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Electrical Signal

Electrical signals are usually used within the cell, as part of a signaling pathway to communicate intracellularly, and most often to move the signal rapidly from one place in the cell to another.

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Principles of Bioreactor Design for Tissue Engineering

Sarindr Bhumiratana, . Gordana Vunjak-Novakovic, in Principles of Tissue Engineering (Fourth Edition) , 2020

Electrical Signals

Electrical signals are inherent to most tissues. Depending on the specific state of the tissue (development, damage, or homeostasis) the electrical signals change with the specific tissue activities [25] . Electrical currents have been measured in tissue development, wound healing and preconditioning of engineered tissues [25] . These studies have shown the importance of electrical currents in the differentiation, maturation and assembly of electrically excitable cells.

The presence of large electrical currents within the embryo shows that electrical signals are important for stem cell differentiation and early development. Studies of animal embryos show the presence of an electrical field during first cell divisions. Notably, the reversal of the direction of this electrical field caused developmental defects [25] . Equipping bioreactors with electrical fields has enabled the use of electrical signals to modulate stem cell differentiation. By applying an alternating current with a 1 Hz frequency, neural stem cells were induced to differentiate into astrocytes [26] . Similarly, a 15 Hz frequency of a pulsed electromagnetic field was able to increase osteogenic differentiation of human mesenchymal stem cells [27] .

In wounds, an electrical field is generated across the damaged tissue. Its strength depends on the tissue type, but in general, a stronger field is recorded at the edges of a wound in comparison to its center [25] . The upregulation of sarcoma and inositol-phospholipid signaling pathways establish the directionality of the electrical field, which mediates cell migration into the wound [28] . Similar electrical fields also promoted directed cell migration [28] .

The incorporation of electrical stimuli in the bioreactor design for electrically active tissues, such as muscle and nerves, allows for better tissue development and augmented functionality of engineered tissues. For example, the application of pulsed electrical signals 2 ms duration at a frequency of 1 Hz resulted in significant structural organization of the myocardial grafts [29] . Electrical stimulation resulted in improved cardiac cell alignment and significant improvements of the amplitude and synchronicity of myocyte contractions [29] .

Time-Frequency Diagnosis and Monitoring

15.5.3 Data Acquisition

Electrical signals produced in the brain can be monitored in a non-invasive manner by measuring variations in potential on the scalp. This EEG measurement is achieved by strategically placing several small electrodes on the scalp. One electrode, usually at the base of the skull, acts as a reference (ground) signal, and various channels of data are created by measuring the voltage differences between neighboring electrodes. Five channels of EEG have been recorded in each session using the 10–20 International System of Electrode Placement. The EEG data has been recorded using a sampling frequency of 256 Hz. For artifact detection, three auxiliary signals representing electro-oculogram (EOG), electrocardiogram (ECG), and respiration are also recorded. Data used has been collected at the Royal Women’s Hospital Perinatal Intensive Care Unit in Brisbane, Australia. The EEG signals containing seizures were obtained from two different newborn babies that have been clinically identified to have seizures. The gestational ages of the babies were 35 weeks and 40 weeks and 3 days. The recording lasted 137 minutes and 23 minutes respectively.

