*Editorial* **Multiscale Simulation of Composite Structures: Damage Assessment, Mechanical Analysis and Prediction**

**Stelios K. Georgantzinos**

Laboratory for Advanced Materials, Structures, and Digitalization, Department of Aerospace Science and Technology, National and Kapodistrian University of Athens, 34400 Psachna, Greece<EMAIL_ADDRESS>
**Abstract:** Composites can be engineered to exhibit high strength, high stiffness, and high toughness. Composite structures have been used increasingly in various engineering applications. In recent decades, most fundamentals of science have expanded their reach by many orders of magnitude. Currently, one of the primary goals of science and technology seems to be the quest to develop reliable methods for linking the physical phenomena that occur over multiple length scales, particularly from a nano-/micro-scale to a macroscale. The aim of this Special Issue is to assemble high quality papers that advance the field of multiscale simulation of composite structures, through the application of any modern computational and/or analytical methods alone or in conjunction with experimental techniques, for damage assessment or mechanical analysis and prediction.

**Keywords:** composite structures; multiscale simulation; finite element analysis; damage assessment; mechanical analysis

To engineer composites for high performance and to design advanced structures, the relationship between material nano-/micro-structures and their macroscopic properties must be established in order to accurately predict their mechanical performance and failure. Multiscale simulation is a tool that enables the study and comprehension of complex systems and phenomena that would otherwise be too expensive or dangerous, or even impossible, to study by direct experimentation and, thus, to deal with.

The mechanical characterization of textile composites is a challenging task due to their nonuniform deformation and complicated failure phenomena. However, Zhao et al. [1] introduced a three-dimensional mesoscale finite element model to investigate the progressive damage behavior of a notched single-layer triaxially braided composite subjected to axial tension. The damage initiation and propagation in fiber bundles were simulated using three-dimensional failure criteria and the damage evolution law. A traction–separation law was applied to predict the interfacial damage of fiber bundles. The proposed model was correlated and validated by the experimentally measured full field strain distributions and effective strength of the notched specimen. The progressive damage behavior of the fiber bundles was studied by examining the damage and stress contours at different loading stages. Parametric numerical studies were conducted to explore the role of modeling parameters and geometric characteristics on the internal damage behavior and global measured properties of the notched specimen. Moreover, the correlations of damage behavior, global stress–strain response, and the efficiency of the notched specimen were discussed in detail. The results of this paper delivered a throughout understanding of the damage behavior of braided composites and can help in the specimen design of textile composites.

Accelerated construction in the form of steel–concrete composite beams is among the most efficient methods to construct highway bridges. One of the main problems with this type of composite structure, which has not yet been fully clarified in the case of continuous beam, is the crack zone initiation that gradually expands through the beam width. Gautam et al. [2] proposed a semi-empirical model to predict the size of cracks in terms of small box girder deflection and intensity of the load applied on a structure.

**Citation:** Georgantzinos, S.K. Multiscale Simulation of Composite Structures: Damage Assessment, Mechanical Analysis and Prediction. *Materials* **2022**, *15*, 6494. https:// doi.org/10.3390/ma15186494

Received: 13 September 2022 Accepted: 16 September 2022 Published: 19 September 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

A set of steel–concrete composite small box girders were constructed using steel fibrous concrete and experimentally tested under different caseloads. The results were then used to create a dataset of the box girder response in terms of beam deflection and crack width. The dataset obtained was then utilized to develop a simplified formula providing the maximum width of cracks. The results showed that the cracks were initiated in the hogging moment region when the load exceeded 80 kN. Additionally, it was observed that the maximum cracked zone occurred in the center of the beam due to the maximum negative moment. Moreover, the crack width of the box girder at different loading cases was compared with the test results obtained from the literature. A good agreement has been found between the proposed model and experiment results.

With the aim of damage reduction in person subjected to ballistic impact, polymeric, carbon, and glass fibers are commonly used to develop protective systems. However, during recent decades, it has been found that a combination of high strength yarns in different directions generates flexible woven fabrics, which are light and highly resistant at the same time. Therefore, aramid fibers are one of the protection materials most used today, with a growing trend in the industry. Feito et al. [3] investigated the effect of the impact angle of a projectile during low-velocity impact on Kevlar fabrics using a simplified numerical model. The implementation of mesoscale models is complex and usually involves a long computation time, in contrast with practical industry needs to obtain accurate results rapidly. In addition, when the simulation includes more than one layer of composite ply, the computational time increases even in the case of hybrid models. With the goal of providing useful and rapid prediction tools to the industry, a simplified model has been developed in this work. The model offers an advantage in the reduced computational time compared with a full 3D model (around 90% faster). The proposed model has been validated against equivalent experimental and numerical results reported in the literature, with acceptable deviations and accuracies for the design requirements. The proposed numerical model allows for the study of the influence of the geometry on the impact response of the composite. After a parametric study related to the number of layers and the angle of impact, using a response surface methodology, a mechanistic model and a surface diagram were presented to help with the calculation of the ballistic limit.

Concrete-filled steel tubes (CFSTs) show advantageous applications in the field of construction, especially for a high axial load capacity. The challenge in using such structures lies in the selection of many parameters constituting CFST, which necessitates defining complex relationships between the components and the corresponding properties. The axial capacity (Pu) of CFST is among the most important mechanical properties. Nguyen et al. [4] investigated the possibility of using a feedforward neural network (FNN) to predict Pu. Furthermore, an evolutionary optimization algorithm, namely invasive weed optimization (IWO), was used for tuning and optimizing the FNN weights and biases to construct a hybrid FNN–IWO model and to improve its prediction performance. The results showed that the FNN–IWO algorithm is an excellent predictor of Pu, with a value of R2 of up to 0.979. The advantage of FNN–IWO was also pointed out with gains in accuracy of 47.9%, 49.2%, and 6.5% for root mean square error (RMSE), mean absolute error (MAE), and R2 , respectively, compared with the simulation using the single FNN. Finally, the performance when predicting Pu as a function of structural parameters, such as the depth/width ratio, the thickness of the steel tube, the yield stress of steel, the concrete compressive strength, and the slenderness ratio, was investigated and discussed.

Circular opening steel beams have been increasingly acknowledged in structural engineering because of their many remarkable advantages, including their ability to bridge the span of a large aperture or their lighter weight compared with conventional steel beams. Nguyen et al. [5] investigated and selected the most suitable parameters used in particle swarm optimization (PSO), namely the number of rules (nrule), the population size (npop), the initial weight (wini), the personal learning coefficient (c1), the global learning coefficient (c2), and the velocity limits (fv), in order to improve the performance of the adaptive neuro-fuzzy inference system in determining the buckling capacity of circular opening

steel beams. This is an important mechanical property in terms of the safety of structures under subjected loads. An available database of 3645 data samples was used for the generation of training (70%) and testing (30%) datasets. Monte Carlo simulations, which are natural variability generators, were used in the training phase of the algorithm. Various statistical measurements, such as root mean square error (RMSE), mean absolute error (MAE), Willmott's index of agreement (IA), and Pearson's coefficient of correlation (R), were used to evaluate the performance of the models. The results of the study show that the performance of ANFIS optimized by PSO (ANFIS-PSO) is suitable for determining the buckling capacity of circular opening steel beams but is very sensitive under different PSO investigation and selection parameters. The findings of this study show that nrule = 10, npop = 50, wini = 0.1 to 0.4, c1 = [1, 1.4], c2 = [1.8, 2], and fv = 0.1, which are the most suitable selection values for ensuring the best performance for ANFIS-PSO. In short, this study might help in selecting suitable PSO parameters for the optimization of the ANFIS model.

Commonly, nanocomposite material applications are associated with the simultaneous actions with more than one type of loading. Specifically, the investigation of nanocomposites subjected to both thermal as well as mechanical loads is perhaps one of most interesting fields of research, since high-temperature applications are very frequent. Giannopoulos et al. [6] provided a computationally efficient and reliable hybrid numerical formulation capable of characterizing the thermomechanical behavior of nanocomposites, which was based on a combination of molecular dynamics (MD) and the finite element method (FEM). A polymeric material was selected as the matrix—specifically, poly(methyl methacrylate) (PMMA), commonly known as Plexiglas, due to its extensive applications. On the other hand, the fullerene C240 was adopted as a reinforcement because of its high symmetry and suitable size. The numerical approach was performed at two scales. First, an analysis was conducted at the nanoscale level by utilizing an appropriate nanocomposite unit cell containing C240 at a high mass fraction. A MD-only method was applied to accurately capture all of the internal interfacial effects and, accordingly, its thermoelastic response. Then, a micromechanical, temperature-dependent finite element analysis took place using a representative volume element (RVE), which incorporated the first-stage MD output, to study nanocomposites with small mass fractions, for which a atomistic-only simulation would require a substantial computational effort. To demonstrate the effectiveness of the proposed scheme, numerous numerical results were presented, while the investigation was performed in a temperature range that included the PMMA glass transition temperature, Tg.

