**4. DE-MOMI tuning method**

In order to improve the disturbance rejection response, while retaining the tracking response obtained by the MOMI method, a disturbance estimator has been added to the PID controller *GC*(*s*) (2), as depicted in **Figure 3**.

The disturbance estimator consists of the process model *GM*, the inverse process model *GMI* and the filter *GFD*. In hypothetical case, when the process model is ideal representation of the bi-proper process without time-delay, and the filter *GFD* = 1:

**Figure 3.** *The PID controller with disturbance estimator.*

$$\mathbf{G}\_{M} = \mathbf{G}\_{P}, \mathbf{G}\_{M} = \mathbf{G}\_{M}^{-1}, \mathbf{G}\_{FD} = \mathbf{1},\tag{11}$$

the estimated disturbance *df* equals to the actual disturbance *d*:

$$d\_f = d.\tag{12}$$

*a*1*m* ¼ *A*1 *A*0

> *A*0 þ

The process model delay *Tdelm* is calculated from the third-order equation in

Derivation of disturbance filter parameters depends mainly on desired disturbance rejection performance. It is natural that the disturbance signal reconstruction (*df*) is faster if the filter time constant *TFD* is smaller. In hypothetical case, if

> *e*�*sTdelm* ð Þ 1 þ *sTFD*

With sufficiently small time constant (*TFD* ! 0), where the disturbance filter gain

One remaining parameter of the disturbance filter *GFD* (13) is the gain *KFD*. One would, naturally, expect that the most optimal value should be *KFD* = 1, since only in this case, after some time, *df* becomes the same to *d* (15). Therefore, the process input disturbance *d* is eliminated by the reconstructed disturbance *df*. However, as will be shown below, the optimal disturbance response is obtained with lower values of the gain *KFD*. Namely, due to the disturbance compensator, the external process input signal *d* generates the delayed reconstructed disturbance signal *df* (15). Combined together, the actual process input *u*, due to disturbance *d*, is *d*-*df*. The step-like signal *d*, therefore, generates pulse-like actual process input disturbance signal *d*-*df*. Since the PID controller is present in the loop, and it contains the integrating term, the process output (*y*) deviation in one direction (e.g. above the reference) should be compensated by the process output deviation in the opposite direction (e.g. below the reference). Namely, when using *KFD* = 1, the integral of the control error must be:

*KFD* = 1, and there is no process time delay, *df* ≈ *d*. In this case the reconstructed disturbance signal *df* perfectly compensates the disturbance *d*. On the other hand, smaller disturbance filter time constant significantly increases the process output noise present in the measurements and forwards it to the controller output. Therefore, the *TFD* should be selected according to the tolerated noise gain of the distur-

Now, all the model parameters are known and the disturbance filter *GFD* parameters should be derived. Before continuing the derivation we should be aware of the fact that *GMI*(s) is not proper, so it cannot be realised in practice without the accompanied filter *GFD*(s). Multiplication of both is strictly proper, so the entire

*a*2*m* ¼ � *A*2

(14). The solution is the smallest real positive result [23].

*Improving Disturbance-Rejection by Using Disturbance Estimator*

*DOI: http://dx.doi.org/10.5772/intechopen.95615*

block can be easily implemented inside the controller.

*GM* = *GP*, the reconstructed disturbance signal *df* becomes:

*d f* ¼ *KFD*

bance estimator, as will be discussed in detail in the next sub-chapter.

ð∞ *t*¼0

initial process overshoot due to the disturbance *d*, is inevitable.

the disturbance rejection response is improved (lower figure).

**49**

It means that, by applying *KFD* = 1, the additional process undershoot, after the

**Figure 4** shows an example on delayed second-order process, when applying the

step-wise external process input disturbance signal *d*, and when using *KFD* = 1 (upper figure) and *KFD* = 0.44 (lower figure). The process output undershoot in the upper figure is clearly seen. By appropriately reducing the filter gain to *KFD* = 0.44,

� *Tdelm*

2 (14)

3 *d* (15)

*e t*ð Þ*dt* ¼ 0*:* (16)

*a*1*mA*1 *A*0 þ *T*2 *delm*

In this case the ideal disturbance compensation is achieved. However, in practice, model mismatch may occur (due to changing process characteristics in time or working point, lower-order process model or the process non-linearity), and the inverse of the process usually cannot be obtained, since majority of the actual processes are either strictly proper or they have time delays. Therefore, another strategy is required.

For practical applications, the solution has to be as simple as possible. In this manner we decided to use the following process model, the inverse process model and the disturbance estimator filter:

$$G\_M = \frac{K\_{PRM}e^{-sT\_{ddm}}}{1 + a\_{1m}s + a\_{2m}s^2}$$

$$G\_{MI} = \frac{1 + a\_{1m}s + a\_{2m}s^2}{K\_{PRM}}$$

$$G\_{FD} = \frac{K\_{FD}}{(1 + sT\_{FD})^3} \tag{13}$$

where *KPRM* and *Tdelm* are the process model gain and time delay and *a1m* and *a2m* are the process model dynamic parameters. Parameters *KFD* and *TFD* are the disturbance filter gain and time constant, respectively.

The remaining question is how to obtain the process model if the actual process is of the higher order or if the actual process is not known (e.g. the areas (moments) were calculated directly from the process time-response)? Fortunately, the process model can be calculated directly from the obtained areas (5), as derived in [23]:

$$\frac{T\_{delm}^3}{6} - \frac{A\_1}{2A\_0} T\_{delm}^2 + \left(\frac{A\_1^2}{A\_0^2} - \frac{A\_2}{A\_0}\right) T\_{delm} + \frac{2A\_1 A\_2}{A\_0^2} - \frac{A\_3}{A\_0} - \frac{A\_1^3}{A\_0^3} = \mathbf{0}$$

$$K\_{PRM} = A\_0$$

*Improving Disturbance-Rejection by Using Disturbance Estimator DOI: http://dx.doi.org/10.5772/intechopen.95615*

$$a\_{1m} = \frac{A\_1}{A\_0} - T\_{delm}$$

$$a\_{2m} = -\frac{A\_2}{A\_0} + \frac{a\_{1m}A\_1}{A\_0} + \frac{T\_{delm}^2}{2} \tag{14}$$

The process model delay *Tdelm* is calculated from the third-order equation in (14). The solution is the smallest real positive result [23].

Now, all the model parameters are known and the disturbance filter *GFD* parameters should be derived. Before continuing the derivation we should be aware of the fact that *GMI*(s) is not proper, so it cannot be realised in practice without the accompanied filter *GFD*(s). Multiplication of both is strictly proper, so the entire block can be easily implemented inside the controller.

Derivation of disturbance filter parameters depends mainly on desired disturbance rejection performance. It is natural that the disturbance signal reconstruction (*df*) is faster if the filter time constant *TFD* is smaller. In hypothetical case, if *GM* = *GP*, the reconstructed disturbance signal *df* becomes:

$$d\_f = K\_{FD} \frac{e^{-sT\_{delm}}}{\left(1 + sT\_{FD}\right)^3} d\tag{15}$$

With sufficiently small time constant (*TFD* ! 0), where the disturbance filter gain *KFD* = 1, and there is no process time delay, *df* ≈ *d*. In this case the reconstructed disturbance signal *df* perfectly compensates the disturbance *d*. On the other hand, smaller disturbance filter time constant significantly increases the process output noise present in the measurements and forwards it to the controller output. Therefore, the *TFD* should be selected according to the tolerated noise gain of the disturbance estimator, as will be discussed in detail in the next sub-chapter.

