**Byungryul An**

Department of Civil Engineering, Sangmyung University, Cheonan 31066, Korea<EMAIL_ADDRESS>Tel.: +82-41-550-5497

Received: 29 July 2020; Accepted: 19 September 2020; Published: 21 September 2020

**Abstract:** Amino groups in the chitosan polymer play as a functional group for the removal of cations and anions depending on the degree of protonation, which is determined by the solution pH. A hydrogel beadlike porous adsorbent was used to investigate the functions and adsorption mechanism of the amino groups by removal of Cu(II) as a cation and As(V) as an anion for a single and mixed solution. The uptakes of Cu(II) and As(V) were 5.2 and 5.6 µmol/g for the single solution and 5.9 and 3.6 µmol/g for the mixed solution, respectively. The increased total capacity in the presence of both the cation and anion indicated that the amino group (NH2 or NH3 +) species was directly associated for adsorption. The application of a pseudo second-order (PSO) kinetic model was more suitable and resulted in an accurate correlation coefficient (R2 ) compared with the pseudo first-order (PFO) kinetic model for all experimental conditions. Due to poor linearization of the PFO reaction model, we attempted to divide it into two sections to improve the accuracy. Regardless of the model equation, the order of the rate constant was in the order of As(V)-single > Cu(II)-single > As(V)-mixed > Cu(II)-mixed. Also, the corresponding single solution and As(V) showed a higher adsorption rate. According to intraparticle and film diffusion applications displaying two linear lines and none passing through zero, the rate controlling step in the chitosan hydrogel bead was determined by both intraparticle and film diffusion.

**Keywords:** adsorption; amino group; kinetic; multifunction; cation; anion

### **1. Introduction**

The application of natural biopolymers, chitosan, has been reported for water and wastewater treatment as adsorbents owing to their natural abundance, nontoxicity, hydrophilicity, and biodegradability [1,2]. Chitosan has been utilized in various ways according to their required use, such as (nano)particles [3], beads [1], or membranes [4] in solution, and have demonstrated high effective removal efficiencies and uptakes for cations and anions. Similar to alginate, chitosan bead was formed by covalent crosslinking [5] and H-bonds interaction [6]. Chitosan exhibits the ability to remove cation heavy metals [7]. Moreover, chitosan is useful for removing anions, such as As(V), phosphate [8], dyes [9], and toluene [10]. This is because of the unique characteristic linked to the amino group. The removal by the amino group can be explained by Equation (1) for cations and Equation (2) for anions. The amino functional groups exist as NH3 + and NH2 via protonation and deprotonation, respectively, and these bind with an anion via electrostatic forces and with a cation heavy metal via surface complexation [11].

$$\text{R-NH}\_2 + \text{Cu(II)} \rightarrow \text{R-NH}\_2\text{Cu(II)} \text{ by coordination} \tag{1}$$

$$\text{R-NH}\_3\text{}^+ + \text{HAsO}\_4\text{}^{2-} \rightarrow \text{R-NH}\_3\text{}^+\\\text{HAsO}\_4\text{}^{2-} \text{ by electrostatic force} \tag{2}$$

The adsorption process in water and wastewater treatment has been recently regarded as one of the most effective technologies for pollutant removal from gas or liquid to solid media, owing to the flexible design and easy operation with high removal efficiency [12]. However, because of the presence of cation and anion pollutants in solution, each process was separately installed and operated as a dual or multiple column in the full process to remove cations and anions, respectively. For example, Cr(VI) and Cr(III) have been treated by anion exchange and cation exchange [13]. Otherwise, chemical modification involves preparing a multifunctional adsorbent by grafting [14], coating to inorganic or organic materials, or both [12,15]. Therefore, interest in the simultaneous removal of cations and anions has increased. The degree of protonation is defined by the pKa value, which usually ranges from 6.0 to 7.0 [11]. The preparation of a granular type is possible with a porous adsorbent, which leads to the application of an adsorption process [16]. Moreover, the chemical stability (insoluble chitosan) under acidic conditions is increased by the crosslinking reaction, resulting in an expansion of its use in the field of water and wastewater treatment [17].

Generally, for porous adsorbents, like granular activated carbon (GAC), the adsorption process occurs through four consecutive stages [18]. The migration of the molecular adsorbate is divided into (1) transferring from the bulk solution to a layer of thin film (bulk diffusion), which is excluded when three stages are described; (2) solute transport by diffusion through a liquid boundary layer to the external surface of the adsorbent; (3) penetration from the external (sorbent) surface of the sorbent to the intraparticle pores; and (4) adsorption on both the external and internal available surfaces via interactions of the solute. Because the first and fourth stages are relatively fast, the second and third stages determine the rate of the entire adsorption process, which is called the rate limiting or controlling step [19–21].

Several mathematical models have been introduced and reported to describe the optimized fitting of adsorption kinetics and diffusion. Based on these models, the models can be classified with adsorption reaction and adsorption diffusion [22]. The most common adsorption reaction models are the pseudo first-order (PFO) and pseudo second-order (PSO) models that describe the interaction of pollutants present as ions and molecules on the adsorbent surface, although sometimes erroneous overestimation of the rate constant occurs due to the underestimation of instantaneous driving force for sorption [23]. The nonlinear PFO (NPFO) and PSO (NPSO) should be linearized to calculate the parameters, which can be compared to determine the adsorption mechanism.

Although the adsorption reaction model provides adsorption information, such as uptake, rate, and nature of the adsorption, more valuable adsorption mechanism would be provided from an adsorption diffusion model that describes what controls the overall rate. The adsorption diffusion model has been classified as having two parts: an internal diffusion and external mass transfer model. The internal diffusion model was introduced by Crank [24], Weber and Morris [25], and Bangham [23]. Among the three internal diffusion models, the model by Weber and Morris is the simplest and is widely used to explain the adsorption mechanism. The external mass transfer model was proposed by Spahn and Schlunder (1975) [26] and Boyd (1947) [27].

Since the amino group interacts with Cu(II) and As(V) by coordination and electrostatic force, respectively, each interaction is influenced by the presence of counter-ion. Therefore, it is required to determine the effect of counter-ion on the adsorption characteristic. In the present study, the overall goal was to investigate the multifunctional behavior of the amino group and determine the adsorption rate and sorption mechanism of Cu(II) and As(V) on hydrogel chitosan beads in batch and kinetic sorption tests. The specific objectives were to (1) determine the removal efficiency of Cu(II) and As(V) under single and mixed conditions, (2) observe the sorption kinetics using PFO and PSO, (3) compare the linear and nonlinear models from the adsorption kinetics, and (4) investigate intraparticle and external mass transfer diffusion and the effect of the presence of a counter-ion.

#### **2. Material and Methods**

#### *2.1. Chemicals*

Chitosan was purchased from Sigma-Aldrich (St. Louis, MI, USA) as flakes with a medium molecular weight of ~250,000 g/mol and a 75–85% degree of deacetylation from chitin. The glutaraldehyde solution (25 wt.%) was obtained from SHOWA (Tokyo, Japan). The solution of Cu(II) and As(V) was prepared using CuCl2·2H2O and arsenate (NaHAsO4H2O) purchased from Sigma-Aldrich (St. Louis, MI, USA). All other chemicals, including HCl and NaOH, were purchased from Sigma-Aldrich (St. Louis, MI, USA) and were ACS grade and used without further purification.

#### *2.2. Preparation of Chitosan Beads*

Chemically stable chitosan beads were prepared in three steps [12,28]. Briefly, we first prepared a 2.5% chitosan solution using 1% HCl from chitosan flakes and beads of chitosan were prepared by dropping the chitosan solution in 1M NaOH during mild stirring, and crosslinking was achieved by adding chitosan beads to a 0.5 M glutaraldehyde (GA) solution. Finally, the cross-linked hydrogel chitosan beads were washed several times and stored in Deionized water (DI) until use.

#### *2.3. Batch Adsorption Tests*

A series of batch tests were performed to determine the uptake of Cu(II) and As(V) and determine the adsorption rate for the single and mixed solutions. The adsorption experiment to find the uptake of Cu(II) and As(V) was conducted with 0.15 ± 0.01 g of chitosan bead in 50 mL of 0.031 mmol/L for Cu(II) or 0.021 mmol/L for As(V) at single solution, and 0.032 mmol/L for Cu(II) and 0.027 mmol/L for As(V) at mixed solution. Each sample was rotated at 90 rpm for 48 h.

To determine the adsorption rate, batch kinetic tests were carried out in a 0.5 L glass bottle with 0.75 ± 0.015 g of chitosan bead, including 0.055 and 0.049 mmol/L of Cu(II) and As(V) at single solution, and 0.038 and 0.055 mmol/L of Cu(II) and As(V) at mixed solution, respectively. For both experiments, the initial solution pH of 5.0–5.5 for was adjusted with weak HCl or NaOH solution at a desired time. An aliquot of the sample solution was removed at desired intervals and stored in a refrigerator (4 ◦C). Cu(II) and As(V) in single and mixed solutions are referred to as Cu(II)-single and As(V)-single and Cu(II)-mixed and As(V)-mixed, respectively. The concentration of Cu(II) and As(V) were analyzed with ICP–OES (Prodigy, Reemanlabs, Mason, OH, USA). The uptake of adsorbate was calculated by

$$q\_l = \frac{(\mathcal{C}\_0 - \mathcal{C}\_l)V}{M} \tag{3}$$

#### *2.4. Adsorption Reaction Model*

The experimental data were analyzed by using the following four kinetic models.

