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.

DeepCEL0 for 2D single-molecule localization in fluorescence microscopy.

MOTIVATION: In fluorescence microscopy, single-molecule localization microscopy (SMLM) techniques aim at localizing with high-precision high-density fluorescent molecules by stochastically activating and imaging small subsets of blinking emitters. Super resolution plays an important role in this field since it allows to go beyond the intrinsic light diffraction limit. RESULTS: In this work, we propose a deep learning-based algorithm for precise molecule localization of high-density frames acquired by SMLM techniques whose ℓ2-based loss function is regularized by non-negative and ℓ0-based constraints. The ℓ0 is relaxed through its continuous exact ℓ0 (CEL0) counterpart. The arising approach, named DeepCEL0, is parameter-free, more flexible, faster and provides more precise molecule localization maps if compared to the other state-of-the-art methods. We validate our approach on both simulated and real fluorescence microscopy data. AVAILABILITY AND IMPLEMENTATION: DeepCEL0 code is freely accessible at https://github.com/sedaboni/DeepCEL0.

Personal VOCs Exposure with a Sensor Network Based on Low-Cost Gas Sensor, and Machine Learning Enabled Indoor Localization.

Indoor locations with limited air exchange can easily be contaminated by harmful volatile compounds. Thus, is of great interest to monitor the distribution of chemicals indoors to reduce associated risks. To this end, we introduce a monitoring system based on a Machine Learning approach that processes the information delivered by a low-cost wearable VOC sensor incorporated in a Wireless Sensor Network (WSN). The WSN includes fixed anchor nodes necessary for the localization of mobile devices. The localization of mobile sensor units is the main challenge for indoor applications. Yes. The localization of mobile devices was performed by analyzing the RSSIs with machine learning algorithms aimed at localizing the emitting source in a predefined map. Tests performed on a 120 m2 meandered indoor location showed a localization accuracy greater than 99%. The WSN, equipped with a commercial metal oxide semiconductor gas sensor, was used to map the distribution of ethanol from a point-like source. The sensor signal correlated with the actual ethanol concentration as measured by a PhotoIonization Detector (PID), demonstrating the simultaneous detection and localization of the VOC source.

Apoptosis mapping in space and time of 3D tumor ecosystems reveals transmissibility of cytotoxic cancer death.

The emerging tumor-on-chip (ToC) approaches allow to address biomedical questions out of reach with classical cell culture techniques: in biomimetic 3D hydrogels they partially reconstitute ex vivo the complexity of the tumor microenvironment and the cellular dynamics involving multiple cell types (cancer cells, immune cells, fibroblasts, etc.). However, a clear bottleneck is the extraction and interpretation of the rich biological information contained, sometime hidden, in the cell co-culture videos. In this work, we develop and apply novel video analysis algorithms to automatically measure the cytotoxic effects on human cancer cells (lung and breast) induced either by doxorubicin chemotherapy drug or by autologous tumor-infiltrating cytotoxic T lymphocytes (CTL). A live fluorescent dye (red) is used to selectively pre-stain the cancer cells before co-cultures and a live fluorescent reporter for caspase activity (green) is used to monitor apoptotic cell death. The here described open-source computational method, named STAMP (spatiotemporal apoptosis mapper), extracts the temporal kinetics and the spatial maps of cancer death, by localizing and tracking cancer cells in the red channel, and by counting the red to green transition signals, over 2-3 days. The robustness and versatility of the method is demonstrated by its application to different cell models and co-culture combinations. Noteworthy, this approach reveals the strong contribution of primary cancer-associated fibroblasts (CAFs) to breast cancer chemo-resistance, proving to be a powerful strategy to investigate intercellular cross-talks and drug resistance mechanisms. Moreover, we defined a new parameter, the 'potential of death induction', which is computed in time and in space to quantify the impact of dying cells on neighbor cells. We found that, contrary to natural death, cancer death induced by chemotherapy or by CTL is transmissible, in that it promotes the death of nearby cancer cells, suggesting the release of diffusible factors which amplify the initial cytotoxic stimulus.

A Lab-on-a-Chip Based Automatic Platform for Continuous Nitrites Sensing in Aquaculture.

