investigate or therapies to apply are still controversial. ML could help clinicians to reach an objectiveness in RPL classification and access to care. Our aim was to stratify RPL patients in different risk classes by applying an ML algorithm, through a diagnostic work-up to validate it for the appropriate prognosis and potential therapeutic approach.

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.

Neandertals on the beach: Use of marine resources at Grotta dei Moscerini (Latium, Italy).

Excavated in 1949, Grotta dei Moscerini, dated MIS 5 to early MIS 4, is one of two Italian Neandertal sites with a large assemblage of retouched shells (n = 171) from 21 layers. The other occurrence is from the broadly contemporaneous layer L of Grotta del Cavallo in southern Italy (n = 126). Eight other Mousterian sites in Italy and one in Greece also have shell tools but in a very small number.

Toxicity of Thallium at Low Doses: A Review.

A mini review of the toxicity of Thallium (Tl) at low doses is herein presented. Thallium has severe toxicity. Although its acute biological effects have been widely investigated and are well known, its biological effects on human health and in cell cultures at low doses (<100 μg/L) due, for example, to Tl chronic exposure via consumption of contaminated water or foods, have often been overlooked or underestimated.

5-Aza Exposure Improves Reprogramming Process Through Embryoid Body Formation in Human Gingival Stem Cells.

Embryoid bodies (EBs) are three-dimensional aggregates formed by pluripotent stem cells, including embryonic stem cells and induced pluripotent stem cells. They are used as an model to evaluate early extraembryonic tissue formation and differentiation process. In the adult organisms, cell differentiation is controlled and realized through the epigenetic regulation of gene expression, which consists of various mechanisms including DNA methylation.

Efficient zinc uptake is critical for the ability of Pseudomonas aeruginosa to express virulence traits and colonize the human lung.

We have recently shown that Pseudomonas aeruginosa, an opportunistic pathogen that chronically infects the lungs of patients with cystic fibrosis (CF) and other forms of lung disease, is extremely efficient in recruiting zinc from the environment and that this capability is required for its ability to cause acute lung infections in mice. To verify that P. aeruginosa faces zinc shortage when colonizing the lungs of human patients, we analyzed the expression of three genes that are highly induced under conditions of zinc deficiency (zrmA, dksA2 and rpmE2), in bacteria in the sputum of patients with inflammatory lung disease.

Porphyromonas gingivalis lipopolysaccharide stimulation in human periodontal ligament stem cells: role of epigenetic modifications to the inflammation.

Periodontitis is a chronic oral inflammatory disease produced by bacteria. Gingival retraction and bone and connective tissues resorption are the hallmarks of this disease. Chronic periodontitis may contribute to the risk of onset or progression of neuroinflammatory pathological conditions, such as Alzheimer's disease.

Growth of Pseudomonas aeruginosa in zinc poor environments is promoted by a nicotianamine-related metallophore.

Previous studies have suggested that P. aeruginosa possesses redundant zinc uptake systems. To identify uncharacterized zinc transporters, we analyzed the genome-wide transcriptional responses of P.

Thallium-rich rust scales in drinkable water distribution systems: A case study from northern Tuscany, Italy.

Following the detection of a severe thallium contamination of the drinkable water from the public distribution system of Valdicastello Carducci-Pietrasanta (northern Tuscany, Italy), and the identification of the source of contamination in the Molini di Sant'Anna spring (average Tl content≈15μgL), the replacement of the contaminated water with a virtually Tl-free one (Tl<0.10μgL) caused an increase in Tl concentration in the drinkable water. This suggested that the pipeline interior had become a secondary source of Tl contamination, promoting its mineralogical and geochemical study.

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.

Prosurvival AMBRA1 turns into a proapoptotic BH3-like protein during mitochondrial apoptosis.

Autophagy and apoptosis are 2 stress-response mechanisms that are closely interconnected. However, the molecular interplays between these 2 pathways remain to be clarified. Here we report that the crucial proautophagic factor AMBRA1 can act as a positive mediator of mitochondrial apoptosis.