Although some work has already been conducted on the vibrations of composite structures reinforced by nanoparticles, there are only a few studies that focused on the vibration behavior of carbon fiber-based laminate composites with pure graphene inclusions. Georgantzinos et al. [7] developed a computational procedure to investigate the vibration behavior of laminated composite structures, including graphene inclusions in a matrix. Concerning the size-dependent behavior of graphene, its mechanical properties were derived using nanoscopic empiric equations. Using the appropriate Halpin–Tsai models, the equivalent elastic constants of the graphene reinforced matrix were obtained. Then, the orthotropic mechanical properties of a composite lamina of carbon fibers and hybrid matrix can be evaluated. Considering a specific stacking sequence and various geometric configurations, carbon fiber-graphene-reinforced hybrid composite plates were modeled using conventional finite element techniques. Applying simply support or clamped boundary conditions, the vibrational behavior of the composite structures was finally extracted. Specifically, the modes of vibration for every configuration were derived, and the effect of graphene inclusions in the natural frequencies was calculated. The higher the volume fraction of graphene in the matrix, the higher the natural frequency for every mode. Comparisons with other methods, where possible, were performed to validate the proposed method.

Giannopoulos and Georgantzinos [8] investigated the thermomechanical effects of adding a newly proposed nanoparticle within a polymer matrix such as polyethylene.  In this way, a bone-shaped nanofiber that may offer enhanced thermomechanical characteristics when used as a polymer filler, due to each unique shape and chemical nature, was developed. The investigation was based on molecular dynamics simulations of the tensile stress–strain response of the polymer nanocomposites under a variety of temperatures. The thermomechanical behavior of the bone-shaped nanofiber-reinforced polyethylene was compared with that of an equivalent nanocomposite filled with ordinary capped single-walled carbon nanotubes to reach some coherent fundamental conclusions. That study focused on the evaluation of some basic, temperature-dependent properties of the nanocomposite reinforced with these innovative bone-shaped allotropes of carbon.

The flexural strength of Slender steel tube sections is known to achieve significant improvements upon being filled with concrete; however, this section is more likely to fail by buckling under compression stresses. Al Zand et al. [9] investigated the flexural behavior of a Slender steel tube beam that was produced by connecting two pieces of C-sections and filled with recycled-aggregate concrete materials (CFST beam). The C-section's lips behaved as internal stiffeners for the CFST beam's cross section. A static flexural test was conducted on five large-scale specimens, including one specimen that was tested without concrete (hollow specimen). The ABAQUS software was also employed for the simulation and non-linear analysis of 20 additional CFST models in order to further investigate the effects of varied parameters that were not tested experimentally. The numerical model was able to adequately verify the flexural behavior and failure mode of the corresponding tested specimen, with an overestimation of the flexural strength capacity of about 3.1%. Generally, the study confirmed the validity of using the tubular C-sections in the CFST beam concept, and their lips (internal stiffeners) led to significant improvements in the flexural strength, stiffness, and energy absorption index. Moreover, a new analytical method was developed to specifically predict the bending (flexural) strength capacity of the internally stiffened CFST beams with steel stiffeners, which was well-aligned with the results derived from the current investigation and with those obtained by others.

The composite shear wall has various merits over traditional reinforced concrete walls. Thus, several experimental studies have been reported in the literature to study the seismic behavior of composite shear walls. However, few numerical investigations were found in previous literature because of difficulties in the interaction behavior of steel and concrete. Najm et al. [10] presented a numerical analysis of smart composite shear walls that use an infilled steel plate and concrete. The study was carried out using the ANSYS software. The mechanical mechanisms between the web plate and concrete were investigated thoroughly. The results obtained from the finite element (FE) analysis show excellent agreement with the experimental test results in terms of the hysteresis curves, failure behavior, ultimate strength, initial stiffness, and ductility. The results indicate that increasing the gap between the steel plate and the concrete wall from 0 mm to 40 mm improved the stiffness by 18% compared with the reference model, which led to delaying failures in this model. Expanding the infill steel plate thickness to 12 mm enhanced the stiffness and energy absorption at ratios of 95% and 58%, respectively. This resulted in a gradual decrease in the strength capacity of this model. Meanwhile, increasing the concrete wall thickness to 150 mm enhanced the ductility and energy absorption at ratios of 52% and 32%, respectively, which led to restricting the model and reducing the lateral offset. Changing the distance between shear studs from 20% to 25% enhanced the ductility and energy absorption by about 66% and 32%, respectively.

A major part of the computational cost required for determining an optimal material design with extreme properties using a topology optimization formulation is devoted to solving the equilibrium system of equations derived through the implementation of the finite element method (FEM). To reduce this computational cost, among other methodologies, various model order reduction (MOR) approaches can be utilized. Kazakis and Lagaros [11] presented a simple Matlab code for solving the topological optimization for the design of materials combined with three different model order reduction approaches. The three MOR approaches presented in the code implemented are the proper orthogonal decomposition (POD), the on-the-fly reduced order model construction and the approximate reanalysis (AR) following the combined approximations approach. The complete code, containing all participating functions (including the changes made to the original ones), was also provided.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Conflicts of Interest:** The author declares no conflict of interest.

#### **References**


**2.2 Accumulation of antibiotic-degrading enzymes in the matrix**

The microorganisms that form biofilm are able to collect high amounts of β-lactamases in the biofilm matrix as a defense mechanism.

When *P. aeruginosa* biofilm matrix accumulates β-lactamases, it can lead to increased hydrolysis of antibiotics, such as imipenem and ceftazidime. It is demonstrated that *P. aeruginosa* PAO1-J32 biofilms have shown high promoter (ampC β-lactamases) activity, which is determined by scanning confocal laser photomicrographs [12]. Also, while ampicillin cannot reach the deeper layers of *Klebsiella pneumoniae* biofilms associated with β-lactamase activity, deletion of β-lactamase increases the amount of ampicillin that reaches the deep layer [13].

### **2.3 DNA in biofilm matrix**

Extracellular DNA (eDNA) is a significant and common component ingredient of the bacterial biofilm matrix. The eDNA can be obtained endogenously without quorum sensing-mediated release, from the outer membrane or from the cell integrity-degraded biofilm microorganisms [14]. DNA can increase biofilm resistance to certain antimicrobial agents [15].

One of the mechanisms by which the DNA increases biofilm resistance is that it causes changes in outer membrane because DNA is an anionic molecule; it is able to chelate cations, such as magnesium ions and cause a lowering Mg2+ concentration in membrane. Magnesium restriction in *P. aeruginosa* and *Salmonella enterica* serovar Typhimurium is an environmental signal that induces energizing of the two-component systems PhoPQ and PmrAB to provide antimicrobial resistance [16].

These signal molecules are responsible for the rearrangement of the PA3552-3559 operon. The operon encodes to protein having enzymatic activity that attaches aminoarabinose to Lipid A part of the lipopolysaccharide layer, so it provides resistance against cationic peptide and aminoglycoside [17].

A polyamine, spermidine, localized to the outer membrane contributes to saving the cell from aminoglycosides and cationic peptides that are antimicrobial agents by lowering outer membrane penetrability for these positively charged molecules. Spermidine synthesis is another resistance mechanism induced by eDNA-associated cation restriction in *P. aeruginosa* [18].

Playing a physical role in defense against antibiotics, eDNA has also provided horizontal transfer of antibiotic resistance genes between microorganism cells forming biofilm [14].

### **2.4 Growth rate, stress response, and persistent cells**

During growth in biofilm structures, physiological heterogeneity happens due to the occurrence of oxygen and other nutrients gradient in biofilms. This gradient is created because cells that are close to the surface of the biofilm consume obtainable nutrient sources and oxygen before the nutrients disperse into depth of the biofilm [19]. Nutrient and oxygen concentration gradients develop and cause bacterial populations that display different growth rates [20]. The effect of many antibiotics depends on growth. Because most antibiotics aim at some kind of produced macromolecule, it is unexpected that these agents will have much impact on the microorganisms in biofilm that limit macromolecular production, so conventional antibiotics are usually less affected against metabolically inactive or slow-growing cells.

In biofilms, a small subpopulation of bacteria can be reversibly transformed into slowly growing cells. These cells are known as persistent or dormant cells. Persistent cells are generated stochastically or under endogenous stress (e.g., oxidative stress

and exposure to antibiotics) and are highly resistant to being killed by antibiotics [21, 22]. When these cells are compared with active and rapidly growing bacteria, lower metabolism rate makes these cells less susceptible to antibiotics. High levels of persistent cells are seen in chronic urinary tract infections and the lungs of patients with cystic fibrosis, especially, when the penetration of the immune system components is limited. The dormant phenotype is characterized by down-regulation of functions, such as energy production and biosynthesis.

