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Pain is an alert state of the human body that can be conveyed to the external world through different modalities. A possible communication channel for human pain is represented by facial expressions, whose role in social interactions has been well established. In this work, the link between pain and transfer entropy (TE), passing through facial expressions, is investigated. A new approach to the vision-based measurement (VBM) of pain is presented, which is based on TE among the time-series of facial landmarks positions. The system is composed of three main blocks: A VBM block for the automatic landmarking and the generation of the time-series from the video-sequences; a second block for the evaluation of TE; and finally a classification model based on machine learning algorithms for pain assessment. A public database of video sequences of patients experiencing pain in a controlled scenario was used for the characterization of the system in terms of accuracy and precision. Different uncertainty contributions related to realistic signals interruptions and fluctuations were modeled and propagated to provide a comprehensive evaluation of the proposed measurement system. The obtained results indicate that TE-based approaches can provide great benefits in automatic pain assessment, opening new perspectives for remote management of patients.

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. Cell trajectory prediction becomes crucial especially to increase the trustworthiness in software tools designed to conduct a massive analysis of cell behavior under chemical stimuli. To address this task, we transpose here the exploitation of the presence of “social forces” from the human to the cellular level for motion prediction at microscale by adapting the potential of Social Generative Adversarial Network predictors to cell motility. To demonstrate the effectiveness of the approach, we consider here two case studies: one related to PC-3 prostate cancer cells cultured in 2D Petri dishes under control and treated conditions and one related to an OoC experiment of tumor-immune interaction in fibrosarcoma cells. The goodness of the proposed strategy has been verified by successfully comparing the distributions of common descriptors (kinematic descriptors and mean interaction time for the two scenarios respectively) from the trajectories obtained by video analysis and the predicted counterparts.

Organs On a Chip (OOCs) represent a sophisticated approach for exploring biological mechanisms and developing therapeutic agents. In conjunction with high-quality time-lapse microscopy (TLM), OOCs allow for the visualization of reconstituted complex biological processes, such as multi-cell-type migration and cell–cell interactions. In this context, increasing the frame rate is desirable to reconstruct accurately cell-interaction dynamics. However, a trade-off between high resolution and carried information content is required to reduce the overall data volume. Moreover, high frame rates increase photobleaching and phototoxicity. As a possible solution for these problems, we report a new hybrid-imaging paradigm based on the integration of OOC/TLMs with a Multi-scale Generative Adversarial Network (GAN) predicting interleaved video frames with the aim to provide high-throughput videos. We tested the performance of the predictive capability of GAN on synthetic videos, as well as on real OOC experiments dealing with tumor–immune cell interactions. The proposed approach offers the possibility to acquire a reduced number of high-quality TLM images without any major loss of information on the phenomena under investigation.

We describe a novel method to achieve a universal, massive, and fully automated analysis of cell motility behaviours, starting from time-lapse microscopy images. The approach was inspired by the recent successes in application of machine learning for style recognition in paintings and artistic style transfer. The originality of the method relies i) on the generation of atlas from the collection of single-cell trajectories in order to visually encode the multiple descriptors of cell motility, and ii) on the application of pre-trained Deep Learning Convolutional Neural Network architecture in order to extract relevant features to be used for classification tasks from this visual atlas. Validation tests were conducted on two different cell motility scenarios: 1) a 3D biomimetic gels of immune cells, co-cultured with breast cancer cells in organ-on-chip devices, upon treatment with an immunotherapy drug; 2) Petri dishes of clustered prostate cancer cells, upon treatment with a chemotherapy drug. For each scenario, single-cell trajectories are very accurately classified according to the presence or not of the drugs. This original approach demonstrates the existence of universal features in cell motility (a so called “motility style”) which are identified by the DL approach in the rationale of discovering the unknown message in cell trajectories.

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.

