**6. DC vaccines**

More recent clinical trials using naturally circulating blood DCs have turned to CliniMACS system by Miltenyi to isolate different DC subsets from patients (**Figure 1**). Two completed Phase I clinical trials have used CD1c+ DCs (cDC2) loaded with TAA peptides in hormone refractory metastatic prostate cancer and metastatic melanoma and observed good safety and immunogenicity [71*,* 72]. Another completed Phase I trial using pDCs showed the induction of tumour-Ag specific CTL response as well as an IFN signature [33]. On-going clinical trials, as summarised by Bol et al., are not only isolating single DC subsets for vaccination, but are also trying combination vaccines comprised of cDC2 and pDC subsets and using dual-activating maturation agonists such as single stranded RNA that stimulates TLR8 on cDC2 and TLR7 on pDCs (NCT-02993315, NCT-02574377, NCT-02692976) [67]. However, there are still many challenges in using naturally circulating blood DCs in tumour vaccinations. The methodology for isolation of sufficient CD141+ cDC1 DCs, which comprise only 0.03% PBMCs, is still lacking and will be important to harness due to their superior ability to cross-present dead and necrotic Ag. Furthermore, although improved over the years, the duration of DCs spent *ex vivo* can drastically affect DC viability and functionality and the personalised nature of these vaccines can limit the quantity of patient access to these treatments.

Apart from the *ex vivo* maturation of autologous DCs, another strategy of DC vaccines has been receptor targeting (**Figure 1**). This involves the administration of a monoclonal Ab (mAb) specific for endocytic receptors on various DC subsets to deliver tumour Ags to DCs directly *in vivo* [73]. Tumour Ags are conjugated to these DC-targeting mAb either chemically, through genetic fusion, or attachment to nanoparticles and liposomes [74]. Importantly, the administration of adjuvant, such as TLR3 agonist poly I:C, in conjunction with Ag delivery, is necessary to induce immune priming instead of tolerance, as shown in mice [75–77]. Moreover, the targeting of cross-presenting DC subsets has been particularly attractive, due to their ability to activate CTLs. DEC-205, a C-type lectin that is highly expressed on cDC1 can cross-present Ag when targeted and induce tumour Ag NY-ESO-1-specific

**107**

CD8+

**Figure 1.**

*Dendritic Cells and Their Roles in Anti-Tumour Immunity*

cellular and humoral responses in patients with solid cancers [78*,* 79]. However,

*Overview of potential roles of DC in cancer therapies. To improve current cancer treatments and the activation of tumour-specific CTL, DC may be directly targeted* in vivo *(Section 6) or may themselves be the targets of checkpoint immunotherapies (Section 8).* Ex vivo *manipulation of DC (Section 6) may also be beneficial in some cancer patients.* In vivo *targeting strategies may also be combined with Flt3-L treatment to enhance DC numbers, and adjuvants targeting specific PRR to ensure the DC subset of interest are activated. Created with Biorender.com.*

and monocytes which can affect targeting specificities and efficiencies [79*–*81]. In contrast, another C-type lectin, Clec9a (also known as DNGR-1), is specifically expressed on cDC1 and strategies targeting this molecule have demonstrated highly immunogenic responses without adjuvant in non-human primates, and also superior Ag-specific cross-presentation when targeted *in vitro* and *in vivo* [79*,* 81*,* 82]. Based on these pre-clinical studies, the progression of vaccines targeting Clec9a into

The tumour microenvironment (TME) is a complex niche of tumour cells, stromal cells and tumour infiltrating myeloid and lymphoid immune cells. The dynamic nature of this niche varies with different types and stages of cancer, as well as between patients themselves. It has been established that the infiltration of

cytotoxic T cells have been associated with better treatment outcomes with

DCs, pDCs

DEC-205 is also expressed on many other cell-types including CD1c+

clinical trials is much anticipated.

**7. DC in the tumour microenvironment**

*DOI: http://dx.doi.org/10.5772/intechopen.91692*

*Dendritic Cells and Their Roles in Anti-Tumour Immunity DOI: http://dx.doi.org/10.5772/intechopen.91692*

#### **Figure 1.**

*Current Cancer Treatment*

clinical outcomes.

