A computational model for single cell Lamin-A structural organization after microfluidic compression.

In recent years, nuclear mechanobiology gained a lot of attention for the study of cell responses to external cues like adhesive forces, applied compression, and/or shear-stresses. In details, the Lamin-A protein-as major constituent of the cell nucleus structure-plays a crucial role in the overall nucleus mechanobiological response. However, modeling and analysis of Lamin-A protein organization upon rapid compression conditions in microfluidics are still difficult to be performed. Here, we introduce the possibility to control an applied microfluidic compression on single cells, deforming them up to the nucleus level. In a wide range of stresses (~1-102 kPa) applied on healthy and cancer cells, we report increasing Lamin-A intensities which scale as a power law with the applied compression. Then, an increase up to two times of the nuclear viscosity is measured in healthy cells, due to the modified Lamin-A organization. This is ascribable to the increasing assembly of Lamin-A filament-like branches which increment both in number and elongation (up to branches four-time longer). Moreover, the solution of a computational model of differential equations is presented as a powerful tool for a single cell prediction of the Lamin-A assembly as a function of the applied compression.

HiViPore: a highly viable in-flow compression for a one-step cell mechanoporation in microfluidics to induce a free delivery of nano- macro-cargoes.

BACKGROUND: Among mechanoporation techniques for intracellular delivery, microfluidic approaches succeed in high delivery efficiency and throughput. However, especially the entry of large cargoes (e.g. DNA origami, mRNAs, organic/inorganic nanoparticles) is currently impaired since it requires large cell membrane pores with the need to apply multi-step processes and high forces, dramatically reducing cell viability. RESULTS: Here, HiViPore presents as a microfluidic viscoelastic contactless compression for one-step cell mechanoporation to produce large pores while preserving high cell viability. Inducing an increase of curvature at the equatorial region of cells, formation of a pore with a size of ~ 1 µm is obtained. The poration is coupled to an increase of membrane tension, measured as a raised fluorescence lifetime of 12% of a planarizable push-pull fluorescent probe (Flipper-TR) labelling the cell plasma membrane. Importantly, the local disruptions of cell membrane are transient and non-invasive, with a complete recovery of cell integrity and functions in ~ 10 min. As result, HiViPore guarantees cell viability as high as ~ 90%. In such conditions, an endocytic-free diffusion of large nanoparticles is obtained with typical size up to 500 nm and with a delivery efficiency up to 12 times higher than not-treated cells. CONCLUSIONS: The proposed one-step contactless mechanoporation results in an efficient and safe approach for advancing intracellular delivery strategies. In detail, HiViPore solves the issues of low cell viability when multiple steps of poration are required to obtain large pores across the cell plasma membrane. Moreover, the compression uses a versatile, low-cost, biocompatible viscoelastic fluid, thus also optimizing the operational costs. With HiViPore, we aim to propose an easy-to-use microfluidic device to a wide range of users, involved in biomedical research, imaging techniques and nanotechnology for intracellular delivery applications in cell engineering.

MicroLOCK: Highly stable microgel biosensor using locked nucleic acids as bioreceptors for sensitive and selective detection of let-7a.

Chemically modified oligonucleotides can solve biosensing issues for the development of capture probes, antisense, CRISPR/Cas, and siRNA, by enhancing their duplex-forming ability, their stability against enzymatic degradation, and their specificity for targets with high sequence similarity as microRNA families. However, the use of modified oligonucleotides such as locked nucleic acids (LNA) for biosensors is still limited by hurdles in design and from performances on the material interface. Here we developed a fluorogenic biosensor for non-coding RNAs, represented by polymeric PEG microgels conjugated with molecular beacons (MB) modified with locked nucleic acids (MicroLOCK). By 3D modeling and computational analysis, we designed molecular beacons (MB) inserting spot-on LNAs for high specificity among targets with high sequence similarity (95%). MicroLOCK can reversibly detect microRNA targets in a tiny amount of biological sample (2 µL) at 25 °C with a higher sensitivity (LOD 1.3 fM) without any reverse transcription or amplification. MicroLOCK can hybridize the target with fast kinetic (about 30 min), high duplex stability without interferences from the polymer interface, showing high signal-to-noise ratio (up to S/N = 7.3). MicroLOCK also demonstrated excellent resistance to highly nuclease-rich environments, in real samples. These findings represent a great breakthrough for using the LNA in developing low-cost biosensing approaches and can be applied not only for nucleic acids and protein detection but also for real-time imaging and quantitative assessment of gene targeting both in vitro and in vivo.

