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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.

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. 734 patients were enrolled and divided into 4 risk classes, according to the numbers of miscarriages. ML method, called Support Vector Machine (SVM), was used to analyze data. Using the whole set of 43 features and the set of the most informative 18 features we obtained comparable results: respectively 81.86 ± 0.35% and 81.71 ± 0.37% Unbalanced Accuracy. Applying the same method, introducing the only features recommended by ESHRE, a correct classification was obtained only in 58.52 ± 0.58%. ML approach could provide a Support Decision System tool to stratify RPL patients and address them objectively to the proper clinical management.

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.

In aerobiological monitoring and agriculture there is a pressing need for accurate, label-free and automated analysis of pollen grains, in order to reduce the cost, workload and possible errors associated to traditional approaches. Methods: We propose a new multimodal approach that combines electrical sensing and optical imaging to classify pollen grains flowing in a microfluidic chip at a throughput of 150 grains per second. Electrical signals and synchronized optical images are processed by two independent machine learning-based classifiers, whose predictions are then combined to provide the final classification outcome. Results: The applicability of the method is demonstrated in a proof-of-concept classification experiment involving eight pollen classes from different taxa. The average balanced accuracy is 78.7 % for the electrical classifier, 76.7 % for the optical classifier and 84.2 % for the multimodal classifier. The accuracy is 82.8 % for the electrical classifier, 84.1 % for the optical classifier and 88.3 % for the multimodal classifier. Conclusion: The multimodal approach provides better classification results with respect to the analysis based on electrical or optical features alone. Significance: The proposed methodology paves the way for automated multimodal palynology. Moreover, it can be extended to other fields, such as diagnostics and cell therapy, where it could be used for label-free identification of cell populations in heterogeneous samples.

Pain is a health problem of vast dimension that requires prompt diagnosis. However, the evaluation criteria currently used in clinical settings are based on self-rating scales, which are subjective and prone to error. This may cause administering of improper analgesic treatments, either in dosage or in duration. For the scope of providing objective measures of pain and help devising effective therapeutic choices, we designed a personalized platform for sensing and monitoring pain. As input to the platform, visual, speech and physiological clues expressed by the patient during the pain experience are non-invasively measured, then sent to a cloud server to be integrated and analyzed. In this work, preliminary results obtained with the vision-based block of the platform are presented. Because the pain perceived by each individual is highly subjective, once the data are collected from each subject, quantitative descriptors are extracted and personalized selection of relevant features is performed to train a subject-specific regression model. Video sequences of genuine pain felt were tested and weighted accuracy values of 0.81 (0.19) and 0.84 (0.10) were obtained, respectively, for three-class and two-class scenarios, showing benefits and drawbacks of the proposed personalized platform.

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.