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The accurate identification of the three-dimensional quantitative shape of a cell nucleus is now possible without fluorescent staining by applying computational segmentation to refractive index tomograms recorded in the flow cytometry mode., Quantitative phase imaging has gained popularity in bioimaging because it can avoid the need for cell staining, which, in some cases, is difficult or impossible. However, as a result, quantitative phase imaging does not provide the labelling of various specific intracellular structures. Here we show a novel computational segmentation method based on statistical inference that makes it possible for quantitative phase imaging techniques to identify the cell nucleus. We demonstrate the approach with refractive index tomograms of stain-free cells reconstructed using tomographic phase microscopy in the flow cytometry mode. In particular, by means of numerical simulations and two cancer cell lines, we demonstrate that the nucleus can be accurately distinguished within the stain-free tomograms. We show that our experimental results are consistent with confocal fluorescence microscopy data and microfluidic cyto-fluorimeter outputs. This is a remarkable step towards directly extracting specific three-dimensional intracellular structures from the phase contrast data in a typical flow cytometry configuration.

Automated vehicles are characterized by intelligent functions (many developed using AI-based solutions), autonomy, and high connectivity with the external environment. The high connectivity of AV opens relevant cybersecurity issues due to many potential attack surfaces. Cybersecurity attacks' effects on AV may also determine severe risks in terms of functional safety due to vehicles' high automation and autonomy. Functional safety assurance of AV cannot be disjoint from the analysis and treatment of cybersecurity concerns. In this paper, we define a methodology called ISCA (Integrated Safety and Cybersecurity Assurance) aimed at integrating, in a systematic manner, cybersecurity concerns into the functional safety assurance process of AI-based functions of AV. The ISCA methodology focuses on the emerging scenario-based approach for testing AI-based vehicle functions and is based on creating variants of the scenarios used for functional safety testing. The variants are derived from the original scenarios modified by the effects of highly risky cyberattacks that can occur during the scenario execution. The risk assessment of cyberattacks is based on the fuzzy logic mechanism. ISCA represents a possible way to strengthen the scenario-based functional safety assurance of AI-based vehicle functions by integrating cybersecurity concerns.

Red blood cells on the surface of a lithium niobate crystal can be used as optical lenses for direct writing of laser-induced refractive index changes. The writing process by such a photomask made of biological lenses is due to the photorefractive effect. Wavefront analysis by a digital holographic microscope is performed for deep and accurate evaluation of local refractive index changes. Different focusing properties can be imprinted on the crystal depending on which type of RBC is employed, discocytes or spherical-like RBCs. The possibility to fix into a solid material the optical fingerprint of the RBCs will have an impact on both diagnostics and cell\material interfacing.

Anemia affects about the 25% of the global population and can provoke severe diseases, ranging from weakness and dizziness to pregnancy problems, arrhythmias and hearth failures. About 10% of the patients are affected by rare anemias of which 80% are hereditary. Early differential diagnosis of anemia enables prescribing patients a proper treatment and diet, which is effective to mitigate the associated symptoms. Nevertheless, the differential diagnosis of these conditions is often difficult due to shared and overlapping phenotypes. Indeed, the complete blood count and unaided peripheral blood smear observation cannot always provide a reliable differential diagnosis, so that biomedical assays and genetic tests are needed. These procedures are not error-free, require skilled personnel, and severely impact the financial resources of national health systems. Here we show a differential screening system for hereditary anemias that relies on holographic imaging and artificial intelligence. Label-free holographic imaging is aided by a hierarchical machine learning decider that works even in the presence of a very limited dataset but is enough accurate for discerning between different anemia classes with minimal morphological dissimilarities. It is worth to notice that only a few tens of cells from each patient are sufficient to obtain a correct diagnosis, with the advantage of significantly limiting the volume of blood drawn. This work paves the way to a wider use of home screening systems for point of care blood testing and telemedicine with lab-on-chip platforms.