One remaining parameter of the disturbance filter *GFD* (13) is the gain *KFD*. One would, naturally, expect that the most optimal value should be *KFD* = 1, since only in this case, after some time, *df* becomes the same to *d* (15). Therefore, the process input disturbance *d* is eliminated by the reconstructed disturbance *df*. However, as will be shown below, the optimal disturbance response is obtained with lower values of the gain *KFD*. Namely, due to the disturbance compensator, the external process input signal *d* generates the delayed reconstructed disturbance signal *df* (15). Combined together, the actual process input *u*, due to disturbance *d*, is *d*-*df*. The step-like signal *d*, therefore, generates pulse-like actual process input disturbance signal *d*-*df*. Since the PID controller is present in the loop, and it contains the integrating term, the process output (*y*) deviation in one direction (e.g. above the reference) should be compensated by the process output deviation in the opposite direction (e.g. below the reference). Namely, when using *KFD* = 1, the integral of the control error must be:

$$\int\_{t=0}^{\infty} e(t)dt = 0.\tag{16}$$

It means that, by applying *KFD* = 1, the additional process undershoot, after the initial process overshoot due to the disturbance *d*, is inevitable.

**Figure 4** shows an example on delayed second-order process, when applying the step-wise external process input disturbance signal *d*, and when using *KFD* = 1 (upper figure) and *KFD* = 0.44 (lower figure). The process output undershoot in the upper figure is clearly seen. By appropriately reducing the filter gain to *KFD* = 0.44, the disturbance rejection response is improved (lower figure).

*GM* ¼ *GP*, *GMI* ¼ *G*�1

*Control Based on PID Framework - The Mutual Promotion of Control and Identification…*

In this case the ideal disturbance compensation is achieved. However, in practice, model mismatch may occur (due to changing process characteristics in time or working point, lower-order process model or the process non-linearity), and the inverse of the process usually cannot be obtained, since majority of the actual processes are either strictly proper or they have time delays. Therefore, another strategy is required. For practical applications, the solution has to be as simple as possible. In this manner we decided to use the following process model, the inverse process model

*GM* ¼ *KPRMe*�*sTdelm*

*GMI* ¼ 1 þ *a*1*ms* þ *a*2*ms*

*GFD* ¼ *KFD*

*A*2 1 *A*2 0

� *A*2 *A*0

*KPRM* ¼ *A*0

!

1 þ *a*1*ms* þ *a*2*ms*2

*KPRM*

ð Þ 1 þ *sTFD*

where *KPRM* and *Tdelm* are the process model gain and time delay and *a1m* and *a2m* are the process model dynamic parameters. Parameters *KFD* and *TFD* are the distur-

The remaining question is how to obtain the process model if the actual process is of the higher order or if the actual process is not known (e.g. the areas (moments) were calculated directly from the process time-response)? Fortunately, the process model can be calculated directly from the obtained areas (5), as derived in [23]:

*Tdelm* þ

2*A*1*A*2 *A*2 0

� *A*3 *A*0 � *A*3 1 *A*3 0 ¼ 0

2

the estimated disturbance *df* equals to the actual disturbance *d*:

and the disturbance estimator filter:

*The PID controller with disturbance estimator.*

**Figure 3.**

bance filter gain and time constant, respectively.

*T*3 *delm* 6 � *A*1 2*A*0 *T*2 *delm* þ

**48**

*M* , *GFD* ¼ 1, (11)

3 (13)

*d f* ¼ *d:* (12)

*KIP* ¼ *KIKPRM KPP* ¼ *KPKPRM KDP* ¼ *KDKPRM d*0 ¼ ð Þ 1 þ *KPP*

*Improving Disturbance-Rejection by Using Disturbance Estimator*

*DOI: http://dx.doi.org/10.5772/intechopen.95615*

For the given controller filter (*TF*) and the disturbance filter (*TFD*) time constants (note that the calculation of both time constants, according to the desired level of noise, will be derived in the next sub-chapter), the calculation of remaining controller, model and disturbance filter parameters proceeds as given in **Figure 5**.

To illustrate the proposed design of DE-MOMI method, according to control structure in **Figure 5**, let us calculate the controller, model and disturbance filter

> *GP*1 ¼ *e*�0*:*5*s* ð Þ 1 þ *s* 2

*GP*2 ¼ *e*�0*:*2*s*

The characteristic areas, calculated from (5) and (7), are given in **Table 1**. Next, the PID controller parameters are calculated from (6) and from (9), since we are going to compare the proposed DE-MOMI method with MOMI and DRMO

The process models *GM* and inverse process models *GMI* are then calculated from

1. Calculate the characteristic areas or moments from the process time-response [9,12]

Areas *GP1* 1 2.50 4.13 5.77 7.42 9.07 Areas *GP1* with controller filter 1 2.60 4.39 6.21 8.04 9.87 Areas *GP2* 1 3.20 6.62 11.26 17.12 24.21 Areas *GP2* with controller filter 1 3.30 6.95 11.96 18.32 26.04

methods. The calculated controller parameters are given in **Table 2**.

 or from (5) if the process transfer function is known in advance. 2. Calculate modified areas according to the chosen T*F* from (7).

*The calculated areas for the processes (20) without and with the controller filter.*

(14), where *GMI* is the inverse of *GM* without time-delay:

3. Calculate PID controller parameters from (6). 4. Calculate process model parameters from (14). 5. Calculate the disturbance filter gain K*FD* from (18).

*Calculation of the controller, model and filter parameters.*

**Figure 5.**

**Table 1.**

**51**

Illustrative example 1

parameters for the following processes:

The a-priori chosen filter time constants were:

2

*f* 0 ¼ *a*1*m* þ *KDP* þ *TF* þ *Tdelm* (19)

ð Þ 1 þ *s* 3 (20)

*TF* ¼ *TFD* ¼ 0*:*1 (21)

*A0 A1 A2 A3 A4 A5*

**Figure 4.** *The closed-loop signals when applying step-wise external process input disturbance signal with disturbance filter gains* KFD *= 1 (upper figure) and* KFD *= 0.44 (lower figure).*

The remaining question is how to find the most appropriate filter gain *KFD*. Certainly, the *KFD* should be chosen so as that the disturbance rejection is optimised. Here we can use the same optimisation criteria as in the DRMO tuning method. Therefore, the transfer function *GCLD*(s) between the external disturbance (*d*) and the process output (*y*):

$$G\_{\rm CLD}(\mathbf{s}) = \frac{Y(\mathbf{s})}{D(\mathbf{s})} = \frac{G\_M(\mathbf{1} - G\_{FD}\mathbf{e}^{-sT\_{\rm ddm}})}{\mathbf{1} + G\_M G\_P} \tag{17}$$

should be optimised according to the modified MO criterion [9, 16]. Note that expression (17) holds when the process and the model transfer functions are equivalent. Since the disturbance filter time constant is defined, and all of the controller and the model parameters are calculated, the only optimisation parameter is the gain *KFD*. By using similar derivation as in [9, 16], the optimal filter gain *KFD* is calculated as:

$$K\_{FD} = \frac{-b\_0 + \sqrt{b\_0^2 - 4a\_0c\_0}}{2a\_0} \tag{18}$$

where

$$a\_0 = -d\_0 + 2f\_0 K\_{IP} + K\_{IP}^2 \left(T\_F^2 - 3T\_{FD}^2\right)$$

$$b\_0 = 2d\_0 - 4f\_0 K\_{IP} + K\_{IP}^2 \left(-2T\_F^2 + 12T\_{FD}^2 + 6T\_{FD}T\_{delm} + T\_{delm}^2\right)$$

$$c\_0 = -d\_0 + 2f\_0 K\_{IP} + K\_{IP}^2 T\_F^2$$

*Improving Disturbance-Rejection by Using Disturbance Estimator DOI: http://dx.doi.org/10.5772/intechopen.95615*

$$K\_{IP} = K\_I K\_{PRM}$$

$$K\_{PP} = K\_P K\_{PRM}$$

$$K\_{DP} = K\_D K\_{PRM}$$

$$d\_0 = \left(\mathbf{1} + K\_{PP}\right)^2$$

$$f\_0 = a\_{1m} + K\_{DP} + T\_F + T\_{delm} \tag{19}$$

For the given controller filter (*TF*) and the disturbance filter (*TFD*) time constants (note that the calculation of both time constants, according to the desired level of noise, will be derived in the next sub-chapter), the calculation of remaining controller, model and disturbance filter parameters proceeds as given in **Figure 5**.