In aquaculture, the density of fish stock, use of feeding, and surrounding environmental conditions can easily result in an excessive concentration of harmful compounds that require continuous monitoring. Chemical sensors are available for most of these compounds, however, operative conditions and continuous monitoring in water make the development of sensors suitable for long and unattended deployments difficult. A possible solution is the development of engineered automatic labs where the uptake of sample and the contact with water is reduced and the use of a minimal quantity of reagents enables the implementation of reliable chemical assays. In this paper, a platform for automatic chemical assays is presented. The concept is demonstrated with the detection of nitrites based on the well-known colorimetric Griess reaction. The platform is centered around a lab-on-a-chip where reagents and water samples are mixed. The color of the reaction product is measured with low-cost optoelectronic components. Results show the feasibility of the approach with a minimum detectable concentration of about 0.1 mg/L which is below the tolerance level for aquaculture farms.

Rapid Manufacturing of Multilayered Microfluidic Devices for Organ on a Chip Applications.

Microfabrication and Polydimethylsiloxane (PDMS) soft-lithography techniques became popular for microfluidic prototyping at the lab, but even after protocol optimization, fabrication is yet a long, laborious process and partly user-dependent. Furthermore, the time and money required for the master fabrication process, necessary at any design upgrade, is still elevated. Digital Manufacturing (DM) and Rapid-Prototyping (RP) for microfluidics applications arise as a solution to this and other limitations of photo and soft-lithography fabrication techniques. Particularly for this paper, we will focus on the use of subtractive DM techniques for Organ-on-a-Chip (OoC) applications. Main available thermoplastics for microfluidics are suggested as material choices for device fabrication. The aim of this review is to explore DM and RP technologies for fabrication of an OoC with an embedded membrane after the evaluation of the main limitations of PDMS soft-lithography strategy. Different material options are also reviewed, as well as various bonding strategies. Finally, a new functional OoC device is showed, defining protocols for its fabrication in Cyclic Olefin Polymer (COP) using two different RP technologies. Different cells are seeded in both sides of the membrane as a proof of concept to test the optical and fluidic properties of the device.

Compared EC-AFM Analysis of Laser-Induced Graphene and Graphite Electrodes in Sulfuric Acid Electrolyte.

Flexible and economic sensor devices are the focus of increasing interest for their potential and wide applications in medicine, food analysis, pollution, water quality, etc. In these areas, the possibility of using stable, reproducible, and pocket devices can simplify the acquisition of data. Among recent prototypes, sensors based on laser-induced graphene (LIGE) on Kapton represent a feasible choice. In particular, LIGE devices are also exploited as electrodes for sensing in liquids. Despite a characterization with electrochemical (EC) methods in the literature, a closer comparison with traditional graphite electrodes is still missing. In this study, we combine atomic force microscopy with an EC cell (EC-AFM) to study, in situ, electrode oxidation reactions when LIGE or other graphite samples are used as anodes inside an acid electrolyte. This investigation shows the quality and performance of the LIGE electrode with respect to other samples. Finally, an ex situ Raman spectroscopy analysis allows a detailed chemical analysis of the employed electrodes.

Polylactic is a Sustainable, Low Absorption, Low Autofluorescence Alternative to Other Plastics for Microfluidic and Organ-on-Chip Applications.

Organ-on-chip (OOC) devices are miniaturized devices replacing animal models in drug discovery and toxicology studies. The majority of OOC devices are made from polydimethylsiloxane (PDMS), an elastomer widely used in microfluidic prototyping, but posing a number of challenges to experimentalists, including leaching of uncured oligomers and uncontrolled absorption of small compounds. Here we assess the suitability of polylactic acid (PLA) as a replacement material to PDMS for microfluidic cell culture and OOC applications. We changed the wettability of PLA substrates and demonstrated the functionalization method to be stable over a time period of at least 9 months. We successfully cultured human cells on PLA substrates and devices, without coating. We demonstrated that PLA does not absorb small molecules, is transparent (92% transparency), and has low autofluorescence. As a proof of concept of its manufacturability, biocompatibility, and transparency, we performed a cell tracking experiment of prostate cancer cells in a PLA device for advanced cell culture.

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.

Willingness towards cognitive engagement: a preliminary study based on a behavioural entropy approach.

Faced with a novel task some people enthusiastically embark in it and work with determination, while others soon lose interest and progressively reduce their efforts. Although cognitive neuroscience has explored the behavioural and neural features of apathy, the why's and how's of positive engagement are only starting to be understood. Stemming from the observation that the left hemisphere is commonly associated to a proactive ('do something') disposition, we run a preliminary study exploring the possibility that individual variability in eagerness to engage in cognitive tasks could reflect a preferred left- or right-hemisphere functioning mode. We adapted a task based on response-independent reinforcement and used entropy to characterize the degree of involvement, diversification, and predictability of responses. Entropy was higher in women, who were overall more active, less dependent on instructions, and never reduced their engagement during the task. Conversely, men showed lower entropy, took longer pauses, and became significantly less active by the end of the allotted time, renewing their efforts mainly in response to negative incentives. These findings are discussed in the light of neurobiological data on gender differences in behaviour.