Persistent formation is enhanced by toxin/antitoxin (TA) systems induced by environmental factors or DNA damage. TA systems do the following: (i) inhibition of protein synthesis by phosphorylation of the elongation factor, Ef-Tu (e.g., HipBA), translation inhibition and subsequent tolerance to antibiotics; (ii) expressing the TA modules (e.g., TisB toxin forming an anion channel in the membrane) leading to a decrease in PMF and ATP levels; and (iii) breakdown of mRNA (e.g., RelE and MazF toxins) and inhibition of translation. Prolonged treatment with aminoglycosides and RNA polymerase inhibitor rifampicin may prevent persistent resuscitation with synergistic effects with TA systems [23]. It is suggested that fluoroquinolones can induce TisB toxin by causing DNA damage in *Escherichia coli* [24]. In biofilms, many TA systems are associated with multidrug-tolerant persistent cells. However, this tolerance is limited to specific antibiotics and TA [25].

Bacteria are equipped with a range of stress responses that make them possible to deal with environmental change, such as oxidative stress, unexpected temperature changes, low water activity, deprivation, and DNA damage [26]. These adaptive responses serve to enhance bacterial survivability. Adaptive stress responses can influence antimicrobial susceptibility since these responses impact on many of the same cellular components and processes that are aimed by antimicrobials [27].

Heterogeneity in the biofilm is one of the causes of the stress response [26]. Cells within hypoxic zones have decreased metabolic activity and are in a state like stationary phase [28]. It is known that many of the stress responses result in bacterial cells entering stationary phase.

Nutrient starvation also induces (p)ppGpp production, which mediates a global stress response known as the stringent response. The stringent response and (p) ppGpp signaling contribute to multidrug tolerance in *P. aeruginosa* biofilms. It is shown that ofloxacin, gentamicin, meropenem, and colistin killing increased upon inactivation of the stringent response [29]. Nutrient starvation also induced ofloxacin tolerance in *E. coli* K-12 biofilm through mechanisms dependent on the stringent and SOS response [30].

### **2.5 Quorum sensing**

Despite their self-sufficiency, bacteria interact with neighbors to accomplish collective activities, such as bioluminescence production, biofilm development, and exoenzyme secretion. This cooperation occurs through a mechanism: quorum sensing (QS) [31]. Quorum sensing (QS) is cell-to-cell communication at the molecular level controlled by chemical signaling molecules called autoinducers (AIs) [32]. Due to QS, bacteria can recognize the population density by measuring the accumulation of signaling molecules that are secreted from members of the community. The accumulation of the signal in the extracellular environment is adequate to activate the response only when the population density is high [33].

Recent studies indicate that in many bacterial species, activation of QS happens in the formed biofilm activating the maturation and disassembly of the biofilm. The initial adhesion step seems not suitable for the accumulation of signal molecules. Then, with the next steps, the attached bacteria are divided and form microcolonies, population density rises, and so signal molecules can reach adequate levels

**139**

**Figure 1.**

*Antibiotic Resistance in Biofilm*

and colonize new niches [33].

**2.6 Efflux pumps**

extruded by efflux pumps [39].

which builds the biofilm matrix [34].

ganisms (*Bordetella bronchiseptica* and *C. albicans*) [36].

the biofilm volume, thickness, roughness, and channel formation.

*DOI: http://dx.doi.org/10.5772/intechopen.92388*

to activate the maturation and disassembly of the biofilm in a coordinate manner (**Figure 1**). The time nutrients and other resources become limited and waste products accumulate, biofilm dispersion is imperative to provide bacteria to escape

*P. aeruginosa* harbors two complete AHL circuits*, lasI/lasR* and *rhlI/rhlR*, the *lasI/R* circuit being hierarchically positioned upstream, of the *rhlI/R* circuit. It is reported that *las*-mediated QS inhibits the production of exopolysaccharide, Pel,

Another element controlled by QS in *P. aeruginosa* biofilm development is rhamnolipids production [35]. These biosurfactant rhamnolipids caused bacterial detachment of *Pseudomonas* biofilms or even biofilms produced by other microor-

in *S. aureus* that a specific class of secreted peptides (phenol-soluble modulins, PSMs) that have surfactant-like properties mediates the main impact of Agr in biofilm dispersion. PSM operons transcription is under strict control by AgrA and agr mutants lack PSM production [38]. Also, it is shown that by analysis of biofilm tridimensional structure with confocal laser scanning microscopy, PSMs impacted

In *Staphylococcus aureus*, Agr is a QS regulation system [37]. It was demonstrated

Efflux pumps are membrane proteins that are related to the export of harmful substances from within the bacterial cell into the external environment. They are found in all species of bacteria, and efflux pump genes can be found in bacterial chromosomes or mobile genetic elements, such as plasmids. A wide array of substrates, such as antibiotics, detergents, dyes, toxins, and waste metabolites are

There are five known different classes of bacterial efflux pumps, which are the major facilitator superfamily (MF), the small multidrug resistance family (SMR), the ATP-binding cassette family (ABC), the resistance nodulation-division family (RND), and the multidrug and toxic compound extrusion family (MATE) [40]. To carry out the antimicrobial agent flow, the ABC family system hydrolyzes the ATP

*Stages of bacterial biofilm formation ((a) Planktonic cells adhere to a surface, (b) Initial attachment; cell proliferates to form a monolayer over the surface, (c–d) Increase in cell numbers results in the synthesis of elevated levels of autoinducers and EPS, (e) A mature biofilm with increased resistance to hostile environmental factors, (f) Dispersion of bacteria, : Autoinducers, : eDNA, : Enzymes).*

### *Antibiotic Resistance in Biofilm DOI: http://dx.doi.org/10.5772/intechopen.92388*

*Bacterial Biofilms*

and exposure to antibiotics) and are highly resistant to being killed by antibiotics [21, 22]. When these cells are compared with active and rapidly growing bacteria, lower metabolism rate makes these cells less susceptible to antibiotics. High levels of persistent cells are seen in chronic urinary tract infections and the lungs of patients with cystic fibrosis, especially, when the penetration of the immune system components is limited. The dormant phenotype is characterized by down-regulation of

Persistent formation is enhanced by toxin/antitoxin (TA) systems induced by environmental factors or DNA damage. TA systems do the following: (i) inhibition of protein synthesis by phosphorylation of the elongation factor, Ef-Tu (e.g., HipBA), translation inhibition and subsequent tolerance to antibiotics; (ii) expressing the TA modules (e.g., TisB toxin forming an anion channel in the membrane) leading to a decrease in PMF and ATP levels; and (iii) breakdown of mRNA (e.g., RelE and MazF toxins) and inhibition of translation. Prolonged treatment with aminoglycosides and RNA polymerase inhibitor rifampicin may prevent persistent resuscitation with synergistic effects with TA systems [23]. It is suggested that fluoroquinolones can induce TisB toxin by causing DNA damage in *Escherichia coli* [24]. In biofilms, many TA systems are associated with multidrug-tolerant persistent cells. However, this

Bacteria are equipped with a range of stress responses that make them possible to deal with environmental change, such as oxidative stress, unexpected temperature changes, low water activity, deprivation, and DNA damage [26]. These adaptive responses serve to enhance bacterial survivability. Adaptive stress responses can influence antimicrobial susceptibility since these responses impact on many of the same cellular components and processes that are aimed by antimicrobials [27]. Heterogeneity in the biofilm is one of the causes of the stress response [26]. Cells within hypoxic zones have decreased metabolic activity and are in a state like stationary phase [28]. It is known that many of the stress responses result in bacte-

Nutrient starvation also induces (p)ppGpp production, which mediates a global stress response known as the stringent response. The stringent response and (p) ppGpp signaling contribute to multidrug tolerance in *P. aeruginosa* biofilms. It is shown that ofloxacin, gentamicin, meropenem, and colistin killing increased upon inactivation of the stringent response [29]. Nutrient starvation also induced ofloxacin tolerance in *E. coli* K-12 biofilm through mechanisms dependent on the

Despite their self-sufficiency, bacteria interact with neighbors to accomplish collective activities, such as bioluminescence production, biofilm development, and exoenzyme secretion. This cooperation occurs through a mechanism: quorum sensing (QS) [31]. Quorum sensing (QS) is cell-to-cell communication at the molecular level controlled by chemical signaling molecules called autoinducers (AIs) [32]. Due to QS, bacteria can recognize the population density by measuring the accumulation of signaling molecules that are secreted from members of the community. The accumulation of the signal in the extracellular environment is adequate to activate

Recent studies indicate that in many bacterial species, activation of QS happens in the formed biofilm activating the maturation and disassembly of the biofilm. The initial adhesion step seems not suitable for the accumulation of signal molecules. Then, with the next steps, the attached bacteria are divided and form microcolonies, population density rises, and so signal molecules can reach adequate levels

the response only when the population density is high [33].

functions, such as energy production and biosynthesis.

tolerance is limited to specific antibiotics and TA [25].

rial cells entering stationary phase.

stringent and SOS response [30].

