Projection Micro-Stereolithography to Manufacture a Biocompatible Micro-Optofluidic Device for Cell Concentration Monitoring.

In this work, a 3D printed biocompatible micro-optofluidic (MoF) device for two-phase flow monitoring is presented. Both an air-water bi-phase flow and a two-phase mixture composed of micrometric cells suspended on a liquid solution were successfully controlled and monitored through its use. To manufacture the MoF device, a highly innovative microprecision 3D printing technique was used named Projection Microstereolithography (PµSL) in combination with the use of a novel 3D printable photocurable resin suitable for biological and biomedical applications. The concentration monitoring of biological fluids relies on the absorption phenomenon. More precisely, the nature of the transmission of the light strictly depends on the cell concentration: the higher the cell concentration, the lower the optical acquired signal. To achieve this, the microfluidic T-junction device was designed with two micrometric slots for the optical fibers' insertion, needed to acquire the light signal. In fact, both the micro-optical and the microfluidic components were integrated within the developed device. To assess the suitability of the selected biocompatible transparent resin for optical detection relying on the selected working principle (absorption phenomenon), a comparison between a two-phase flow process detected inside a previously fully characterized micro-optofluidic device made of a nonbiocompatible high-performance resin (HTL resin) and the same made of the biocompatible one (BIO resin) was carried out. In this way, it was possible to highlight the main differences between the two different resin grades, which were further justified with proper chemical analysis of the used resins and their hydrophilic/hydrophobic nature via static water contact angle measurements. A wide experimental campaign was performed for the biocompatible device manufactured through the PµSL technique in different operative conditions, i.e., different concentrations of eukaryotic yeast cells of Saccharomyces cerevisiae (with a diameter of 5 µm) suspended on a PBS (phosphate-buffered saline) solution. The performed analyses revealed that the selected photocurable transparent biocompatible resin for the manufactured device can be used for cell concentration monitoring by using ad hoc 3D printed micro-optofluidic devices. In fact, by means of an optical detection system and using the optimized operating conditions, i.e., the optimal values of the flow rate FR=0.1 mL/min and laser input power P∈{1,3} mW, we were able to discriminate between biological fluids with different concentrations of suspended cells with a robust working ability R2=0.9874 and Radj2=0.9811.

A 3D-Printed Micro-Optofluidic Chamber for Fluid Characterization and Microparticle Velocity Detection.

This work proposes a multi-objective polydimethylsiloxane (PDMS) micro-optofluidic (MoF) device suitably designed and manufactured through a 3D-printed-based master-slave approach. It exploits optical detection techniques to characterize immiscible fluids or microparticles in suspension inside a compartment specifically designed at the core of the device referred to as the MoF chamber. In addition, we show our novel, fast, and cost-effective methodology, dual-slit particle signal velocimetry (DPSV), for fluids and microparticle velocity detection. Different from the standard state-of-the-art approaches, the methodology focuses on signal processing rather than image processing. This alternative has several advantages, including the ability to circumvent the requirement of complex and extensive setups and cost reduction. Additionally, its rapid processing speed allows for real-time sample manipulations in ongoing image-based analyses. For our specific design, optical signals have been detected from the micro-optics components placed in two slots designed ad hoc in the device. To show the devices' multipurpose capabilities, the device has been tested with fluids of various colors and densities and the inclusion of synthetic microparticles. Additionally, several experiments have been conducted to prove the effectiveness of the DPSV approach in estimating microparticle velocities. A digital particle image velocimetry (DPIV)-based approach has been used as a baseline against which the outcomes of our methods have been evaluated. The combination of the suitability of the micro-optical components for integration, along with the MoF chamber device and the DPSV approach, demonstrates a proof of concept towards the challenge of real-time total-on-chip analysis.

A Regression Approach to Model Refractive Index Measurements of Novel 3D Printable Photocurable Resins for Micro-Optofluidic Applications.