Illustrative example 1

To illustrate the proposed design of DE-MOMI method, according to control structure in **Figure 5**, let us calculate the controller, model and disturbance filter parameters for the following processes:

$$\mathbf{G}\_{P1} = \frac{\mathbf{e}^{-0.5\mathbf{s}}}{\left(\mathbf{1} + \mathbf{s}\right)^{2}}$$

$$\mathbf{G}\_{P2} = \frac{\mathbf{e}^{-0.2\mathbf{s}}}{\left(\mathbf{1} + \mathbf{s}\right)^{3}}\tag{20}$$

The a-priori chosen filter time constants were:

$$T\_F = T\_{FD} = \mathbf{0.1} \tag{21}$$

The characteristic areas, calculated from (5) and (7), are given in **Table 1**.

Next, the PID controller parameters are calculated from (6) and from (9), since we are going to compare the proposed DE-MOMI method with MOMI and DRMO methods. The calculated controller parameters are given in **Table 2**.

The process models *GM* and inverse process models *GMI* are then calculated from (14), where *GMI* is the inverse of *GM* without time-delay:

#### **Figure 5.**

The remaining question is how to find the most appropriate filter gain *KFD*. Certainly, the *KFD* should be chosen so as that the disturbance rejection is optimised. Here we can use the same optimisation criteria as in the DRMO tuning method. Therefore, the transfer function *GCLD*(s) between the external disturbance (*d*) and

*The closed-loop signals when applying step-wise external process input disturbance signal with disturbance*

*Control Based on PID Framework - The Mutual Promotion of Control and Identification…*

*D s*ð Þ ¼ *GM* 1 � *GFDe*�*sTdelm*

should be optimised according to the modified MO criterion [9, 16]. Note that expression (17) holds when the process and the model transfer functions are equivalent. Since the disturbance filter time constant is defined, and all of the controller and the model parameters are calculated, the only optimisation parameter is the gain *KFD*. By using similar derivation as in [9, 16], the optimal filter gain *KFD* is

q

2*a*0

*F* þ 12*T*2

� � 1 þ *GMGP*

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi *b*2

*IP T*2

*F* � 3*T*2 *FD* � �

� �

*IPT*2 *F*

*FD* þ 6*TFDTdelm* þ *T*2

*delm*

0 � 4*a*0*c*0

(17)

(18)

*GCLD*ðÞ¼ *s*

*filter gains* KFD *= 1 (upper figure) and* KFD *= 0.44 (lower figure).*

*Y s*ð Þ

*KFD* ¼ �*b*0 þ

*a*0 ¼ �*d*0 þ 2 *f* 0*KIP* þ *K*2

*IP* �2*T*2

*c*0 ¼ �*d*0 þ 2 *f* 0*KIP* þ *K*2

*b*0 ¼ 2*d*0 � 4 *f* 0*KIP* þ *K*2

the process output (*y*):

**Figure 4.**

calculated as:

where

**50**

*Calculation of the controller, model and filter parameters.*

**Table 1.**

*The calculated areas for the processes (20) without and with the controller filter.*

*Control Based on PID Framework - The Mutual Promotion of Control and Identification…*

#### **Table 2.**

*The calculated controller parameters for the processes (20) for MOMI (6) and DRMO (9) method, taking into account the chosen controller filter TF = 0.1.*

$$G\_{M1} = \frac{e^{-0.5\epsilon}}{1 + 2\epsilon + \epsilon^2}$$

$$G\_{M11} = 1 + 2\epsilon + \epsilon^2$$

$$G\_{M2} = \frac{e^{-0.616\epsilon}}{1 + 2.58\epsilon + 1.84\epsilon^2} \tag{22}$$

$$G\_{M2} = 1 + 2.58\epsilon + 1.84\epsilon^2$$

Finally, the disturbance filter gain *KFD*, when taking into account the chosen *TFD* = 0.1, is then calculated from (18):

$$K\_{FD1} = 0.57$$

$$K\_{FD2} = 0.59 \tag{23}$$

**Figure 6.**

**Figure 7.**

**53**

*The closed-loop responses on the process* GP1*, when using the MOMI, DRMO and DE-MOMI method.*

*Improving Disturbance-Rejection by Using Disturbance Estimator*

*DOI: http://dx.doi.org/10.5772/intechopen.95615*

*The closed-loop responses on the process* GP2*, when using the MOMI, DRMO and DE-MOMI method.*

Therefore, the complete inverse of the models with accompanying disturbance filters (see **Figure 3**) are the following:

$$\mathbf{G}\_{\rm MI1}\mathbf{G}\_{\rm FD1} = \frac{\mathbf{0}.57(\mathbf{1} + 2\mathbf{s} + s^2)}{\left(\mathbf{1} + \mathbf{0}.\mathbf{1}\mathbf{s}\right)^3}$$

$$\mathbf{G}\_{\rm MI2}\mathbf{G}\_{\rm FD2} = \frac{\mathbf{0}.59(\mathbf{1} + 2.58\mathbf{s} + \mathbf{1}.84\mathbf{s}^2)}{\left(\mathbf{1} + \mathbf{0}.\mathbf{1}\mathbf{s}\right)^3} \tag{24}$$

The closed-loop responses, obtained with the calculated controller, model and filter parameters, for the MOMI, DRMO and the proposed DE-MOMI method, are given in **Figures 6** and **7**. At *t* = 0 s, the reference value (*r*) was changed from 0 to 1 and at half of experiment time the process input disturbance (*d*) was changed from 0 to 1. It is obvious that the disturbance rejection performance of the DE-MOMI method is the best. Note that when applying the DE-MOMI method, due to the difference between the actual process and the process model in the second example (*GP2*), the process input signal, during the reference change, is not smooth. This is expected, since the inverse process model with filter is amplifying the difference between the actual process and the process model. In this case, the response can be made smoother by increasing the disturbance filter time constant (*TFD*). Note that a possible limitation of the control signal can also help to smooth out the oscillations after the reference step [24].

The disturbance rejection performance of the DE-MOMI method can be increased by decreasing the disturbance filter time constant *TFD*. However, as already mentioned above, the process input signal can become oscillatory when the actual process and the process model differ. In this case, too small *TFD* can even render the closed-loop system unstable. Besides that, the process noise (signal *n* in


**6. Addison's disease**

#### **6.1 Background**

In 1849 Thomas Addison firstly described a group of patients characterized by anemia and disease of adrenal glands. Addison's disease is an insidious, chronic disorder of the adrenal cortex resulting in decreased production of glucocorticoids, mineralocorticoids, and androgens. There is a concomitant increased secretion of ACTH from the pituitary gland aimed to stimulate the adrenal gland. In developed countries an autoimmune process is recognized as the most common etiological factor of adrenal gland insufficiency (70-90%); the second cause is tuberculosis of the adrenal gland (10 to 20%). Three clinical forms of adrenal insufficiency are recognized: Addison disease within syndromes characterized by autoimmune involvement of several organs and named Autoimmune Polyendocrine Syndromes (APS-1 and APS-2), and Addison disease as an isolated condition.