**2.5 Quorum sensing**

**138**

to activate the maturation and disassembly of the biofilm in a coordinate manner (**Figure 1**). The time nutrients and other resources become limited and waste products accumulate, biofilm dispersion is imperative to provide bacteria to escape and colonize new niches [33].

*P. aeruginosa* harbors two complete AHL circuits*, lasI/lasR* and *rhlI/rhlR*, the *lasI/R* circuit being hierarchically positioned upstream, of the *rhlI/R* circuit. It is reported that *las*-mediated QS inhibits the production of exopolysaccharide, Pel, which builds the biofilm matrix [34].

Another element controlled by QS in *P. aeruginosa* biofilm development is rhamnolipids production [35]. These biosurfactant rhamnolipids caused bacterial detachment of *Pseudomonas* biofilms or even biofilms produced by other microorganisms (*Bordetella bronchiseptica* and *C. albicans*) [36].

In *Staphylococcus aureus*, Agr is a QS regulation system [37]. It was demonstrated in *S. aureus* that a specific class of secreted peptides (phenol-soluble modulins, PSMs) that have surfactant-like properties mediates the main impact of Agr in biofilm dispersion. PSM operons transcription is under strict control by AgrA and agr mutants lack PSM production [38]. Also, it is shown that by analysis of biofilm tridimensional structure with confocal laser scanning microscopy, PSMs impacted the biofilm volume, thickness, roughness, and channel formation.

### **2.6 Efflux pumps**

Efflux pumps are membrane proteins that are related to the export of harmful substances from within the bacterial cell into the external environment. They are found in all species of bacteria, and efflux pump genes can be found in bacterial chromosomes or mobile genetic elements, such as plasmids. A wide array of substrates, such as antibiotics, detergents, dyes, toxins, and waste metabolites are extruded by efflux pumps [39].

There are five known different classes of bacterial efflux pumps, which are the major facilitator superfamily (MF), the small multidrug resistance family (SMR), the ATP-binding cassette family (ABC), the resistance nodulation-division family (RND), and the multidrug and toxic compound extrusion family (MATE) [40]. To carry out the antimicrobial agent flow, the ABC family system hydrolyzes the ATP

#### **Figure 1.**

*Stages of bacterial biofilm formation ((a) Planktonic cells adhere to a surface, (b) Initial attachment; cell proliferates to form a monolayer over the surface, (c–d) Increase in cell numbers results in the synthesis of elevated levels of autoinducers and EPS, (e) A mature biofilm with increased resistance to hostile environmental factors, (f) Dispersion of bacteria, : Autoinducers, : eDNA, : Enzymes).*

while the MF family, the MATE family, and the RND family act as secondary carriers and catalyze the drug ion antiproton [41].

Efflux pumps play a role in the natural resistance to antibiotics in some pathogens. These pumps also cause acquired resistance by overexpression and contribute to other resistance mechanisms [20]. Overproduction of the efflux pump can lead to multidrug resistance. Bacterial efflux pumps perform multidrug resistance (MDR) phenotype [42]. The efflux pump slows down the diffusion of hydrophilic solutes by downregulating the "porin" production in several pathogenic bacteria, such as *E. coli*, *Enterobacter aerogenes,* and *Klebsiella pneumonia*, thereby decreasing the transmembrane diffusion of lipophilic solutes [43].

Some multidrug efflux pumps contribute significantly to biofilm formation and this mechanism can be used to help bacteria overcome attacks from several classes of antibiotics. Extremely reduced biofilm formation has been reported for mutant *E. coli* that does not have the various genes associated with efflux pumps [44].

Upregulation of some efflux pumps (MexAB-OprM and MexCD-OprJ) in resistant *P. aeruginosa* biofilms has been observed in the presence of azithromycin [45]. It is reported that flow pump PA 1874-1877 is associated with biofilm-specific resistance to antibiotics. When these genes are mutated, lower resistance to aminoglycosides and fluoroquinolones is seen in biofilm conditions [46].

Efflux pumps may play different roles in biofilm formation; several studies proposed that efflux of EPSs and QS molecules to facilitate biofilm matrix formation and regulate QS, respectively, lead to indirect regulation of genes involved in biofilm formation and influence aggregation by promoting or preventing adhesion to surfaces and other cells [39].

### **2.7 Genetic diversity**

Genetic diversity provides bacterial adaptation, evolution, and survival in hostile environments. Biofilms are considered as a reservoir of genetic diversity. In biofilms, the emergence and spread of antibiotic resistance genes increase with horizontal gene transfer (HGT). HGT can happen through the transfer of plasmids among microorganism cells in a biofilm by conjugation. Actually, studies that are practiced by certain researchers have demonstrated that plasmid moving among bacterial cells might be more effective in biofilms than planktonic cells and that probably arises from proximity of microorganism cells in planktonic shape. In addition, some bacteria have the ability to pick up DNA from the biofilm matrix. The highly hydrated matrix provides favorable conditions for natural transformation [47]. Incidence of antibiotic resistance gene cassettes is determined more than 100-fold higher in biofilms than in planktonic cells [48].

Mutation frequency can be another factor that increases antibiotic resistance or tolerance. There is proof in the literature that cells in biofilms accumulate mutations at a higher rate than planktonic cells and these mutations may contribute to increase of antibiotic resistance [16]. Some bacteria have ability to pick up DNA from the biofilm matrix in addition.

### **2.8 Multispecies interactions**

Many laboratory studies about biofilm associated with antibiotic resistance and tolerance mechanisms have focused on monospecies biofilms in the literature and this issue is becoming progressively apparent. Interactions among microorganisms that are different species in a biofilm can alter the general antimicrobial resistance of the population. When we regard that many infections are polymicrobial, these interactions may be considered clinically important [49].

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*Antibiotic Resistance in Biofilm*

*DOI: http://dx.doi.org/10.5772/intechopen.92388*

micin tolerance, is not known [50].

single-species biofilms [54].

11 to 87% compared to antibiotic alone.

Studies are showing that antimicrobial resistance in multispecies biofilms is much higher than that in monospecies biofilms in available literature. For instance, it is determined that in vivo *P. aeruginosa* growing in a monospecies biofilm is twice more vulnerable to gentamicin antibiotic than that growing in multispecies biofilm consisting of *S. aureus, Enterococcus faecalis,* and *Finegoldia magna*. The molecular mechanism that underlies this multispecies biofilm model, which increases genta-

A clinically important model of multispecies biofilm infection includes *Moraxella catarrhalis* and *Streptococcus pneumoniae*. These bacteria play a role in the pathogenesis of otitis media, a biofilm-mediated infection that may be multi microbial. When antibiotic therapy is required, otitis media is commonly treated by amoxicillin. However, in stubborn cases, second-line treatments, such as amoxicillin-associated β-lactamase inhibitor or azithromycin, are applied. It is determined that in the biofilm consisting of two species, *M. catarrhalis* produces β-lactamase that provides resistance of *S. pneumoniae* against amoxicillin. Reciprocally, *S. pneumoniae* protects

*M. catarrhalis* from azithromycin with an unknown mechanism [51].

**3. Approaches aimed at overcoming biofilm resistance**

Interactions between different microorganisms and their effects on biofilm susceptibility to antibiotics have also been examined in polymicrobial biofilms. *C. albicans*, an opportunistic fungal pathogen, and *S. aureus* have a high resistance to vancomycin in a dual species. In a biofilm that is composed of *C. albicans* and *S. aureus*, *S. aureus* is associated with the fungal hyphae via the *C. albicans* Als3p adhesin and becomes covered with biofilm matrix probably derived from *C. albicans* [52]. Owing to the fungal matrix component, β-1,3-glucan, which is thought to act as a barrier to vancomycin diffusion into the biofilm, Staphylococcal resistance to vancomycin is increased in polymicrobial biofilms formed with *C. albicans* [53].

In polymicrobial biofilms, molecular basis may increase antibiotic resistance. In a study focused on *P. aeruginosa* and *S. maltophilia* two-species biofilm, it is determined that an intercellular signaling molecule that is secreted by *S. maltophilia* is sensed by the two-component sensor, BptS in *P. aeruginosa*, inducing upregulation of the PmrA-regulated PA3552–3559 and PA4773–4775 genes. These two operon gene products provide resistance to polymyxins, which is a cationic antimicrobial peptide. Actually, *P. aeruginosa* cultured in a biofilm with *S. maltophilia* have reduced vulnerability to polymyxin B and colistin compared to *P. aeruginosa,*

Biofilm infections can be treated and dispersed by the mixture of traditional antibiotics and substances called biofilm disrupting. The dissolution of biofilm is the first step in the ability of the host organism's immune system to remove microbial pathogens [55]. The combined antibiotics with the biofilm-dispersing medicines can bring a promising outcome. Most biofilm-dispersing medicines do not kill the pathogenic cells when they are used alone. For instance, patulin was analyzed with the aim of acyl-homoserine lactone removal in *P. aeruginosa*, but it had no effect on the existence of *P. aeruginosa* cells in a given biofilm. Although only patulin had no effect on the *P. aeruginosa,* the combination of patulin with antibiotic tobramycin was more effective and caused serious killing of the bacterial cells [56]. Another study showed that the mixture of the quorum controlling compounds with the antibiotic tigecycline increased the susceptibility of *S. aureus* fourfold compared to tigecycline alone [57]. Furthermore, the treatment of *S. aureus* with the mixture of cis-2-decenoic acid and ciprofloxacin is improved from

### *Antibiotic Resistance in Biofilm DOI: http://dx.doi.org/10.5772/intechopen.92388*

*Bacterial Biofilms*

and catalyze the drug ion antiproton [41].

to surfaces and other cells [39].