In this work, a quadratic polynomial regression model was developed to aid practitioners in the determination of the refractive index value of transparent 3D printable photocurable resins usable for micro-optofluidic applications. The model was experimentally determined by correlating empirical optical transmission measurements (the dependent variable) to known refractive index values (the independent variable) of photocurable materials used in optics, thus obtaining a related regression equation. In detail, a novel, simple, and cost-effective experimental setup is proposed in this study for the first time for collecting the transmission measurements of smooth 3D printed samples (roughness ranging between 0.04 and 2 µm). The model was further used to determine the unknown refractive index value of novel photocurable resins applicable in vat photopolymerization (VP) 3D printing techniques for manufacturing micro-optofluidic (MoF) devices. In the end, this study proved how knowledge of this parameter allowed us to compare and interpret collected empirical optical data from microfluidic devices made of more traditional materials, i.e., Poly(dimethylsiloxane) (PDMS), up to novel 3D printable photocurable resins suitable for biological and biomedical applications. Thus, the developed model also provides a quick method to evaluate the suitability of novel 3D printable resins for MoF device fabrication within a well-defined range of refractive index values (1.56; 1.70).

Cell counting and velocity algorithms for hydrodynamic study of unsteady biological flows in micro-channels.

In this paper, the combination of two algorithms, a cell counting algorithm and a velocity algorithm based on a Digital Particle Image Velocimetry (DPIV) method, is presented to study the collective behavior of micro-particles in response to hydrodynamic stimuli. A wide experimental campaign was conducted using micro-particles of different natures and diameters (from 5 to 16 µ m ), such as living cells and silica beads. The biological fluids were injected at the inlet of a micro-channel with an external oscillating flow, and the process was monitored in an investigated area, simultaneously, through a CCD camera and a photo-detector. The proposed data analysis procedure is based on the DPIV-based algorithm to extrapolate the micro-particles velocities and a custom counting algorithm to obtain the instantaneous micro-particles number. The counting algorithm was easily integrated with the DPIV-based algorithm, to automatically run the analysis to different videos and to post-process the results in time and frequency domain. The performed experiments highlight the difference in the micro-particles hydrodynamic responses to external stimuli and the possibility to associate them with the micro-particles physical properties. Furthermore, in order to overcome the hardware and software requirements for the development of a real-time approach, it was also investigated the possibility to detect the flows by photo-detector signals as an alternative to camera acquisition. The photo-detector signals were compared with the velocity trends as a proof of concept for further simplification and speed-up of the data acquisition and analysis. The algorithm flexibility underlines the potential of the proposed methodology to be suitable for real-time detection in embedded systems.

Cortical networks underlying successful control of nociceptive processing using real-time fMRI.

Real-time fMRI (rt-fMRI) enables self-regulation of neural activity in localized brain regions through neurofeedback. Previous studies showed successful up- and down-regulation of neural activity in the anterior cingulate cortex (ACC) and the insula (Ins) during nociceptive stimulation. Such self-regulation capacity is, however, variable across subjects, possibly related to the ability of cognitive top-down control of pain. Moreover, how specific brain areas interact to enable successful regulation of nociceptive processing and neurofeedback-based brain modulation is not well understood. A connectivity analysis framework in the frequency domain was used to examine the up- or down-regulation in the ACC and Ins and pain intensity and unpleasantness ratings were assessed. We found that successful up- and down-regulation was mediated by the ACC and by its functional connectivity with the Ins and secondary somatosensory cortex. There was no significant relationship between successful up- or downregulation and pain ratings. These findings demonstrate functional interactions between brain areas involved in nociceptive processing during regulation of ACC and Ins activity, and the relevance of the frequency domain connectivity analysis for real-time fMRI. Moreover, despite successful neural regulation, there was no change in pain ratings, suggesting that pain is a complex perception, which may be more difficult to modify than other sensory or emotional processes.

3D Printing Manufacturing of Polydimethyl-Siloxane/Zinc Oxide Micro-Optofluidic Device for Two-Phase Flows Control.

Tailored ZnO surface functionalization was performed inside a polydimethyl-siloxane (PDMS) microchannel of a micro-optofluidic device (mofd) to modulate its surface hydrophobicity to develop a method for fine tuning the fluid dynamics inside a microchannel. The wetting behavior of the surface is of particular importance if two different phases are used for system operations. Therefore, the fluid dynamic behavior of two immiscible fluids, (i) air-water and (ii) air-glycerol/water in PDMS mofds and ZnO-PDMS mofds was investigated by using different experimental conditions. The results showed that air-glycerol/water fluid was always faster than air-water flow, despite the microchannel treatment: however, in the presence of ZnO microstructures, the velocity of the air-glycerol/water fluid decreased compared with that observed for the air-water fluid. This behavior was associated with the strong ability of glycerol to create an H-bond network with the exposed surface of the zinc oxide microparticles. The results presented in this paper allow an understanding of the role of ZnO functionalization, which allows control of the microfluidic two-phase flow using different liquids that undergo different chemical interactions with the surface chemical terminations of the microchannel. This chemical approach is proposed as a control strategy that is easily adaptable for any embedded micro-device.