Anemia; Microfluídica; Enfermedad de células falciformes Anèmia; Microfluídica; Malaltia de cèl·lules falciformes Anaemia; Microfluidics; Sickle cell disease Combining microfluidics technology with machine learning represents an innovative approach to conduct massive quantitative cell behavior study and implement smart decision-making systems in support of clinical diagnostics. The spleen plays a key-role in rare hereditary hemolytic anemia (RHHA), being the organ responsible for the premature removal of defective red blood cells (RBCs). The goal is to adapt the physiological spleen filtering strategy for in vitro study and monitoring of blood diseases through RBCs shape analysis. Then, a microfluidic device mimicking the slits of the spleen red pulp area and video data analysis are combined for the characterization of RBCs in RHHA. This microfluidic unit is designed to evaluate RBC deformability by maintaining them fixed in planar orientation, allowing the visual inspection of RBC’s capacity to restore their original shape after crossing microconstrictions. Then, two cooperative learning approaches are used for the analysis: the majority voting scheme, in which the most voted label for all the cell images is the class assigned to the entire video; and the maximum sum of scores to decide the maximally scored class to assign. The proposed platform shows the capability to discriminate healthy controls and patients with an average efficiency of 91%, but also to distinguish between RHHA subtypes, with an efficiency of 82%. This work was supported by Networking Biomedical Research Center (CIBER), Spain. CIBER is an initiative funded by the VI National R&D&i Plan 2008–2011, Iniciativa Ingenio 2010, Consolider Program, CIBER Actions, and the Instituto de Salud Carlos III (RD16/0006/0012), with the support of the European Regional Development Fund. This work was funded by the European Commission H2020-MSCA-ITN-2019, Grant Agreement N860436, “EVIDENCE” and by the CERCA Programme and by the Commission for Universities and Research of the Department of Innovation, Universities, and Enterprise of the Generalitat de Catalunya (2017 SGR 1079), it has been developed in the context of AdvanceCat with the support of ACCIÓ (Catalonia Trade and Investment; Generalitat de Catalunya) under the Catalonian ERDF operational program (European Regional Development Fund) 2014–2020.

Summary 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., Highlights • Monitoring of cell phenotype changes by fluorescence label-free time-lapse microscopy • Adaptive semantic segmentation for the robust detection of cell shape • TE to correlate morphological and textural soma descriptors along time • Directed TE graph for the representation of mutual relationship among descriptors, The bigger picture One of the most challenging frontiers for the automatic understanding of biological systems is fluorescent label-free imaging in which the behavior changes of living being are characterized without cell staining. To this aim, we present here the NeuriTES platform that revisits standard paradigms of video analysis to detect unlabeled objects and correlate the analysis to phenotype evolution of the mechanisms under observation. Through the exploitation of adaptive algorithms and of transfer entropy measures, the platform assures regular cell detection and the possibility to extract reliable parameters related to the evolving cell system. As a proof-of-concept, NeuriTES is applied to two fascinating phenotype investigation scenarios, amyotrophic lateral sclerosis (ALS) disease mechanism and the study of the effects of a chemotherapy drug on living prostate cancer cells (PC3) cultures. Directed graphs assist the biologists with a visual understanding of the mechanisms identified., Nowadays, observational traits (phenotype) of biological entities complement with genomics offering the possibility to monitor phenomenon evolutions and underlying mechanism (neurodegeneration, cancer cell replication, etc.). By complementing video, data analysis, and machine learning approaches with label-free time-lapse microscopy, NeuriTES tool allows automatically analyzing such traits and extracting as outcomes proof of concepts and visual graph-based representation of the phenotypical rationale behind.

Facial expression is an important component of the pain measurement process, which can be utilized by clinicians in patients with impaired communicative or cognitive abilities. However, given the complex nature of the pain and the absence of a standard reference, accurate measurement of pain in hospital settings is a difficult practice. In this paper, we present a vision-based measurement system aimed at automatically measuring the pain intensity values through the analysis of the facial expression, based on deep-learning strategies. For the scope of providing a metrological characterization of the system, we designed a benchmark for the estimation of the measurement precision via multiexpert judgments. As a major contribution of this paper, we investigated the interobserver and intraobserver variability as sources of uncertainty at the calibration level in order to provide a robust pain estimation via an automatic framework. Reproducibility and standard reference uncertainty (due to interobserver and intraobserver variability, respectively) have been estimated, modeled, and propagated through the proposed platform using the Guide to the expression of Uncertainty in Measurement (GUM)-compliant procedures. Numerical results, provided in terms of average values and confidence intervals of the accuracy of classification, indicate that such a system can facilitate decision-making in healthcare. Moreover, the proposed benchmark offers new hints to incorporate, rationally, subjective expert judgments within automatic measurement systems.