**2.7 Genetic diversity**

biofilm matrix in addition.

**2.8 Multispecies interactions**

transmembrane diffusion of lipophilic solutes [43].

glycosides and fluoroquinolones is seen in biofilm conditions [46].

100-fold higher in biofilms than in planktonic cells [48].

interactions may be considered clinically important [49].

while the MF family, the MATE family, and the RND family act as secondary carriers

Efflux pumps play a role in the natural resistance to antibiotics in some pathogens. These pumps also cause acquired resistance by overexpression and contribute to other resistance mechanisms [20]. Overproduction of the efflux pump can lead to multidrug resistance. Bacterial efflux pumps perform multidrug resistance (MDR) phenotype [42]. The efflux pump slows down the diffusion of hydrophilic solutes by downregulating the "porin" production in several pathogenic bacteria, such as *E. coli*, *Enterobacter aerogenes,* and *Klebsiella pneumonia*, thereby decreasing the

Some multidrug efflux pumps contribute significantly to biofilm formation and this mechanism can be used to help bacteria overcome attacks from several classes of antibiotics. Extremely reduced biofilm formation has been reported for mutant *E. coli* that does not have the various genes associated with efflux pumps [44]. Upregulation of some efflux pumps (MexAB-OprM and MexCD-OprJ) in resistant *P. aeruginosa* biofilms has been observed in the presence of azithromycin [45]. It is reported that flow pump PA 1874-1877 is associated with biofilm-specific resistance to antibiotics. When these genes are mutated, lower resistance to amino-

Efflux pumps may play different roles in biofilm formation; several studies proposed that efflux of EPSs and QS molecules to facilitate biofilm matrix formation and regulate QS, respectively, lead to indirect regulation of genes involved in biofilm formation and influence aggregation by promoting or preventing adhesion

Genetic diversity provides bacterial adaptation, evolution, and survival in hostile environments. Biofilms are considered as a reservoir of genetic diversity. In biofilms, the emergence and spread of antibiotic resistance genes increase with horizontal gene transfer (HGT). HGT can happen through the transfer of plasmids among microorganism cells in a biofilm by conjugation. Actually, studies that are practiced by certain researchers have demonstrated that plasmid moving among bacterial cells might be more effective in biofilms than planktonic cells and that probably arises from proximity of microorganism cells in planktonic shape. In addition, some bacteria have the ability to pick up DNA from the biofilm matrix. The highly hydrated matrix provides favorable conditions for natural transformation [47]. Incidence of antibiotic resistance gene cassettes is determined more than

Mutation frequency can be another factor that increases antibiotic resistance or tolerance. There is proof in the literature that cells in biofilms accumulate mutations at a higher rate than planktonic cells and these mutations may contribute to increase of antibiotic resistance [16]. Some bacteria have ability to pick up DNA from the

Many laboratory studies about biofilm associated with antibiotic resistance and tolerance mechanisms have focused on monospecies biofilms in the literature and this issue is becoming progressively apparent. Interactions among microorganisms that are different species in a biofilm can alter the general antimicrobial resistance of the population. When we regard that many infections are polymicrobial, these

**140**

Studies are showing that antimicrobial resistance in multispecies biofilms is much higher than that in monospecies biofilms in available literature. For instance, it is determined that in vivo *P. aeruginosa* growing in a monospecies biofilm is twice more vulnerable to gentamicin antibiotic than that growing in multispecies biofilm consisting of *S. aureus, Enterococcus faecalis,* and *Finegoldia magna*. The molecular mechanism that underlies this multispecies biofilm model, which increases gentamicin tolerance, is not known [50].

A clinically important model of multispecies biofilm infection includes *Moraxella catarrhalis* and *Streptococcus pneumoniae*. These bacteria play a role in the pathogenesis of otitis media, a biofilm-mediated infection that may be multi microbial. When antibiotic therapy is required, otitis media is commonly treated by amoxicillin. However, in stubborn cases, second-line treatments, such as amoxicillin-associated β-lactamase inhibitor or azithromycin, are applied. It is determined that in the biofilm consisting of two species, *M. catarrhalis* produces β-lactamase that provides resistance of *S. pneumoniae* against amoxicillin. Reciprocally, *S. pneumoniae* protects *M. catarrhalis* from azithromycin with an unknown mechanism [51].

Interactions between different microorganisms and their effects on biofilm susceptibility to antibiotics have also been examined in polymicrobial biofilms. *C. albicans*, an opportunistic fungal pathogen, and *S. aureus* have a high resistance to vancomycin in a dual species. In a biofilm that is composed of *C. albicans* and *S. aureus*, *S. aureus* is associated with the fungal hyphae via the *C. albicans* Als3p adhesin and becomes covered with biofilm matrix probably derived from *C. albicans* [52].

Owing to the fungal matrix component, β-1,3-glucan, which is thought to act as a barrier to vancomycin diffusion into the biofilm, Staphylococcal resistance to vancomycin is increased in polymicrobial biofilms formed with *C. albicans* [53].

In polymicrobial biofilms, molecular basis may increase antibiotic resistance. In a study focused on *P. aeruginosa* and *S. maltophilia* two-species biofilm, it is determined that an intercellular signaling molecule that is secreted by *S. maltophilia* is sensed by the two-component sensor, BptS in *P. aeruginosa*, inducing upregulation of the PmrA-regulated PA3552–3559 and PA4773–4775 genes. These two operon gene products provide resistance to polymyxins, which is a cationic antimicrobial peptide. Actually, *P. aeruginosa* cultured in a biofilm with *S. maltophilia* have reduced vulnerability to polymyxin B and colistin compared to *P. aeruginosa,* single-species biofilms [54].


*Edited by Kai Seng Koh and Voon Loong Wong*

Fluidics, an increasingly examined topic in nanoscience and nanotechnology is often discussed with regard to the handling of fluid flow, material processing, and material synthesis in innovative devices ranging from the macroscale to the nanoscale. *Nanoemulsions—Properties, Fabrications and Applications* reviews key concepts in nanoscale fluid mechanics, its corresponding properties, as well as the latest trends in nanofluidics applications. With attention to the fundamentals as well as advanced applications of fluidics, this book imparts a solid knowledge base and develops skill for future problem-solving and system analysis. This is a vital resource for upper-level engineering students who want to expand their potential career opportunities and familiarize themselves with an increasingly important field.

ISBN 978-1-78984-175-6

Nanoemulsions - Properties, Fabrications and Applications

Published in London, UK © 2019 IntechOpen © assistantua / iStock


**1. Introduction**

The first generation of induced pluripotent stem cells (iPSCs) by Yamanka and co-workers in 2006 was a milestone for stem cell research as it allows the in vitro production of human cells without ethical concerns. Like embryonic stem cells, iPSCs have the capability to differentiate into any cell type, including cardiomyocytes, therefore providing an easy accessible cellular source for the generation of cardiac organoids and tissue structures [1–3].

One possible application for iPSC-derived cardiomyocytes (CMs) is their use in cell therapy replacing damaged tissue by in vitro generated CMs. As cardiovascular diseases are the major cause of death worldwide such regenerative approaches are needed for the development of novel treatment options. The potential and feasibility of iPSC-CM transplantation has been investigated in small and large animal models [4–7].

Thereby, an important future option of iPSC-CMs will be their generation from patient specific tissue enabling the implementation of autologous cell transplantation strategies. In this respect, iPSC-CMs can be used for the development of personalized drug screening approaches and clinically relevant diseases models. Therefore, iPSCs enable cost-effective methods to identify potential drug targets, even more accurately than animal models or other in vitro cell systems. Successful pre-clinical application of iPSC-derived cardiomyocytes for drug screening assays has been lately demonstrated by the CiPA initiative, which was initiated to assess the proarrhythmic risk of novel cardio therapeutics. A myriad of studies investigated in vitro drug effects on different ion channels of iPSC-CMs [8–11], reflecting the importance of electrophysiological measurements using stem cell derived cardiac cells.