Exploring altered bovine sperm trajectories by sperm tracking in unconfined conditions.

Mammalian sperm motility is getting more relevant due to rising infertility rates worldwide, generating the need to improve conventional analysis and diagnostic approaches. Nowadays, computer assisted sperm analysis (CASA) technologies represent a popular alternative to manual examination which is generally performed by observing sperm motility in very confined geometries. However, under physiological conditions, sperm describe three-dimensional motility patterns which are not well reconstructed by the limited depth of standard acquisition chambers. Therefore, affordable and more versatile alternatives are needed. Here, a motility analysis in unconfined conditions is proposed. In details, the analysis is characterized by a significant longer duration -with respect to conventional systems- with the aim to observe eventually altered motility patterns. Brightfield acquisition in rectangular glass capillaries captured frozen-thawed bovine spermatozoa which were analyzed by means of a self-written tracking routine and classified in sub-populations, based on their curvilinear velocity. To test the versatility of our approach, cypermethrin -a commonly used pesticides- known to be responsible for changes in sperm motility was employed, assessing its effect at three different time-steps. Experimental results showed that such drug induces an increase in sperm velocity and progressiveness as well as circular pattern formation, likely independent of wall interactions. Moreover, this resulted in a redistribution of sperm with the rapid class declining in number with time, but still showing an overall velocity increase. The flexibility of the approach permits parameter modifications with the experimental needs, allowing us to conduct a comprehensive examination of sperm motility. This adaptability facilitated data acquisition which can be computed at different frame rates, extended time periods, and within deeper observation chambers. The suggested approach for sperm analysis exhibits potential as a valuable augmentation to current diagnostic instruments.

Unknown cell class distinction via neural network based scattering snapshot recognition.

Neural network-based image classification is widely used in life science applications. However, it is essential to extrapolate a correct classification method for unknown images, where no prior knowledge can be utilised. Under a closed set assumption, unknown images will be inevitably misclassified, but this can be genuinely overcome choosing an open-set classification approach, which first generates an in-distribution of identified images to successively discriminate out-of-distribution images. The testing of such image classification for single cell applications in life science scenarios has yet to be done but could broaden our expertise in quantifying the influence of prediction uncertainty in deep learning. In this framework, we implemented the open-set concept on scattering snapshots of living cells to distinguish between unknown and known cell classes, targeting four different known monoblast cell classes and a single tumoral unknown monoblast cell line. We also investigated the influence on experimental sample errors and optimised neural network hyperparameters to obtain a high unknown cell class detection accuracy. We discovered that our open-set approach exhibits robustness against sample noise, a crucial aspect for its application in life science. Moreover, the presented open-set based neural network reveals measurement uncertainty out of the cell prediction, which can be applied to a wide range of single cell classifications.

Hydrogel particles-on-chip (HyPoC): a fluorescence micro-sensor array for IgG immunoassay.

Novel microparticles have generated growing interest in diagnostics for potential sensitivity and specificity in biomolecule detection and for the possibility to be integrated in a micro-system array as a lab-on-chip. Indeed, bead-based technologies integrated in microfluidics could speed up incubation steps, reduce reagent consumption and improve accessibility of diagnostic devices to non-expert users. To limit non-specific interactions with interfering molecules and to exploit the whole particle volume for bioconjugation, hydrogel microparticles, particularly polyethylene glycol-based, have emerged as promising materials to develop high-performing biosensors since their network can be functionalized to concentrate the target and improve detection. However, the limitations in positioning, trapping and mainly fine manipulation of a precise number of particles in microfluidics have largely impaired point-of-care applications. Herein, we developed an on-chip sandwich immunoassay for the detection of human immunoglobulin G in biological fluids. The detection system is based on finely engineered cleavable PEG-based microparticles, functionalized with specific monoclonal antibodies. By changing the particle number, we demonstrated tuneable specificity and sensitivity (down to 3 pM) in serum and urine. Therefore, a controlled number of hydrogel particles have been integrated in a microfluidic device for on-chip detection (HyPoC) allowing for their precise positioning and fluid exchange for incubation, washing and target detection. HyPoC dramatically decreases incubation time from 180 minutes to one minute and reduces washing volumes from 3.5 ml to 90 µL, achieving a limit of detection of 0.07 nM (with a dynamic range of 0.07-1 nM). Thus, the developed approach represents a versatile, fast and easy point-of-care testing platform for immunoassays.

Direct, precise, enzyme-free detection of miR-103-3p in real samples by microgels with highly specific molecular beacons.