**6. DC vaccines**

and have been extensively characterised [67*,* 68].

of autologous DCs. In this method, DCs are isolated from patient peripheral blood mononuclear cells (PBMCs) obtained via leukapheresis, incubated with maturation stimuli and tumour Ags, and vaccinated back into the patient. Because this method requires a large number of DCs, and naturally circulating blood DCs are rare, the majority of clinical trials have previously used moDCs for this type of DC vaccine

Thus far, a wide variety of moDC vaccine strategies have been trialled [68]. moDCs have been differentiated and matured using monocyte conditioned medium

More recent clinical trials using naturally circulating blood DCs have turned to CliniMACS system by Miltenyi to isolate different DC subsets from patients

loaded with TAA peptides in hormone refractory metastatic prostate cancer and metastatic melanoma and observed good safety and immunogenicity [71*,* 72]. Another completed Phase I trial using pDCs showed the induction of tumour-Ag specific CTL response as well as an IFN signature [33]. On-going clinical trials, as summarised by Bol et al., are not only isolating single DC subsets for vaccination, but are also trying combination vaccines comprised of cDC2 and pDC subsets and using dual-activating maturation agonists such as single stranded RNA that stimulates TLR8 on cDC2 and TLR7 on pDCs (NCT-02993315, NCT-02574377, NCT-02692976) [67]. However, there are still many challenges in using naturally circulating blood DCs in tumour vaccinations. The methodology for isolation of sufficient

 cDC1 DCs, which comprise only 0.03% PBMCs, is still lacking and will be important to harness due to their superior ability to cross-present dead and necrotic Ag. Furthermore, although improved over the years, the duration of DCs spent *ex vivo* can drastically affect DC viability and functionality and the personalised nature of these vaccines can limit the quantity of patient access to these treatments. Apart from the *ex vivo* maturation of autologous DCs, another strategy of DC vaccines has been receptor targeting (**Figure 1**). This involves the administration of a monoclonal Ab (mAb) specific for endocytic receptors on various DC subsets to deliver tumour Ags to DCs directly *in vivo* [73]. Tumour Ags are conjugated to these DC-targeting mAb either chemically, through genetic fusion, or attachment to nanoparticles and liposomes [74]. Importantly, the administration of adjuvant, such as TLR3 agonist poly I:C, in conjunction with Ag delivery, is necessary to induce immune priming instead of tolerance, as shown in mice [75–77]. Moreover, the targeting of cross-presenting DC subsets has been particularly attractive, due to their ability to activate CTLs. DEC-205, a C-type lectin that is highly expressed on cDC1 can cross-present Ag when targeted and induce tumour Ag NY-ESO-1-specific

DCs (cDC2)

(**Figure 1**). Two completed Phase I clinical trials have used CD1c+

with various supplements of cytokines (TNF-α, GM-CSF, IL-4, IFN-α), TLR agonists (LPS) and other factors such as prostaglandin E2 [67–69]. There is also variety in the type of Ags loaded into DCs such as peptides from tumour-associated Ags (TAA), TAA-encoding mRNA and whole tumour lysates [67]. More recently, the electroporation of synthetic mRNA encoding DC-maturation factors such as CD40 ligand, constitutively active TLR4 and CD70 together with fusion proteins DC-LAMP and melanoma-associated Ags into autologous moDCs (TriMixDC-MEL) have proven safe and immunogenic in phase 1 clinical trials in metastatic melanoma [70]. However, the variation in the aforementioned vaccine factors as well as the route of DC administration (intranodal, i.v.) and lack of standardised method of moDC generation has shown variable efficacies of moDC vaccines in

**106**

CD141+

*Overview of potential roles of DC in cancer therapies. To improve current cancer treatments and the activation of tumour-specific CTL, DC may be directly targeted* in vivo *(Section 6) or may themselves be the targets of checkpoint immunotherapies (Section 8).* Ex vivo *manipulation of DC (Section 6) may also be beneficial in some cancer patients.* In vivo *targeting strategies may also be combined with Flt3-L treatment to enhance DC numbers, and adjuvants targeting specific PRR to ensure the DC subset of interest are activated. Created with Biorender.com.*

cellular and humoral responses in patients with solid cancers [78*,* 79]. However, DEC-205 is also expressed on many other cell-types including CD1c+ DCs, pDCs and monocytes which can affect targeting specificities and efficiencies [79*–*81]. In contrast, another C-type lectin, Clec9a (also known as DNGR-1), is specifically expressed on cDC1 and strategies targeting this molecule have demonstrated highly immunogenic responses without adjuvant in non-human primates, and also superior Ag-specific cross-presentation when targeted *in vitro* and *in vivo* [79*,* 81*,* 82]. Based on these pre-clinical studies, the progression of vaccines targeting Clec9a into clinical trials is much anticipated.