However, the maturation of iPSC derived CMs is still a critical point for their application in cardiovascular research as well as for clinical applications. Besides metabolic and structural maturation, proper ion channel composition is crucial for the development of a mature cardiac phenotype. During the last decade, extensive analyses have been performed on the electrophysiological properties of iPSC-CMs [12–15]. Several ion channels and ion currents have been found to be present in iPSC-CMs, including sodium (INa), potassium (IK1 and IKr), L-type and T-type calcium channels, etc. Although multiple differentiation protocols have been developed, researchers failed to generate fully mature cardiomyocytes in vitro, possessing identical electrophysiological properties as their native adult counterparts [16–18]. Moreover, it has largely been shown that iPSC-CMs represent a heterogeneous population of electrophysiological phenotypes, i.e., atrial, ventricular and nodal-like cells [19], each characterized by a specific electrical profile. Therefore, it is important to obtain electrophysiological data for detailed characterization of iPSC-CMs, in particular when differentiation into a certain cardiac subtype is desired [20,21].

Typical approaches to investigate the electrophysiological properties of stem cell derived CMs will be discussed in the following paragraph.

#### **2. Methods for Electrophysiological Characterization of iPSC-CMs**

Several different techniques exist to study the electrophysiological properties of cardiac cells, including patch clamp analysis, MEA measurement and fluorescence dye-based assessment of the membrane potential. Each of these techniques has its own advantages and limitations, which are described in detail in the following.

#### *2.1. Patch Clamping*

Patch clamping is the gold standard technique for the acquisition of ion current data and detailed measurement of action potential (AP) properties in individual cells. The basic principle of patch clamp relies on a blunt ended glass pipette that is sealed onto the cellular membrane to obtain a so-called gigaseal [22].

In the "current patch clamp" mode the membrane potential is recorded while the current applied by the patch pipette is controlled by the operator [23]. The current patch clamp technique allows detection of APs that occur spontaneously or after stimulation with a current change induced by the recording pipette. Considering the fact that iPSC-CMs also contain non-beating populations, current patch clamp methodology allows the detection of AP patterns in these quiescent cells [23,24]. Moreover, detailed AP features, such as AP duration, amplitude, beating rate and mean diastolic potential can reliably be acquired with current patch clamping [25].

When precise characterization of ion channel subtypes is desired, "voltage patch clamp" is performed to measure individual ion currents. Unlike in the current patch clamp mode, the operator keeps the membrane potential at a certain value that enables detection of the net membrane current. In hiPSC-CMs, voltage patch clamp has been successfully applied to obtain data about ion channel density, voltage dependency and activation/deactivation characteristics [23].

However, these manual patch clamp methods are complex, technically challenging procedures that require high operator skills as well as a biophysical background for data interpretation. Another limitation is the low throughput since measurements are usually performed on the single cell level. Therefore, automated patch clamp devices have been developed to overcome the aforementioned drawbacks of manual patch clamp approaches [26]. Automatic platforms profoundly increase the efficiency of electrophysiological data recording by assessment of 10–700 cells at the same time [27]. However, while automated systems are capable to analyze hundreds of cells under variable experimental conditions, the accuracy of obtained data is reduced if compared to manual patch clamping [28,29]. High-throughput analysis is realized by analysis of single cell suspensions, in contrast to manual patch clamping where cells are usually processed in an adherent state. Recently developed systems are equipped with temperature control, optical stimulation and internal perfusion systems to ensure

high data quality and reproducibility [27,30]. Data consistency and robustness is further determined by the homogeneity and density of the applied single cell suspension—a point that is particularly important for iPSC-CMs that are sensitive to dissociation as it can affect membrane proteins and electrical physiology of the cell, including ion channel expression [31,32]. Automatic techniques also do not provide the possibility of selective cell capturing. Hence, the system demands highly purified cell populations, which could be challenging when working with CMs differentiated from iPSCs that commonly represent a mixture of different cardiac subtypes [29].

#### *2.2. Optical Recordings of the Membrane Potential*

An indirect technique to assess electrophysiological data of iPSC-CMs is the application of voltage-sensitive dyes that change fluorescence intensity or emission spectra upon alteration of the membrane potential. Considering fluorescence microscopy as one of the most commonly used methods in cell research, utilizing voltage-sensitive dyes is operationally simple and does not require special instrumentation. Moreover, it is less invasive and it enables monitoring of voltage dynamics over thousands of cells with very high temporal resolution [33]. Several studies have proven feasibility of voltage sensor probes for drug screening experiments in iPSC-CMs [34–36]. Recently, Takaki and colleagues [37] applied voltage sensitive dyes for the identification of distinct cardiac subtypes in an iPSC-CM population. Further, the authors were able to detect differences in the AP pattern in iPSC-CMs obtained from patients suffering from the long QT syndrome, compared to control cells.

Alternatively, voltage sensitive probes can be engineered as fluorescent proteins that are stably expressed in target cells. Compared to voltage-sensitive dyes, these proteins possess lower phototoxicity, thus, facilitating long-term measurements. These genetically encoded probes are designed by conjugating a voltage-sensing domain to a single fluorescent protein, a fluorescence resonance energy transfer (FRET) pair or rhodopsin proteins [38,39]. Changes of the membrane potential induce conformational rearrangement of the voltage-sensor, which in turn modulates the emission spectra of the attached fluorescent protein. The latest generation of genetically encoded voltage sensors, such as ArcLight, Archer1 or QuasAr1, show large fluorescence alteration upon depolarizing events (40%–80% for a 100 mV depolarization) associated with faster on/off kinetics (1–10 ms). Shaheen et al. generated ArcLight expressing human iPSC-CMs to establish a 2D cardiac tissue platform for optical mapping and pharmacological studies [40]. Former data confirmed the suitability of genetically encoded voltage sensors for iPSC-CM drug screening applications and disease modeling attributed to altered AP phenotypes [41–44]. However, there are certain limitations of this technology. Like fluorescence dyes, genetically encoded voltage indicators provide only relative, not absolute values for the membrane potential [45]. The lower on/off kinetics increase the probability of losing high frequency AP elements [41,45]. Furthermore, introduction of voltage-sensitive proteins like ArcLight could affect the electrophysiological properties of iPSC-CMs. This needs to be carefully addressed by the operator as well as proper folding and membrane integration of the voltage sensitive probe.


A Brief Overview of CRC Implementation for 5G NR

*Hao Wu*

## **Abstract**

In fifth generation (5G) new radio (NR), the medium access control (MAC) layer organizes the data into the transport block and transmits it to the physical layer. The transport block consists of up to million bits. When the transport block size exceeds a threshold, the transport block is divided into multiple equal size code blocks. The code block consists of up to 8448 bits. Both the transport block and the code block have a cyclic redundancy check (CRC) attached. Due to the difference in the size of the transport block and the code block, the CRC processing scheme suitable for the transport block and that suitable for the code block are different. This chapter gives an overview of the CRC implementation in 5G NR.

**Keywords:** 5G, NR, CRC, transport block, code block

### **1. Introduction**

In order to provide high data transmission rates, the bandwidth of mobile communication systems is increasing. In fourth generation (4G) long term evolution (LTE), the maximum transmission bandwidth for one component carrier is 20 MHz [1]. In fifth generation (5G) new radio (NR), the frequency bands are divided into two parts: frequency range 1 (FR1) below 6 GHz and frequency range 2 (FR2) above 24.25 GHz. The maximum transmission bandwidth for one component carrier is 100 MHz and 400 MHz in FR1 and FR2 respectively [2]. The increasing system bandwidth brings new problems to the design of the transmitter and the receiver. In this chapter of the book, we focus on the cyclic redundancy check (CRC) implementation in 5G NR.

In 5G NR, there are many physical channels defined in the downlink and the uplink [3]. The downlink physical channels consist of the physical downlink shared channel (PDSCH), the physical downlink control channel (PDCCH), the physical broadcast channel (PBCH), etc. The uplink physical channels consist of physical uplink shared channel (PUSCH), the physical uplink control channel (PUCCH), the physical random access channel (PRACH), etc. The PDSCH and the PDSCH are mainly used to transmit data. The usage scenarios of 5G NR consist of enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) and ultra-reliable and low latency communications (URLLC) [4, 5]. The usage scenario of the eMBB requires high data transmission rates. As a consequence, we focus on the PDSCH and the PUSCH in this chapter.