Low abundance, small size, and sequence similarities render microRNA (miRNAs) detection challenging, particularly in real samples, where quantifying weakly expressed miRNAs can be arduous due to interference of more abundant molecules. The standard quantitative reverse transcription polymerase chain reaction (qRT-PCR) requires multiple steps, thermal cycles, and costly enzymatic reactions that can negatively affect results. Here we present a direct, precise, enzyme-free assay based on microgels particles conjugating molecular beacons (MB) capable of optically detecting low abundant miRNAs in real samples. We validate the applicability of microgels assay using qRT-PCR as a reference technology. As a relevant case, we chose miR-103-3p, a valuable diagnostic biomarker for breast cancer, both in serum samples and MCF7 cells. As a result, microgels assay quantifies miRNA molecules at room temperature in a single step, 1 h (vs. 4 hrs for qRT-PCR) without complementary DNA synthesis, amplification, or expensive reagents. Microgels assay exhibits femtomolar sensitivity, single nucleotide specificity, and a wide linear range (102-107 fM) (wider than qRT-PCR), with low sample consumption (2 µL) and excellent linearity (R2= 0.98). To test the selectivity of the microgel assay in real samples, MCF7 cells were considered where the pool of 8 other miRNAs were further upregulated with respect to miRNA 103-3p. In such complex environments, microgels assay selectively detects the miRNA target, mainly due to MB advanced stability and specificity as well as high microgel antifouling properties. These results show the reliability of microgels assay to detect miRNAs in real samples.

Cell deformability heterogeneity recognition by unsupervised machine learning from in-flow motion parameters.

Cell deformability is a well-established marker of cell states for diagnostic purposes. However, the measurement of a wide range of different deformability levels is still challenging, especially in cancer, where a large heterogeneity of rheological/mechanical properties is present. Therefore, a simple, versatile and cost-effective recognition method for variable rheological/mechanical properties of cells is needed. Here, we introduce a new set of in-flow motion parameters capable of identifying heterogeneity among cell deformability, properly modified by the administration of drugs for cytoskeleton destabilization. Firstly, we measured cell deformability by identification of in-flow motions, rolling (R), tumbling (T), swinging (S) and tank-treading (TT), distinctively associated with cell rheological/mechanical properties. Secondly, from a pool of motion and structural cell parameters, an unsupervised machine learning approach based on principal component analysis (PCA) revealed dominant features: the local cell velocity (VCell/VAvg), the equilibrium position (YEq) and the orientation angle variation (Δφ). These motion parameters clearly defined cell clusters in terms of motion regimes corresponding to specific deformability. Such correlation is verified in a wide range of rheological/mechanical properties from the elastic cells moving like R until the almost viscous cells moving as TT. Thus, our approach shows how simple motion parameters allow cell deformability heterogeneity recognition, directly measuring rheological/mechanical properties.

Single cell classification of macrophage subtypes by label-free cell signatures and machine learning.

Pro-inflammatory (M1) and anti-inflammatory (M2) macrophage phenotypes play a fundamental role in the immune response. The interplay and consequently the classification between these two functional subtypes is significant for many therapeutic applications. Albeit, a fast classification of macrophage phenotypes is challenging. For instance, image-based classification systems need cell staining and coloration, which is usually time- and cost-consuming, such as multiple cell surface markers, transcription factors and cytokine profiles are needed. A simple alternative would be to identify such cell types by using single-cell, label-free and high throughput light scattering pattern analyses combined with a straightforward machine learning-based classification. Here, we compared different machine learning algorithms to classify distinct macrophage phenotypes based on their optical signature obtained from an ad hoc developed wide-angle static light scattering apparatus. As the main result, we were able to identify unpolarized macrophages from M1- and M2-polarized phenotypes and distinguished them from naive monocytes with an average accuracy above 85%. Therefore, we suggest that optical single-cell signatures within a lab-on-a-chip approach along with machine learning could be used as a fast, affordable, non-invasive macrophage phenotyping tool to supersede resource-intensive cell labelling.

Wide-range viscoelastic compression forces in microfluidics to probe cell-dependent nuclear structural and mechanobiological responses.