#### 2.4.1. Pseudo First-Order Model

The assumption of pseudo first-order (PFO) is that (1) the rate of occupation of sorption sites is proportional to the number of unoccupied sites and (2) sorption only occurs on localized sites and there is no interaction between the sorbed ions, which correspond to the monolayer of adsorbates on the adsorbent surface [29,30]. The equation of PFO, which was first proposed by Lagergreen (1907), can be represented as follows [31]:

$$\frac{dq\_t}{dt} = K\_1(q\_\ell - q\_t) \tag{4}$$

#### 2.4.2. Pseudo Second-Order Model

The pseudo second-order (PSO) model was first described by Ho and McKay (1998) for the kinetic process of the adsorption of divalent metal ions onto peat [32]. While the PFO model estimated the initial stage of adsorption, the adsorption behavior was described during the entire range or final stage of adsorption process via the PSO model [18,33]. The following PSO Equation (5) can be presented:

$$\frac{dq\_t}{dt} = \mathcal{K}\_2(q\_\ell - q\_t)^2\tag{5}$$

#### *2.5. Adsorption Di*ff*usion Model*

#### 2.5.1. Intraparticle Diffusion

The intraparticle diffusion model, which considers pore diffusion, was developed and proposed by Weber and Morris (1963) as follows [25]:

$$q\_t = K\_i t^{0.5} + \mathbb{C} \tag{6}$$

The internal diffusion model assumes that internal diffusion of the adsorbate is the slowest step, resulting in the rate-controlling step during the adsorption process, and the adsorption is instantaneous [33].

#### 2.5.2. External Mass Transfer

To identify the external mass transfer, it is assumed that the diffusion of the adsorbate is controlled by the liquid film around the adsorbent. Spahn and Schlunder (1975) suggested the following equation [26]:

$$\mathbf{V}\frac{d\mathbf{C}}{dt} = -\mathbf{K}\_{\mathbf{s}}A(\mathbf{C} - \mathbf{C}\_{\mathbf{s}})\tag{7}$$

#### **3. Results and Discussion**

#### *3.1. Batch Test*

A series of batch tests was conducted for the single and mixed solutions of Cu(II) and As(V) at pH ~5.5, and the uptakes are shown in Figure 1. The individual uptakes were ~5.2–5.6 µmol/g for Cu(II) and As(V) for the single solution, indicating the difference of two uptakes is less than 10%. For the mixed solution, the uptakes for Cu(II) and As(V) were 3.6 (30% decrease) and 5.9 (5% increase) µmol/g, respectively, resulting in a total capacity increase to 9.4 µmol/g, which reached 87% of the sum of each ion. This increased total capacity of chitosan can be explained by the hybrid characteristic of the amino group in chitosan. The available sorption sites are separated for Cu(II) and As(V) based on Equations (1) and (2), respectively. As a result, Cu(II) and As(V) can independently interact with NH2 and NH3 +, respectively. However, Figure 2, calculated based on the Katchalsky equation [34], shows that over 80% of amino groups are protonated as NH3 + at a pKa of 6.0–7.0, which enhances the adsorption for As(V) via electrostatic forces. Theoretically, when the pKa (depending on the molecular weight and the degree of deacetylation) is estimated to be 5.5 for the inner chitosan polymer, the reaction is proportionally related to the presence of amino groups. Another assumption is additional association with Cu(II) except for the amino groups. Domard (1987) suggested that the oxygen on the OH group in the chitosan polymer also contributes to chelation with heavy cation metals [35].

**7**

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**single**

**Figure 1.** Removal uptake of Cu(II) and As(V) in a presence of single and/or mixed solution. **Figure 1.** Removal uptake of Cu(II) and As(V) in a presence of single and/or mixed solution.

**pH in solution 456789 Figure 2.** Degree of protonation (α) of chitosan as a function of pH and pKa. **Figure 2.** Degree of protonation (α) of chitosan as a function of pH and pKa.

#### *3.2. Removal E*ffi*ciency*

0.25–0.35 mm-sized chitosan [37].

0.25–0.35 mm-sized chitosan [37].

**Figure 2.** Degree of protonation (α) of chitosan as a function of pH and pKa. *3.2. Removal Efficiency*  The adsorption rates of Cu(II) and As(V) in the form of removal efficiency (%) vs. time (h) for single and mixed solutions are shown in Figure 3. The dash lines indicate the point of 90% uptake. For all cases, it is likely that the adsorption rate was initially faster and then became slower. This phenomenon may be governed by the chance of collision between ions and the adsorbent. At the beginning of the reaction, the higher concentration in the experiment led to surface adsorption with the higher driving force, which reduced the mass transfer resistance in the bulk and film layer [21]. Then, 90% of the sorption removal for Cu(II) was reached at 6 and 24 h, and for As(V), at 0.5 and 6 h for the single and mixed solutions, respectively. The sorption equilibrium for As(V) was more quickly *3.2. Removal Efficiency*  The adsorption rates of Cu(II) and As(V) in the form of removal efficiency (%) vs. time (h) for single and mixed solutions are shown in Figure 3. The dash lines indicate the point of 90% uptake. For all cases, it is likely that the adsorption rate was initially faster and then became slower. This phenomenon may be governed by the chance of collision between ions and the adsorbent. At the beginning of the reaction, the higher concentration in the experiment led to surface adsorption with the higher driving force, which reduced the mass transfer resistance in the bulk and film layer [21]. Then, 90% of the sorption removal for Cu(II) was reached at 6 and 24 h, and for As(V), at 0.5 and 6 h for the single and mixed solutions, respectively. The sorption equilibrium for As(V) was more quickly achieved than for Cu(II), and the single solution was at least four times faster than the mixed solution. The adsorption rates of Cu(II) and As(V) in the form of removal efficiency (%) vs. time (h) for single and mixed solutions are shown in Figure 3. The dash lines indicate the point of 90% uptake. For all cases, it is likely that the adsorption rate was initially faster and then became slower. This phenomenon may be governed by the chance of collision between ions and the adsorbent. At the beginning of the reaction, the higher concentration in the experiment led to surface adsorption with the higher driving force, which reduced the mass transfer resistance in the bulk and film layer [21]. Then, 90% of the sorption removal for Cu(II) was reached at 6 and 24 h, and for As(V), at 0.5 and 6 h for the single and mixed solutions, respectively. The sorption equilibrium for As(V) was more quickly achieved than for Cu(II), and the single solution was at least four times faster than the mixed solution. The relative long equilibrium of Cu(II) is similar to that in Kannamba et al. (2010) [36], who reported that 12 h is required for equilibrium using chitosan flakes. As(V) reached equilibrium at 5 h using 0.25–0.35 mm-sized chitosan [37].

was because of the competition derived from the increased mass of the adsorbate for the limited absorbable sites despite their separation. According to prior studies [38], increasing the initial

was because of the competition derived from the increased mass of the adsorbate for the limited absorbable sites despite their separation. According to prior studies [38], increasing the initial

concentration retards the equilibrium despite the higher concentration gradient.

concentration retards the equilibrium despite the higher concentration gradient.

A longer equilibrium time was required for the mixed solution regardless of the species. This

achieved than for Cu(II), and the single solution was at least four times faster than the mixed solution. The relative long equilibrium of Cu(II) is similar to that in Kannamba et al. (2010) [36], who reported

The relative long equilibrium of Cu(II) is similar to that in Kannamba et al. (2010) [36], who reported that 12 h is required for equilibrium using chitosan flakes. As(V) reached equilibrium at 5 h using

**Figure 3.** Removal efficiency (%) of Cu(II) and As(V) in the presence of single and/or mixed solutions (dash lines indicate 90% removal efficiency (%), and the dotted line for Cu(II)-single and Cu(II)-mixed were very close). **Figure 3.** Removal efficiency (%) of Cu(II) and As(V) in the presence of single and/or mixed solutions (dash lines indicate 90% removal efficiency (%), and the dotted line for Cu(II)-single and Cu(II)-mixed were very close).

*3.3. Adsorption Reaction Kinetics*  3.3.1. Nonlinear Pseudo First-Order (PFO) and Second-Order (PSO) Models A longer equilibrium time was required for the mixed solution regardless of the species. This was because of the competition derived from the increased mass of the adsorbate for the limited absorbable sites despite their separation. According to prior studies [38], increasing the initial concentration retards the equilibrium despite the higher concentration gradient.

#### From Equation (4) for PFO, the integrating form for the boundary conditions of *t = 0* to *t = t* and *3.3. Adsorption Reaction Kinetics*

single, Cu(II)-mixed, and As(V)-mixed, respectively.