Quantitative Phase Imaging (QPI) has gained popularity because it can avoid the staining step, which in some cases is difficult or impossible. However, QPI does not provide the well-known specificity to various parts of the cell (e.g., organelles, membrane). Here we show a novel computational segmentation method based on statistical inference that bridges the gap between the specificity of Fluorescence Microscopy (FM) and the label-free property of QPI techniques to identify the cell nucleus. We demonstrate application to stain-free cells reconstructed through the holographic learning and in flow cyto-tomography modality. In particular, by means of numerical simulations and two cancer cell lines, we demonstrate that the nucleus-like regions can be accurately distinguished within the stain-free tomograms. We show that our experimental results are consistent with confocal FM data and microfluidic cytofluorimeter outputs. This is a significant step towards extracting the three-dimensional (3D) intracellular specificity directly from the phase-contrast data in a typical flow cytometry configuration.

Water pollution is one of the main global emergencies that must be quickly addressed in order to hinder its growing effects, which represent a danger for the environmental and the human health. It is mostly due to the production of microplastics from the larger plastic items released more and more into the water environment during the last decades. Therefore, the accurate identification of microplastics is strongly requested in order to assess the degree of pollution of a certain water area and then to apply suitable ecological strategies to clean it. However, this goal has not yet been achieved, because of the wide heterogeneity of objects that can be found in a water sample, and their microscopic dimensions. In particular, a lot of species of diatoms (i.e., unicellular microalgae) share the same range size and shape with microplastics. Digital holography (DH) has been exploited to realize a quantitative analysis of the microplastics’ and diatoms’ properties at the microscopic scale. Recently, we demonstrated the fruitful combination between the DH and the fractal geometry, in order to measure a very distinctive fractal signature related to the scattering properties of the imaged samples. Here we demonstrate that, by properly combining some of these fractal parameters with each other, also subsets of the fractal features allow to reach high accuracy in detecting microplastics among diatoms. These results, combined to the flow cytometric applications of DH, could open the route for fast and accurate tools for the microplastics’ identification and quantification in water.

Interaction of nanoparticles (NPs) with cells is of fundamental importance in biology and biomedical sciences. NPs can be taken up by cells, thus interacting with their intracellular elements, modifying the life cycle pathways, and possibly inducing death. Therefore, there is a great interest in understanding and visualizing the process of cellular uptake itself or even secondary effects, for example, toxicity. Nowadays, no method is reported yet in which 3D imaging of NPs distribution can be achieved for suspended cells in flow-cytometry. Here we show that, by means of label-free tomographic flow-cytometry, it is possible to obtain full 3D quantitative spatial distribution of nanographene oxide (nGO) inside each single flowing cell. This can allow the setting of a class of biomarkers that characterize the 3D spatial intracellular deployment of nGO or other NPs clusters, thus opening the route for quantitative descriptions to discover new insights in the realm of NP-cell interactions.

A lab-on-chip platform for blood cells analysis will be presented that combine phase-contrast label-free imaging, microfluidics and artificial intelligence. Such platform will have important impact in the aerospace field quantifying the effect on the blood cells of astronauts stresses. Furthermore, smart and innovative platform for blood analysis could be the key element for innovative biomedicine and telemedicine solutions in aerospace applications.

Liquid biopsy (LB) is a promising oncological tool that aims to the early diagnosis of tumors and to improve the therapeutical efficacy by following patient treatment. It is based on the detection and analysis of circulating tumor cells (CTCs) released into the bloodstream from primary or metastatic tumors. However, a reliable label-free LB tool has not yet been developed. Therefore, here we discuss the advantages derived from combining microfluidics and label-free quantitative imaging, to access the full three-dimensional (3D) information of a biological specimen by performing the tomographic reconstruction at single-cell level in high throughput modality. Experimental results on some CTCs are reported.

Digital holography (DH) has been demonstrated as a very powerful tool for micro-plastics (MPs) imaging and recognition, thanks to its unique capabilities such as label-free 3D imaging, flexible focusing and high-throughput. Moreover, the use of machine learning approaches has permitted to surpass main processing limitations in classifying MPs. In particular, the quantitative phase signature provided by DH permits to identify the unique fingerprint for MPs that is crucial to improve the accuracy in features based classification task. In this paper, we investigate new optical, morphological and texture features that can be calculated from phase images of MPs only.