The cell nucleus plays a critical role in mechanosensing and mechanotransduction processes, by adaptive changes of its envelope composition to external biophysical stimuli such as substrate rigidity and tensile forces. Current measurement approaches lack precise control in stress application on nuclei, thus significantly impairing a complete mechanobiological study of cells. Here, we present a contactless microfluidic approach capable to exert a wide range of viscoelastic compression forces (10-103 µN)-as an alternative to adhesion-related techniques-to induce cell-specific mechano-structural and biomolecular changes. We succeed in monitoring substantial nuclear modifications in Lamin A/C expression and coverage, diffusion processes of probing molecules, YAP shuttling, chromatin re-organization and cGAS pathway activation. As a result, high compression forces lead to a nuclear reinforcement (e.g. up to +20% in Lamin A/C coverage) or deconstruction (e.g. down to -45% in Lamin A/C coverage with a 30% reduction of chromatin condensation state parameter) up to cell death. We demonstrate how wide-range compression on suspended cells can be used as a tool to investigate nuclear mechanobiology and to define specific nuclear signatures for cell mechanical phenotyping.

PEG-based cleavable hydrogel microparticles with controlled porosity for permiselective trafficking of biomolecular complexes in biosensing applications.

In the last decade, PEG-based hydrogels have been extensively used for the production of microparticles for biosensing applications. The biomolecule accessibility and mass transport rate represent key parameters for the realization of sensitive microparticles, therefore porous materials have been developed, mainly resorting to the use of inert porogens and copolymers with different chain lengths. However, very limited information is reported regarding the addition of cleavable crosslinkers to modulate the network porosity. In this scenario, the aim of this work is to design, synthesize and characterize hydrogel microparticles, based on the copolymerization between PEG-diacrylate and N,N'-(1,2-dihydroxyethylene)-bisacrylamide, a cleavable crosslinker that simultaneously produces pores and reactive groups for bioprobe 3D bioconjugation. The results show great accessibility of these microparticles to antibodies and their complexes, without affecting their diffusion rate. Furthermore, the presence of a well-defined number of reactive aldehydes, produced by the cleavage reaction, allows modulating biosensor sensitivity through a fine control of the conjugation degree. The antibody-conjugated microparticles can efficiently capture the analyte down to a few picograms. These novel microparticles could be used as a highly sensitive platform for biomacromolecule detection in complex fluids, exploiting the combined effects of PEG's anti-fouling properties, large network porosity and interconnections, and three-dimensional bioconjugation.

Hydrogel Microparticles for Fluorescence Detection of miRNA in Mix-Read Bioassay.

Herein we describe the development of a mix-read bioassay based on a three-dimensional (3D) poly ethylene glycol-(PEG)-hydrogel microparticles for the detection of oligonucleotides in complex media. The key steps of hydrogels synthesis and molecular recognition in a 3D polymer network are elucidated. The design of the DNA probes and their density in polymer network were opportunely optimized. Furthermore, the diffusion into the polymer was tuned adjusting the polymer concentration and consequently the characteristic mesh size. Upon parameters optimization, 3D-PEG-hydrogels were synthetized in a microfluidic system and provided with fluorescent probe. Target detection occurred by double strand displacement assay associated to fluorescence depletion within the hydrogel microparticle. Proposed 3D-PEG-hydrogel microparticles were designed for miR-143-3p detection. Results showed 3D-hydrogel microparticles with working range comprise between 10-6-10-12 M, had limit of detection of 30 pM and good specificity. Moreover, due to the anti-fouling properties of PEG-hydrogel, the target detection occurred in human serum with performance comparable to that in buffer. Due to the approach versatility, such design could be easily adapted to other short oligonucleotides detection.

New Trends in Precision Medicine: A Pilot Study of Pure Light Scattering Analysis as a Useful Tool for Non-Small Cell Lung Cancer (NSCLC) Diagnosis.

Background: To date, in personalized medicine approaches, single-cell analyses such as circulating tumour cells (CTC) are able to reveal small structural cell modifications, and therefore can retrieve several biophysical cell properties, such as the cell dimension, the dimensional relationship between the nucleus and the cytoplasm and the optical density of cellular sub-compartments. On this basis, we present in this study a new morphological measurement approach for the detection of vital CTC from pleural washing in individual non-small cell lung cancer (NSCLC) patients. Materials and methods: After a diagnosis of pulmonary malignancy, pleural washing was collected from nine NSCLC patients. The collected samples were processed with a density gradient separation process. Light scattering analysis was performed on a single cell. The results of this analysis were used to obtain the cell's biophysical pattern and, later on, as basis for Machine Learning (ML) on unknown samples. Results: Morphological single-cell analysis followed by ML show a predictive picture for an NSCLC patient, screening that it is possible to distinguish CTC from other cells. Moreover, we find that the proposed measurement approach was fast, reliable, label-free, identifying and count CTC in a biological fluid. Conclusions: Our findings demonstrate that CTC Biophysical Profile by Pure Light Scattering in NSCLC could be used as a promising diagnostic candidate in NSCLC patients.