**1. Introduction**

A significant number of previous studies have emphasized the ubiquitous presence of microplastics (MPs) in the oceans [1], freshwater bodies [2,3], and soil [4], along with food items, drinks [5], seasonings [6], and aquatic organisms (Figure 1). Microplastic pollution in the environment can be caused by several factors, including landfills [7]; dumping and application of sewage sludge [8]; fiber shedding of synthetic textiles; transportation (wear of tires, brakes, road signs, etc.) [9]; and other human activities, including industrial plastic pellet preproduction [10], plastic mulching and grinding in agricultural [11], fisheries, and tourism [12]. After being released into the environment through different pathways, these microplastics experience the process of degradation (physicochemical fragmentation, chemical aging, biological degradation, etc.) [11] and translocation [13] under different environmental conditions. Finally, they enter animals and humans through skin contact, oral ingestion, inhalation, and other ways and continue to get enriched [14]. After entering the body, microplastics may produce a variety of negative effects, such as decreased growth rate [15], inflammatory response [16] and oxidative stress [17], and metabolic disorders [18]. In severe cases, they can penetrate organs [19,20], tissues [21], and even cells [22], causing toxic effects. Because microplastics have a large specific surface area and strong adsorption capacity, they easily adsorb various inorganic pollutants, such as Cu, Pb, and Cd, [23] and

**Citation:** Yang, H.; He, Y.; Yan, Y.; Junaid, M.; Wang, J. Characteristics, Toxic Effects, and Analytical Methods of Microplastics in the Atmosphere. *Nanomaterials* **2021**, *11*, 2747. https:// doi.org/10.3390/nano11102747

Academic Editor: Vivian Hsiu-Chuan Liao

Received: 24 September 2021 Accepted: 14 October 2021 Published: 17 October 2021

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

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

organic pollutants, e.g., PCBs [24], PAHs [18], and polybrominated diphenyl ethers [25]. Even some microplastics themselves contain additives with a certain toxicity, and some studies have shown that microplastics combined with these pollutants pose a serious threat to organisms.

**Figure 1.** Cycle of microplastic pollution in ecosystems.

MPs in the air have been identified as particulate air pollutants and paid great attention recently [26]. However, at present, the research on environmental MPs is mainly focused on the aquatic ecosystem, and the number of studies on atmospheric MPs is limited, which is a limitation to further understanding the environmental characteristics and negative effects of atmospheric MPs. The small size of MPs, especially nanoplastics (NPs), facilitates their emissions into the air [27] and long-distance transportation [28] and can cause adverse effects on animals and humans through respiratory inhalation [29]. Critical analysis is urgently needed to open new ways of thinking about atmospheric MPs in the future.

In this review, the research progress on atmospheric MPs in recent years is summarized, including the following: (1) the global distribution of atmospheric MPs and their influencing factors, (2) the origin and fate of atmospheric MPs, (3) advances in sampling and analysis of atmospheric MPs, (4) toxicological impacts of MPs in the atmosphere on animals and humans, and last but not the least (5) the existing gaps in each part and the corresponding future research directions.

### **2. The Global Distribution of Atmospheric MPs and Associated Influencing Factors**


The schematic of the experimental setup is shown in **Figure 2** and the results prepared for the measurements performed are shown in **Figure 3**. A pump probe delay time is varied in order to obtain the photoconductivity kinetics while the gating delay time is fixed at the peak of the terahertz electric field (*E*THz). The pump-probe delay time is scanned within an interval of 1500 s to record the transmitted terahertz electric field. It should be noted that the photoconductivity obtained with this process represents the lower limit of the mobility (**Figure 3(c)**). The transmitted terahertz electric field (*E*GS and *E*EXC) responses can be fitted to

#### **Figure 2.**

*Schematic diagram of the terahertz setup used in probing charge carrier dynamics in hydrogenated amorphous silicon passivation layer. The transmitted electric fields, E*GS *(ground state) and E*EXC *(excited state), are Fourier transformed and used the conductivity Eq. (34) to obtain the photoconductivity kinetics at the maximum of terahertz electric field (E*THz*).*

#### **Figure 3.**

*Surface passivation effectiveness of a layer of hydrogenated amorphous silicon (a-Si:H). (a) Schematic of the double-layered sample under observation, it consists of one-sided growth of 100 nm SiO2 and on the other side 30 nm a = Si:H is deposited through PECVD process. (b) Scanning Electron microscopy (SEM) image of the top surface of the sample where an uniform layer of a-Si:H is clearly visible due to contrast difference between Si and a-Si:H. (c) the photoconductivity kinetics of the sample under study normalized to the excitation density where N*EXC *=* 1 1015 *and e*0 *is the elementary electronic charge. The effectiveness of surface passivation from a-Si:H can be stated from the difference in the terahertz conductivity between the passivated Si sample and nonpassivated Si sample. (d) the results of Fourier transform infrared spectroscopy (FTIR) of the a-Si:H sample compared with the Si substrate to highlight the presence of Si H and Si H*2 *bonds in the passivation layer.*

Drude or Drude-Smith model to elucidate the change generation efficiency (quantum yield) and the mobility independently. The results of the photoconductivty point to a very important parameter that controls charge dynamics in photovoltaic materials: defects and dangling bonds, as evident by the difference in the photoconductivity kinetics of a-Si:H-passivated silicon sample and non-passivated silicon sample (**Figure 3(c)**). Through the use of time resolved terahertz spectroscopy, these early time processes and parameters of novel solar cell materials can be investigated in nanometer scale. The presence of hydrogen (H) in the passivation layer is by using Fourier Transform Infrared Spectroscopy (FTIR) and the many repetitive measurements were taken to check that no hydrogen loss takes place with time. The integration times were sufficient so as to reduce the statistical errors to a few percent of the average value. Infrared absorption measurements were made with a Perkin Elmer spectrometer on films deposited on crystalline silicon. The infrared spectrum of hydrogenated a-Si generally displays two groups of bands due to the stoichiometric ratio of silicon and hydrogen i.e. Si H*X* bonds. The first group is designated to the bond wagging (rocking, rolling) band at ≈640 cm1 which is directly proportional to the hydrogen concentrations and the second group corresponds to the band in the region of wavenumber 2000 cm1 where the vibrations are designated by the stretching of the Si-H bonds.

### **6. Conclusions**

Terahertz radiation, in particular the theoretical framework, has become a requirement to design faster and energy efficient devices. Successful first-pass design of components of a system requires the ability to accurately predict the dissipative losses due to charge-carrier scattering in semi-conductor and metallic materials used in waveguides, filters, antennas and optics. A comprehensive description of different formalisms was presented that allows the complex conductivity of semiconductor materials to be calculated at terahertz frequencies by including the energy dependence of the scattering rate. The generalized conductivity reduces to the Drude model in the limit of zero temperature or an energy independent scattering rate. Terahertz time-domain spectroscopy was used to determine the conductivity and the photoconductivity of hydrogen-doped amorphous silicon, which was semi-quantitatively accounted for by the generalized conductivity model. The breadth and depth of the studies of conductive solid state materials using terahertz radiation continues to advance and as the field is very broad, it is very important to understand the fundamental models to better utilized materials for future. Terahertz based spectroscopy methods can be valuable to not only in revealing fundamental processes or micro/nano strategies but also in providing insights for possible optimization of manufacturing procedures for solar cell industry.

### **Acknowledgements**

Rahul Goyal would like to thank Ministry of Human Resource and Development, Government of India, for providing financial support and Centre for Nanoscience and Engineering for the research facilities.

### **Conflict of interest**

The authors declare no conflict of interest.

### **Author details**

Rahul Goyal1 \* and Akash Tiwari2

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 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.

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### *Edited by Borwen You and Ja-Yu Lu*

Electromagnetic waves within a terahertz frequency range are becoming critical to investigating molecules, materials, and possible applications that are operated by both visible light and infrared rays. This book discusses sensing, imaging, and optoelectronic technologies of terahertz electromagnetic waves in theory and experiments. Most terahertz technologies can be explained by fundamentals of applied physics that have been demonstrated in other spectral ranges. However, the optoelectronic technology and corresponding configurations of imaging and sensing techniques are so special for various terahertz material polarization waves, which are excited in solid-state media by high-peak power lasers and waveguide transportation. Thus, this book also specifies terahertz parameters and available technologies.