Vision-based measurement (VBM) systems are powerful tool to extract quantitative information by acquiring video sequence or static images. When a VBM is applied to the evaluation of nominal properties, such as image characteristics, the term VBM is substituted with vision-based evaluation (VBE) by extending the framework of operation unit to a new concept of evaluation unit (EU) for the image analysis and machine learning phases. To this regard, deep learning (DL) approaches have gained an exponential interest in the research and industrial community, thanks to incredible flexibility toward visual words and the possibility to abandon subjective feature extraction procedures. From such an explosiveness of applications, it emerges the need to conduct studies on the capability of DL strategies to deal with uncertainty contributions, i.e., definitional uncertainty related to the measurand and reference uncertainty that may occur during the calibration process. In order to present a benchmark platform to analyze the effect of the major sources of uncertainties estimated, we use here an atomic force microscopy (AFM) imaging scenario for the evaluation of the effect of nanoparticles exposure on human cells in the laboratory. These studies are nowadays fundamental in toxicity analysis for monitoring the health conditions of workers and for protecting people from atherosclerosis disease. The performance of the proposed VBE-DL system to recognize cell alterations from the AFM images is related to three different sources of uncertainty and a critical analysis of the results achieved is provided.

Cell-cell interactions are an observable manifestation of underlying complex biological processes occurring in response to diversified biochemical stimuli. Recent experiments with microfluidic devices and live cell imaging show that it is possible to characterize cell kinematics via computerized algorithms and unravel the effects of targeted therapies. We study the influence of spatial and temporal resolutions of time-lapse videos on motility and interaction descriptors with computational models that mimic the interaction dynamics among cells. We show that the experimental set-up of time-lapse microscopy has a direct impact on the cell tracking algorithm and on the derived numerical descriptors. We also show that, when comparing kinematic descriptors in two diverse experimental conditions, too low resolutions may alter the descriptors’ discriminative power, and so the statistical significance of the difference between the two compared distributions. The conclusions derived from the computational models were experimentally confirmed by a series of video-microscopy acquisitions of co-cultures of unlabelled human cancer and immune cells embedded in 3D collagen gels within microfluidic devices. We argue that the experimental protocol of acquisition should be adapted to the specific kind of analysis involved and to the chosen descriptors in order to derive reliable conclusions and avoid biasing the interpretation of results.

Combining microfluidics technology with machine learning represents an innovative approach to conduct massive quantitative cell behavior study and implement smart decision-making systems in support of clinical diagnostics. The spleen plays a key-role in rare hereditary hemolytic anemia (RHHA), being the organ responsible for the premature removal of defective red blood cells (RBCs). The goal is to adapt the physiological spleen filtering strategy for in vitro study and monitoring of blood diseases through RBCs shape analysis. Then, a microfluidic spleen-like filtering unit and video data analysis are combined for the characterization of RBCs in RHHA. A filtering unit is designed to measure deformability by maintaining fixed the RBC orientation to study its capacity of restoring the original shape after crossing microconstrictions. Two cooperative learning approaches are used for the analysis: the majority voting scheme, in which the most voted label for all the cell images is the class assigned to the entire video; and the maximum sum of scores to decide the maximally scored class to assign. The proposed platform shows the capability to discriminate healthy controls and patients with an average efficiency of 91%, but also to distinguish between RHHA subtypes, with an efficiency of 82%.