Holographic Tomography (HT) is an emerging label-free technique for microscopic bioimaging applications, that allows reconstructing the three-dimensional (3D) refractive index (RI) distribution of biological specimens. Recently, an in-flow HT technique has been proposed in which multiple digital holograms are recorded at different viewing angles around the sample while it flows and rotates within a microfluidic channel. However, unlike conventional HT methods, there is no a priori information about cell 3D orientations, that are instead requested to perform any tomographic algorithm. Here we investigate a tracking-based rolling angles recovery method, showing robustness against the sample’s features. It is based on a phase images similarity metric recently demonstrated, that exploits the local contrast phase measurements to recognize a full cell rotation within the microfluidic channel. Hence, the orientations of the flowing cells are retrieved from their positions, which are in turn computed through the 3D holographic tracking. The performances of the rolling angles recovery method have been assessed both numerically, by simulating a 3D cell phantom, and experimentally, by reconstructing the 3D RI tomograms of two cancer cells. Both the numerical and the experimental analysis have been performed at different spatial resolutions. This rolling angles recovery method, not depending on the cell shapes, the RI contents, and the optical experimental conditions, could pave the way to the study of circulating tumor cells (CTCs) in the challenging tool of liquid biopsy.

Nano graphene-based materials offer interesting physicochemical and biological properties for biotechnological applications due to their small size, large surface area and ability to interact with cells/tissues. Among carbon-based nanomaterials, graphene oxide is one of the most used in biological field. There is an increasing interest in shedding light on the interaction mechanisms of nanographene oxide (nGO) with cells. In fact, the effects on human health of GO, and its toxicological profile, are still largely unknown. Here we show that, by minimizing the oxidation degree of GO, its toxicity is significantly reduced in NIH 3T3 cells. Moreover, we show that mild oxidation of graphene nanoplatelets produces nGO particles, which are massively internalized into the cell cytoplasm. MTT(3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide) assay was performed to analyze cell viability. Transmission electron microscopy (TEM) analysis was performed to evaluate nGO internalization mechanism into the cytoplasm under different oxidation degree and concentrations. For the first time, we evaluated quantitatively, the cell volume variation after nGO internalization in live fibroblasts through a label-free digital holography (DH) imaging technique and in quasi-real-time modality, thus avoiding the time-consuming and detrimental procedures usually employed by electron-based microscopy. In conclusion, here we have demonstrated that DH can be a viable tool to visualize and display 3D distributions of nano graphene oxide (nGO) uptake by fibroblast cells. DH opens the route for high-throughput investigation at single cell level for understanding how in different conditions nanoparticles aggregates distribute inside the cells.

Diatoms are one of the largest groups of microalgae present in marine, freshwater and transitional environments and their reactivity to environmental changes makes them suitable to be employed as biomarkers for monitoring tasks. Anyway, their presence in a large number of species makes it arduous to perform diatoms taxonomy during monitoring tasks considering that, to date, analysis is conducted by marine biologists on the basis of their own experience and, hence, in a subjective way. Hence, the need for automatic and objective methodologies for the identification and classification of diatoms samples rises. Research efforts in the field of Computer Vision led to a plethora of highly effective deep learning strategies surpassing human capabilities for image classification, as showed in the recent Imagenet challenge editions where they were initially introduced. Despite the very promising results of the proposed solutions, the difficulty arises to determine which technique is most suitable among them for real tasks and in particular for diatoms classification. This work proposes an end-to-end pipeline for automatic recognition of diatoms, acquired by means of holographic microscopy in water samples, employing deep learning techniques. In particular the most recently introduced Convolution Neural Networks (CNNs) architectures have been deeply investigated and compared in order to highlight the pros and cons of each of them. Moreover, in order to feed the CNNs training stages with a suitable amount of labeled data, a strategy to build a synthetic dataset, starting from a single image per class available from commercial glass slides specifically prepared for taxonomy purposes, is introduced. Besides, models ensembling strategies, in order to improve the single model scores, have been exploited. Finally, the proposed approach has been validated employing a dataset built up of holographic images of diatoms sampled in natural water bodies.

The detection of CTCs in a blood sample is a challenging task due to their rarity and variety. We develop a new label-free and all-optical approach at the lab-on-chip scale for the detection of CTCs based on morphological biomarkers. In particular, we design a microfluidic device to be combined with a phase-contrast tomography system to carry out quantitative measurements of the three-dimensional structure of each single cell in a blood sample. In such device, two aspects are conjugated: on the one hand, the cells need to perform at least one complete rotation within the field of view of the imaging apparatus; on the other hand, the highest possible throughput has to be achieved, yet without deforming the cells significantly, which would impede their tomographic reconstruction. In this contribution, the finite-element-simulation-based preliminary design of a microfluidic device that would allow the achievement of the aforementioned objectives for cells with different shape and deformability is presented.