௧ = {1 − [

#### for *qt = 0* to *qe = qt* is expressed as nonlinear (Equation (8)). 3.3.1. Nonlinear Pseudo First-Order (PFO) and Second-Order (PSO) Models

௧ = (1−ିభ௧) (8) From Equation (4) for PFO, the integrating form for the boundary conditions of *t* = 0 to *t* = *t* and for *qt* = 0 to *qe* = *qt* is expressed as nonlinear (Equation (8)).

$$q\_t = q\_t \{ 1 - e^{-K\_1 t} \} \tag{8}$$

௧ = ଶ 1+ଶ ଶ (9) After separating the variables from Equation (5) for PSO and by applying the boundary condition (*t* = 0 to *t* = *t* and *qt* = 0 to *qe* = *qt*), the nonlinear PSO follows Equation (9).

$$q\_t = \frac{K\_2 q\_t t}{1 + K\_2 q\_c^2 t} \tag{9}$$

comparing the R2, although both NPFO and NPSO showed good agreement (>0.94), except for Cu(II) mixed, NPSO achieved higher accuracy for all experimental conditions. Additionally, the qecal. was closer to qeexp. for NPSO than for NPFO. As shown in Figure 4, the simulated NPFO line did not likely represent a sudden change in qe at 3–6 h leading to a lower R2. Moreover, the pseudo n order equation, Equation (10), based on adsorption capacity was simulated to determine the accurate kinetic order (n) [39]. The calculated n values were 1.98, 2.01, 1.84, and 1.98 for Cu(II)-single, As(V)- Nonlinear PFO (NPFO) and nonlinear PSO (NPSO) were modeled using Equations (8) and (9), and the calculated variables, including qecal., K, and R2 , are listed in Figure 4 and Table 1. Upon comparing the R2 , although both NPFO and NPSO showed good agreement (>0.94), except for Cu(II)-mixed, NPSO achieved higher accuracy for all experimental conditions. Additionally, the qecal. was closer to qeexp. for NPSO than for NPFO. As shown in Figure 4, the simulated NPFO line did not likely represent a sudden change in qe at 3–6 h leading to a lower R2 . Moreover, the pseudo n order equation, Equation (10), based on adsorption capacity was

kinetic model with a higher applicability for PSO, indicating that the adsorption process is mainly associated with chemisorption [18,41], whereas PFO proceeds by diffusion through a boundary [22].

1

ିଵ) ] ଵ

ିଵ} (10)

(1+(−1)

PNO (a)

3.3.2. Linear PFO and PSO

(12), respectively.

simulated to determine the accurate kinetic order (n) [39]. The calculated n values were 1.98, 2.01, 1.84, and 1.98 for Cu(II)-single, As(V)-single, Cu(II)-mixed, and As(V)-mixed, respectively.

$$q\_t = q\_\varepsilon \{ 1 - \left[ \frac{1}{\left( 1 + K\_n(n-1) q\_\varepsilon^{n-1} t \right)} \right]^{\frac{1}{n-1}} \} \tag{10}$$

Although methodological bias is introduced for PSO [40], the NPSO is the more appropriate kinetic model with a higher applicability for PSO, indicating that the adsorption process is mainly associated with chemisorption [18,41], whereas PFO proceeds by diffusion through a boundary [22]. *Processes* **2020**, *8*, x FOR PEER REVIEW 7 of 15

**Figure 4.** Nonlinear pseudo first-order (dots) and second-order (solid lines) kinetic models. **Figure 4.** Nonlinear pseudo first-order (dots) and second-order (solid lines) kinetic models.

The rate constant K was considered to determine the adsorption equilibrium [42]. Both absolute **Table 1.** Kinetic parameters using nonlinear PFO and PSO models for Cu(II) and As(V) in single and mixed solutions.

qecal. (c) 1.64 1.07 2.30 2.62 (a) indicates pseudo n order. (b) indicates experimental qe. (c) indicates calculated qe.

PFO K 2.16 0.187 3.96 2.10 R2 0.969 0.819 0.997 0.942 PSO qecal. 1.71 1.17 2.34 2.74 K 2.19 0.258 5.28 1.28 The rate constant K was considered to determine the adsorption equilibrium [42]. Both absolute K1 and K2 are in the order of As(V)-single > Cu(II)-single > As(V)-mixed > Cu(II)-mixed. This observation shows that a higher K was calculated for the single solution and As(V), regardless of the kinetic model.

R2 0.986 0.891 0.999 0.979

qecal. 1.45 0.813 2.03 2.32 K 15.7 4.04 36.3 12.4

Linearization of PFO and PSO was attempted to determine the best-fitting model by minimizing

Equations (11) and (12) are frequently applied to fit experiment data instead of nonlinear PFO

and PSO models. The calculated parameters are listed in Table 2 and shown in Figures 5 and 6 for LPFO and LPSO, respectively. Note that Equation (12) is one of the linearized PSO expressions among the five equations derived from Equation (9) and is the most valid and widely used expression,

1

ln( − ௧) = ln − ଵ (11)

(12)

the error distribution between experimental and predicted values. Linear PFO (LPFO) and PSO (LPSO) were derived from Equations (8) and (9), respectively, and expressed by Equations (11) and

> ௧

=1 ଶ ଶ +

As(V)

mixed 2.92 0.959 0.07

#### 3.3.2. Linear PFO and PSO

Linearization of PFO and PSO was attempted to determine the best-fitting model by minimizing the error distribution between experimental and predicted values. Linear PFO (LPFO) and PSO (LPSO) were derived from Equations (8) and (9), respectively, and expressed by Equations (11) and (12), respectively.

$$\ln(q\_{\ell} - q\_{t}) = \ln q\_{\ell} - K\_{1}t \tag{11}$$

$$\frac{t}{q\_t} = \frac{1}{\mathcal{K}\_2 q\_\varepsilon^2} + \frac{1}{q\_\varepsilon} t \tag{12}$$

Equations (11) and (12) are frequently applied to fit experiment data instead of nonlinear PFO and PSO models. The calculated parameters are listed in Table 2 and shown in Figures 5 and 6 for LPFO and LPSO, respectively. Note that Equation (12) is one of the linearized PSO expressions among the five equations derived from Equation (9) and is the most valid and widely used expression, showing the highest R2 and similarity between qeexp. and qecal. [43,44]. Our study also obtained the most valid R 2 from Equation (12) (data not shown).

**Table 2.** Kinetic parameters for the linear PFO and PSO models for Cu(II) and As(V) in single and mixed solutions.

**Figure 5.** Linear PFO model for (**a**) Cu(II) and (**b**) As(V) with two sections. **Figure 5.** Linear PFO model for (**a**) Cu(II) and (**b**) As(V) with two sections.

**Table 2.** Kinetic parameters for the linear PFO and PSO models for Cu(II) and As(V) in single and mixed solutions.  **PFO PSO Without Separation t ≤ 1 t ≥ 3 qeexp. qe-cal. K1 R2 qecal. K1 R2 qe-cal. K1 R2 qecal. K2 R2** Cu(II)s ingle 1.85 0.529 0.04 370.526 1.69 1.38 0.952 0.268 0.0231 0.514 1.75 5.37 0.999 Cu(II) mixed 1.25 1.03 0.10 20.990 1.17 0.489 0.979 1.11 0.104 0.992 1.23 1.86 0.981 As(V)s ingle 2.4 0.259 0.03 71 0.240 1.95 2.39 0.918 0.0816 0.0021 20.0129 2.32 12.5 0.999 Table 2 and Figures 5 and 6 clearly verify the effect of linearization. While the fitting accuracy by R2 increased from 0.891 to 0.981 for Cu(II)-mixed and others were enhanced to 0.999 for LPSO, the accuracy decreased from ~0.9 to 0.526 and 0.240 for Cu(II)-single and As(V)-single, respectively, for the LPFO model. Similarly, poor values from linearization were reported in several studies [45,46]. Attempts have been made to overcome the poor relation for the LPFO model. Na and Park (2011) [47] and Simonin (2016) [40] separated the times in terms of the initial from the other times. Both results show that the calculated parameters at the initial time can be relatively compared to the nonlinear equation and PSO; however, the rest of the region should be ignored, indicating that a different adsorption mechanism is expected [47]. In this study, Figure 5 is shown with two regions and Table 2 includes the separate parameters clarified from previous studies. For the condition of 1 ≤ h, the R2 and qecal. are comparable to NLFO, but at 3 ≥ h, both R2 and qecal. were not comparable Thus,

accuracy decreased from ~0.9 to 0.526 and 0.240 for Cu(II)-single and As(V)-single, respectively, for the LPFO model. Similarly, poor values from linearization were reported in several studies [45,46]. Attempts have been made to overcome the poor relation for the LPFO model. Na and Park (2011) [47] and Simonin (2016) [40] separated the times in terms of the initial from the other times. Both results show that the calculated parameters at the initial time can be relatively compared to the nonlinear equation and PSO; however, the rest of the region should be ignored, indicating that a different adsorption mechanism is expected [47]. In this study, Figure 5 is shown with two regions and Table 2 includes the separate parameters clarified from previous studies. For the condition of 1 ≤ h, the R2 and qecal. are comparable to NLFO, but at 3 ≥ h, both R2 and qecal. were not comparable Thus, we concluded that linearization of the PFO model included unreasonable data that were influenced by

the failure to represent the kinetics and limited by the initial adsorption time [40].

000.820 2.54 1.21 0.860 0.552 0.0530 0.954 2.89 24.2 0.999

we concluded that linearization of the PFO model included unreasonable data that were influenced by the failure to represent the kinetics and limited by the initial adsorption time [40].

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**Figure 6.** Linear PSO model for Cu(II) and As(V). **Figure 6.** Linear PSO model for Cu(II) and As(V).