The blood-brain barrier (BBB) is a dynamic cellular barrier that regulates brain nutrient supply, waste efflux, and paracellular diffusion through specialized junctional complexes. Finding a system to mimic and monitor BBB integrity (i.e., to be able to assess the effect of certain compounds on opening or closing the barrier) is of vital importance in several pathologies. This work aims to overcome some limitations of current barrier integrity measuring techniques thanks to a multi-layer microfluidic platform with integrated electrodes and Multi-frequency Trans-Endothelial Electrical Resistance (MTEER) in synergy with machine learning algorithms. MTEER measurements are performed across the barrier in a range of frequencies up to 10 MHz highlighting the presence of information on different frequency ranges. Results show that the proposed platform can detect barrier formation, opening, and regeneration afterwards, correlating with the results obtained from immunostaining of junctional complexes. This model presents novel techniques for a future biological barrier in-vitro studies that could potentially help on elucidating barrier opening or sealing on treatments with different drugs., This work was supported by Networking Biomedical Research Center (CIBER), Spain. CIBER is an initiative funded by the VI National R&D&i Plan 2008–2011, Iniciativa Ingenio 2010, Consolider Program, CIBER Actions, and the Instituto de Salud Carlos III (RD16/0006/0012), with the support of the European Regional Development Fund. This work was funded by the CERCA Programme and by the Commission for Universities and Research of the Department of Innovation, Universities, and Enterprise of the Generalitat de Catalunya (2017 SGR 1079). This work has been developed in the context of AdvanceCat with the support of ACCIÓ (Catalonia Trade and Investment; Generalitat de Catalunya) under the Catalonian ERDF operational program (European Regional Development Fund) 2014–2020. This work was funded by the Spanish Ministry of Economy and Competitiveness (MINECO) through the projects MINDS (Proyectos I + D Excelencia + FEDER) TEC2015-70104-P, CTQ2016-75870-P, BIOBOT (Programa Explora Ciencia / Tecnología), TEC2015- 72718-EXP and RTI2018-097038-B-C21.

The temperature modulation effects of metal oxide based gas sensors have been widely investigated for the volatile compounds detection and, to this aim, several interface circuits were designed for optimization of the sensors properties. In this paper instead of improving only the sensors operation, we propose a novel interface that can also maximize the information that can be collected from the sensor, through a feedback-based thermal modulation method and an active digital control. The self-adaptive modulation method exploits the changes of the sensor resistance to drive its operating temperature. This is implemented in a novel digital interface based on a microcontroller that can drive an array of four sensors independently and provides a real-time control over the modulation parameters. In this way, the behavior of the sensor can be changed during the measurements, enabling the drift compensation, the fault identification and more importantly the minimization of the mutual information between similar sensors. The proposed interface has been tested in two different scenarios: a lab calibration experiment, by recognizing different concentrations of known gases and a practical application, by distinguishing complex VOCs to evaluate the stress level of plants. A multivariate data analysis has been conducted, highlighting a clear effectiveness of the proposed approach and better performances with respect to the standard thermal modulation techniques.

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.

Aflatoxins are fungal metabolites extensively produced by many different fungal species that may contaminate a wide range of agricultural food products. They have been studied extensively because of being associated with various chronic and acute diseases, especially immunosuppression and cancer, and their presence in food is strictly monitored and regulated worldwide. Aflatoxin detection and measurement relies mainly on chemical methods usually based on chromatography approaches, and recently developed immunochemical based assays that have advantages but also limitations, since these are expensive and destructive techniques. Nondestructive, optical approaches are recently being developed to assess presence of contamination in a cost and time effective way, maintaining acceptable accuracy and reproducibility. In this paper are presented the results obtained with a simple portable device for nondestructive detection of aflatoxins in almonds. The presented approach is based on the analysis of fluorescence spectra of slurried almonds under 375 nm wavelength excitation. Experiments were conducted with almonds contaminated in the range of 2.7–320.2 ng/g total aflatoxins B (AFB1 + AFB2) as determined by High Performance Liquid Chromatography with Fluorescence Detection (HPLC/FLD). After applying pre-processing steps, spectral analysis was carried out using a binary classification model based on Support Vector Machine (SVM) algorithm. A majority vote procedure was then performed on the classification results. In this way we could achieve, as best result, a classification accuracy of 94% (and false negative rate 5%) with a threshold set at 6.4 ng/g. These results illustrate the feasibility of such approach in the great challenge of aflatoxin detection for food and feed safety.