Bell pepper is the common name of the berry obtained from some varieties of the Capsicum annuum species. This agro-food is appreciated all over the world and represents one of the key ingredients of several traditional dishes. It is used as a fresh product, or dried and ground as a seasoning (e.g., paprika). Specific varieties of sweet pepper present organoleptic peculiarities and they have been awarded by quality marks as a further confirmation of their unicity (e.g., Piment d’Espelette, Pimiento de Herbón, Peperone di Senise). Due to the market value of this aliment, it can be subjected to frauds, such as adulterations and sophistication. The present study lays on these considerations and aims at developing a spectroscopy-based approach for authenticating Senise bell pepper and for detecting its adulteration with common paprika. In order to achieve this goal, 60 pure samples of bell pepper from Senise were analyzed by mid- and near-infrared spectroscopies. Then, in order to mimic the adulteration, 40 mixtures of Senise bell pepper and paprika were prepared and analyzed (by the same spectroscopic techniques). Eventually, two different multi-block classification approaches (sequential and orthogonalized partial least squares linear discriminant analysis and sequential and orthogonalized covariance selection linear discriminant analysis) were used to discriminate between pure and adulterated Senise bell pepper samples. Both proposed procedures achieved extremely successful results in external validation, correctly classifying all the (thirty-five) test samples, indicating that both approaches represent a winning solution for the investigated classification problem.

We propose a way to perform the classification of holographic phase maps using machine learning models in a web application. The system can perform acquisition, processing, reconstruction, segmentation, and classification steps straight from the browser.

We investigate the ability of machine learning to provide an accurate classification of cancer cell in microfluidics when only raw digital holograms are used as input data. Comparison among different learning strategies is addressed.

Diatoms are among the dominant phytoplankters in marine and freshwater habitats, and important biomarkers of water quality, making their identification and classification one of the current challenges for environmental monitoring. To date, taxonomy of the species populating a water column is still conducted by marine biologists on the basis of their own experience. On the other hand, deep learning is recognized as the elective technique for solving image classification problems. However, a large amount of training data is usually needed, thus requiring the synthetic enlargement of the dataset through data augmentation. In the case of microalgae, the large variety of species that populate the marine environments makes it arduous to perform an exhaustive training that considers all the possible classes. However, commercial test slides containing one diatom element per class fixed in between two glasses are available on the market. These are usually prepared by expert diatomists for taxonomy purposes, thus constituting libraries of the populations that can be found in oceans. Here we show that such test slides are very useful for training accurate deep Convolutional Neural Networks (CNNs). We demonstrate the successful classification of diatoms based on a proper CNNs ensemble and a fully augmented dataset, i.e., creation starting from one single image per class available from a commercial glass slide containing 50 fixed species in a dry setting. This approach avoids the time-consuming steps of water sampling and labeling by skilled marine biologists. To accomplish this goal, we exploit the holographic imaging modality, which permits the accessing of a quantitative phase-contrast maps and a posteriori flexible refocusing due to its intrinsic 3D imaging capability. The network model is then validated by using holographic recordings of live diatoms imaged in water samples i.e., in their natural wet environmental condition.

Photothermal therapy (PTT) is an efficient method of inducing localized hyperthermia and can be achieved using gold nanoparticles as photothermal agents. However, there are many hurdles to get over before this therapy can safely reach the clinics, including nanoparticles' optimal shape and the accurate prediction of cellular responses. Here, we describe the synthesis of gold nanorods and nanoprisms with similar surface plasmon resonances in the near-infrared (NIR) and comparable photothermal conversion efficiencies and characterize the response to NIR irradiation in two biological systems, melanoma cells and the small invertebrate Hydra vulgaris. By integrating animal, cellular, and molecular biology approaches, we show a diverse outcome of nanorods and nanoprisms on the two systems, sustained by the elicitation of different pathways, from necrosis to programmed cell death mechanisms (apoptosis and necroptosis). The comparative multilevel analysis shows great accuracy of in vivo invertebrate models to predict overall responses to photothermal challenging and superior photothermal performance of nanoprisms. Understanding the molecular pathways of these responses may help develop optimized nanoheaters that, safe by design, may improve PTT efficacy for clinical purposes.