Published in London, UK © 2022 IntechOpen © wacomka / iStock

Terahertz Technology

Terahertz Technology

*Edited by Borwen You and Ja-Yu Lu*


**7. Conclusion**

In summary, although there has been progress made in identifying factors outlining OA disease progression, a more detailed analysis of the factors surrounding the epigenetics should be conducted in order to reveal any potential therapies. The control of chondrogenesis via bone morphogenic protein signalling, transforming growth signalling, fibroblast growth factor signalling, connective tissue growth factor and insulinlike growth factor all play important roles in chondrocyte formation and destruction. This in addition to the fact that cellular mechanisms controlled by gene expression and epigenetic changes including DNA methylation, histone modification, and microRNAs can all help us gain an understanding of regenerative cartilage therapies.

### **Acknowledgements**

The authors acknowledge the support and funding from the Deanship of Scientific Research, King Saud University, Riyadh.


**1. Introduction**

Bladder cancer is one of the most common malignant tumors of the urinary system. The cancer statistics released in 2022 state that, in the United States, bladder cancer has an estimated incidence of 81,800 cases and a mortality rate of 17,100 cases [1]. These

**Citation:** Gao, Y.; Nie, Z.; Cao, H.; Huang, D.; Chen, M.; Xiang, Y.; Yu, X.; Zhang, S. Scabertopin Derived from *Elephantopus scaber* L. Mediates Necroptosis by Inducing Reactive Oxygen Species Production in Bladder Cancer In Vitro. *Cancers* **2022**, *14*, 5976. https://doi.org/10.3390/ cancers14235976

Academic Editors: Barbara De Filippis, Alessandra Ammazzalorso and Marialuigia Fantacuzzi

Received: 30 September 2022 Accepted: 1 December 2022 Published: 2 December 2022

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

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

rates are slightly lower than those reported in 2021 [2]. In China, bladder cancer had an incidence of approximately 80,500 and a mortality rate of 32,900 in 2015 [3]. Uroepithelial cancer is the main pathological type of bladder cancer. Approximately 75% and 25% of the cases of this type of cancer are non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC), respectively. The current clinical treatment of bladder cancer remains based on surgery combined with postoperative radiotherapy [4,5]. Mitomycin C, epirubicin, adriamycin, methotrexate, vincristine, pirarubicin, and cisplatin are used as the first-line chemotherapeutic agents for bladder cancer in clinical practice [6]. However, the use of these drugs is greatly limited by their unsatisfactory efficacy, the widely reported drug tolerance of bladder cancer, and their adverse effects. Although the rapid development of molecular biology and genetics research has provided new therapeutic tools for the clinical development of immunotherapy, gene therapy, and targeted therapy, these approaches fail to exert an ideal treatment effect on bladder cancer because they use a large number of chemically synthesized drugs. This approach results in high treatment costs, drug side effects, hepatotoxicity, and drug resistance. Therefore, the search for new drugs with low toxicity and side effects has been a hot issue in tumor treatment.

In recent years, many natural compound products have played an important role in the treatment of tumors. Finding compounds with significant tumor-inhibiting activity from natural products is an important approach and means for the research and development of anticancer drugs [7]. Scabertopin is a sesquiterpene lactone compound that is mainly derived from the chemical constituents of *Elephantopus scaber* L. It is a kind of herbaceous plant belonging to the phylum Angiosperm, class Dicotyledonous, order Campanulaceae, family Asteraceae, and genus Elephantopus [8], which is widely distributed worldwide, particularly in East Asia, Southeast Asia, Africa, Australia, India, and South America, and has been reported to have various pharmacological properties, such as anti-bacterial, antidiabetic, anti-inflammatory, and anti-cancer efficacy [9,10]. An increasing number of studies have shown that the antitumor activity of *Elephantopus scaber* L. is attributed to sesquiterpene lactones, which execute significant antitumor activities in nasopharyngeal [11,12], cervical [13], breast [14], colon [15,16], and hepatocellular carcinomas [17]. For example, in HCT116 and RKO cells, deoxyelephantopin (DET), another sesquiterpene lactone compound derived from *Elephantopus scaber* L. can significantly increase reactive oxygen species (ROS) levels, thereby activating the JNK signaling pathway and triggering cell death. In the mouse breast cancer cell line TS/A, DET can inhibit TNF-α-induced NF-κB activity and down-regulate the NF-κB regulated gene products of matrix metalloproteases (MMP)-2 and MMP-9, thereby inhibiting the migration and invasion in vitro and in vivo [14]. In addition, sesquiterpenoids exhibit different cytotoxicities toward tumor cells and normal cells, whereby they selectively target tumor cells and show little systemic toxicity [18,19]. However, the research on the anticancer effect and mechanism of scabertopin in bladder cancer has not been reported.

Necroptosis is a unique form of programmed cell death, which is characterized by caspase-independent activation and is morphologically accompanied by the swelling of organelles and the rupture of cell membranes, resulting in the spillage of the cellular contents into the surrounding cells and, consequently, triggering inflammatory response [20,21]. Various drugs and active sites of traditional Chinese medicine have been confirmed to exert their anticancer activity through necroptosis [22,23]. Receptor-interacting protein (RIP)1, RIP3, and mixed lineage kinase-like (MLKL) are three key factors in the regulation of necroptosis; RIP3 has been well-established to recruit MLKL and induce its phosphorylation. Phosphorylated-MLKL (p-MLKL) then undergoes oligomerization and translocates to the plasma membrane to execute cellular necrosis [24–26]. However, the pathway through which RIP1 undergoes autophosphorylation remains controversial. ROS have been recently reported to act as upstream cell signaling molecules to activate and drive necroptosis [27]. RIP1 can sense ROS by modifying three key cysteine residues (C257, C268, and C586) and, subsequently, induces their autophosphorylation at the S161 site, which in turn results in the recruitment of RIP3 to form a necrosome [28]. Previous studies have also shown

a correlation between necroptosis and drug-induced ROS. For example, artesunate can inhibit the proliferation of renal cancer cell lines in vitro by inducing elevations in ROS levels and, thus, promoting RIP1-dependent necrotic apoptosis [29]. In addition, emodin promotes the occurrence of necrotic apoptosis in renal cell carcinoma (RCC) cell lines, thereby restricting tumor development by inducing the production of ROS that promote increases in the levels of RIP1 and MLKL phosphorylation [30]. In conclusion, the positive correlation of necroptosis with ROS implies that regulating ROS to induce necroptosis in tumor cells is a potential strategy for cancer therapy.

In this study, we aimed to evaluate the inhibitory effects and potential molecular mechanisms of scabertopin on bladder cancer cells in vitro. Scabertopin is one of the four sesquiterpene lactones with the highest content in *Elephantopus scaber* L. Since DET has shown a good anti-tumor effect in a variety of tumor cell lines, we speculated that scabertopin would also have similar efficacy. Therefore, we determined our research objective was to study the effect of scabertopin on bladder cancer cell lines and explore its way to promote the death of bladder cancer cell lines. Our results showed that scabertopin with antitumor properties inhibited the growth, migration, and invasion of the bladder cancer cell line J82. Further in-depth studies demonstrated that the above effects of scabertopin may be related to the promotion of ROS generation, the induction of focal adhesion kinase (FAK) phosphorylation, and the activation of necroptotic signaling pathways.