The medium access control (MAC) layer organizes the data into the transport block and transmits it to the physical layer. In 5G NR, the maximum transport block

#### *Moving Broadband Mobile Communications Forward - Intelligent Technologies for 5G…*

In the following sections, we mainly consider the case that there is a segmenta-

Note that the procedure of the transport block size determination guarantees

where 0 ≤ *j*≤ *K* � 25. In the following, the processing of the transport block includes: QC-LPDC encoding, rate matching, bit interleaving and code block concatenation. The encoded transport block is transmitted over the air after the symbol

At the receiver side, the following steps are carried out for the transport block:

decoding, code block concatenation. We need to check whether each code block and

*ej* ¼ *dv*

In this section, we give two properties of the CRC. These properties are useful in the CRC implementation. Before giving these properties, we define some variables. Let *A x*ð Þ and *B x*ð Þ be the polynomials. Let *g x*ð Þ be the cyclic generator polynomial. CRC*g x*ð Þ½ � *A x*ð Þ is defined as the remainder when *A x*ð Þ is divided by *g x*ð Þ. The two

CRC*g x*ð Þ½ �¼ *A x*ð Þ*B x*ð Þ CRC*g x*ð Þ CRC*g x*ð Þ½ � *A x*ð Þ CRC*g x*ð Þ½ � *B x*ð Þ (8)

Property 1 implies that CRC*g x*ð Þ½ � *A x*ð Þ*B x*ð Þ can be obtained by computing the

where *v* ¼ ⌊ *j=*ð Þ *K* � 24 ⌋, *u* ¼ modð Þ *j*, *K* � 24 and 0≤ *j*≤*L* þ *N* � 1. The undetected error probability is required to be less than 10�6 in 5G NR [8, 9]. Since the parity check capacity of QC-LDPC codes alone cannot meet the undetected error probability requirement of 5G NR [8, 9], we need to use the CRC check to

code block segmentation, bit de-interleaving, de-rate matching, QC-LPDC

received code block after the hard decision and *e* ¼ ½ � *e*0,*e*1, … ,*eL*þ*N*�1 be the

*K*�23, … ,*ci*

*K*�24,*ci*

*c i*

which is generated by the cyclic generator polynomial

the transport block are correctly received. Let *di* ¼ *di*

determine whether *di* and *e* are correctly received.

**3. Properties of the CRC**

properties are listed as follows.

CRC of *A x*ð Þ and *B x*ð Þ independently.

**Property 1**.

**71**

received transport block after the hard decision. *ej* is equal to

be the *i*th code block, where *K* is the code block size

*K*�1

*g*24Bð Þ¼ *x x*24 þ *x*23 þ *x*6 þ *x*5 þ *x* þ 1 (5)

*K* ¼ ð Þ *L* þ *N =C* þ 24 (4)

is the code block level CRC,

*j* ¼ *ai K*ð Þþ �24 *j* (6)

0, *di*

1, … , *di*

*K*�1 be the *i*th

*u* (7)

tion. Let *ci* ¼ *ci*

and is equal to

*ci*

*j* is equal to

level processing.

0,*ci* 1, … ,*ci K*�1

*A Brief Overview of CRC Implementation for 5G NR DOI: http://dx.doi.org/10.5772/intechopen.91790*

that ð Þ *L* þ *N* is divisible by *C*. *ci*

**Figure 1.**

*The transport block and the code block.*

size is 1,277,992 [6]. The processing of the transport block is shown in **Figure 1** [7]. If the transport block size is larger than 3824, a 16-bit CRC is added at the end of the transport block. Otherwise, a 24-bit CRC is added at the end of the transport block. The transport block is divided into multiple equal size code blocks when the transport block size exceeds a threshold. For quasi-cyclic low-density parity-check code (QC-LDPC) base graph 1, the threshold is equal to 8448. For QC-LDPC base graph 2, the threshold is equal to 3840. In 5G NR, the maximum code block size number is 8448. An additional 24-bit CRC is added at the end of each code block when there is a segmentation. Due to the difference in the size of the transport block and the code block, the CRC processing scheme suitable for the transport block and that suitable for the code block are different.

The rest of this chapter is organized as follows. Section 2 describes the system model of the transport block and the code block in 5G NR. Section 3 gives two properties of the CRC. Section 4 presents the overview of the CRC implementation. Finally, Section 5 gives the conclusion.

#### **2. System model**

Let *a* ¼ ½ � *a*0, *a*1, … , *aL*�1, *aL*, *aL*þ1, … , *aL*þ*N*�1 be the transport block including the transport block level CRC, where *L* is the transport block size and *N* is the transport block level CRC size. Note that *p* ¼ ½ � *aL*, *aL*þ1, … , *aL*þ*N*�1 is the transport block level CRC. If *L* is smaller than or equal to 3824, then *N* is equal to 16 and *p* is generated by the following cyclic generator polynomial:

$$\mathcal{g}\_{16}(\mathbf{x}) = \mathbf{x}^{16} + \mathbf{x}^{12} + \mathbf{x}^5 + \mathbf{1} \tag{1}$$

If *L* is larger than 3824, then *N* is equal to 24 and *p* is generated by the following cyclic generator polynomial:

$$\mathbf{g\_{24A}}(\mathbf{x}) = \mathbf{x}^{24} + \mathbf{x}^{23} + \mathbf{x}^{18} + \mathbf{x}^{17} + \mathbf{x}^{14} + \mathbf{x}^{11} + \mathbf{x}^{10} + \mathbf{x}^7 + \mathbf{x}^6 + \mathbf{x}^5 + \mathbf{x}^4 + \mathbf{x}^3 + \mathbf{x} + 1 \tag{2}$$

When *L* þ *N* is larger than *M*, the transport block including the transport block level CRC is segmented into multiple code blocks. Let *R* be code rate of the initial transmission indicated by the modulation and coding scheme (MCS) index. If *L*>292 and *R*>0*:*67 or *L*>3824 and *R*>0*:*25, then QC-LDPC base graph 1 is used and *M* is equal to 8448. Otherwise, QC-LDPC base graph 2 is used and *M* is equal to 3840.

When there is no segmentation, the number of code blocks *C* is equal to 1. When there is a segmentation, the number of code blocks *C* is equal to

$$\mathbf{C} = \lceil (L+N)/(\mathbf{M}-\mathbf{24}) \rceil \tag{3}$$

#### *A Brief Overview of CRC Implementation for 5G NR DOI: http://dx.doi.org/10.5772/intechopen.91790*

In the following sections, we mainly consider the case that there is a segmentation. Let *ci* ¼ *ci* 0,*ci* 1, … ,*ci K*�1 be the *i*th code block, where *K* is the code block size and is equal to

$$K = (L+N)/C + \text{24} \tag{4}$$

Note that the procedure of the transport block size determination guarantees that ð Þ *L* þ *N* is divisible by *C*. *ci K*�24,*ci K*�23, … ,*ci K*�1 is the code block level CRC, which is generated by the cyclic generator polynomial

$$\mathcal{g}\_{24\mathbb{B}}(\mathbf{x}) = \mathbf{x}^{24} + \mathbf{x}^{23} + \mathbf{x}^6 + \mathbf{x}^5 + \mathbf{x} + \mathbf{1} \tag{5}$$

*ci j* is equal to

size is 1,277,992 [6]. The processing of the transport block is shown in **Figure 1** [7]. If the transport block size is larger than 3824, a 16-bit CRC is added at the end of the transport block. Otherwise, a 24-bit CRC is added at the end of the transport block. The transport block is divided into multiple equal size code blocks when the transport block size exceeds a threshold. For quasi-cyclic low-density parity-check code (QC-LDPC) base graph 1, the threshold is equal to 8448. For QC-LDPC base graph 2, the threshold is equal to 3840. In 5G NR, the maximum code block size number is 8448. An additional 24-bit CRC is added at the end of each code block when there is a segmentation. Due to the difference in the size of the transport block and the code block, the CRC processing scheme suitable for the transport block and that

*Moving Broadband Mobile Communications Forward - Intelligent Technologies for 5G…*

The rest of this chapter is organized as follows. Section 2 describes the system model of the transport block and the code block in 5G NR. Section 3 gives two properties of the CRC. Section 4 presents the overview of the CRC implementation.

Let *a* ¼ ½ � *a*0, *a*1, … , *aL*�1, *aL*, *aL*þ1, … , *aL*þ*N*�1 be the transport block including the transport block level CRC, where *L* is the transport block size and *N* is the transport block level CRC size. Note that *p* ¼ ½ � *aL*, *aL*þ1, … , *aL*þ*N*�1 is the transport block level CRC. If *L* is smaller than or equal to 3824, then *N* is equal to 16 and *p*

If *L* is larger than 3824, then *N* is equal to 24 and *p* is generated by the following

*g*24Að Þ¼ *x x*24 þ *x*23 þ *x*18 þ *x*17 þ *x*14 þ *x*11 þ *x*10 þ *x*7 þ *x*6 þ *x*5 þ *x*4 þ *x*3 þ *x* þ 1

When *L* þ *N* is larger than *M*, the transport block including the transport block level CRC is segmented into multiple code blocks. Let *R* be code rate of the initial transmission indicated by the modulation and coding scheme (MCS) index. If *L*>292 and *R*>0*:*67 or *L*>3824 and *R*>0*:*25, then QC-LDPC base graph 1 is used and *M* is equal to 8448. Otherwise, QC-LDPC base graph 2 is used and *M* is equal

When there is no segmentation, the number of code blocks *C* is equal to 1. When

there is a segmentation, the number of code blocks *C* is equal to

*g*16ð Þ¼ *x x*16 þ *x*12 þ *x*5 þ 1 (1)

*C* ¼ d e ð Þ *L* þ *N =*ð Þ *M* � 24 (3)

(2)

is generated by the following cyclic generator polynomial:

suitable for the code block are different.