Microplastics (MPs) are a major environmental concern due to their possible impact on water pollution, wildlife, and the food chain. Reliable, rapid, and high‐throughput screening of MPs from other components of a water sample after sieving and/or digestion is still a highly desirable goal to avoid cumbersome visual analysis by expert users under the optical microscope. Here, a new approach is presented that combines 3D coherent imaging with machine learning (ML) to achieve accurate and automatic detection of MPs in filtered water samples in a wide range at microscale. The water pretreatment process eliminates sediments and aggregates that fall out of the analyzed range. However, it is still necessary to clearly distinguish MPs from marine microalgae. Here, it is shown that, by defining a novel set of distinctive “holographic features,” it is possible to accurately identify MPs within the defined analysis range. The process is specifically tailored for characterizing the MPs' “holographic signatures,” thus boosting the classification performance and reaching accuracy higher than 99% in classifying thousands of items. The ML approach in conjunction with holographic coherent imaging is able to identify MPs independently from their morphology, size, and different types of plastic materials.

Circulating tumor cells (CTCs) are rare tumor cells released from primary, metastatic, or recurrent tumors in the peripheral blood of cancer patients. CTCs isolation from peripheral blood and their molecular characterization represent a new marker in cancer screening, a diagnostic tool called “liquid biopsy” (LB). Compared to traditional tissue biopsy that is invasive and does not reveal tumor heterogeneity, LB is noninvasive and reflects in “real‐time” tumor dynamism and drug sensitivity. In the frame of LB, a new paradigm based on single‐cell and label‐free analysis based on morphological analysis is emerging. Here, we review the latest research developments in this emerging vision of LB. In particular, we survey and discuss recent improvements in microfluidics, imaging label‐free diagnosis and cell classification by artificial intelligence and how to combine them to realize an intelligent platform based on lab‐on‐chip technology. This prospect appears to open up promising and intriguing new scenarios for cancer management through single‐cell analysis that will revolutionize the future of early cancer diagnosis and therapeutic choice with disruptive impact on the society.

An effective strategy, that combines Digital Holography with machine learning, for achieving accurate and automatic identification of microplastics in filtered water sample, is proposed, reaching over 99% in classification performance among microplastics and diatoms.

Graphene family materials (GFMs) have large perspectives for drug-delivery applications, but their internalization in live cells is under investigation in a wide variety of studies in order to assess the best conditions for efficient cellular uptake. Here we show that mild oxidation of graphene nanoplatelets produces nanographene oxide (nGO) particles, which are massively internalized into the cell cytoplasm. This remarkable uptake of nGO in NIH-3T3 cells has never been observed before. We performed vitality tests for demonstrating the biocompatibility of the material and analyzed the internalization mechanism under different oxidation degrees and concentrations. Moreover, we evaluated quantitatively, for the first time, the cell volume variation after nGO internalization in live cells through a label-free digital holographic imaging technique and in quasi-real-time modality, thus avoiding the time-consuming and detrimental procedures usually employed by electron-based microscopy. The results demonstrate that nGO formulations with a tailored balance between the exposed surface and content of functional groups are very promising in drug-delivery applications.

A review on the last achievements of Digital Holography is reported in this paper, showing that this powerful method can be a key metrological tool for the quantitative analysis and non-invasive inspection of a variety of materials, devices and processes. Nowadays, its range of applications has been greatly extended, including the study of live biological matter and biomedical applications. This paper overviews the main progresses and future perspectives of digital holography, showing new optical configurations and investigating the numerical issues to be tackled for the processing and display of quantitative data.

High-throughput single-cell analysis is a challenging target for implementing advanced biomedical applications. An excellent candidate for this aim is label-free tomographic phase microscopy (TPM). In this paper, some of the methods used to obtain TPM are reviewed, analyzing advantages and disadvantages of each of them. Moreover, an alternative tomographic technique is described for live cells analysis, and future trends of the method are foreseen. In particular, by exploiting random rolling of cells while they are flowing along a microfluidic channel, it is possible to obtain phase-contrast tomography thus obtaining complete retrieval of both 3D-position and orientation of rotating cells. Thus, a priori knowledge of such information is no longer needed. This approach extremely simplifies the optical system avoiding any mechanical/optical scanning of light source. The proof is given for different classes of biosamples, red-blood-cells (RBCs) and diatom algae. Accurate characterization of each type of cells is reported and compared to that obtained by other tomographic techniques.