Upon comparison of nonlinear and linear PFO and PSO models, the highest R2 was acquired for LPSO followed by NPSO and NPFO, and the relative lowest was for the LPFO model. This trend is the same as shown by Kumar (2006), who studied methylene blue on activated carbon [44]. Moreover, the qecal. was compared with qeexp. for all conditions. Based on the results of comparison with R2 and qe-cal., to estimate the appropriate parameters, the nonlinear application was more suitable and acceptable in this study. Although the comparison of R2 cannot describe a sufficient criterion [48], Upon comparison of nonlinear and linear PFO and PSO models, the highest R2 was acquired for LPSO followed by NPSO and NPFO, and the relative lowest was for the LPFO model. This trend is the same as shown by Kumar (2006), who studied methylene blue on activated carbon [44]. Moreover, the qecal. was compared with qeexp. for all conditions. Based on the results of comparison with R 2 and qe-cal., to estimate the appropriate parameters, the nonlinear application was more suitable and acceptable in this study. Although the comparison of R2 cannot describe a sufficient criterion [48], Simonin (2016) suggested that the PSO model better describes a diffusion-controlled process than the PFO model [40].

Simonin (2016) suggested that the PSO model better describes a diffusion-controlled process than the PFO model [40]. Before discussing the value of K, the effect of the initial concentration should be first considered because a higher initial concentration contributes to a higher concentration gradient, which can enhance the initial sorption rate and require more time to reach equilibrium [49]. However, it can cause a decrease in the entire sorption rate due to the higher competition between adsorbate and adsorbent active sites [50]. In this study, Cu(II) and As(V) in the single and mixed solutions were 0.055 and 0.049 mmol/L and 0.038 and 0.055 mmol/L, respectively, where ~31% of the Cu(II) concentration was decreased in the mixed solution and a ~l0% difference in concentration between Cu(II) and As(V) in single solution was found. According to the K1 and K2 from NPFO, NPSO, and Before discussing the value of K, the effect of the initial concentration should be first considered because a higher initial concentration contributes to a higher concentration gradient, which can enhance the initial sorption rate and require more time to reach equilibrium [49]. However, it can cause a decrease in the entire sorption rate due to the higher competition between adsorbate and adsorbent active sites [50]. In this study, Cu(II) and As(V) in the single and mixed solutions were 0.055 and 0.049 mmol/L and 0.038 and 0.055 mmol/L, respectively, where ~31% of the Cu(II) concentration was decreased in the mixed solution and a ~l0% difference in concentration between Cu(II) and As(V) in single solution was found. According to the K1 and K2 from NPFO, NPSO, and LPSO (LPFO was excluded), the K value was in the following order regardless of kinetic model: As(V)-single > Cu(II)-single > As(V)-mixed >> Cu(II)-mixed. The effect of the initial concentration difference for Cu(II) was negligible because the K value for Cu(II)-mixed showed 3–11 times lower than for other conditions, although the concentration of Cu(II) in the mixed solution showed a 31% decrease.

LPSO (LPFO was excluded), the K value was in the following order regardless of kinetic model: As(V)-single > Cu(II)-single > As(V)-mixed >> Cu(II)-mixed. The effect of the initial concentration difference for Cu(II) was negligible because the K value for Cu(II)-mixed showed 3–11 times lower than for other conditions, although the concentration of Cu(II) in the mixed solution showed a 31% decrease. Based on the order of K, As(V) and single solution had higher rate constants than Cu(II) and the mixed solution. Generally, the value of K increased with increased initial concentration as chemical conditions [49] and shaking speed as physical conditions [50]. The phenomena indicate that for the mixed condition, As(V) and Cu(II) competed with each other at limited activated sites, and the rate declined. As mentioned in Section 3.1, the functional group in chitosan, the amino group, is present in the form of NH2 or NH3 + depending on the solution pH, and it has been suggested that

electrostatic forces initially occurred and led to the rapid adsorption rate of As(V).

mixed condition, As(V) and Cu(II) competed with each other at limited activated sites, and the rate declined. As mentioned in Section 3.1, the functional group in chitosan, the amino group, is present in the form of NH2 or NH3+ depending on the solution pH, and it has been suggested that Cu(II) can coordinate with NH2 and As(V) can react with NH3+ via electrostatic forces [15]. Therefore,

Based on the order of K, As(V) and single solution had higher rate constants than Cu(II) and the

Cu(II) can coordinate with NH2 and As(V) can react with NH3 + via electrostatic forces [15]. Therefore, electrostatic forces initially occurred and led to the rapid adsorption rate of As(V).

#### *3.4. Di*ff*usion Model*

Both diffusion and adsorption on the activated site were influenced by the surface area, the reactivity of the surface, and liquid film thickness for external diffusion, and by the reaction of surface and pore structure for internal diffusion. Generally, film diffusion is controlled in a specific system followed at poor mixing, low concentration, small particle sizes of the adsorbent, and a higher affinity of the adsorbate for the adsorbent; whereas, good mixing, larger particle size of the adsorbent, high concentration of adsorbate, and a low affinity of the adsorbate for the adsorbent are controlled at intraparticle diffusion [21,49,51].

#### 3.4.1. Intraparticle Diffusion

A plot of adsorbate uptake versus the square root of time (t0.5) is shown in Figure 7, based on Equation (6), and some calculated parameters are listed in Table 3. There are theoretical interpretations of the intraparticle diffusion equation. C is an arbitrary constant representing the boundary layer thickness, and a larger value of C represents a thicker boundary layer [18,21,52]. If the value of C is zero, which corresponds to no boundary layer, the linear line should pass through the origin. Consequently, film diffusion could be ignored due to no or less thickness, and thus, intraparticle diffusion would remain as the rate-controlling step through the entire adsorption kinetic process. However, this is only the theoretical explanation using Equation (6). Many studies have reported nonzero intercepts, indicating that the rate-limiting step involves both intraparticle and film diffusion in most adsorption processes. As shown in Figure 7 and Table 3, owing to the very low R2 with the single regression line, it is unable to predict one linear line for the experiment data; instead, it could be divided with two different segments for all cases. Thus, Figure 7 was segregated with two linear regressions, illustrating that both film and intraparticle diffusion control the adsorption diffusion. Moreover, another study [53] suggested that intraparticle diffusion has three regressions in the macro, meso, and micro pores with a horizontal line as the equilibrium. Another study observed three linear regions, including initially rapid surface loading, then pore diffusion, and finally horizontal equilibrium [21]. However, it was not easy to differentiate three or four regions in this study. Consequently, the two linear regions were primarily observed and clearly explained by the adsorption process between the film and intraparticle diffusion [49,54]. Additionally, the initial rapid increase was represented by film diffusion [21,55]. As a result, two linear lines and the value of C (*y*-axis) both revealed diffusion was coincidentally involved in the adsorption of Cu(II) and As(V).

Some observations were made based on the data in Table 2. The highest and lowest values of Ki were for As(V)-single and Cu(II)-mixed in the first regression line, respectively, and the order was reversed to Cu(II)-mixed and As(V)-single in the second line. As mentioned regarding the first rapid adsorption related to film diffusion, the higher Ki1 for As(V) compared with that of Cu(II) indicated a higher surface adsorption reaction with an active site leading to an electrostatic force between NH3 + and HAsO4 −. According to the assumption where close to the zero of slope (K) in the second section represents intraparticle diffusion, which stands for the equilibrium process [21], the single solutions of Cu(II) and As(V) quickly accomplished equilibrium, whereas mixed solutions required more time to reach equilibrium, which was the same result as with the PSO model.

The order of C was As(V), As(V)-Cu(II), Cu(II), and Cu(II)-As(V) regardless of separation, which is related to boundary diffusion or surface adsorption. As a result, the higher C for As(V) demonstrated that As(V) removal was primarily associated with surface adsorption [22].

The order of C was As(V), As(V)-Cu(II), Cu(II), and Cu(II)-As(V) regardless of separation, which

is related to boundary diffusion or surface adsorption. As a result, the higher C for As(V)

**Figure 7.** Weber and Morris intraparticle diffusion model with two sections. **Figure 7.** Weber and Morris intraparticle diffusion model with two sections.

3.4.2. External Mass Transfer **Table 3.** Diffusion parameters for the Weber and Morris intraparticle model for Cu(II) and As(V) in single and mixed solutions.

#### through zero regardless of Ks, A, or V, it suggests that film diffusion governs the adsorption kinetic 3.4.2. External Mass Transfer

process [56]. To carefully predict the external diffusion, Figure 8a was segregated with two regions in Figure 8b, and then showed that the slope sharply increased at the beginning (≤ 1 h) and gradually To identify the external mass transfer, Equation (7) was expressed with Equation (13) by plotting the values between Ct /Co and ln (Ct /Co), respectively.

Like the intraparticle diffusion model, when the plot using Equation (13) is linear and passes

$$-\ln\_{C\_0}^{C\_t} = K\_s \frac{A}{V} t \tag{13}$$

similar to Figure 5 in the current study. The rapid slope (Ks∙A/V) of the regression line indicates that external mass transfer occurred [57], and the lowest slope for the Cu(II) mixed solution can be assumed to be relatively associated with a strong effect of intraparticle diffusion. Therefore, both intraparticle and external diffusion simultaneously influence the adsorption kinetics. Like the intraparticle diffusion model, when the plot using Equation (13) is linear and passes through zero regardless of Ks, A, or V, it suggests that film diffusion governs the adsorption kinetic process [56]. To carefully predict the external diffusion, Figure 8a was segregated with two regions in Figure 8b, and then showed that the slope sharply increased at the beginning (≤ 1 h) and gradually decreased after 1 h, which is a similar trend as that found in a prior study [47]. Note that the simplified Equation (13) is comparable with the LPFO model in Equation (9); additionally, Figure 8 is very similar to Figure 5 in the current study. The rapid slope (Ks·A/V) of the regression line indicates that external mass transfer occurred [57], and the lowest slope for the Cu(II) mixed solution can be assumed to be relatively associated with a strong effect of intraparticle diffusion. Therefore, both intraparticle and external diffusion simultaneously influence the adsorption kinetics.