Modern optical sensors coupled with time-lapse microscopy devices and dedicated software tools allow the miniaturization of laboratories for biological experiments leading to the Organ-On-Chip (OoC) framework. OoCs allow performing massive measurements on a large number of cells under the assumption of reproducibility conditions, permitting to investigate the cell dynamics in terms of motility and shape changes over time. In this work, we present the OoC platform used in a preliminary study of the Rare Haemolytic Anaemia (RHA) disease, a group of rare diseases characterized by haemolysis, which is the premature loss of red blood cells (RBCs). Preliminary results demonstrate the effectiveness of shape measurement for the diagnosis of RHA.

The appraisal of stress in plants is of great relevance in agriculture and any time the transport of living plants is involved. Wireless sensor networks (WSNs) are an optimal solution to simultaneously monitor a large number of plants in a mostly automatic way. A number of sensors are readily available to monitor indicators that are likely related to stress. The most common of them include the levels of total volatile compounds and CO2 together with common physical parameters such as temperature, relative humidity, and illumination, which are known to affect plants&rsquo, behavior. Recent progress in microsensors and communication technologies, such as the LoRa protocol, makes it possible to design sensor nodes of high sensitivity where power consumption, transmitting distances, and costs are optimized. In this paper, the design of a WSN dedicated to plant stress monitoring is described. The nodes have been tested on European privet (Ligustrum Jonandrum) kept in completely different conditions in order to induce opposite level of stress. The results confirmed the relationship between the release of total Volatile Organic Compounds (VOCs) and the environmental conditions. A machine learning model based on recursive neural networks demonstrates that total VOCs can be estimated from the measure of the environmental parameters.

Organ-on-chips are miniaturised devices aiming at replacing animal models for drug discovery, toxicology and studies of complex biological phenomena. The field of Organ-On-Chip has grown exponentially, and has led to the formation of companies providing commercial Organ-On-Chip devices. Yet, it may be surprising to learn that the majority of these commercial devices are made from Polydimethylsiloxane (PDMS), a silicone elastomer that is widely used in microfluidic prototyping, but which has been proven difficult to use in industrial settings and poses a number of challenges to experimentalists, including leaching of uncured oligomers and uncontrolled adsorption of small compounds. To alleviate these problems, we propose a new substrate for organ-on-chip devices: Polylactic Acid (PLA). PLA is a material derived from renewable resources, and compatible with high volume production technologies, such as microinjection moulding. PLA can be formed into sheets and prototyped into desired devices in the research lab. In this article we uncover the suitability of Polylactic acid as a substrate material for Microfluidic cell culture and Organ-on-a-chip applications. Surface properties, biocompatibility, small molecule adsorption and optical properties of PLA are investigated and compared with PDMS and other reference polymers.SignificanceOrgan-On-Chip (OOC) technology is a powerful and emerging tool that allows the culture of cells constituting an organ and enables scientists, researchers and clinicians to conduct more physiologically relevant experiments without using expensive animal models. Since the emergence of the first OOC devices 10 years ago, the translation from research to market has happened relatively fast. To date, at least 28 companies are proposing body and tissue on-a chip devices. The material of choice in most commercial organ-on-chip platforms is an elastomer, Polydymethyloxane (PDMS), commonly used in microfluidic R&D. PDMS is however subject to poor reproducibility, and absorbs small molecule compounds unless treated. In this study we show that PLA overcomes all the drawbacks related to PDMS: PLA can be prototyped in less than 45 minutes from design to test, is transparent, not autofluorescent, and biocompatible. PLA-based microfluidic platforms have the potential to transform the OOC industry as well as to provide a sustainable alternative for future Lab-On-Chip and point-of-care devices.

Objective: The ability of cells to collectively move is essential in various biological contexts including cancer metastasis. In this paper, we propose an automatic video analysis tool to correlate the cell movement inhibition with replication block induced by dose-dependent chemotherapy administration. Methods: The novel approach combines individual and collective cell kinematic analysis performed over time-lapse microscopy video frames. Cells are first localized and tracked, and then kinematic descriptors are extracted for each track. Selective track identification is performed assuming diversified cell roles within the same cluster (spontaneously forming groups of cells), and finally individual results are grouped exploiting consensus of coordinated motility within cell clusters. Results: Recognition performance of three different experimental conditions (no drug, 0.5–5 μ M merged in the same condition, and 50 μ M) reached an average accuracy value of 88% over 958 different tracks collected in 36 clusters of diverse dimensions in eight independent experiments. Conclusion: An extensive application of this methodology could give a different point of view of the cancer mechanisms.