Modeling Neural Systems

30.4 Spatiotemporal Model for the Electroencephalogram

The electrical signal that can be recorded from the scalp, the EEG, reflects the currents generated by the brain’s nerve cells. If we ignore electrical signals from other more remote sources (e.g., muscle, heart) and nonbiological artifacts, we can assume that the EEG is the weighted sum of the activities of the underlying neural network. Due to volume conduction responsible for attenuating signals propagating across the tissue between source and electrode, the weights in this weighted sum are determined by the geometry and the tissue’s conductivity properties. The EEG signal is used both in research and clinical settings. Because a single EEG signal includes the activity of millions of nerve cells, the relationship between smaller networks of the brain and the EEG signal is not necessarily a trivial one. Therefore this relationship has been the target of many modeling studies; the framework developed by physicist Paul Nunez is an example. Because it is generally thought that the slower synaptic potentials, especially those of the neocortical pyramidal cells, contribute most to the EEG signals on the surface of cortex and scalp, Nunez’ model focused on describing synaptic activity across the cortex (e.g., Nunez, 1974, 1995 ). The spatiotemporal model of cortical activity is based on the schematic of the neocortex shown in Fig. 30.9 . This diagram shows the effect of action potential firing activity g produced by the left cube on the synaptic activity h of the right cube. The synaptic activity has subscripts e and i for the excitatory and inhibitory synapses, respectively. Both activity variables g and h are a function of location: r → 1 (left cube), r → (right cube), and time t. The remaining variables in Fig. 30.9 are v for conduction velocity and u ( r → , t ) for external, subcortical input. Consequently the delay for action potentials arriving at the right cube that were generated in the left cube is | r → − r → 1 | v ; therefore the action potential firing function from the left cube arriving at the right cube is indicated as: g ( r → 1 , t − | r → − r → 1 | v ) . Furthermore, the connectivity within the cortex is governed by functions RE and RI for the excitatory and inhibitory connections, respectively. These functions also depend on the locations r → , r → 1 , and fiber system (with conduction velocity v), i.e., R E = R E ( r → , r → 1 , v ) . The equation that describes the excitatory synaptic activity as a function of the input to the right cube can now be formulated as:

Figure 30.9 . Diagram of the model presented by Nunez showing the interaction between cortical volume units. The output g of one volume at position r → 1 arrives at its target at position r → with a delay determined by conduction velocity (v). Each volume unit is characterized by its synaptic activity, both for excitatory (he) and inhibitory synapses (hi).

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For the inhibitory synaptic activity a simpler expression can be used since there are only local inhibitory fibers all with similar conduction velocity, i.e., R I = R I ( r → , r → 1 ) . Further, because of the local character of inhibition, one can neglect the delays for the inhibitory fibers: i.e., now the action potential firing function is simply g ( r → 1 , t ) . For the inhibitory expression we get:

The external input u0 represents subcortical input, which is assumed to be constant for a given state. Because the physiological basis for this input isn’t obvious, Nunez dropped this term in later versions of the model. In the following it isn’t very important since the effect of u0 vanishes when linearizing the expression. Note that both in Eqs. (30.16) and (30.17) the connectivity functions RE and RI and/or activity function g must contain a component that translates the action potential rate into a level of synaptic activity. In his development Nunez assumes this component to be linear by using a simple gain/attenuation factor. The same can be said for external input functions u ( r → , t ) and u0.

Based on the comments above, it is clear that while the expressions in Eqs. (30.16) and (30.17) explicitly relate spike input to synaptic activity (pulse-to-wave conversion), there is also the effect of the synaptic activity on the spike output (wave-to-pulse conversion) to be considered. The nonlinear relationship for both synaptic activities hE and hI on action potential rate function g is commonly modeled by a sigmoid function (e.g., Nunez, 1995 ). Nunez linearizes this relationship about an assumed fixed state g0. If we consider a small change of the action potential rate δg around state g0 and relate this to small changes in the synaptic activities δhE and δhI, we get:

with the changes of notation indicated by the curly brackets, we obtain ( Eq. 11.5 in Nunez, 1995 ):

Following the same procedure for Eq. (30.17) gives (Nunez’ Eq. 11.6 ):

One of the strong aspects of Nunez’ model is that it acknowledges different conduction velocities, the weak part is that it linearizes the relationship between synaptic activity and action potential rate, and vice versa.

Muscular Biopolymers

Mohsen Shahinpoor, in Engineered Biomimicry , 2020

6.1.3 Electromyography

The electrical signal associated with the contraction of a muscle is called an electromyogram or EMG. Electromyography, which is the study of EMG, has revealed some basic information. Voluntary muscular activity results in an EMG that increases in magnitude with tension. However, other variables influencing the signal at any given time are velocity of shortening or lengthening of the muscle, rate of tension buildup, fatigue, and reflex activity.