**Figure 8.** Spahn and Schlunder film diffusion model shown for (**a**) the entire experimental time scale **Figure 8.** Spahn and Schlunder film diffusion model shown for (**a**) the entire experimental time scale and (**b**) less than 2 h.

#### and (**b**) less than 2 h. **4. Conclusions**

**4. Conclusions**  In this study, we described the interaction between the amino group in the chitosan polymer and the cation of Cu(II) and anion of As(V) to determine the adsorption mechanism. Additionally, the effect of each ion on adsorption was also studied for single and mixed solutions. According to the increased capacity of the Cu(II) and As(V) mixed solution, the amino group in chitosan was in the form of NH2 and NH3+, and each functional group directly participated in the removal of the anion and cation, respectively. The PSO model is more suitable than PFO, indicating that surface chemisorption occurred primarily for both Cu(II) and As(V) adsorption. Because the linearization of PFO expanded the error, lowering the correlation coefficient (R2), two segregation regression lines In this study, we described the interaction between the amino group in the chitosan polymer and the cation of Cu(II) and anion of As(V) to determine the adsorption mechanism. Additionally, the effect of each ion on adsorption was also studied for single and mixed solutions. According to the increased capacity of the Cu(II) and As(V) mixed solution, the amino group in chitosan was in the form of NH2 and NH3 +, and each functional group directly participated in the removal of the anion and cation, respectively. The PSO model is more suitable than PFO, indicating that surface chemisorption occurred primarily for both Cu(II) and As(V) adsorption. Because the linearization of PFO expanded the error, lowering the correlation coefficient (R2 ), two segregation regression lines dependent on time were effective at describing the adsorption. Because the two types of diffusion models showed no passing through of the zero regression line, both intraparticle and film diffusion were considered as rate-controlling steps for Cu(II) and As(V) adsorption. A higher rate constant (K) for As(V) than for Cu(II) was obtained from the electrostatic interaction between NH3 + and HAsO4 2−.

dependent on time were effective at describing the adsorption. Because the two types of diffusion models showed no passing through of the zero regression line, both intraparticle and film diffusion were considered as rate-controlling steps for Cu(II) and As(V) adsorption. A higher rate constant (K) **Funding:** This research was supported by the National Research Foundation of Korea (NRF-2019R1A2C1009129). **Conflicts of Interest:** The author declares no conflict of interest.

#### for As(V) than for Cu(II) was obtained from the electrostatic interaction between NH3+ and HAsO42−. **Nomenclature**

	- *Ct* Liquid phase concentration at any time, mg/L
	- *K*2 Sorption rate constant (g/mg·min) at PSO
	- *Kn* n order diffusion rate constant

*C0* Initial concentration in the solution, mg/L *Ct* Liquid phase concentration at any time, mg/L

*Ks* The liquid-film mass transfer coefficient (m2/s)

*qe* Adsorbate uptake at equilibrium (mg/g) *qt* Adsorbate uptake at any time (mg/g)

*K1* Sorption rate constant (min−1) at PFO

*Kn* n order diffusion rate constant

*M* Mass of adsorbent (g)

*n* Kinetic order


**4. Discussion**

The use of anti-gene therapy was also effective in another AS approach, targeted toward the molecule, TGF-beta [79]. TGF-beta2 plays a role in tumor progression by regulating key mechanisms including proliferation, metastasis, and angiogenesis. The approach of AS TGF-beta using an AS oligodeoxynucleotide—compound AP 12009, has given satisfactory results [122–124]. AP-12009 treatment was well tolerated and tumor response has been observed [123]. Two patients experienced long-lasting complete tumor remission [124]. In another clinical AS TGF-beta study, a phase I clinical trial in grade IV astrocytoma (GBM) was performed using autologous tumor cells modified by a AS TGF-beta2 vector [79]. There were indications of humoral and cellular immunity induced by the vaccine [57, 79].

As far as AS TGF-beta therapy of GBM is concerned, the treatment of patients with recurrent or refractory malignant (high-grade) glioma, WHO grade III or IV has been shown to produce results, similar to results obtained with anti IGF-I treatment. The role of peripheral blood mononuclear cells in the immune antitumor response, and prolonged survival in both anti TGF beta and anti IGF-I approaches was comparatively examined. In the case of TGF-beta, it is also important to mention, that although the first successful clinical results were published in 2006–2008, the solid experimental data, using AS technology were obtained in 1994/95 [79], which means, that long periods of research on the mechanism of AS TGF-beta and AS IGF I are required before achieving significant clinical results, confirming the usefulness of gene therapy in cancer treatment [90, 122, 123, 125–130].

In AS IGF-I or AS TGF-beta approaches, immune antitumor response, mediated by TCD8 and APC cells, was signaled as a principal mechanism of AS technology inhibiting growth factors and their signaling pathway [12, 79]. These cells being involved in HS (heat shock) protein mechanism, the inhibition of HS was introduced in clinical trials as a new direction for cancer therapy [131].

More recent clinical successful strategies of gliomas treatment, generally as a combination therapy using different types of inhibitors (i.e., imatinib, gefitinib) including antibodies (i.e., avastin) targeting growth factors and their receptors [132–136], are now focusing on anti-gene strategy, especially on antisense or triple helix technologies used alone or combined also with pharmacological treatment. As far as antisense and triple helix strategies are compared, the triple helix blockade of

**65**

provided the original work is properly cited.

1 Laboratories Legrand, Bogota, Colombia

2 ICGT–International Cancer Gene Therapy, Bogota, Colombia

3 ICGT–International Cancer Gene Therapy, Paris, France

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 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,

*Oncoproteins Targeting: Antibodies, Antisense, Triple-helix. Case of Anti IGF-I Cancer…*

on translation (AS) and transcription (TH) levels, respectively.

growth factor has given better results *in vitro* and *in vivo*. But combining both strategies, the final result is most effective—stopping the expression of the oncoproteins

Among the new strategies in the efforts to successfully treat GBM, the use of AS approach targeting IGF-I, TGF-beta or VEGF, their receptors and their downstream transduction signaling elements [9, 137], appears to offer a promising solution. The final result of the signal transduction pathway element inhibition is an immune response mediated *in vivo* by lymphocytes T CD8 and APC cells. Using cancer immunogene therapy of anti-gene anti IGF-I approach, the median survival of treated glioblastoma patients has reached 22–24 months. But the near future in treating this group of disorders belongs to a combination of treatment: classical surgery, radiotherapy with immunotherapy, pharmacologic therapy, growth factor inhibitors, and the use of the antisense/triple helix gene blockade approach targeting signal transduction pathway elements of cancer processes [16, 22, 23, 36, 66, 90,

*DOI: http://dx.doi.org/10.5772/intechopen.82548*

**5. Conclusion**

125, 138–147].

**Conflict of interest**

**Author details**

Jerzy Trojan1,2,3,4\*

4 INSERM, Paris, France

No conflict of interest.

*Oncoproteins Targeting: Antibodies, Antisense, Triple-helix. Case of Anti IGF-I Cancer… DOI: http://dx.doi.org/10.5772/intechopen.82548*

growth factor has given better results *in vitro* and *in vivo*. But combining both strategies, the final result is most effective—stopping the expression of the oncoproteins on translation (AS) and transcription (TH) levels, respectively.


**1. Introduction**

The creativity and innovation are essential elements which bloom the Muslim and Hijab fashion industry which is becoming a lucrative and a productive industry worldwide, in particular, Malaysia [1]. The emergence of Muslim fashion industry has been a product or outcome of the rise of the Islamic Resurgence in Malaysia since 1970s. The recent growth of the Muslim Hijab fashion industry is fundamentally associated with the rise of Islamic awareness on the Muslim-Shariah compliance in Muslim-majority countries, not the rise of capitalism as claimed by Farhad [2]. Inside the booming of Muslim fashion industry, the global fashion industry is now recognising the credibility of Muslim designers with Islamic faith to reflect the dynamic of Muslim fashion industry.

Such phenomenon can be seen not only in the Muslim countries like Malaysia, but also in Europe. What's long been referred to as the "modest" fashion of Muslim women has grown into a major industry generating hundreds of billions of dollars annually in the United States of America (US). For the first time, U.S. museum exhibited the Islamic culture within the context of fashion and it was opened in San Francisco's de Young Museum in 2018. Jeffrey Brown [3] reports on why Muslim fashion that has previously been ignored is now gaining new attention. "It's a fashion show in a museum, with an unusual focus. This is an exhibition of contemporary Muslim fashion, the first of its kind in the U.S., put together by the de Young Museum in San Francisco".

The hijab fashion industry can be considered as the fastest growing consumer segment in the world and is viewed by designers and entrepreneurs as a critically significant segment in Malaysia [1]. Therefore, this paper aims to explore the contributing factors which lead to the involvement of the Malay Muslim women entrepreneurs in the Muslim fashion industry within the boundaries of Islamic principles. This paper also discusses the importance of creativity and innovation in the pursuit of the Muslimah fashion industry among the Malay Muslim women entrepreneurs of the middle class in Selangor. Additionally, this study also analyses the element of creativity, innovation and Shariah compliance in the Muslimah and hijab fashion trends among Malay women entrepreneurs in Selangor, an urban state of Malaysia.