Remote Ultrasound Scan Procedures with Medical Robots: Towards New Perspectives between Medicine and Engineering.

BACKGROUND: This review explores state-of-the-art teleoperated robots for medical ultrasound scan procedures, providing a comprehensive look including the recent trends arising from the COVID-19 pandemic. METHODS: Physicians' experience is included to indicate the importance of their role in the design of improved medical robots. From this perspective, novel classes of equipment for remote diagnostics based on medical robotics are discussed in terms of innovative engineering technologies. RESULTS: Relevant literature is reviewed under the system engineering point of view, organizing the discussion on the basis of the main technological focus of each contribution. CONCLUSIONS: This contribution is aimed at stimulating new research to obtain faster results on teleoperated robotics for ultrasound diagnostics in response to the high demand raised by the ongoing pandemic.

3D-Printed micro-optofluidic device for chemical fluids and cells detection.

In this work, it is presented a micro-optofluidic flow detector used for on-chip biological and chemical samples investigation. It is made in Poly-dimethyl-siloxane using a master-slave approach based on the 3D-Printing techniques. The micro-optofluidic device is made by assembling a microfluidic T-junction with a micro-optical section that consists of two optical fiber insertions and a PDMS gold-spattered micro-waveguide. The working principle in the detection is based on a different light transmission correlated to the fluid interfering with the laser beam in a micro-channel section. The proposed solution allows to realize a PDMS micro-device taking the advantage of 3D- Printing and goes beyond the restriction in the material selection. The device's performances were tested in the fluids detection and in the evaluation of the cell concentrations. Additionally, the micro-device was used as a real-time two-phase fluids flow detector. The two-phases flows were successfully monitored in different experimental conditions, varying both hydrodynamic and optical external stimuli.

A polymeric micro-optical interface for flow monitoring in biomicrofluidics.

We describe design and miniaturization of a polymeric optical interface for flow monitoring in biomicrofluidics applications based on polydimethylsiloxane technology, providing optical transparency and compatibility with biological tissues. Design and ray tracing simulation are presented as well as device realization and optical analysis of flow dynamics in microscopic blood vessels. Optics characterization of this polymeric microinterface in dynamic experimental conditions provides a proof of concept for the application of the device to two-phase flow monitoring in both in vitro experiments and in vivo microcirculation investigations. This technology supports the study of in vitro and in vivo microfluidic systems. It yields simultaneous optical measurements, allowing for continuous monitoring of flow. This development, integrating a well-known and widely used optical flow monitoring systems, provides a disposable interface between live mammalian tissues and microfluidic devices making them accessible to detectionprocessing technology, in support or replacing standard intravital microscopy.

An environment for complex behaviour detection in bio-potential experiments.

We propose BioS (Bio-potential Study) as a new virtual data analysis and management environment. It was devised to cope with the physiological signals, in order to manage different data using advanced methods of analysis and to find a simple way to decode and interpret data. BioS has been structured as a flexible, modular, and portable environment. It includes several modules as data importing and loading, data visualization (1D, 2D, 3D), pre-processing (frequency and saturation filtering, statistical analysis), spatiotemporal processing such as power spectrum, independent component analysis (ICA) in spatial and time domain, and nonlinear analysis for the extraction of the maximum Lyapunov exponent and d-infinity using optimized algorithms. The environment provides a user-friendly Graphic User Interface that allows inexperienced users to perform complex analyses and to speed up experimental data processing.

BioS: a new tool for biopotential experiments.