1.2 Types of optical fibers

The commonly known optical fibers are step index and graded index (GR-IN) optical fibers. The former means that the core's refractive index is homogeneous while it suffers an abrupt change at the boundary with the clad. For a GR-IN optical fiber, the core does not have a constant value of refractive index but it rather has a radial distribution of refractive index. These two types of optical fibers can be classified into either single-mode or multi-mode optical fibers. Single-mode optical fiber only sustains one mode of propagation while the multi-mode optical fiber can sustain up to hundreds of propagation modes [1, 3]. The number of the propagation modes is related to the numerical aperture of the fiber, which, in turn, depends on the refractive indices of both core and clad.

### 1.3 Characterization of optical fibers

Accurate characterization of optical fibers is required in order to know about their functions and performances. There are many methods of optical fibers characterization such as optical microscopy, electron microscopy, X-ray spectrometry, infrared spectroscopy, light diffraction, light scattering, optical interferometry, and digital holography [1, 3, 12–29]. Optical interferometry is an effective accurate tool for studying and characterizing optical fibers. It depends on the determination of the phase difference between a ray of light transmitting the fiber's cross-section and a reference ray reaching the interference plane directly without crossing the fiber. This phase shift can be transformed into a refractive index map representing the radial distribution of the refractive index across the fiber or, in other words, the refractive index profile (RIP). Interferometry can detect tiny changes in refractive index if an external effect is applied on the fiber. The change of refractive index can be in situ detected if the interferometer is developed to achieve this task. Interference patterns can be digitally processed and analyzed in order to increase the accuracy of the obtained results [1, 17, 22, 26, 27, 30–35].

Interference techniques can be classified into either two-beam interferometers such as Michelson, Mach-Zehnder, Pluta polarizing microscope, Lioyd's mirror, etc., or multiple-beam interferometers such as Fabry-Pérot and Fizeau interferometers [1, 3, 25, 36–39]. A two-beam interferometer produces a pattern of alternate bright and dark fringes of equal thicknesses when two beams, usually, of equal intensities Io suffering a relative phase difference δ are superposed. The resultant intensity distribution I of the interference pattern is given as:

$$I = I\_o \cos^2\left(\frac{\delta}{2}\right) \tag{1}$$

Multiple-beam interference takes place when light rays fall on two parallel optical plates enclosing a small distance between each other while their inner surfaces are highly reflecting and partially transparent. The intensities of both reflected, I (r), and transmitted, I (t), light distributions that are redistributed due to the multiple-beam interference are given as [40]

Optical Fibers Profiling Using Interferometric and Digital Holographic Methods DOI: http://dx.doi.org/10.5772/intechopen.91265

$$I^{(r)} = \frac{(2 - 2\cos\delta)R}{1 + R^2 - 2R\cos\delta} I^{(i)} \tag{2}$$

$$I^{(t)} = \frac{T^2}{1 + R^2 - 2R\cos\delta} I^{(i)} \tag{3}$$

where, I (i) is the intensity of the incident light,T and R are the products of the transmission and reflection coefficients of the two surfaces, respectively, while δ is the phase difference between any two consecutive interfered rays.

On the other hand, holography was firstly presented by Gabor in 1947 as a lensless process for image formation by reconstruction of wave-fronts [41–43]. It offers 3D characterization such as the depth of field from recording and reconstructing the whole optical wave field, intensity, and phase [41, 42]. Holographic interferometry is a non-destructive, contactless tool that can be used for measuring shapes, deformations and refractive index distributions [44, 45]. The modern digital holography was introduced in 1994 [46–48]. Moreover, the phase shifting interferometric (PSI) technique was introduced by Hariharan et al. as an accurate method for measuring interference fringes in the real time [49]. Recently, digital holographic phase shifting interferometry (DHPSI) was used to investigate some optical parameters of fibrous materials [17, 18, 21, 26–29].