*3.5. Laser Scanning Microscopy (LSM)*

LSM recordings of a collagen–elastin hydrogel with primary collagen antibody staining support the above claims of lateral collagen–elastin polymerization (Figure 5). The left image (Figure 5a) represents an exemplary primary collagen antibody-stained sample. In total, images from seven random positions were recorded which all displayed the discussed features.

**Figure 4.** (**a**) Polar plot of the 2D anisotropy analysis of each 10 random positions in one 2 mg/mL collagen network and one collagen–elastin (33.3 w% elastin) network. The coordinates refer to the determined angle (polar angle) and the standard deviation (radial length) while the circles refer to the median values of the distributions of the standard deviations, i.e., elastin increases the standard deviation of the network and, thereby, the 2D anisotropy. (**b**) Comparison between the standard deviation of the angle distribution of the samples already displayed in Figure 4. Significance was tested with the Mann–Whitney U test. This standard deviation is referred to as "2D anisotropy".

**Figure 5.** Fluorescence images of a 33.3 w% elastin–collagen gel. (**a**) collagen type I antibody and (**b**) Col-F. The dots in each image are most likely clustered elastin–collagen monomers.

The right image (Figure 5b) shows all the network features (collagen + elastin). A comparison with the primary collagen antibody-stained image reveals identical features in both images while any observable differences must be attributed to the brightness thresholding of the image software. The left image contains the well-known features of the R + G collagen mixture, namely, the nodes and fibers, while the right image does not convey any additional structural features; therefore, a lateral alignment of the elastin and collagen monomers appears likely. This is further plausible, given the architecture of the elastic fiber under physiological conditions. Elastin is synthesized in the endoplasmic reticulum and then transported outside of a cell by binding to an elastin-binding protein. Upon binding of this protein to the galactosugars of micro fibrils outside of a cell, elastin is released from the elastin-binding protein and interacts then with the microfibrils. Elastin is then incorporated in a complex way into the microfibrils resulting finally in a fiber which contains elastin on the inside and a microfibrillar shell outside [15]. Basically, elastin needs a scaffold to be deposited on and several proteins of the fibrillin class as well as MAGP-1 where it is shown to interact with elastin [34]. Furthermore, although the elastin–fibrillin interaction is very complex, it has been shown that elastin binds to a glycine and proline-rich region in fibrillin-2 [35]. Consequently, some homology to collagen is given, which lends credibility to the elastin–collagen interaction as seen in the above described LSM recordings; however, other proteins such as fibulin-5 are also central to elastic fiber formation [36]. The list of important proteins continues and their non-existence in our system may be a likely explanation for the lack of formation of distinct elastic fibers. This consideration hardens our claim of a lateral, or at least, homogenous incorporation of elastin into collagen fibers, as elastin monomers simply do not experience guidance and as such are subjected to following collagen fibrillogenesis.

The image in Figure 5 contains the clustered collagen and elastin which was already discussed in the section, "plate reader", where the high elastin concentration sample displayed an absorption peak prior to the main maximum. These clusters are said to contribute to clouding as the early binding of elastin to collagen might form these clusters which, by chance, are not polymerized into the final network. As we can see the clusters also in the "collagen only" channel (Figure 5a), they must have at least contained some collagen, but as the clusters also appeared after the elastin addition, they must have also contained elastin.

An interesting accordance is seen when the 3D pore size of the networks is compared (Figure 6). The addition of elastin lowered the median pore size by only ~4%. Additionally, the interquartile range was smaller after the elastin addition (0.46 μm for the collagen and 0.32 μm for the collagen–elastin). A likely explanation for this effect is an increase in the fiber diameter because of a lateral fiber alignment between the elastin and collagen chains. The resulting thicker fibers would automatically lead to a decreased pore size when the network architecture remains unchanged, which was shown earlier. Indeed, Figure 6 reveals an increase in the fiber thickness by ~10% which is, again, similar to the percentage changes for the pore size. In fact, other imaginable polymerization types, i.e., a branched fiber alignment, should significantly reduce the median fiber thickness. Consider also the network illustration shown in Figure 7. If the addition of elastin to the network would connect random points along the turquois fibers, the pore size would be halved or at least significantly reduced. This effect must lead to a significant decrease in the median pore size which, presently however, was not observed.

**Figure 6.** (**a**) Pore diameter of a 2 mg/mL collagen gel and a 33.3 w% elastin collagen gel. (**b**) Fiber diameter of the same gel. Ten positions for each condition were used and 100 planes were summed each prior to analysis. Significance was tested with the Mann–Whitney U test.

**Figure 7.** Drawn intersection of collagen fibers (blue lines), which enclose a pore (circle); however, a hypothetical elastin fiber (red line) will divide the pore in two much smaller pores. As we did not see a significant decrease in the pore size, the only other mechanism must be lateral polymerization.


**5.1 Alzheimer's disease**

*Proteoforms - Concept and Applications in Medical Sciences*

**4. Mass spectrometry**

sor ions [73].

isoforms in histone family and combined this separation with RPLC online in the first dimension to better separation and characterization of intact histones [71].

Proteomic experiments, MS based on comprehensive and total characterization of proteoform from a biological system, besides efficient separation, employ a combination of sensitive detection and accuracy of intact proteins. The technology for identification by MS to top-down proteomics has gained impulse. The accuracy of mass spectrometric characterization of polypeptides involves improvement on ionization, fragmentation and detection conditions. Tandem MS can confirm the protein identification based on the daughter ions and characteristics of the obtained peptide map and primary structure, which thereafter provide exact localization of post-translational or other modification sites. Data-independent acquisition (DIA) methods have been alternatively used to analyze proteoforms particularly suited to the study of PTMs [72]. DIA focuses on the identification and quantitation of fragment ions that are generated from multiple peptides contained in the same selection window of several to tens of m/z, that is, the fragmentation spectra of all the peptides are acquired in each cycle time without any preselection of the precur-

The mass spectrometers are compounded basically into a sample inlet, an ion source, a mass analyzer and a detector [74, 75]. Although MS appeared more than a century ago, its application to protein analysis began in the 1990s, because existing ion sources only allowed the ionization and analysis of inorganic molecules and small organic molecules and proteins are not easily transferred to the gas phase and ionized by the size [76, 77]. Advancement of mass spectrometry technology occurred with the new instrumentation ionizer, matrix-assisted laser desorption/ ionization (MALDI) and electrospray ionization (ESI) [78–80]. The development of the mass analyzer applied to analyze intact proteins contributed to the mass spectrometry identification of the proteoforms. Mass analyzers with a high level of resolving power and sensitivity as time-of-flight (TOF), Orbitrap, Fourier Transform Ion Cyclotron Resonance (FT-ICR), or the combination of multiple mass analyzers in series, created a powerful tool for top-down MS characterization of proteoforms [78, 81, 82]. Most top-down proteomics (TDP) studies have used some form of tandem-MS fragmentation techniques, for intact proteins sequencing with greatly resolving power and high mass accuracy as: collisionally activated dissociation (CAD), collision-induced dissociation (CID), electron transfer dissociation (ETD), electron-capture dissociation (ECD), higher-energy collisional dissociation (HCD), infrared multiphoton dissociation (IRMPD) and ultraviolet photodissociation (UVPD). These examples of fragmentation strategies can provide additional information on the amino acid sequence and PTMs for identification of proteoforms [74, 75, 79, 83]. The mass spectrometer sample introduction can be through the traditional RPLC-MS, by CZE-MS or embedded in a matrix on a target plate [74, 84]. Mass spectrometers that use different types of analyzers for the first and second stages of mass analysis (hybrid MS instruments) are employed to maximize proteoform characterization top-down MS-based. Still, software tools for the identification and quantification of proteoforms need to be continuously developed to keep up with a demand to quickly and automatically analyze the data generated. Many a comprehensive proteoform software tools for proteoform identification and construction of proteoform families are freely available: MASH Suite, MetaMorpheus, MSPathFinder, Proteoform Suite, TDPortal, TopMG and TopPIC

**16**

In the diagnosis of Alzheimer's disease (AD), quantification of total Tau protein (T-tau), threonine-phosphorylated Tau181 form (P-Tau181), and the 42 amino acid peptide, alpha-amyloid isoform (Aβ) are well established as markers present in cerebrospinal fluid (CSF). However, there is a constant need for new diagnostic markers to identify the disease at a very early stage [87]. A review about the role of proteoforms in the pathophysiology process of Alzheimer's disease was described in [88]. The mass spectrometry performance of three canonical proteins, clusterin, secretogranin-2, or chromogranin A, was presented. Variations on the levels of Apo A-1, a protein with antioxidant and anti-inflammatory properties, in the serum or in CSF, are also indicated as a potential marker for AD diagnosis and progression. Apo A-1 exhibits [86] and inhibits the aggregation and neurotoxicity of an amyloid-β peptide in AD [89]. The possible association between apolipoproteins increased Apo A-1 levels that were correlated with decreasing risk of dementia [87], raising the possibility of a novel role of Apo A-1 in protection against neurological disorders [87, 89].