Aflatoxin detection currently relies mainly on chemical methods usually based on chromatography approaches, and recently developed immunochemical based assays that are fairly accurate, however, they are time-consuming, expensive and destructive. Non-destructive, optical approaches are recently being developed in order to assess the presence of contamination in a cost and time-effective way, maintaining high levels of accuracy and reproducibility, but are usually based on the benchtop and expensive instruments. Here we will present the evolution of results of the analysis of fluorescence spectra of contaminated almond samples during the development of an optical multi-sensor device in the framework of PhasmaFOOD project. The aim of the project was to develop a low cost and portable instrument comprising multispectral and imaging capabilities, conjugated with a cloud reference database and analysis toolbox for food features analysis. One of the use cases of the project is the fast, reliable and non-destructive detection of mycotoxin (in particular aflatoxin) contamination in food products. For this use case, we used in particular fluorescence spectroscopy and different approaches to data analysis. After the first feasibility tests in the range of mg/g contamination range with a simple and effective analysis that led to highly reliable results, the work was focused on the detection limits with samples (almond) in the range of 0-291 ng/g acquired with a simple portable device and excitation light at 365 nm wavelength. An ad hoc processing strategy based on a feature selection steps coupled with a nonlinear classifier has been developed and test with two different datasets collected one month from the other another. The system performances have been evaluated training the classification model with one dataset and testing its with the other. The results have shown an accuracy higher than 80% with a threshold lower than 10ppb as contamination level.

t: 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. Keywords: digital manufacturing; rapid prototyping; organ on a chip; microfluidics

A major challenge in cancer research is the complexity of the tumor microenvironment and the necessity to take into account the host immunological setting. An innovative way to address these problems is to exploit the emerging technology of organ-on-chip to achieve co-cultures in microfluidic devises that recapitulate ex vivo the tumor ecosystem. We generated tumors-on-chip integrating four cell populations (cancer, immune, endothelial cells and fibroblasts) in a 3D collagen matrix, reconstituting HER2 breast cancer ecosystem. By time-lapse microscopy, differential live staining and a novel automated analysis method, we visualized, and quantified the complex dynamics of this ecosystem, in absence or in presence of the drug trastuzumab (Herceptin), a targeted antibody therapy directed against the HER2 receptor. We uncovered the capacity of the drug trastuzumab to specifically promote long cancer-immune interactions (≳ 60 min), recapitulating an anti-tumoral ADCC (antibody-dependent cell-mediated cytotoxicity) immune response. Cancer-associated fibroblasts (CAFs) on the contrary inhibited cancer-immune interactions, antagonizing the action of the trastuzumab. These observations constitute a proof-of-concept that tumors-on-chip are novel powerful platforms that can be exploited to study ex vivo immunocompetent tumor microenvironments, to characterize ecosystem-level responses to anti-cancer drugs, and to dissect the roles of the various cellular components of the stroma.

Engineered nanomaterials play an even more relevant role in nanotechnology advances. However, care must be taken due to their suspected detrimental effects on human cells. Such alterations can be monitored through Atomic Force Microscopy (AFM) equipment and image digitalization. With the purpose to depict a metrological compliant scenario, a novel vision-based evaluation system is proposed with an evaluation unit based on a deep learning architecture. Inspired by the recent trends in trying to extend the standard concept of quantities to nominal properties and measurement to evaluation, we proposed here a platform for the evaluation of morphological alterations in AFM images of human cells exposed to different concentrations of carbon nanotubes. Results reveal the feasibility to automatically investigate such alterations with the aim to improve occupational medicine protocols and cells cataloguing procedures.