Muscle tissue conducts electrical potentials somewhat similarly to axons of the nervous system. Motor unit action potential (m.u.a.p.) is an electrical signal generated in the muscle fibers because of the recruitment of fibers as the motor unit. Electrodes placed on the surface of a muscle or inside the muscle tissue will record the algebraic sum of all m.u.a.p.’s being transmitted along the muscle fibers at that point in time. Those motor units away from the electrode site will result in a smaller m.u.a.p. than those of similar size near the electrode.

For a given muscle there can be a variable number of motor units, each controlled by a motor neuron through special synaptic junctions called motor end plates. An action potential transmitted down the motor neuron arrives at the motor end plate and triggers a sequence of electrochemical events. A quantum of acetylcholine (ACh) is released. It then crosses the synaptic gap (200–500 Å wide) and causes a depolarization of the postsynaptic membrane. Such a depolarization can be recorded by a suitable microelectrode and is called an end plate potential (EPP). In normal circumstances, the EPP is large enough to reach a threshold level and an action potential is initiated in the adjacent muscle fiber membrane.

The beginning of the m.u.a.p. starts at the Z-disc of the contractile element by means of an inward spread of the stimulus along the transverse tubular system. This results in a release of Ca 2+ in the SR. Ca 2+ rapidly diffuses to the contractile filaments of actin and myosin where ATP is hydrolyzed to produce ADP plus heat plus mechanical energy (tension). The mechanical energy manifests itself as an impulsive force at the cross-bridges of the contractile element.

The depolarization of the transverse tubular system and the SR results in a depolarization wave along the direction of the muscle fibers. It is this depolarization wave front and the subsequent repolarization wave that are seen by the recording electrodes.

Two general types of EMG electrodes have been developed. Surface electrodes consist of disks of metal, usually silver/silver chloride, of about 1 cm in diameter. These electrodes detect the average activity of superficial muscles and give more reproducible results than do in-dwelling types. In-dwelling electrodes are required, however, for the assessment of fine movements or to record from deep muscles. Aneedle electrode is a fine hypodermic needle with an insulated conductor located inside and bared to the muscle tissue at the open end of the needle. The needle itself forms the other conductor.

In-dwelling electrodes are influenced by both waves that actually pass by their conducting surface and by waves that pass within a few millimeters of the bare conductor. The same is true for surface electrodes.

ATP is an important molecule for the life of living cells. It provides energy for various cellular activities such as muscular contraction, movement of chromosomes during cell division, movement of cytoplasm within cells, transporting substances across cell membranes, and putting together larger molecules from smaller ones during synthetic reactions. Structurally, ATP consist of three phosphate groups attached to an adenosine unit composed of adenine and five-carbon sugar ribose.

ATP is the energy reserve of living systems. When a reaction requires energy, ATP can transfer just the right amount, because it contains two high-energy phosphate bonds. When the terminal phosphate group P is hydrolyzed by addition of a water molecule, the reaction releases energy. This energy is used by the cell to power its activities. The resulting molecule, after removal of the terminal phosphate groups, is ADP. This reaction may be represented as follows:

The energy supplied by the catabolism of ATP into ADP is constantly being used by the cell. Since the supply of ATP at any given time is limited, a mechanism exists to replenish it. Aphosphate group is added to ADP to manufacture more ATP. The reaction may be represented as follows:

The energy required to attach phosphate groups to ADP to make ATP is provided by breakdown of glucose in the cellular respiration process, which has two phases:

Anaerobic. In the absence of oxygen, glucose is partially broken down by the glycolysis process into pyruvic acid. Each glucose that is converted into a pyruvic acid molecule yields two molecules of ATP.

Aerobic. In the presence of oxygen, glucose is completely broken down into carbon dioxide and water. These reactions generate heat and ATP molecules from each glucose molecule.