Non-invasive and label-free identification of human natural killer cell subclasses by biophysical single-cell features in microfluidic flow.

Natural killer (NK) cells are indicated as favorite candidates for innovative therapeutic treatment and are divided into two subclasses: immature regulatory NK CD56bright and mature cytotoxic NK CD56dim. Therefore, the ability to discriminate CD56dim from CD56bright could be very useful because of their higher cytotoxicity. Nowadays, NK cell classification is routinely performed by cytometric analysis based on surface receptor expression. Here, we present an in-flow, label-free and non-invasive biophysical analysis of NK cells through a combination of light scattering and machine learning (ML) for NK cell subclass classification. In this respect, to identify relevant biophysical cell features, we stimulated NK cells with interleukine-15 inducing a subclass transition from CD56bright to CD56dim. We trained our ML algorithm with sorted NK cell subclasses (≥86% accuracy). Next, we applied our NK cell classification algorithm to cells stimulated over time, to investigate the transition of CD56bright to CD56dim and their biophysical feature changes. Finally, we tested our approach on several proband samples, highlighting the potential of our measurement approach. We show a label-free way for the robust identification of NK cell subclasses based on biophysical features, which can be applied in both cell biology and cell therapy.

Small Oligonucleotides Detection in Three-Dimensional Polymer Network of DNA-PEG Hydrogels.

The control of the three-dimensional (3D) polymer network structure is important for permselective materials when specific biomolecule detection is needed. Here we investigate conditions to obtain a tailored hydrogel network that combines both molecular filtering and molecular capture capabilities for biosensing applications. Along this line, short oligonucleotide detection in a displacement assay is set within PEGDA hydrogels synthetized by UV radical photopolymerization. To provide insights on the molecular filter capability, diffusion studies of several probes (sulforhodamine G and dextrans) with different hydrodynamic radii were carried out using NMR technique. Moreover, fluorometric analyses of hybridization of DNA oligonucleotides inside PEGDA hydrogels shed light on the mechanisms of recognition in 3D, highlighting that mesh size and crowding effect greatly impact the hybridization mechanism on a polymer network. Finally, we found the best probe density and diffusion transport conditions to allow the specific oligonucleotide capture and detection inside PEGDA hydrogels for oligonucleotide detection and the filtering out of higher molecular weight molecules.

Easy Surface Functionalization and Bioconjugation of Peptides as Capture Agents of a Microfluidic Biosensing Platform for Multiplex Assay in Serum.

The development of assays for protein biomarkers in complex matrices is a demanding task that still needs implementation of new approaches. Antibodies as capture agents have been largely used in bioassays but their low stability, low-efficiency production, and cross-reactivity in multiplex approaches impairs their larger applications. Instead, synthetic peptides, even with higher stability and easily adapted amino acid sequences, still remain largely unexplored in this field. Here, we provide a proof-of-concept of a microfluidic device for direct detection of biomarker overexpression. The multichannel microfluidic polydimethylsiloxane (PDMS) device was first derivatized with PAA (poly(acrylic acid)) solution. CRP-1, VEGF-114, and ΦG6 peptides were preliminarily tested to respectively bind the biomarkers, C-reactive protein (CRP), vascular endothelial growth factor (VEGF), and tumor necrosis factor-alpha (TNF-α). Each PDMS microchannel was then respectively bioconjugated with a specific peptide (CRP-1, VEGF-114, or ΦG6) to specifically capture CRP, VEGF, and TNF-α. With such microdevices, a fluorescence bioassay has been set up with sensitivity in the nanomolar range, both in buffered solution and in human serum. The proposed multiplex assay worked with a low amount of sample (25 µL) and detected biomarker overexpression (above nM concentration), representing a noninvasive and inexpensive screening platform.

Mechanical phenotyping of breast cell lines by in-flow deformation-dependent dynamics under tuneable compressive forces.