**3. Results**

### *3.1. Initial Surface and Material Analysis*

The initial surface was measured by WLI, and characteristic sections were analyzed by fast Fourier transformation (FFT). The surfaces exhibit two kinds of marks, which are a result of the mechanical preparation process. The directions of the grinding marks are strongly heterogeneous but exhibit a clearly dominant orientation. Therefore, the profile analysis shows significant differences in the spatial frequency spectrum for horizontal or vertical sections through the initial surface. While Figure 4 focuses primarily on the macroroughness of the surface at spatial frequencies smaller than 50 mm−1, Figure 5 primarily highlights the micro-roughness up to spatial frequencies of 200 mm−1. Figure 4 gives a representative impression of the initial surface, while the results displayed in Figure 5 will be used for an in-depth analysis of changes in micro-roughness and their corresponding spatial frequencies and wavelengths, respectively.

**Figure 4.** (**a**) WLI image of initial surface roughness; (**b**) Profile of cross-section along horizontal white dashed line; (**c**) Onedimensional (1D) Fourier analysis of profile along cross-section; (**d**) Profile of longitudinal section along vertical white dashed line; (**e**) One-dimensional (1D) Fourier analysis of longitudinal section.

**Figure 5.** (**a**) WLI image of initial micro-roughness; (**b**) Profile of cross-section along horizontal white dashed line; (**c**) Onedimensional (1D) Fourier analysis of profile along cross-section; (**d**) Profile of longitudinal section along vertical white dashed line; (**e**) One-dimensional (1D) Fourier analysis of longitudinal section.

In addition to surface roughness, the initial microstructure and distribution of chemical elements at the surface was investigated by scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX). In contrast to the experimental investigation, the surface was manually polished before SEM and EDX to get a clearer picture and less errors in measurement.

Figure 6 shows SEM images in different magnifications. While Figure 6a gives an overview of the overall distribution of chromium carbides (dark gray) in the steel matrix (light gray), Figure 6b provides a close-up of chromium carbides regarding sizes and geometrical shapes.

**Figure 6.** (**a**,**b**) SEM images of manually polished surface in two different magnifications to visualize size and distribution of chromium carbides (dark gray spots).

Firstly, the dark and light gray phases were characterized by EDX, and it was confirmed that the dark gray spots were chromium-enriched carbides (C39.55; Si 0; V3.69; Cr 31.83; Mn 0.39; Mo 1.04; Fe Bal.; in weight %), while the lighter gray areas were the steel matrix of AISI D2 containing less chromium (C 14.37; Si; 0.91; V 0.35; Cr 7.06; Mn 0.18; Mo 0.44; Fe Bal. in weight %). The size of the chromium carbides was primarily in the range from one to three micrometers, while the shape was predominantly ellipsoid and often spherical (Figure 6b). However, also, agglomerations of multiple chromium carbides as well as areas with a smaller density of chromium carbides were observed (Figure 6a). The size, shape, and distribution of chromium carbides are a characteristic feature of the powder metallurgical production process of 1.2379 (AISI D2). Therefore, this enhanced version of the material is typically referred to as 1.2379+ in comparison to 1.2379 (standard).

### *3.2. Surface Topography after Pulsed Laser Remelting*

The surface topography and thus surface roughness after laser polishing were investigated in a full factorial design for three different laser beam dimensions—Q100, Q200, and Q400—and ten different laser fluences ranging from 3 to 12 J/cm2 in equidistant steps of 1 J/cm2. Figure 7 shows micrographs in a matrix form for selected laser fluences and for the laser beams Q100, Q200, and Q400. Figure 7a represents the initial surface topography since a fluence of 4 J/cm2 at Q100 was not sufficient to introduce any topographical change in the initial surface.

**Figure 7.** Micrographs of surfaces after laser micro polishing using tools Q100 (**a**–**f**), Q200 (**g**–**l**), and Q400 (**m**–**r**) for laser fluences ranging from 4 to 12 J/cm2.

The smallest laser fluence investigated was *F* = 3 J/cm2, but this fluence was not sufficient for any of the laser beam dimensions to remelt the surface at least partially. Remelting starts at a fluence of approximately *F* = 4 J/cm2 for Q200 and Q400, while a fluence of approximately *F* = 6 J/cm2 is required for Q100. The micrographs for these fluences show that various small, circular craters were formed during the remelting process. In general, it is observed that surface topography is significantly different for the same fluence at different laser beam dimensions (e.g., Figure 7b,h,n). A purely topographical description shows that small craters are preferably observed for smaller laser beams and fluences (Figure 7b,g,m). Larger laser beams and higher fluences, on the other hand, tend to

produce dents, i.e., comparatively long-wavelength dimples or depressions (Figure 7j,o,p), which clearly differ in their topology from the craters at small beam sizes. At small fluences, basically only comparatively small, circular craters are visible in the laser-polished surface. A very large crater density was observed particularly at Q100 and 10 J/cm2 (Figure 7e). Using larger beam sizes, surfaces with wavy depressions were formed. In particular, at large laser beam sizes and high fluences, surface ripples are formed in addition to the characteristic stripe texture (Figure 7q,r).

Additionally, visual process observation enables identifying certain characteristic process regimes and points toward different mechanisms of surface structure formation in the remelting process. Particularly, material ablation is a significant effect that is visible for all laser beam sizes at high fluences. However, the fluence required for material ablation strongly depends on the laser beam dimensions. Significant evaporation is visible for Q100 at approximately 12 J/cm2, in comparison to approximately 10 J/cm2 at Q200, and approximately 9 J/cm2 at Q400. Laser ablation often coincides with clearly visible, parallel stripes on the surface (Figure 7k,l,q,r). The distance between the stripes corresponds to the track offset and is typically an indication for a continuous remelting process. These characteristic stripes get only visible for Q100 at the highest fluence of 12 J/cm2 (Figure 7f). For Q200, these stripes are already hardly observable at 8 J/cm2 (Figure 7j) but get clearly visible at 10 J/cm2 (Figure 7k). Qualitatively, the same is true for Q400, but for lower fluences. Remelting stripes get hardly visible at approximately 7 J/cm2 (Figure 7o) but get more pronounced for fluences of approximately 8–10 J/cm2 (Figure 7p,q).

In particular, the surfaces remelted at high laser fluence appear to radiate a greater gloss (Figure 7q,r), i.e., to be significantly smoother at the micro-roughness level. The observed evaporation might lead to a comparatively small micro-roughness at the same time.


The updated online version of the original chapter can be found at **https://doi.org/10.1007/978-3-319-99713-1\_8**

# **Index**

#### **A**

*A priori* dimensionality reduction method, 139 Access policies, 47, 48 Accuracy, 110 Advanced CDSS, 153 Aggregation terminologies, 23 Alert fatigue, 158, 159 α level, 102 Analyze phase of DMAIC cycle, 189 Android (Google) apps, 171 Anonymisation, 45, 60 Anonymous data, 60 Apple HealthKit, 5, 172 Area Under the Curve (AUC), 113 Artificial Intelligence (AI), 122 Artificial neural networks (ANN) activation function, 128 advantages, 128 architecture, 126–128 classification, 127 disadvantages, 128, 129 dropout, 128 network weight initialization, 128 Autoregressive (AR) modeling, 96, 97

#### **B**

Bandpass filter, 90 Band-reject filter, 90 Bias-variance tradeoff, 106, 107 Big (clinical) data, *See* Big data Big data, 5

exchange barriers, 16 outcome and cost measurement, 195 validity, 14 variety, 13 velocity, 13 veracity, 13, 14 virality, 14 viscosity, 14 volatility, 14 volume, 13 Biopsychosocial model, of medicine, 199 Brier score, 109

#### **C**

Calibration plot, 114, 115 Care process, 182–184 optimization (*see* Operational Excellence (OE)) (*see* Process mining) Categorical variables, 104 CDSS, *See* Clinical decision support systems (CDSS) CGA model, 30–32 CHARMS checklist, 145, 148 Clinical data, *See* Big data Clinical data standards, 20, 21 clinical terminology systems, 23–25 (*see also* Healthcare data standards, implementation of) quality and usability, 25, 26 semantic interoperability, 25