A muscle fiber is about 100 μm in diameter and consists of fibrils about 1 μm in diameter. Fibrils in turn consist of filaments about 100 Å in diameter. These further are of smaller units of molecular chains called actin, myosin, and elastic elements. Electron micrographs of fibrils show the basic mechanical structure of the interacting actin and myosin filaments. The darker and wider myosin protein bands are interlaced with the lighter and smaller actin protein bands, as seen in electron micrographs. The space between them consists of a cross-bridge structure where the tension is created and elongation/contraction takes place. The term contractile element is used to describe the part of the muscle that generates the tension, and it is this part that shortens and lengthens as positive or negative work is done. The sarcomere, which is a basic length of the myofibril, is the distance between the Z-discs. It can vary from 1.5 μm at full shortening to 2.5 μm at resting length to about 4 μm at full lengthening.

The structure of the muscle is such that many filaments are in parallel and many sarcomere elements are in series to make up a single contractile element. Consider a motor unit of a cross-sectional area of 0.1 cm 2 and a resting length of 10 cm. The number of sarcomere contractile elements in series would be 10 cm/2.5 μm = 40,000 and the number of filaments (each with an area of 10 −8 cm 2 ) in parallel would be 0.1/10 −8 = 10 7 . Thus the number of contractile elements of sarcomere length packed into this motor unit would be 4 x 10 11 .

The active contractile elements are contained within the fascia. These tissue sheaths enclose the muscles, separating them into layers and groups and ultimately connecting them to the tendons at either end. The mechanical characteristics of connective tissue are important in the overall biomechanics of the muscle. Some of the connective tissue is in series with the contractile element; some is in parallel. These tissues are modeled as springsand viscous dampers for modeling purposes.

Each muscle has a finite number of motor units (motor neuron plus muscle fibers it innervates), each of which is controlled individually by a separate nerve ending. Excitation of each unit is an all-or-none event. The electrical indication is a motor unit action potential with the mechanical result being a tension twitch. An increase in tension can be accomplished in two ways: by increasing the stimulation rate for the motor unit or by the excitation (recruitment) of an additional motor unit.

It is now generally accepted that the motor units are recruited according to the size principle, which states that the size of the newly recruited motor unit increases with the tension level at which it is recruited. This means that the smallest unit is recruited first and the largest unit last. In this manner, low-tension movements can be achieved in finely graded steps. Conversely, those movements requiring high forces but not needing fine control are accomplished by recruiting the larger motor units.

Successive recruitment can be described as follows: The smallest motor unit (MU-1) is recruited first, usually at an initial frequency ranging from about 5–13 Hz. Tension increases as MU-1 fires more rapidly until a certain tension is reached, at which MU-2 is recruited. Here MU-2 starts firing at its initial low rate, and further tension is achieved by the increased firing of both MU-1 and 2. At a certain tension, MU-1 reaches its maximum firing range (15–60 Hz) and therefore generates its maximum tension. This process of increasing tension reaching new thresholds and recruiting another larger motor unit continues until maximum voluntary contraction is reached. At that point, all motor units will be firing at their maximum frequencies. For a detailed discussion of mammalian muscles, the reader is refered to Bobet and Stein [1] and Ding et al. [2, 3] .

In the following section we present a brief review of electroactive polymers (EAP) as artificial muscles, in general.

Cardiac Muscle Tissue Engineering

Amandine Godier-Furnemont, Gordana Vunjak-Novakovic, in Biomaterials Science (Third Edition) , 2020

Mechanical Conditioning

As an electrical signal propagates through the gap junctions of cardiomyocytes in the form of ions each individual cardiac myocyte contracts, resulting in the synchronized contraction of the heart muscle. Fink et al. (2000) proposed that directly applying the mechanical stretch to embryonic chick or neonatal rat heart cells cultured in hydrogel would increase cell alignment and function. In that and later studies ( Zimmermann et al., 2006a,b ), unidirectional and cyclic stretch of the hydrogel constructs resulted in mature, adult like engineered heart tissue, and in vivo, led to stable muscle grafts that prevented deterioration of heart function and exhibited electromechanical integration with the host tissue. While the mechanism by which heart cells sense and generate force in response to exogenous factors is not totally understood, these research groups have demonstrated that their role is instrumental in forming functional cardiac tissues. Various mechanical stimulation studies are discussed in detail in the section on Culture in Hydrogel with Mechanical Stimulation.