#### 1.4 Digital holographic phase shifting interferometry (DHPSI)

In DHPSI, frequently a set of four [20] or five [23, 33] phase shifted holograms with known mutual phase shifts starting with 00 and having 900 separations have to be recorded [21]. These recorded holograms can be represented by:

$$I\_n = a(\zeta, \eta) + b(\zeta, \eta) \cos \left(\wp(\zeta, \eta) + \wp\_{\text{Ru}}\right), n = 1, 2, 3, \dots \tag{4}$$

where a(ζ, η) and b(ζ, η) are the additive and the multiplicative distortions and φRn is the phase shift of the reference wave. In case of four and five phase shifted holograms, the complex wavefield [37] in the hologram plane can be calculated using Eqs. (5) and (6), respectively.

$$h(\zeta,\eta) = [I\_1(\zeta,\eta) - I\_3(\zeta,\eta)] + i[I\_4(\zeta,\eta) - I\_2(\zeta,\eta)]\tag{5}$$

or,

$$h(\zeta,\eta) = \left[4I\_1(\zeta,\eta) - I\_2(\zeta,\eta) - 6I\_3(\zeta,\eta) - I\_4(\zeta,\eta) + 4I\_5(\zeta,\eta)\right] + i\left[7(I\_4(\zeta,\eta) - I\_2(\zeta,\eta))\right] \tag{6}$$

In digital holography, the recorded wavefield is reconstructed, based on Fresnel diffraction integral, by multiplying the stored hologram by the complex conjugate of the reference wave r\*(ζ, η) to calculate the diffraction field b'(x', y') in the image plane, see Figure 1. This can be calculated using the finite discrete form of the Fresnel approximation to the diffraction integral as:

$$b'(n\Delta\mathbf{x}', m\Delta\mathbf{y}') = \mathbf{A} \sum\_{j=0}^{N-1} \sum\_{l=0}^{M-1} h(j\Delta\zeta, l\Delta\eta) r^\* \left(j\Delta\zeta, l\Delta\eta\right)$$

$$\times \exp\left\{\frac{i\pi}{d'\lambda} \left(j^2 \Delta\zeta^2 + l^2 \Delta\eta^2\right)\right\} \exp\left\{2i\pi \left(\frac{jn}{N} + \frac{lm}{M}\right)\right\} \tag{7}$$

Figure 1. Geometry of digital holographic axes and the planes systems.

The parameters used in this formula depend on the used CCD array of N � M pixels and the pixel pitches Δζ and Δη. The distance between the hologram and the image plane is denoted by d'. The pixel spacings in the reconstructed field of image are:

$$
\Delta \mathbf{x}' = \frac{d'\lambda}{N\Delta \zeta} \text{ and } \Delta \mathbf{y}' = \frac{d'\lambda}{M\Delta \eta} \tag{8}
$$

The convolution of h(ζ, η)r\*(ζ, η) can be used as alternative of Fresnel approximation [37]. The resulting pixel spacing for this convolution approach is

$$
\Delta \mathfrak{x}' = \Delta \zeta \,\, \mathfrak{a} \\
\text{and } \Delta \mathfrak{y}' = \Delta \eta \,\, \tag{9}
$$

In addition, the phase shifted holograms are used to overcome the problems of the d.c. term and twin image, in which the calculated complex wavefield is used instead of a real hologram in the convolution approach.

The intensity and phase distributions in the reconstruction plane are given by

$$I(\mathbf{x}', \mathbf{y}') = \left| b'(\mathbf{x}', \mathbf{y}') \right|^2 \tag{10}$$

$$\rho(\mathbf{x}',\mathbf{y}') = \arctan\left\{\frac{\operatorname{Im}|b'(\mathbf{x}',\mathbf{y}')|}{\operatorname{Re}\left|b'(\mathbf{x}',\mathbf{y}')|}\right|\right\}\tag{11}$$

So, the optical phase differences due to phase objects can be extracted.

Mach-Zehnder interference-like system is used as a digital holographic setup as shown in Figure 2 [20, 23, 29, 33]. The optical waveguide sample, such as optical fiber, is immersed in a liquid of refractive index nL near or matching the cladding refractive index nclad of the sample. The interference patterns are recorded using a charge-coupled device, that is, CCD camera.