Online Feature Selection for Robust Classification of the Microbiological Quality of Traditional Vanilla Cream by Means of Multispectral Imaging.

The performance of an Unsupervised Online feature Selection (UOS) algorithm was investigated for the selection of training features of multispectral images acquired from a dairy product (vanilla cream) stored under isothermal conditions. The selected features were further used as input in a support vector machine (SVM) model with linear kernel for the determination of the microbiological quality of vanilla cream. Model training (n = 65) was based on two batches of cream samples provided directly by the manufacturer and stored at different isothermal conditions (4, 8, 12, and 15 °C), whereas model testing (n = 132) and validation (n = 48) were based on real life conditions by analyzing samples from different retail outlets as well as expired samples from the market. Qualitative analysis was performed for the discrimination of cream samples in two microbiological quality classes based on the values of total viable counts [TVC ≤ 2.0 log CFU/g (fresh samples) and TVC ≥ 6.0 log CFU/g (spoiled samples)]. Results exhibited good performance with an overall accuracy of classification for the two classes of 91.7% for model validation. Further on, the model was extended to include the samples in the TVC range 2-6 log CFU/g, using 1 log step to define the microbiological quality of classes in order to assess the potential of the model to estimate increasing microbial populations. Results demonstrated that high rates of correct classification could be obtained in the range of 2-5 log CFU/g, whereas the percentage of erroneous classification increased in the TVC class (5,6) that was close to the spoilage level of the product. Overall, the results of this study demonstrated that the UOS algorithm in tandem with spectral data acquired from multispectral imaging could be a promising method for real-time assessment of the microbiological quality of vanilla cream samples.

Conductive Photo-Activated Porphyrin-ZnO Nanostructured Gas Sensor Array.

Chemoresistors working at room temperature are attractive for low-consumption integrated sensors. Previous studies show that this feature can be obtained with photoconductive porphyrins-coated ZnO nanostructures. Furthermore, variations of the porphyrin molecular structure alter both the chemical sensitivity and the photoconductivity, and can be used to define the sensor characteristics. Based on these assumptions, we investigated the properties of an array of four sensors made of a layer of ZnO nanoparticles coated with porphyrins with the same molecular framework but different metal atoms. The array was tested with five volatile organic compounds (VOCs), each measured at different concentrations. Results confirm that the features of individual porphyrins influence the sensor behavior, and the differences among sensors are enough to enable the discrimination of volatile compounds disregarding their concentration.

An Exploration of the Metal Dependent Selectivity of a Metalloporphyrins Coated Quartz Microbalances Array.

Several studies in the last two decades have demonstrated that metalloporphyrins coated quartz microbalances can be fruitfully used in many diverse applications, spanning from medical diagnosis to environmental control. This large versatility is due to the combination of the flexibility of metalloporphyrins molecular design with the independence of the quartz microbalance signal from the interaction mechanisms. The nature of the metal atom in the metalloporphyrins is often indicated as one of the most effective tools to design differently selective sensors. However, the properties of sensors are also strongly affected by the characteristics of the transducer. In this paper, the role of the metal atom is investigated studying the response, to various volatile compounds, of six quartz microbalance sensors that are based on the same porphyrin but with different metals. Results show that, since quartz microbalances (QMB) transducers can sense all the interactions between porphyrin and volatile compounds, the metal ion does not completely determine the sensor behaviour. Rather, the sensors based on the same molecular ring but with different metal ions show a non-negligible common behaviour. However, even if limited, the different metals still confer peculiar properties to the sensors and might drive the sensor array identification of the pool of tested volatile compounds.

Identification of a Large Pool of Microorganisms with an Array of Porphyrin Based Gas Sensors.

The association between volatile compounds (VCs) and microorganisms, as demonstrated by several studies, may offer the ground for a rapid identification of pathogens. To this regard, chemical sensors are a key enabling technology for the exploitation of this opportunity. In this study, we investigated the performance of an array of porphyrin-coated quartz microbalance gas sensors in the identification of a panel of 12 bacteria and fungi. The porphyrins were metal complexes and the free base of a functionalized tetraphenylporphyrin. Our results show that the sensor array distinguishes the VC patterns produced by microorganisms in vitro. Besides being individually identified, bacteria are also sorted into Gram-positive and Gram-negative.