Semiconducting silicon nanowire array fabrication for high throughput screening in the biosciences

9.5.4 Temporal and spatially resolved recording of extracellular and intracellular signals from cells

Recording the electrical signals from cells and tissues is fundamentally important to the understanding of basic biophysical phenomena ( Hille, 2001; Rutten, 2002; Huang et al., 1999 ). Micropipette electrodes and patch clamp electrodes are commonly used to measure electrical signal propagation through individual neuron cells ( Hille, 2001 ). But these techniques do not allow for multiplexed measurements ( Patolsky et al., 2006; Cohen-Karni et al., 2009 ). Micro-fabricated multi-electrode arrays and planar FET do allow for multiplexed detections; however, these techniques suffer from the limits of low signal-to-noise ratios and low spatial resolution in the cell detection area ( Banks et al., 2002; Prohaska et al., 1986; Sekirnjak et al., 2006 ). Recently, NW and CNT FET have been employed as nano-electrode arrays to detect nucleic acids, proteins and viruses, and to record cellular signals, demonstrating high signal-to-noise ratios and excellent sensitivity and selectivity ( Patolsky et al., 2006; Timko et al., 2009; Gao et al., 2020; Pui et al., 2009; Cellot et al., 2009; Stern et al., 2007; Gruner, 2006; Zheng et al., 2005 ). SiNW FET arrays can be used to record the electrical signals from multiple points of a single neuron ( Patolsky et al., 2006 ), cardiomyocyte cells ( Cohen-Karni et al., 2009; Timko et al., 2009 ), acute brain slices ( Qing et al., 2020 ) and detect the released neuro-transmitter of CgA ( Wang et al., 2007 ).

Patolsky et al. integrated the SiNW FET arrays with the individual axons and dendrites of live mammalian neurons to study the neuronal signals under stimulations and inhibitions ( Patolsky et al., 2006 ). The SiNW FET arrays were fabricated via a bottom-up method as discussed previously in this chapter. Polylysine was then used as both the adhesion and growth factor to define the neuron cell growth with respect to the SiNW FET device elements. Using a photolithography method, square regions of a 30 × 60 μm polylysine pattern were generated to boost the cell body adhesion, and 2 μm wide lines are projected to define the subsequent neurite growth. To survive the harsh cell culture and actual sample measurements, the SiNW–metal contacts were passivated such that the survival rate of devices is greater than 90% after 10 days at 37 °C ( Patolsky et al., 2006 ). Using such a unique integrated system of SiNW FET-cell arrays, the propagation and back-propagation of the action potential spikes in axon and dendrites can be measured separately and simultaneously. It was found that there is a clear reduction in FET conductance and temporal spreading in the dendrites, whereas very little change was observed for the axon, which is consistent with passive and active propagation mechanisms for dendrites and axon, respectively. The signal propagation rates in axon and dendrites are 0.46 and 0.15 m/s, respectively, as calculated from the conductance data collected from different SiNW FET that are in contact with different positions of dendrites and axon. The signal propagation can be blocked by applying an input voltage of 0.9 V. They also demonstrated that as many as 50 addressable NW FET with a 10 μm inter-device spacing can be integrated with a single axon with an 86% yield of functional devices ( Patolsky et al., 2006; 2007 ). The spacing can be further reduced to only 400 nm, indicating their potential applications in high throughput screening for real-time cellular testing, drug testing and discovery.