Clinical decision support systems (CDSS) adaption in practice, 165 advanced, 153 basic/simple, 153 challenges, 158 high adoption and effective use, 158–160 integration in clinical workflow, 160 updated clinical rules, 161, 162 computerized physician order entry, 154, 157 decision tree models, 155, 156 decision-making process/model, 155 electronic health records, 154 future perspectives, 166 high-quality, 154 human computer interaction, 155 medication related, 157, 158 software verification and validation, 162, 163 structured development and validation of clinical rules, 163–165 style of communication, 155 types of, 154–157 user requirement specification, 162 Clinical rules, development and validation, 163 post-implementation prospective validation, 165 pre-implementation prospective validation, 165 technical validation, 164 therapeutic retrospective validation, 164 Clinical Trials Regulation (CTR), 65, 67 Clinician burnout, 207 Co-creation concept, 31 Confidence levels, 102 Conformité Européenne (CE), 176 Confusion matrix, 109, 110 Connectathons, 26 Context factors, 159, 160 Continuous variables, 104 Convolutional Neural Networks (CNN), 128 CoreML technology, 176 Cox proportional hazards regression model, 106, 79 Cox regression, 106 Critical appraisal, 136, 145, 146 Cross-validation, 117 *See also* Monte Carlo cross-validation

#### **D**

Data and reality, 20 Data elements, definition of, 7 Data exchange barriers, big data, 16 Data explosion, 12 Data fragmentation, 11 Data interchange formats, 7 Data landscape, 15 Data leakage, 117 Data management plan (DMP), 41 Data sharing policy anonymity, 52 with commercial party, 52 general considerations, 51, 52 Data sources big data, 5 electronic medical records, 3, 4 medical information systems, 3, 4 mobile apps, 4, 5 social media, 5 Data standards, 7 Data stewardship collaborating with patients, 41 data access, 43 data analysis plan, 49, 50 raw data preparation, 48, 49 in data collection phase, 45 data management infrastructure, 46 metadata, 46, 47 monitoring and validation, 46 security, 47, 48 data management plan, 41 definition, 38 FAIR Principles, 38, 39 Open Science, 38 operational workflow, 41, 42 privacy and autonomy, 43–45 care and research environment, 44, 45 informed consent, 44 research data anonymisation and pseudonymisation, 45 responsibilities of people involved, 39, 40 reusing data, 40, 41 scientific data archiving, 50 selecting file formats, 42, 43 sharing data policies, 51, 52 statistical analysis plans, 41 study design and registration, 40 Data types

free text, 7 images and videos, 7 tabular data, 6 time series data, 6 Data-driven medicine, 203 Deep learning, 128 Dimensionality reduction techniques, 108 Donabedian approach, 193

#### **E**

Economic inflexibility, 184 e-Health, 201 Electronic health records (EHRs), 3, 154, 198 Electronic medical records (EMRs), 3, 4 Electronic phenotyping, 196 Equiripple design, 91 Error types, in statistical testing, 103, 102 *e*Standards initiative *e*Standards compass alignment concept, 32 co-creation, 31 governance, 31, 32 respecting perspectives of stakeholders, 30 evidence-based roadmap, 28 reusing eHealth artefacts, 30 roadmap development methodology, 30–33 sustainable adoption and evolution, 28 trusted data flow, 29 EU data protection law, 56, 57 EU's General Data Protection Regulation (GDPR), 6 checklists, 70 and Clinical Trials Regulation, 65–67 compliance criteria, 64 compliance obligations, 55 controller and processor, 61 Data Protection Officer (DPO) appointment, 64, 65 data subject rights, 63, 64 hallmark of, 58 lawful processing of personal data, 61, 62 legal basis for data processing operation, 62, 63 legitimate interest legal basis, 69 modes of consent, in research context, 69 notion of processing, 61 omnibus approach, 57 personal data categories, 58–60

personal data transfer to third countries, 65 research exemption, 65, 67, 68 risk-based approach, 57 sensitive data and explicit consent, 63 technological-neutral approach, 58 temporal scope, 58 territorial scope, 61 Evidence-based clinical outcomes, 198 Evidence-based medicine (EBM) guideline, 164, 203 External validation, 117

#### **F**

F1-score, 111 FAIR principles, for clinical data, 21 False negative rate (FNR), 111 False positive rate (FPR), 111 Fast Fourier Transform (FFT), 94 Feature extraction methods, 108 frequency-domain processing, 93–97 time-domain processing, 85–93 time-frequency processing, 98, 99 time-series, 85 Feature selection, 108 Feed-forward neural networks, 127 Filter design considerations, 92, 93 Finite impulse response (FIR) filters, 91, 92 Flat passband/stopband, 92 Flexible algorithms, 106 Frequency-domain processing, 93 band power, 93, 94 Fourier transform, 93 spectral analysis autoregressive modeling, 96, 97 Fast Fourier Transform (FFT), 94, 95 Hamming and Hanning windows, 96 Fully homomorphic encryption (FHE), 60 Fundamental rights character of EU data protection law, 56

#### **G**

General Data Protection Regulation (GDPR), *See* EU's General Data Protection Regulation (GDPR) Google Fit, 5, 172 Google's Deepmind project, 7

#### **H**

Health Informatics Standards Life Cycle, 28 Healthcare data standards, implementation of conformance audit, 26 Connectathons, 26 *e*Standards initiative, 28–33 interoperability tools, 27 purposes, 26 shared tools, 27 W3C standards, 27 Hierarchical clustering, 131, 132 Hosmer-Lemeshow test, 114 Hotspotting method, 196 Human computer interaction, 155 Humanistic clinical practice, 207 Hybrid apps, 171 Hybrid framework, 171

#### **I**

Images and videos, 7 Improvement phase of DMAIC cycle, 189 Imputation method, 82, 83 Index-event bias, 77 Infinite impulse response (IIR) filter, 91, 92 Inflexibility, types of, 184 Inflexible algorithms, 106 Information model standard, on arterial blood pressure, 22 Institution-shifted validation cohort, 140 Integrity and confidentiality, of personal data, 62 Intellectual Property Rights, 43 Interface terminologies, 22 Internal validation techniques, 116, 117 International Consortium of Healthcare Outcomes Measurement (ICHOM), 197 Internet of Things (IoT), 5 iOS (Apple) apps, 171

#### **K**

Kaplan-Meier curves, 106 Karasek's high demand-low control model, 190 *k*-fold cross-validation, 116, 117 K-means, 129, 130 Knowledge creation, 205 Knowledge management for clinical information systems, 205 description, 205 Knowledge representation, 7

#### **L**

Laboratory information management system (LIMS), 4 Laboratory information system (LIS), 4 Lean Six Sigma, 186, 187 Lean Thinking, 185 Learning health system, 203, 204 Linear regression, 104 Logistic regression, 104

#### **M**

Machine learning in clinical prediction modelling, 122 online courses for, 105 semi-supervised algorithms, 123 supervised algorithms, 122 artificial neural networks, 126–129 random forests, 125, 126 support vector machines, 123, 125 unsupervised algorithms goal, 122, 123 hierarchical clustering, 131, 132 K-means, 129, 130 Medical data standards, 24, 25 Medication related CDSS, 157, 158 MEDLINE, 172 MijnIBDcoach software platform, 202 Missing at random (MAR), 82 Missing completely at random (MCAR), 82 Missing data, 12, 144 handling complete case analysis, 82 imputation methods, 82, 83 Missing not at random (MNAR), 82 Mobile apps, 4 bring your own device strategy, 175 health data collection, 172 mCDSS, 174–176 medical device regulations (MDR), 176 MEDLINE, 172 operating systems, 171 randomized clinical trial, 172–175 systematic review, 172 Monte Carlo cross-validation, 116 Multimorbidity, 198, 199 Multiple imputation, 83 Multivariate prediction modelling, 138