Finally, Section 5 gives the conclusion.

**2. System model**

**Figure 1.**

*The transport block and the code block.*

cyclic generator polynomial:

to 3840.

**70**

$$\mathfrak{c}\_{j}^{i} = \mathfrak{a}\_{i(K-24)+j} \tag{6}$$

where 0 ≤ *j*≤ *K* � 25. In the following, the processing of the transport block includes: QC-LPDC encoding, rate matching, bit interleaving and code block concatenation. The encoded transport block is transmitted over the air after the symbol level processing.

At the receiver side, the following steps are carried out for the transport block: code block segmentation, bit de-interleaving, de-rate matching, QC-LPDC decoding, code block concatenation. We need to check whether each code block and the transport block are correctly received. Let *di* ¼ *di* 0, *di* 1, … , *di K*�1 be the *i*th received code block after the hard decision and *e* ¼ ½ � *e*0,*e*1, … ,*eL*þ*N*�1 be the received transport block after the hard decision. *ej* is equal to

$$e\_j = d\_u^v \tag{7}$$

where *v* ¼ ⌊ *j=*ð Þ *K* � 24 ⌋, *u* ¼ modð Þ *j*, *K* � 24 and 0≤ *j*≤*L* þ *N* � 1. The undetected error probability is required to be less than 10�6 in 5G NR [8, 9]. Since the parity check capacity of QC-LDPC codes alone cannot meet the undetected error probability requirement of 5G NR [8, 9], we need to use the CRC check to determine whether *di* and *e* are correctly received.


**7. Abdominal contouring**

Our algorithm for abdominal contouring is simple. It pivots on four determining factors.

The first factor is the skin quality, laxity and excess. The second the rectus sheath its weakness, bulge, divarication and any hernia/s. The third is fat, its excess and distribution. The fourth is patients' aspirations and whether they are achievable, if so, how best to achieve them safely and efficiently.

If the skin quality is poor with laxity and significant excess, then a surgical excision is warranted (**Figure 6**). If the rectus sheath shows weakness with bulge and divarication, a plication is required. If a hernia is detected on ultrasound prior to surgery, then a multidisciplinary repair of this hernia with a general surgeon is planned.

Fat excess warrants liposculpture; however, the fat distribution influences the way we create curves and the time it takes. Patients' aspirations will have an influence on us as plastic surgeons; however, we must respect the confines of what is scientifically achievable within the realms of safety yet obtaining the best aesthetic results.

We must on occasions when a patient has unrealistic expectations protect that patient by being honest with them. Although we let them down gently, however, we will offer them the reasoning as to why and the support required.

Abdominoplasty with rectus sheath plication is a common procedure. Our aspirations and goals are to push the surgical outcomes of abdominoplasty to new heights. This is achievable when the aesthetic results of surgery; significantly outweighs the size of scarring whilst creating the desired contours and appropriate proportions. To do so, an open mind utilising a hybrid approach must be contemplated (**Figures 7** and **8**).

As plastic surgeons we aspire to always better our surgical outcomes and improve safety. Understanding the abdominal vascular territories and blood supply meant we could incorporate liposculpture with surgical excision yet reduce surgical complications [18].

#### **Figure 6.**

*Forty-nine-year-old female 6 months following VASER liposculpture to abdomen and flanks with abdominoplasty and plication of the rectus sheath. She also had bilateral breast reduction with fat transfer to upper poles to improve upper pole fullness.*

#### **Figure 7.**

*Forty-three-year-old female 6 months following 360 VASER liposculpture of abdomen and flanks with first stage fat transfer to breasts. Followed by a second stage VASER liposculpture of thighs and back.*

#### **Figure 8.**

*Forty-three-year-old female 6 months following 360 VASER liposculpture of abdomen, and flanks with first stage fat transfer to breasts. Followed by a second stage VASER liposculpture of thighs and back.*

VASER has added a safety margin to abdominoplasty as it produces less traumatic fat removal from the abdominal flap, thus decreasing the chances of complications associated with lipo-abdominoplasty [19].

Suction-assisted liposuction does not impair the regenerative potential of adiposederived stem cells [20]. As the lipo-aspiration pressures are much lower following VASER than traditional liposuction, this also reduces risks to the skin [21].

The Vent-X, which is the vacuum pump designed to work with VASER, the vacuum it generates ranges from 0 to 760 mmHg. The dial starts at zero and goes all the *Body Contouring and VASER Technology, the Fourth Dimension DOI: http://dx.doi.org/10.5772/intechopen.108935*

way to 30. If we are to perform lipo-aspiration with fat transfer in mind, then we set the dial at 10–12, which equates to a third of atmosphere, ~250 mmHg.  VASER also reduces surgeon's fatigue as the more fluid fat the easier it is to aspirate.

 The shorter the time, the better the uptake with a critical period of 2 hours [22]. With VASER making fat harvest more time-efficient, this could further aid in better fat retention.

We must consider whether there is a limit of the lipoaspirate volume when performing an abdominoplasty; however, overall, the literature appears to support that current limits on liposuction volumes in lipo-abdominoplasty are arbitrary and do not reflect valid thresholds for increased complications in the hands of an experienced plastic surgeon [23].

#### **8. Breast contouring**

Accepting when we think of the word breast/s contouring does not normally stem to mind. When talking about breast contouring in the majority, we tend to focus on augmentation, reduction, mastopexy and mastopexy augmentation. However, if we go back to the basics, the word contouring is to change the shape of something, hence by changing the shape of the breast/s we are effectively contouring them [24–26].

Fat transfer to the breasts has become popularised following a change in the views about its safety by the American Society of Plastic Surgeons in 2009. It was more than two decades when they condemned this procedure due to fear that it may obscure the detection of breast cancer [24–26].

In this section, we will focus on breast contouring and fat transfer in the context of autogenous augmentation, mastopexy augmentation, asymmetry correction, and volume replacement (**Figure 9**).

The hybrid approach, which includes silicone implants with fat transfer, will also be considered. In selected patients, fat transfer is performed at the same time as breast reduction surgery; thus, adding fullness to the upper poles and cleavage of the breasts (**Figure 6**).

We must accept that not all patients desire one or the other treatment/s and fat is not always available to achieve the goals set out in the agreed pre-treatment planning schedule.

Considerations when injecting fat in the breasts include targeting the subcutaneous space and pre-pectoral plane, in addition to the breast tissue. Respecting the views of others and their practices, however, we do not see a case for transferring fat into or under the pectoral muscles and feel that this could add unnecessary risks without necessarily improving the aesthetic outcomes.

It is important to keep in mind that the pre-operative breasts' volume has an influence on the volume that ought to be transferred. Hence, it is important to appropriately counsel the patients prior to treating them as fat resorption will occur and can vary between 30 and 70% of the transferred volume within 12 months of transfer [27].

In females who are undergoing fat transfer to the breasts, we include a routine mammogram as a baseline in patients ≥40 years of age. As an added safety net, we routinely perform abdominal ultrasound screening if we are using the abdomen as a fat donor site to exclude any clinically undetected hernias and rectus sheath pathology or abnormal anatomy.

#### **Figure 9.**

*Thirty-year-old female. She had VASER liposculpture to abdomen. She had 340 cc of fat transferred to right breast and 220 cc to left breast for the purpose of asymmetry correction and augmentation.*

 Early on we used a syringe-based mechanical aspiration of the fat and the Coleman centrifuge [29].

However, we now use Vaser for the larger volume fat harvest with a 3.7 mm cannula. Our technique includes allowing the aspirated fat to self-separate over a period of 30 minutes rather than spinning it with centrifuge, as we believe this reduces the trauma to the harvested fat.

The fat is then injected into the breasts using a 10 cc syringe and 2 mm cannula. For further refinement, a 5 or 1 cc syringe can be used when required.

Although the literature supports the view that when using the larger harvest cannula, the more fat survives. Nonetheless, a balance must be struck between cosmetic outcomes and scarring extent [30].

Our experience tells us like others that the less we handle the fat, the better is the survival rate when transferred into other tissues [31].

It is important to add that it is best to transfer the fat without significant time delay from the time it was harvested and to use a closed system when harvesting the fat with the least exposure to air [32].

The fat must be infused with small volume injections and distributed in different levels of the breast with the least traumatic method using a retrograde injection technique to minimise injections into vascular spaces. We aspire to limit the fat transfer volume per breast to 250–300 cc (**Figures 6** and **7**).

If larger volume of fat is required for transfer in a small breast, then pre-surgery external expansion is recommended [33]. However, in our practice and contrary to the experience of others we find uptake and acceptance for this method of external expansion amongst our patients thus far has been very limited.