Combining porphyrins and pH indicators for analyte detection.

High sensitivity and cross-selectivity are mandatory properties for sensor arrays. Although metalloporphyrins and pH indicators are among the most common and appropriate choices for the preparation of optical sensor arrays, the sensitivity spectrum of these dyes is limited to those analytes able to induce an optical response. To extend the receptive field of optical sensors, we explore the design of composite materials, where the molecular interaction among the subunits enriches their sensing working mechanisms. We demonstrate that blends of single metalloporphyrins and pH indicators, tested with a transduction apparatus based on ubiquitous and easily available hardware, can be endowed with sensing properties wider than those of single constituents, enabling the recognition of a broad range of volatiles.

Twenty years of in vitro nanotoxicology: how AI could make the difference.

More than two decades ago, the advent of Nanotechnology has marked the onset of a new and critical field in science and technology, highlighting the importance of multidisciplinary approaches to assess and model the potential human hazard of newly developed advanced materials in the nanoscale, the nanomaterials (NMs). Nanotechnology is, by definition, a multidisciplinary field, that integrates knowledge and techniques from physics, chemistry, biology, materials science, and engineering to manipulate matter at the nanoscale, defined as anything comprised between 1 and 100 nm. The emergence of nanotechnology has undoubtedly led to significant innovations in many fields, from medical diagnostics and targeted drug delivery systems to advanced materials and energy solutions. However, the unique properties of nanomaterials, such as the increased surface to volume ratio, which provides increased reactivity and hence the ability to penetrate biological barriers, have been also considered as potential risk factors for unforeseen toxicological effects, stimulating the scientific community to investigate to which extent this new field of applications could pose a risk to human health and the environment.

3D Cell Migration Chip (3DCM-Chip): A New Tool toward the Modeling of 3D Cellular Complex Systems.

3D hydrogel-based cell cultures provide models for studying cell behavior and can efficiently replicate the physiologic environment. Hydrogels can be tailored to mimic mechanical and biochemical properties of specific tissues and allow to produce gel-in-gel models. In this system, microspheres encapsulating cells are embedded in an outer hydrogel matrix, where cells are able to migrate. To enhance the efficiency of such studies, a lab-on-a-chip named 3D cell migration-chip (3DCM-chip) is designed, which offers substantial advantages over traditional methods. 3DCM-chip facilitates the analysis of biochemical and physical stimuli effects on cell migration/invasion in different cell types, including stem, normal, and tumor cells. 3DCM-chip provides a smart platform for developing more complex cell co-cultures systems. Herein the impact of human fibroblasts on MDA-MB 231 breast cancer cells' invasiveness is investigated. Moreover, how the presence of different cellular lines, including mesenchymal stem cells, normal human dermal fibroblasts, and human umbilical vein endothelial cells, affects the invasive behavior of cancer cells is investigated using 3DCM-chip. Therefore, predictive tumoroid models with a more complex network of interactions between cells and microenvironment are here produced. 3DCM-chip moves closer to the creation of in vitro systems that can potentially replicate key aspects of the physiological tumor microenvironment.

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.

Nonenzymatic Potentiometric Detection of Ascorbic Acid with Porphyrin/ZnO-Functionalized Laser-Induced Graphene as an Electrode of EGFET Sensors.

Laser-induced graphene (LIG) has emerged as a highly versatile material with significant potential in the development of electrochemical sensors. In this paper, we investigate the use of LIG and LIG functionalized with ZnO and porphyrins-ZnO as the gate electrodes of the extended gate field effect transistors (EGFETs). The resultant sensors exhibit remarkable sensitivity and selectivity, particularly toward ascorbic acid. The intrinsic sensitivity of LIG undergoes a notable enhancement through the incorporation of hybrid organic-inorganic materials. Among the variations tested, the LIG electrode coated with zinc tetraphenylporphyrin-capped ZnO nanoparticles demonstrates superior performance, reaching a limit of detection of approximately 3 nM. Furthermore, the signal ratio for 5 µM ascorbic acid relative to the same concentration of dopamine exceeds 250. The practical applicability of these sensors is demonstrated through the detection of ascorbic acid in real-world samples, specifically in a commercially available food supplement containing l-arginine. Notably, formulations with added vitamin C exhibit signals at least 25 times larger than those without, underscoring the sensors' capability to discern and quantify the presence of ascorbic acid in complex matrices. This research not only highlights the enhanced performance of LIG-based sensors through functionalization but also underscores their potential for practical applications in the analysis of vitamin-rich supplements.

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.