Cell mechanical properties are powerful biomarkers for label-free phenotyping. To date, microfluidic approaches assay mechanical properties by measuring changes in cellular shape, applying extensional or shear flows or forcing cells to pass through constrictions. In general, such approaches use high-speed imaging or transit time measurements to evaluate cell deformation, while cell dynamics in-flow after stress imposition have not yet been considered. Here, we present a microfluidic approach to apply, over a wide range, tuneable compressive forces on suspended cells, which result in well distinct signatures of deformation-dependent dynamic motions. By properly conceiving microfluidic chip geometry and rheological fluid properties, we modulate applied single-cell forces, which result in different motion regimes (rolling, tumbling or tank-treating) depending on the investigated cell line. We decided to prove our approach by testing breast cell lines, with well-known mechanical properties. We measured a set of in-flow parameters (orientation angle, aspect ratio, cell deformation and cell diameter) as a backward analysis of cell mechanical response. By such an approach, we report that the highly invasive tumour cells (MDA-MB-231) are much more deformable (6-times higher) than healthy (MCF-10A) and low invasive ones (MCF-7). Thus, we demonstrate that a microfluidic design with tuneable rheological fluid properties and direct analysis of bright-field images can be suitable for the label-free mechanical phenotyping of various cell lines.

CD4+ versus CD8+ T-lymphocyte identification in an integrated microfluidic chip using light scattering and machine learning.

T lymphocytes are a group of cells representing the main effectors of human adaptive immunity. Characterization of the most representative T-lymphocyte subclasses, CD4+ and CD8+, is challenging, but has a significant impact on clinical decisions. Up to now, T lymphocytes have been identified by quite complex cytometric assays, which are based on antibody labeling. However, a label-free approach based on pure biophysical evaluation at a single-cell level could enable the ability to distinguish between these subclasses. Here, we report a light-scattering approach, supported by accurate data mining, to evaluate cell biophysical properties on an integrated microfluidic chip. In order to perform single-cell optical analysis in viscoelastic fluids, such a chip is composed of mixing, alignment, readout and collection sections. In particular, we measured the cell dimensions, the refractive index of the cell nucleus, the refractive index of the cytosol, and the nucleus-to-cytosol ratio. Combining measurement of biophysical properties and machine learning allows us to both distinguish and count human CD4+ and CD8+ cells with an accuracy of 79%. An enhanced identification accuracy of 88% can be achieved by stimulating the cells with a selective anti-apoptotic protein, which results in increased biophysical differences between CD4+ and CD8+ cells. This approach has been successfully validated by analysis of samples that recapitulate physiological and pathological scenarios (CD4+/CD8+ ratios). The results are encouraging for the possible application of our approach in hematological clinical routines, as well as in diagnosis and follow-up of specific pathologies, such as human immunodeficiency virus (HIV) progression.

Comparative spallation performance of silicone versus Tygon extracorporeal circulation tubing.

OBJECTIVES: Reports ranged from mixed to marginal tubing wear and spallation effects as a complication of roller pumps in cardiopulmonary bypass (CPB). Because the rollers constantly compress part of the tubing, we sought to determine whether circuit materials behave differently under a 3-h simulation of CPB. METHODS: Two different tubing materials (silicone and Tygon) were tested with a customized experimental circuit, designed to allow in vitro simulation of CPB with priming volumes, pressures, revolutions per minute and temperatures equivalent to the clinical scenario. Samples were analysed with optical and field-emission scanning electron microscopy. We collected 200-ml fluid samples at 4 different times: before starting the CPB (T0), when the predicted revolutions per minute corresponded to about 2 min of CPB (T1), at 90 min (T2) and at 180 min (T3). At the end of CPB, we harvested 2 samples of tubing. Lastly, optical investigations and field-emission scanning electron microscopy observations were used for qualitative and quantitative analysis of circulating fragments. RESULTS: T2 and T3 fluid samples showed more particles than T1 samples. Significant differences in terms of particle numbers were detected: silicone tubing released more fragments per millilitre than Tygon tubing, with both materials releasing particles from 5 to 500 µm. Silicone tubing was associated with a time-dependent increase in small particles released (P = 0.04), whereas this did not apply to large particles or to Tygon tubing. Yet, bootstrap estimates suggested that silicone tubing was associated with the release of more small particles whereas Tygon tubing released more large particles (both P < 0.01). Unlike silicone, Tygon samples taken from the portion of the circuit not subjected to the action of the roller pump did not show any erosion on their surfaces. Samples of both materials taken from the portion subjected to the compression of the roller pump showed signs of significant deterioration. CONCLUSIONS: Silicone showed a worse spallation performance than Tygon, thus appearing less safe for more complex surgery of prolonged duration or for patients with a prior cerebral ischaemic event. Additional risk and cost-effectiveness comparisons to determine the potential benefits of one type of tubing material over the other are warranted to further expand our findings.