In this chapter, we illustrate some featured work on interferometric characterization (sometimes, implying digital holographic interferometry) of different optical fibers done by our research group during the last three decades. In Section 2, interferometric characterization of conventional step-index and GR-IN optical fibers is presented. Section 3 illustrates characterization of the conventional optical fibers when they are suffering mechanical bending. In Section 4, interferometric characterization of a special type of optical fibers called polarization maintaining (PM) optical fibers is presented. In the last section, we elucidate thick optical

Optical Fibers Profiling Using Interferometric and Digital Holographic Methods DOI: http://dx.doi.org/10.5772/intechopen.91265

#### Figure 2.

Mach-Zehnder digital holographic interferometric set-up, S F: spatial filter, L: collimating lens, BS: beam splitter, M: mirror, and MO: microscopic objective.

fibers and their interferometric characterization with a special interferometric system, developed in our laboratory, called lens-fiber interferometry (LFI).


**5. Conclusion**

In controlling and managing infectious diseases through social isolation, distancing or vaccination, the role of individual choice is becoming an increasingly important driver that subsequently affects underlying disease burden among the population. In particular, human behavior and social interactions played a significant role affecting the magnitude of the COVID-19 pandemic. Major factors behind such behavioral interactions are losing jobs and forgoing daily income from social distancing, fatigue from social isolation, and/or conscious or unconscious exploitation of uncertainty due to lack of awareness and knowledge. Thus, the dynamics of controlling infection through social isolation is a potentially complex interplay between individual behaviors and disease dynamics, informed by the perceived cost of being socially isolated and infection risks [69]. This complex interplay can be seen as a strategic game and is conveniently modeled and analyzed using the mathematical framework provided by Game Theory [70, 71]. Such behaviorprevalence game theoretical models have already explored vaccine exemption behavior for endemic diseases [72] but there is less emphasis on behavioral interactions like social distancing, especially analysis from the perspective of cultural dimensions of populations and also their socioeconomic conditions. The current study opens up a forum for further research on how individual choice, especially at the population level, is of utmost concern for public health policymakers to curb a pandemic.

Our model scenario highlights the interplay between economic impact and human choice in social distancing measures. Individuals with limited resources must choose between complying with public health guidance (a collectivist approach where personal actions can help the population) at the expense of losing income that is necessary for basic sustenance (an individualist approach). Changes in public policy are essential to combat the long-standing problems associated with health and economic inequities since these are more pronounced during a health care crisis, such as the COVID-19 pandemic.

To address these inequities there needs to be changes in public policy during inter-pandemic phases to ensure planning in place that is activated at the beginning of an outbreak. Policies should act to provide increased resilience and capacity at the beginning of an outbreak to minimize economic losses. Both the public and private sectors can put planning in place to reduce the magnitude of the economic disruption from NPI compliance in the workforce, supply chains, and healthcare system to prevent unforeseen economic crises.

It was suggested that sharing or pooling of available resources and networking can occur at several different levels including: the individual, household, local community, city, state or province, regional and national scale as a strategy to increase resilience and avoid negative mental health and economic outcomes [73].

Pandemic crises such as COVID-19 have particular characteristics within a complex system requiring a number of different types of resilience be addressed including population health resilience (the population recovering from the disease), healthcare system resilience (the recovery of the healthcare system), economic resilience (recovery from the economic consequences) and psychological resilience (individual recovery from fear, anxiety, depression) [74].

In the context of the COVID-19 pandemic drawing on the different types of resilience can reduce psychosocial effects such as depression, anxiety, stress and non-compliance to public health NPIs during curfew, self-isolation and lockdowns. Indeed, previous studies have shown that resilience decreases the negative effects of stress both at the individual and regional levels [75, 76].

*Human Cultural Dimensions and Behavior during COVID-19 Can Lead to Policy Resistance… DOI: http://dx.doi.org/10.5772/intechopen.96689*

The city, regional and country-level attention and support for designated essential workers is important to ensure that they are adequately equipped and compensated for vital services performed to maintain public health standards [74, 75, 77].


**Author details**

Adrian Suarez\*, Jorge Victoria, Jose Torres, Pedro A. Martinez, Andrea Amaro and Julio Martos School of Engineering of the University of Valencia, Valencia, Spain

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**"Initial attacks" of MMD** [12].

The research committee has identified nine types of initial episodes of MMD. Hemorrhagic type. Epileptic type. Infarction type (can be same side or alternating hemiplegia). TIA type. Frequent TIA type (two or more attacks per month). Headache type. Asymptomatic type. Other types. Details unknown type.