In this work, the optical behavior of Red Blood Cells (RBCs) under an optically-induced mechanical stress was studied. Exploiting the new findings concerning the optical lens-like behavior of RBCs, the variations of the wavefront refracted by optically-deformed RBCs were further investigated. Experimental analysis have been performed through the combination of digital holography and numerical analysis based on Zernike polynomials, while the biological lens is deformed under the action of multiple dynamic optical tweezers. Detailed wavefront analysis provides comprehensive information about the aberrations induced by the applied mechanical stress. By this approach it was shown that the optical properties of RBCs in their discocyte form can be affected in a different way depending on the geometry of the deformation. In analogy to classical optical testing procedures, optical parameters can be correlated to a particular mechanical deformation. This could open new routes for analyzing cell elasticity by examining optical parameters instead of direct but with low resolution strain analysis, thanks to the high sensitivity of the interferometric tool. Future application of this approach could lead to early detection and diagnosis of blood diseases through a single-step wavefront analysis for evaluating different cells elasticity. © 2017 International Society for Advancement of Cytometry.

Red Blood Cells (RBCs) deformability is an important parameter for evaluating their health status. Its quantification is performed through the measurement of erythrocyte's shape stiffness when subjected to external stimuli. Here, we exploit the hydrodynamic deformation of RBCs in microfluidic channels to quantify the shape variations through quantitative phase imaging by digital holography. In particular, two main processing steps have been employed, i.e., the morphological analysis based on quantitative phase variations and a new way to monitor the entire cell's deformation, based on modeling RBCs as a micro-lenses array. In fact, taking advantage of the RBC lens behavior, it is possible to correlate optical aberrations generated by the mechanical stimulus to the entire membrane deformation itself, through a numerical analysis based on Zernike polynomials. We provide a proof of principle of how to use Zernike analysis for characterizing RBCs' deformability under hydrodynamic stress. We demonstrate that new optical parameters of RBCs can be measured and analyzed, thus opening the route to the exploitation of the bio-lens model as a biomechanical marker of RBCs.

We propose a new diagnostic tool for anemias identification based on quantitative phase imaging. We introduce a panel of label-free optical markers to identify red blood cell (RBC) phenotypes, demonstrating that an optical fingerprint of RBC is related to erythrocyte disease through modeling RBC as biological lens.

Microplastics are worrisome water pollutants that are more and more spread in deep sea and coastal waters. Plastic items can take decades to biodegrade, have the potential to affect the food chain and are harmful to marine life. Hence, there is the urgent need to define protocols and to create reliable tools to map the presence of microplastics in heterogeneous liquid samples. However, well established protocols and tools to identify microplastics in water have not been proposed yet. Here we investigate this class of objects by means of coherent imaging, in particular relying on Digital Holography (DH) microscopy. We provide a DH characterization of the "plastic" class that can be used as a global identifier independently on the plastic material under analysis. We probe microplastics of various materials through our DH microscope and show that the phase contrast map of microplastics can be used to define a fingerprint for the microplastics population. Thanks to the DH flexible refocusing, volumetric counting of microplastics in flow is feasible by DH with high-throughput. Remarkably, field-deployable, cost effective DH microscopes exist that can bring the DH characterization potential out of the lab for in situ environmental monitoring.

Nowadays, digital holography can be considered as one of the most powerful imaging modality in several research fields, from the 3D imaging for display purposes to quantitative phase image in microscopy and microfluidics. At the same time, Machine learning in imaging applications has been literally reborn to the point of being considered the most exploited field by optical imaging researchers. In fact, the use of deep convolutional neural networks has permitted to achieve impressive results in the classification of biological samples obtained by holographic imaging, as well as for solving inverse problems in holographic microscopy. Definitely, Machine learning approaches in digital holography has been used mainly to improve the performance of the imaging tool. Here we show a reverse modality in which holographic imaging boosts the performance of Machine leaning algorithms. In particular, we identify several descriptors solely related to the type of data to be classified, i.e. the holographic image. We provide some case studies which demonstrate how the holographic imaging can improve the performance of a plain classifier.