BioS (Bio-potential Study), a novel environment for Biopotential analysis is here proposed. It provides the processing of biopotentials of any number of channels distributed with different geometry. It includes several features as data importing, data visualization (1D, 2D, 3D), preprocessing (frequency & saturation filtering, statistical analysis), spatio-temporal processing (power spectrum analysis, nonlinear analysis, independent component analysis both in the spatial and time domain). BioS, also, provides a user-friendly Graphic User Interface designed to allow all user to speed up data analysis experiments.

Mapping heart dynamics by using nonlinear indicators.

A novel approach for the nonlinear characterization of Electrocardiogram (ECG) signals has been developed. The new developed methodology is based on a numerical algorithm that extracts the value of dinfinity (d-infinite) characterizing the asymptotic chaotic behavior of a system. This algorithm also extracts a measure of the maximum Lyapunov exponent and it is applicable to time series where the knowledge of the system structure and laws is not necessary. In order to prove the significance of the extracted parameters, the presented algorithm was applied on a statistically significant number of ECG signals taken from the MIT-BIH database and including normal subjects and subjects affected by arrhythmia and ventricular arrhythmia. A systematic study, analyzing how dinfinity varies with initial condition was performed showing the sensitivity of such parameter to the initial conditions. Furthermore, two maps, one presenting the maximum Lyapunov exponent and the other the dinfinity versus a control parameter II, as a measure of the rate variation, were drawn using the parameters extracted by the experimental data. They clearly show three distinguishable zones where the normal subjects and the subjects affected by the two different pathologies can be mapped and discriminated. Concluding, the newly presented algorithm, thanks to its implementation features and its effectiveness, it lends itself to future real-time implementation for clinical application in the early diagnosis of cardiac pathologies.

An innovative opto-sensing workbench for bio-microfluidics monitoring and control.

A non-invasive real-time testing workbench for the opto-electric characterization of microfluidic phenomena and particle transport tracking is proposed. It consists of an automated monitoring of microfluidodynamic phenomena, exploiting suitable opto-sensing setup, analog-digital and analog real-time monitoring, and an adaptive control system. The opto-sensing setup is based on a customized opto-mechanic system designed and developed using discrete optic components mounted on a breadboard. The monitoring system exploits different technologies from standard CCD-based to alternative operation mode based on integrated technology, from hybrid analog/digital architecture to totally analog Cellular Nonlinear Networks (CNNs) technology for sensing and computation process. It exploits the optic information analysis as a mean to extract microfluidic phenomena characteristics. The general purpose design of such system makes it suitable for in vitro and in vivo experimental setup and therefore for microfluidic modelling and applications in the biomedical field and for research studies in microcirculation such as blood flow monitoring.

Complementary methods for interpreting brain signals: linear versus nonlinear techniques.

Magnetoencephalography (MEG) brain signals are characterized using both linear and nonlinear dynamical methods. The linear approach employs the power analysis in a spatial visualization. The nonlinear approach estimates the value of d(infinity) to characterize the system's asymptotic chaotic behavior using a computationally less onerous method than the conventional one for d(infinity). Both methods are applied here to study a female patient with obsessive compulsive disorder and an age-sex matched normal subject. MEG time series were obtained using dual 37-channel bio-magnetometers (4-D Neuroimaging, San Diego, CA).

Complex spatio-temporal features in meg data.

Magnetoencephalography (MEG) brain signals are studied using a method for characterizing complex nonlinear dynamics. This approach uses the value of d(infinity) (d-infinite) to characterize the system's asymptotic chaotic behavior. A novel procedure has been developed to extract this parameter from time series when the system's structure and laws are unknown. The implementation of the algorithm was proven to be general and computationally efficient. The information characterized by this parameter is furthermore independent and complementary to the signal power since it considers signals normalized with respect to their amplitude. The algorithm implemented here is applied to whole-head 148 channel MEG data during two highly structured yogic breathing meditation techniques. Results are presented for the spatio-temporal distributions of the calculated d(infinity) on the MEG channels, and they are compared for the dirrerent phases of the yogic protocol. The algorithm was applied to six MEG data sets recorded over a three-month period. This provides the opportunity of verifying the consistency of unique spatio-temporal features found in specific protocol phases and the chance to investigate the potential long term effects of these yogic techniques. Differences among the spatio-temporal patterns related to each phase were found, and they were independent of the power spatio-temporal distributions that are based on conventional analysis. This approach also provides an opportunity to compare both methods and possibly gain complementary information.