Cohen-Karni et al. recently reported the direct recording of electrical signals from embryonic chicken cardiomyocytes using SiNW FET arrays that were also fabricated using a bottom-up method ( Cohen-Karni et al., 2009 ). The cultured cardiomyocyte cells were first transferred to the top of thin transparent polydimethylsiloxane (PDMS) sheets and then attached to the surface of SiNW FET for electrical measurements, which will greatly increase the HTS speed in a real practical application compared with culturing cells directly on FET devices. While water gate-voltage potentials range from − 0.5 to + 0.1 V, the corresponding conductance amplitudes change from 31 to 7 nS. The conductance is also sensitive to the distance between the cardiomyocyte cells and the FET. For example, if the PDMS film coated with cells is 9.8 μm closer to the FET, the NW FET device exhibited an increase in conductance from 44 to 77 nS. NW FET arrays are also suitable for the measurement of signal propagations and temporal shifts in the cells. The measured signal propagation in cardiomyocytes ranges from 0.07 to 0.21 m/s, which is close to the literature reports ( Fast and Kléber, 1994 ). Similarly, Timko et al. reported the direct monitoring of electrical signals from different parts of embryonic hearts using flexible and transparent SiNW FET arrays ( Timko et al., 2009 ). The SiNW FET arrays are built on a flexible substrate, Kapton, making it more suitable for measuring multiple positions of bulk samples like tissues and organs ( Timko et al., 2009 ). In addition, SiNW FET arrays can also be employed to detect neurotransmitters released from living cells ( Wang et al., 2007 ).

Components

Basics of interpretation of an ECG

Once these electrical signals have been obtained, an external microcontroller performs an algorithmic ECG analysis to determine the parameters necessary for the desired application. For this to happen, a range of technical things need to be taken into account:

correct calibration, proved by a calibration signal which is visible on the plot;

the 12 derivations containing various complexes, and a plot which is longer by at least one derivation, so that we can clearly visualize the heart rate;

a plot which (as far as possible) is free of parasitic electrical signals on all the derivations, and has a straight (rather than undulating) baseline;

it is necessary to perform a test with the electrodes in the wrong position. The P wave must be negative in aVR and positive in D1, D2 and V6. In addition, the morphology and amplitude of the complexes Q, R and S must progress harmoniously in the precordial derivations.

Furthermore, to read and interpret an ECG requires familiarity, which can only be gained through regular practice. It should also be noted that ECG is merely one tool among an extensive range of others which lend medical professionals ammunition to support their diagnosis. Hence, interpreting an ECG is a matter for a professional. In parallel, on the market, there are software packages delivered with certain electrocardiographs or certain integrated circuits with their own application libraries which can help with diagnosis. Under no circumstances, though, may they substitute the opinion of a doctor or a specialist.

Obviously, knowing that the electrical plot of an ECG contains multiple repetitive successions/irregularities called “waves”, the signal curve obtained is divided over time into different intervals: the P wave, the PR segment, the QRS complex, the delay in recording of the intrinsicoid deflection, the point J, the interval QT, the segment ST and finally the T wave (see Figure 9.24 ).

Figure 9.24 . Division of an ECG into several intervals. For a color version of this figure, see www.iste.co.uk/paret/wearables.zip

By analyzing this detailed representation, we are able to study cardiac rhythm and heart rate (number of QRS per unit time) and arrhythmia. Many accessory Wearables or certain items of commercial smart apparel in the fields of “well-being” or “health” claim to be able to do this. How, though, is it done? On what is it based? Strictly speaking, this ECG representation can only show us the value of a regular “sinusoidal” heart rate, with a constant R-R interval, and thus we can determine a heart rate equal to the inverse of the R-R interval (multiplied by 60, so it can be expressed in number of beats per minute), and claim to have invented the famous heart rate monitor which is so beloved of manufacturers/sellers of watches, bracelets and sporting equipment, but so far from qualifying as a “medical device”.

Applications and conclusions in relation to smart textiles and apparel

All of the above highlights that: –

well-being, fitness, sport, PPE and medical focus on the heart and cardiography, but at different levels of finesse of the measurements and interpretations;

the interpretation of an ECG is a matter for specialists, who will be able to understand it;

the algorithms must be set in stone from a medical point of view;

only accredited medical devices can boast an ECG;

thus, at different levels, the number of analog sensors which need to be connected means that we need to include smart fabric (second skin) which must be supple, washable, affordable, etc.;

and, finally, once again, Wearables for well-being, fitness and sport are not in the same league as Wearables for PPE and medical devices.

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