#### **N**

Natural language processing, 7 Negative predictive value (NPV), 110 Network medicine, 203 Network-based thinking, 203

Index

Non-computerized tools, 153 Null hypothesis, 102, 103

#### **O**

On-demand care services, 206, 207 Ontologies, 16, 23 Operational Excellence (OE) coaching style, 191 DMAIC-cycle, 184, 186 goal of, 184 leadership, 191 Lean Six Sigma, 186, 187 Lean Thinking, 185 Six Sigma technique, 186 sociotechnical systems, 191 Outcome measures, 194 Overfitting, 114, 138 reducing risk of *a priori* dimensionality reduction, 139 increasing sample size, 139 internal cross-validation, 138 repeated cross-validation, 139

#### **P**

Passive CDSS, 155 Patient-reported experience measures (PREMs), 197 Patient-reported outcome measures (PROMs), 197 Patient-reported outcomes, 197 Picture Archiving and Communication System (PACS), 4, 11 Population-shifted validation cohort, 140 Positive predictive value (PPV), 110 Post-implementation prospective validation, of clinical rules, 165 Pragmatics, 22 Prediction modelling, *See* Prediction models Prediction models, 77, 137 applicability, 146, 147 case control design, 78 clinical significance, 145 context, 142, 143 critical appraisal, 145, 146 cross-sectional design, 77 data pre-processing, 80 categorizing predictor variables, 81 transforming predictor variables, 80 visualizing data, 81 impact assessment and clinical implementation, 141 limitations, 145 meta-analysis, 148

missing data, 81–83, 144 model development, 137–139, 143 patient selection, 78 presentation, 144, 145 prospective cohort study, 77 quality reporting, 136, 141, 142 randomized controlled trial, 77, 78 retrospective cohort design, 77 risk of biased estimation, 147, 148 sample size considerations, 79–80 overfitted models, 79 predictor variables, 79 sample size rules-of-thumb, 79–80 sample size, predictors and predictor selection, 143 specification and performance, 144 statistical model, 144 systematic reviews, 148 transparent reporting, 136 transparent reporting guidelines, 142 updates, 140, 141 validation, 138–140 Prediction tools, types of, 76 Predictive model bias-variance tradeoff, 106, 107 calibration, 111–114 dimensionality reduction techniques, 108 discrimination performance, 110–114 external validation, 117 internal validation techniques, 116, 117 using linear and logistic regression, 103, 104 online courses for, 105 overfitting, 114 performance metrics confusion matrix, 109–111 general, 109 software packages for, 104, 105 splitting training/test sets, 114 underfitting, 115 validation, 114–117 Pre-implementation prospective validation, of clinical rules, 165 Prevalence, defined, 109 Principle of accountability, 58 PRISMA guidelines, 148 Probability of detection, 111 Process measures, 194 Process mining, 6 conformance checking, 189 discovery, 189 enhancement, 189, 190 event log, 187, 188 objectives and types, 187, 188 Prognostic modelling, 147

Pseudonymisation, 45 of personal data, 59 p-value, 102

#### **Q**

Quality improvement collaborative (QIC) model, 205 Quantitative clinical prediction models, 136

#### **R**

Radiology information system (RIS), 4 Random forests (RF), 125, 126 Random split, 116 Recall, 111 Receiver Operating Characteristic (ROC) curve, 112, 113 Recurrent neural networks, 127 Reference terminology, 22 Regression techniques, 75 Regularization methods, 108 Research data stewardship, *See* Data stewardship ResearchKit, 172 ResearchStack.org., 172 Reusable data sources, 41 Rippled passband/stopband, 92 R-squared measures of goodness of fit, 109 Rubin's Rules, 83

#### **S**

Safety measures, for research data, 48 Scientific data archiving, 50, 51 Secure multi-party computing (SMC), 60 Semantics, 22 Semi-supervised algorithms, 123 Sensitive personal data, 56, 59 Sensitivity, 111 Setting-shifted validation cohort, 140 Six Sigma technique, 186 SNOMED CT concept, 23 Social determinants, of health, 199 Social media, 5 Sociotechnical approach, to health information technology, 204 Sociotechnical systems (STS), 190, 191 Socio-technique, *See* Sociotechnical systems (STS) Special categories of personal data, 56, 59 Specificity, 111 Staff inflexibility, 184

Statistical analysis plans, 41 Statistical hypothesis testing, 101–103 Structural measures, 194 Supervised algorithms, 122 artificial neural networks, 126–129 random forests, 125, 126 support vector machines, 123–125 Support vector machines (SVMs) advantages and disadvantages, 124, 125 examples, 123, 124 kernel, 124 margin, defined, 124 soft margin constant, 124 Survival analysis, 106 Syntax, 22 Systematic reviews, of prediction models, 148

#### **T**

Tabular data, 6 Technical inflexibility, 184 Technical validation, of clinical rules, 164 Terminologies, 7 Therapeutic retrospective validation, of clinical rules, 164 Thesauri, 23 Time-domain processing, 85 peak-picking, 85 template matching, 87 time-locked averaging, 86, 87 weighted moving averages with feedback, 91–93 frequency filtering, 88–93 Time-driven activity-based costing (TDABC) approach, 200, 201 Time-frequency processing, 98, 99 Time-series, 6 feature extraction methods (*see* Feature extraction methods) Time-shifted validation cohort, 140 Time-to-event analysis, 106 Tracebook, 161 Triggers, 159 TRIPOD checklist, 142, 144, 145 True positive rate (TPR), 111 Type I error, 102 Type II error, 102

## **U**

Underfitting, 115 Unstructured data, 12 Unstructured information, 12 Index

Unsupervised algorithms goal, 123 hierarchical clustering, 131, 132 K-means, 129, 130 User requirement documentation (URD), 162 User requirement specification (URS), 162

#### **V**

Value agenda, 193, 194, 206 Value in health care, 193 Value-based health care costing analysis, 201 education, 206 and innovation, 201, 202 learning health system, 203, 204 on-demand care services, 206

outcome measurement, 195 cohort identification, 195–197 evidence-based clinical outcomes, 198 multimorbidity, 198, 199 patient-reported outcome measures, 197 social determinants, 199 sociotechnical considerations, 204–206 Variance, 106 Vendor Neutral Archive (VNA), 4

#### **W**

W3C standards, 27 Waiting time *vs.* utilization rate, 183 Waiting time, for patient, 182 Wavelets, 98, 99


*2.4. Stability Tests*

Sample preparation and stability tests were performed according to UNI EN ISO 10993-9 [34], ISO 10993-13 [35] and ISO 13871:1995 [36] guidelines. The test was performed in triplicate, and was designed over 5 study times (from a few seconds to 15 days). For each study time, DAC® hydrogel was uniformly distributed over the roughened surface of the titanium disk. The system was topped with a second titanium disk, obtaining a "sandwich". Each double disk system was immersed in PBS solution at pH 7.2, each container was closed and placed at 37 ◦C, without stirring, to simulate the physiological condition of the medullary cavity. At each study time, for each sample, the double disk system was separated from its buffered solution, and disk samples and solutions were freeze-dried separately. For the initial study time, sudden disk removal, no sample is recovered from the buffer solution. For the final study time, 15 days, no sample is recovered from the interface of the two disks. A total of 24 samples are therefore recovered for the High Performance Liquid Chromatography (HPLC) and NMR study (Table 1).

**Table 1.** The 24 samples of HA-PLA (Novagenit DAC®) recovered for HPLC and NMR study both from gel on disk and in buffer solution as indicated (see text).

Sterile titanium disks 8.0 cm diameter and 0.4 cm thick were purchase by Adler Ortho (Via dell'Innovazione, 9, 20032 Cormano, Italy). Titanium disks 6.5 cm diameter and 0.4 cm thick were purchased by Adler and sterilized by gamma irradiation.