**3. Measuring data quality**

Based on the definitions of data quality, several DQ measurement methods have been developed, that can generally be divided into objective and subjective methods. While objective methods tend to evaluate data quality rather from the perspective of the data producer based on hard criteria, subjective methods rather take the user's perspectives and beliefs into account.

### **3.1 Objective DQ measurement methods**

Measurements of data quality are generally intended to assess the dimensions of data quality as defined in the previous section. As a first step, a framework must

### *Scientometrics Recent Advances*

be set up with the indicators that one wants to assess. Next, a proper reference for verification of the data within the data systems must be determined.

Ideally, the data are compared using real world data, which allows for validation and, if required immediate corrective actions. This method is termed *data auditing* and is the only way of measuring the quality level of dimensions like accuracy, completeness. Furthermore, by going through the data itself, one can discover data quality issues that were unexpected and therefore are of great value for taking corrective measures to improve data quality. However, data auditing comes at a high cost as it is very time consuming and the need of experts in the respective field is required. Furthermore, data auditing can be also very labor-intensive and requires that data controllers have access to the actual data.

For example, consider the metadata of publications that are contained in publication databases. If a data controller validates the content of the metadata fields with the metadata as indicated on the publications, inaccuracies can be detected. These can contain expected flaws like spelling errors but can also provide valuable information on unexpected errors that also might be highly relevant in the context of bibliometric analyses.

If the conditions for data auditing are not met, data controllers can use *rulebased checking* in order to determine data quality. This method heavily relies on business rules that are drafted based upon the domain knowledge and experience that the data controllers have with regards to the data. Consequently, these rules can only check for flaws that were anticipated by the data controllers. However, rule-based checking also offers important advantages, especially as they can be automated after conversion to validation rules, which allows for the identification of the errors (or possibly correct outliers!) via data mining techniques. Nevertheless, the presumed errors still need to be corrected, which remains labor-intensive.

### **3.2 Subjective DQ measurement methods**

Some dimensions, however, cannot be measured objectively because of their intrinsic properties. For example, the dimension relevancy pertains to the extent to which data is applicable and helpful for the stated objective. Obviously, this dimension can only be evaluated using the *perception of the users*. Although this results in a subjective scoring, user evaluations are the only way to measure dimensions that describe external data quality attributes. Internal data quality dimensions on the contrary are preferably measured using objective DQ measurement methods as described above.

Regardless of which methodology is chosen to measure data quality, it is always important to provide information about the measurement method and parameters in addition to the dimension under evaluation, in order that the measurement results can be interpreted correctly by everyone. Furthermore, although a lot of attention always goes to correcting errors, it is important to stress that eliminating the root cause should always be the ultimate goal [7].

## **4. Data quality management**

### **4.1 Data quality frameworks**

As data are extremely valuable resources in today's society, a plethora of data quality management frameworks have been published in the last decades that all strive to preserve the quality of data and to make it accessible for future use.

**7**

**Figure 1.**

*The cornerstone of data quality frameworks.*

*Data Quality Management*

**4.2 Critical success factors**

inter-dependencies with each other.

*4.2.1 Operational processes*

*DOI: http://dx.doi.org/10.5772/intechopen.86819*

found throughout the literature that show slight differences.

• DAMA DMBOK's Data governance model [8]

• Oracle's Data Quality Management Process [10]

• EWSolutions' EIM Maturity Model [9]

The most popular models are listed below, however more DQM frameworks can be

All frameworks are basically centered around three basic elements, that is, the metadata associated with the data, the processes involved in the registration, organization and (re)use of the data, and the organizational context in relation to the data (**Figure 1**). The quality of each individual element, as well as the interplay in between them, ultimately determines the quality and thus the true value of an organization's data heritage. Ideally, an organization uses metadata standards that are understandable throughout the organization and aligned with the organization's processes, business strategies and goals. Rather than describing all popular frameworks, we will describe critical success factors that are useful for developing effective DQ management strategies, and that can be found in all DQ frameworks.

Critical success factors, also termed CSFs, have been defined by Milosevic and Patanakul as '*characteristics, conditions, or variables that can have a significant impact on the success of i.e., a company or a project when properly sustained, maintained, or managed*' [11]. In 2014, Baskarada described 11 CSFs in the field of information quality management that provide valuable means for developing effective DQ management strategies [12]. These CSFs can be clustered into four major groups, that is, training, governance, management and operational processes, that have

The first group of critical success factors deals with the operational processes involved in the collection, storage, analysis and security of the data, which are all highly interdependent. As data is a valuable good, its quality should be managed throughout its entire lifecycle. In practice this comes down to taking measures that maximize, whenever possible, the **automated capture** of data in **real-time**, directly from its **original source**. This minimizes the risk of errors introduced by manual data entry, which can result in typo's, inaccuracies, missing values, erroneous data

*Scientometrics Recent Advances*

of bibliometric analyses.

labor-intensive.

described above.

be set up with the indicators that one wants to assess. Next, a proper reference for

Ideally, the data are compared using real world data, which allows for validation and, if required immediate corrective actions. This method is termed *data auditing* and is the only way of measuring the quality level of dimensions like accuracy, completeness. Furthermore, by going through the data itself, one can discover data quality issues that were unexpected and therefore are of great value for taking corrective measures to improve data quality. However, data auditing comes at a high cost as it is very time consuming and the need of experts in the respective field is required. Furthermore, data auditing can be also very labor-intensive and requires

For example, consider the metadata of publications that are contained in publication databases. If a data controller validates the content of the metadata fields with the metadata as indicated on the publications, inaccuracies can be detected. These can contain expected flaws like spelling errors but can also provide valuable information on unexpected errors that also might be highly relevant in the context

If the conditions for data auditing are not met, data controllers can use *rulebased checking* in order to determine data quality. This method heavily relies on business rules that are drafted based upon the domain knowledge and experience that the data controllers have with regards to the data. Consequently, these rules can only check for flaws that were anticipated by the data controllers. However, rule-based checking also offers important advantages, especially as they can be automated after conversion to validation rules, which allows for the identification of the errors (or possibly correct outliers!) via data mining techniques. Nevertheless, the presumed errors still need to be corrected, which remains

Some dimensions, however, cannot be measured objectively because of their intrinsic properties. For example, the dimension relevancy pertains to the extent to which data is applicable and helpful for the stated objective. Obviously, this dimension can only be evaluated using the *perception of the users*. Although this results in a subjective scoring, user evaluations are the only way to measure dimensions that describe external data quality attributes. Internal data quality dimensions on the contrary are preferably measured using objective DQ measurement methods as

Regardless of which methodology is chosen to measure data quality, it is always important to provide information about the measurement method and parameters in addition to the dimension under evaluation, in order that the measurement results can be interpreted correctly by everyone. Furthermore, although a lot of attention always goes to correcting errors, it is important to stress that eliminating

As data are extremely valuable resources in today's society, a plethora of data quality management frameworks have been published in the last decades that all strive to preserve the quality of data and to make it accessible for future use.

verification of the data within the data systems must be determined.

that data controllers have access to the actual data.

**3.2 Subjective DQ measurement methods**

the root cause should always be the ultimate goal [7].

**4. Data quality management**

**4.1 Data quality frameworks**

**6**

The most popular models are listed below, however more DQM frameworks can be found throughout the literature that show slight differences.

All frameworks are basically centered around three basic elements, that is, the metadata associated with the data, the processes involved in the registration, organization and (re)use of the data, and the organizational context in relation to the data (**Figure 1**). The quality of each individual element, as well as the interplay in between them, ultimately determines the quality and thus the true value of an organization's data heritage. Ideally, an organization uses metadata standards that are understandable throughout the organization and aligned with the organization's processes, business strategies and goals. Rather than describing all popular frameworks, we will describe critical success factors that are useful for developing effective DQ management strategies, and that can be found in all DQ frameworks.


**Author details**

*Prebiotics and Probiotics - Potential Benefits in Nutrition and Health*

more aging society.

**Conflicts of interest**

**Author contribution**

The authors declare no conflicts of interest.

The authors contributed equally to this work.

systems. It is now clear that there is a bidirectional interaction between microbiota and nervous system, microbiota and immunity, microbiota and bones, and eventually microbiota and mitochondria. Probiotics are getting more and more attention due to the increase of evidence of their benefits in many degenerative disorders. It shows the capacity of microbiota to restore gut and vaginal and oral microbiota, thus attenuating various severe inflammatory responses. All these findings suggest that probiotics could play a role in clinical procedure and therapy approaches to decrease the risk of morbidity and mortality related to CNS diseases, cardiovascular diseases, and bone degenerations. The shared information presented on this chapter may also demonstrate that the traditional view on gut microbiota and microbiome has changed and may be eventually useful as a prospective medium for the delivery of superior, more precise, and personalized treatments in the achievement of better protective health benefits for a more and

**128**

Ciro Gargiulo Isacco1,2,3,4, Andrea Ballini5,6\*, Danila De Vito6 , Angelo Michele Inchingolo2 , Stefania Cantore2 , Gregorio Paduanelli2 , Kieu Cao Diem Nguyen1,2, Alessio Danilo Inchingolo2 , Gianna Dipalma2 and Francesco Inchingolo2

1 Human Stem Cells Research Center, Ho Chi Minh City, Vietnam

2 Department of Interdisciplinary Medicine (DIM), School of Medicine, University Aldo Moro, Bari, Italy

3 Pham Chau Trinh University of Medicine, Danang, Vietnam

4 International Institute of Gene and Immunology, Ho Chi Minh City, Vietnam

5 Department of Biosciences, Biotechnology and Biopharmaceutics, University of Bari Aldo Moro, Bari, Italy

6 Department of Basic Medical Sciences, Neurosciences and Sense Organs, University of Bari Aldo Moro, Bari, Italy

\*Address all correspondence to<EMAIL_ADDRESS><EMAIL_ADDRESS>
© 2019 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.