The identification and classification of biological samples is high-demanded in biomedical imaging for diagnostic purposes. Among all imaging modalities, digital holography has gained credits as a powerful solutions, thanks to its ability to perform full-field and label –free quantitative phase imaging. On the other hand, machine learning is nowadays the most used approach for classification purposes. The robustness and the accuracy of the classification depend of the features used for the training step. Therefore, the identification of micro-organism becomes strictly related to the features that can be extracted from their images. In other word, the more the image contains information, the higher the possibility of extracting highly distinctive descriptors to differentiate biological phenotypes. Digital holography can be considered one of the richest in terms of information content due to the fact that a single digital hologram encode both amplitude and phase information about the imaged cells. This opens the way to improve the features extraction, thus making more accurate the classification step. In this paper we analyze a test case by using a holographic image dataset for classification, by extracting unique features that can be solely obtained by holographic images.

All-optical activation of electric fields in smart materials is a challenging issue for avoiding complex and multiple steps in material processing. Photo-activation of electrodes can be realized in photorefractive (PR) crystals where the interaction with light of suitable wavelength and power generates charge displacements and stable space electric fields. In past years many efforts have been spent for studying PR fields in ferroelectric material such as Lithium Niobate (LN) [1,2] and many applications have been demonstrated for dielectrophoretic (DEP) trapping of microparticles [3,4] and for sensing in microfluidic environment [6,7]. Recently it has been investigated the interaction of such fields with biological samples thus proving its biocompatibility for tissue growth [8] of for biosensing applications [9].

According to the International agency for Research on Cancer, cadmium (Cd) is considered as a human carcinogen. Cadmium may induce cell death by apoptosis in various cell types, although the underlying mechanisms are still unclear. Nowadays, the cytotoxic potential of heavy metals is commonly evaluated by different cellular endpoints as reactive oxygen species formation, cell viability or cell death. Heavy metals cytotoxicity testing is based on in-vitro methods such as MTT assay, for the colorimetric detection of mitochondrial activity, propidium iodide-staining of DNA, as cell death marker, fluorometric detection of ROS generation to evaluate the stress response and colorimetric detection of cytokine secretion for the inflammatory reaction by ELISA method. In this work, we present a label-free digital holography (DH) based technique as an in-vitro cytotoxicity assay, which overcomes the limitations of conventional in vitro test based on color or fluorescence read outs. In particular, we show how DH is able to quantify the evolution of key biophysical parameters of cells during the exposure to cadmium. Murine embryonic fibroblasts NIH 3T3 are chosen here as cellular model for studying the cadmium effects. The results demonstrate that DH is able to retrieve the temporal evolution of different key parameters such as cell volume, projected area, cell thickness and dry mass, thus providing a full quantitative characterization of the cell physical behaviour during cadmium exposure. This demonstrates DH as an elegant label-free tool for heavy metals toxicity analysis.

Gold standard methods for anaemia diagnosis are the complete blood count and the peripheral smear observation. However, they do not allow for a complete differential diagnosis, which requires biochemical assays, thus being labeldependent techniques. On the other hand, recent studies focus on label-free quantitative phase imaging (QPI) of blood samples to investigate blood diseases by using video-based morphological methods. However, when sick cells are very similar to healthy ones in terms of morphometric features, identification of a blood disease becomes challenging even by morphometric analysis as well as QPI. Here we exploit in-flow tomographic phase microscopy to retrieve the exact 3D rendering of Red Blood Cells (RBCs) from anaemic patients and to identify the pathology, distinguishing it from healthy samples. Moreover, we introduce a Label-free Optical Marker (LOM) to detect RBC phenotypes demonstrating that a single set of all-optical parameters can clearly identify a signature directly related to the erythrocytes disease by modelling each RBC as a biolens. We tested this novel bio-photonic analysis by proofing that several inherited anaemias, specifically Iron-deficiency Anaemia, Thalassemia, Hereditary Spherocytosis and Congenital Dyserythropoietic Anaemia, can be identified and sorted thus opening a novel route for blood diagnosis on a completely different concept based on LOMs.