Aim Aim of this study is to analyze the real movement, influenced by anatomical difficulties, of nickel-titanium rotary instruments within root canal systems; then the objective is to point out the physical and geometrical characteristics of an ideal instrument, able to overcome the most complex anatomies. Methodology At first, observation of the behavior of nickel-titanium rotary instruments within root canal systems and of the influence on them of root canal anatomy. Then, attempt to avoid the anatomical obstructions exploiting, with manual rotation, the advantages of a zero/low torque. Results Given that, in some root canals the severity of the curves prevents instruments to advance in rotation, we obtained significant results by manually advancing and rotating NiTi rotary instruments. Conclusions Therefore, in some cases, we would need an instrument that can reconcile efficiency with a reduction of mass and torque; the ideal instrument should have a very contained working part, combining efficiency with the decrease of mass and, consequently, of torsional stresses too.

In the last years, we introduced a method aimed at creating a self-adaptive temperature profile that is generated by the sensor itself according to the characteristics of the sample at which it is exposed. Starting from the preliminary experimental evidences, in this work we introduced a novel baseline drift counteraction method to improve the stability of the responses of an array of three commercial self-adapted temperature modulation gas sensors. To further validate the proposed method, we have considered two experiments. In the first experiment, the performances of the proposed method have been compared with those obtained with the standard modulation as thermal ramp in the identification of three volatile compounds (Carbon monoxide, Hydrogen, Methane) at different concentrations. In the second experiment the proposed approach has been tested in the discrimination of three different bacteria strain culture lines.

In this paper we discuss the applicability of numerical descriptors and statistical physics concepts to characterize complex biological systems observed at microscopic level through organ on chip approach. To this end, we employ data collected on a microfluidic platform in which leukocytes can move through suitably built channels toward their target. Leukocyte behavior is recorded by standard time lapse imaging. In particular, we analyze three groups of human peripheral blood mononuclear cells (PBMC): heterozygous mutants (in which only one copy of the FPR1 gene is normal), homozygous mutants (in which both alleles encoding FPR1 are loss-of-function variants) and cells from ‘wild type’ donors (with normal expression of FPR1). We characterize the migration of these cells providing a quantitative confirmation of the essential role of FPR1 in cancer chemotherapy response. Indeed wild type PBMC perform biased random walks toward chemotherapy-treated cancer cells establishing persistent interactions with them. Conversely, heterozygous mutants present a weaker bias in their motion and homozygous mutants perform rather uncorrelated random walks, both failing to engage with their targets. We next focus on wild type cells and study the interactions of leukocytes with cancerous cells developing a novel heuristic procedure, inspired by Lyapunov stability in dynamical systems.

Summary: A major challenge in cancer research is the complexity of the tumor microenvironment, which includes the host immunological setting. Inspired by the emerging technology of organ-on-chip, we achieved 3D co-cultures in microfluidic devices (integrating four cell populations: cancer, immune, endothelial, and fibroblasts) to reconstitute ex vivo a human tumor ecosystem (HER2+ breast cancer). We visualized and quantified the complex dynamics of this tumor-on-chip, in the absence or in the presence of the drug trastuzumab (Herceptin), a targeted antibody therapy directed against the HER2 receptor. We uncovered the capacity of the drug trastuzumab to specifically promote long cancer-immune interactions (>50 min), recapitulating an anti-tumoral ADCC (antibody-dependent cell-mediated cytotoxicity) immune response. Cancer-associated fibroblasts (CAFs) antagonized the effects of trastuzumab. These observations constitute a proof of concept that tumors-on-chip are powerful platforms to study ex vivo immunocompetent tumor microenvironments, to characterize ecosystem-level drug responses, and to dissect the roles of stromal components. : Inspired by the emerging technology of tumor-on-chip, Nguyen et al. reconstituted ex vivo a human tumor microenvironment (HER2+ breast cancer), characterized the ecosystem-level responses to the drug trastuzumab (Herceptin), and dissected the roles of stromal components (immune cells and fibroblasts), demonstrating the power of immunocompetent tumors-on-chip for preclinical drug studies. Keywords: tumor microenvironment, organ-on-chip, tumor-on-chip, trastuzumab, HER2+ breast cancer, cancer-associated fibroblasts, live cell imaging, microfluidics, pre-clinical models, immunotherapy