**4. Conclusion**

72 Remote Sensing of Biomass – Principles and Applications

Fig. 10. Comparison of FOTO- (Proisy et al. 2007) and P-HV-derived (from Mougin et al.

In tropical forest, both gaps and multi-strata organization are often observed. Gaps are due to accidental tree falls or natural decaying of some canopy trees (Fig. 11, left). In presence of gaps, r-spectra tend to be skewed towards low frequencies and this may be erroneously interpreted as if the canopy contained large tree crowns (Fig. 9, r-spectrum of the decaying stage). In fact, gap-influenced r-spectra cannot be automatically related to the same biomass levels and must be removed from the PCA analysis to avoid biases in the AGB-FOTO relationship. Identically, the method was so far tested principally on evergreen forests. Further studies are needed regarding deciduous forests, not only because of the seasonal changes of the canopy aspect, but also because biomass of understorey vegetation often found in such forest type is not necessarily negligible. As spectral properties of the understorey may influence the overall reflectance of the corresponding pixels, this may be all more confusing if there is no intermediate stratum beneath the highest deciduous trees. An example of this is provided by the so-called *Maranthaceae* forest in Africa (Fig. 11, right), which presents a fairly closed albeit deciduous canopy and a very scarce intermediate tree storey. Such a structure allows the development of a dense herbaceous cover. Without relevant field information, results of the FOTO approach may be confusing in those forests. Their standing biomass is probably less than for evergreen closed forests since woody intermediate storey is missing, whereas both canopies are dominated by trees with large crowns. At least, statistical relationships between FOTO indices and AGB should be

1999) biomass estimates in mangroves of French Guiana

**3.3 Present limitations of the methods and prerequisite**

The canopy grain approach is largely original. It combines common techniques, i.e. Fourier transform and principal component analysis to characterize tropical canopy aspect and beyond forest structure from images of metric resolution. It can be implemented without prior radiometric correction, such as reflectance calibration or histogram range concordance. Regarding the increasing availability of metric to sub-metric optical images, the FOTO canopy grain analysis demonstrated its potential to capture gradients of forest structural characteristics in tropical regions. Within this context, the possible contribution of the canopy grain approach to the challenging task of estimating tropical above-ground biomass is worth being assessed at very broad scale. Such aim requires conducting simultaneously observational and simulation studies aiming at better understanding how canopy grain is sensitive to forest structure or biomass in various types of forests under various conditions of image acquisitions. There is particularly an important field of research in simulating multi-spectral and metric reflectance images from realistic forest 3D templates to identify, for instance, the range of conditions for which inversing above ground biomass of tropical forests appears possible. Considering the extreme complexity of most the tropical forests, it would be illusory to believe that only one remote sensing technique can provide all the information required to the AGB inversion. We thus believe that combining canopy grain analysis with low frequencies radar-based studies can provide new insights on this problem.

Biomass Prediction in Tropical Forests: The Canopy Grain Approach 75

Couteron, P. (2002). Quantifying change in patterned semi-arid vegetation by Fourier

Couteron, P., Pélissier, R., Nicolini, E. & Paget, D. (2005). Predicting tropical forest stand

Couteron, P., Barbier, N. & Gautier, D. (2006). Textural ordination based on Fourier spectral

Fromard, F., Vega, C. & Proisy, C. (2004). Half a century of dynamic coastal change affecting

Englhart, S., Keuck, V. & Siegert, F. (2011). Aboveground biomass retrieval in tropical forests

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*Remote Sensing,* Vol.25, No.1, (January 2004), pp.73-96, ISSN: 0143-1161 Gillespie, T. W., Brock, J. & Wright, C. W. (2004). Prospects for quantifying structure,

Letouzey, R. (1968), *Etude phytogéographique du Cameroun*. Lechevalier Eds., Paris.

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Malhi, Y. & Román-Cuesta, R. M. (2008). Analysis of lacunarity and scales of spatial

Ouma, Y. O., Ngigi, T. G. & Tateishi, R. (2006). On the optimization and selection of wavelet

Ploton, P. 2010. Analyzing Canopy Heterogeneity of the Tropical Forests by Texture

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Analysis of Very-High Resolution Images - A Case Study in the Western Ghats of


*1.5.4 Outcome measure*

*Neurostimulation and Neuromodulation in Contemporary Therapeutic Practice*

The term "spastic dystonia" was coined by Denny-Brown in 1966 to define tonic-chronic muscle activity that is present in a spasticity pattern, during rest [15]. Thus, spastic dystonia could be described as a spontaneous overactivity at rest, not induced by a primary triggering factor [14–16]. It is easy to recognise it in patients with spastic paresis, as spastic dystonia causes specific bad postures in joints and body. For example, in the upper limb, the shoulder can stay internally rotated and adducted with a flexed and pronated elbow and flexed wrist and fingers. Equinovarus deformity represents a specific spastic dystonia in the lower limb, and it is characterised by plantar flexors and/or toe flexors, which may be painful and

Spastic co-contraction is defined as an "unwanted, excessive, level of antagonistic muscle activity during voluntary command on an agonist muscle, which is aggravated by tonic stretch in the co-contracting muscle" [13]. Spastic co-contraction in spasticity pattern is a descending phenomenon, most probably due to misdirection of the supraspinal drive. It may be caused by loss of reciprocal inhibition during voluntary command [9, 10]. So, voluntary command of an agonist muscle is the first step, which induces spastic co-contraction. In patients with good or fairly good motor control, spastic co-contraction is certainly the most disabling form of muscle overactivity, because it obstacles muscle physiological muscle voluntary

For each movement evaluated, the corresponding muscles and joints are stretched at a very slow speed, in order to keep below the threshold for eliciting a stretch reflex. The angle at which soft tissue offers a maximum resistance is defined

For each movement evaluated, the clinician should stretch the corresponding muscles and joints as fast as possible for the examiner. The spasticity grade is determined by the joint angle at which catch or clonus appears, according to Tardieu

For each passive movement evaluated at first, the clinician asks the patient to carry out an active movement at maximal range, until the active movement produced by the agonist muscles is contrasted by the passive resistance together with the spastic co-contraction of antagonist ones. This angle measure is the effective

• Velocity-dependent.

• Length-dependent.

disabling during walking.

**1.4 Spastic co-contraction**

recruitment.

**1.5 Clinical evaluation**

*1.5.1 Passive range of motion*

*1.5.3 Active range of motion*

active range of motion [18].

as the passive range of motion for that joint [17].

*1.5.2 Angle of catch or clonus and spasticity grade*

**1.3 Spastic dystonia**

**116**

scale [18].

Tardieu score is a scale realised to measure spasticity that evaluates resistance to passive movement at both slow and fast speed. Individuals are evaluated both in in sit and supine position. There are two types of measures:

The quality of muscle reaction is scored as follows (range 0–4):

In order to consider joint angle, speed movement has to be defined:

Regarding the joint angle, modified Tardieu describes:

The angle of full ROM (R2) is defined at a very slow speed (V1). The angle of muscle reaction (R1) is detected when a catch or clonus appears during a quick stretch (V3) [19].

Ashworth scale, original version (1964), is a test which quantifies resistance to passive movement, with respect to a joint and with varying degrees of velocity. Scores range from 0 to 4:

0.No increase in tone.

3.Considerable increase in tone, passive movement difficult.

4.Limb rigid, sometimes fixed in flexion or extension.

The modified Ashworth scale (Bohannon & Smith, 1987) is similar to the original one, except for a 1+ scoring category to indicate resistance through less than half of the movement [20].


**Weiwei Wu, Shengnan Zhu, Qianqian Chen, Yan Lin, Jiang Tian and Cuiyue Liang \***

Root Biology Center, State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, South China Agricultural University, Guangzhou 510642, China<EMAIL_ADDRESS>(W.W<EMAIL_ADDRESS>(S.Z<EMAIL_ADDRESS>(Q.C<EMAIL_ADDRESS>(Y.L<EMAIL_ADDRESS>(J.T.) **\*** Correspondence<EMAIL_ADDRESS>Tel.: +86-20-8528-0156; Fax: +86-20-8528-1829

Received: 2 August 2019; Accepted: 14 October 2019; Published: 23 October 2019

**Abstract:** Phosphorus is one of the mineral nutrient elements essential for plant growth and development. Low phosphate (Pi) availability in soils adversely affects crop production. To cope with low P stress, remodeling of root morphology and architecture is generally observed in plants, which must be accompanied by root cell wall modifications. It has been documented that cell wall proteins (CWPs) play critical roles in shaping cell walls, transmitting signals, and protecting cells against environmental stresses. However, understanding of the functions of CWPs involved in plant adaptation to P deficiency remains fragmentary. The aim of this review was to summarize advances in identification and functional characterization of CWPs in responses to P deficiency, and to highlight the critical roles of CWPs in mediating root growth, P reutilization, and mobilization in plants.

**Keywords:** cell wall protein; phosphate starvation; Pi mobilization; roots