**6. Techniques and technologies**

164 Otolaryngology

The method anesthesia induction and patients' positioning is similar for most patients undergoing tonsillectomy, regardless of which technique is used to remove the tonsils. The patient is placed in the supine position and orally intubated.The endotracheal tube is taped to the patient's chin in the midline. Alternatively, some practitioners prefer to use a laryngeal mask airway.14,15The bed is turned 90°-180° so that the surgeon can sit or stand at the head of the bed. The patient is positioned at the edge of the bed, and a small shoulder roll is placed. Either a Crowe-Davis, McIvor, or Dingman mouth gag is inserted and expanded to keep the mouth open for the duration of the procedure.Tissue tension during complete tonsillectomy is achieved by strong medial traction of Allis clamps and torsion of

50 patients (30 males, 20 females) were studied. 28(56%) had adenotonsillectomy, while 22(44%) had only tonsillectomy alone. The data was obtained by the author from three centres:University of Ilorin teaching hospital, Kwara state, IBB specialized hospital Minna,

35(70%) of the surgeries were performed using cold surgical dissection technique, while 15(30%) were done using bipolar electrocautry. All the surgeries were performed by one of two experienced surgeons. All the children were kept overnight; some were discharged the following day after they can tolerate liquid diet. Routine antibiotics and analgesics were

For the 28 patients who had adenotonsillectomy, the mean operating time for the 8 bipolar

For the 22 patients who had tonsillectomy alone, the mean operating time was 31.4 minutes

Complication of hemorrhage were seen in 3(6.0%) of all the patients, 2(66.7) were in cold

electrocautries was 42.0 minutes and 47.2 minutes for the 20 cold dissection patients.

for the 7 bipolar electrocautries, while 34.2 minutes for the 15 cold dissections.

The tonsils are then removed using 1 of the techniques described later.

**4.2 Surgical procedure**

the tonsils medially (Figure 3).

Fig. 3. Medial Traction on the Tonsil.

Niger state and Federal medical centre, Azare, Bauchi state.

dissection group and 1(33.3%) in bipolar electrocautry group.

**5. Patients and methods**

given to all the patients.

The techniques of Tonsillectomy can be broadly divided into 2 major categories: extracapsular (total tonsillectomy, subcapsular) and intracapsular (partial tonsillectomy). Intracapsular is also known as "subtotal," and this procedure is referred to as tonsillotomy in some literatures. Extracapsular tonsillectomy involves dissecting lateral to the tonsil in the plane between the tonsillar capsule and the pharyngeal musculature, and the tonsil is generally removed as a single unit. Partial tonsillectomy, or tonsillotomy, involves removal of most of the tonsil, while preserving a rim of lymphoid tissue and tonsillar capsule in the most recent iteration of this older technique.16 Preservation of this margin of tissue, this "biologic dressing," may promote an easier recovery, with lower hemorrhage rates and better recovery of diet and activity reported in comparison with traditional monopolar tonsillectomy techniques.17,18The most common extracapsular techniques use a "cold" knife (sharp dissection), monopolar electrocautery, bipolar cautery (or bipolar scissors),or harmonic scalpel. Intracapsular techniques may use the microdebrider, bipolar radiofrequency ablation (which can also be used to remove the entire tonsil), and carbon dioxide laser. Either extracapsular or intracapsular tonsillectomy can be performed for the pediatric patient with obstructive sleep apnea, but only extracapsular techniques should be used for patients undergoing tonsillectomy as a result of tonsillitis or peritonsillar abscess. In addition, tonsils can be ablated using a laser or monopolar radiofrequency (somnoplasty) in a cooperative adult in a clinic setting.


**1. Introduction**

*Mycobacterium tuberculosis*, the etiological agent of human tuberculosis (TB), represents a global challenge to human health since it is the main cause of death by an infectious disease worldwide. Estimations by the World Health Organization (WHO) reported that the tubercle bacillus latently infects approximately one fourth of the world's population, and it is responsible for more than one million deaths every year [1]. Additional factors such as immunodeficiencies [2] and diabetes [3] increase the risk of developing active TB.

The currently available anti-TB therapy is composed of four antibiotics (rifampicin, isoniazid, pyrazinamide and ethambutol) that must be administered for at least 6 months to patients affected by drug-sensitive pulmonary TB [4]. However, the increasing number of multi- and extensively drug-resistant TB cases [5] requires the use of second- or even third-line anti-TB medications, which are characterized by frequent severe side-effects that reduce patients' compliance [6].

Feeding the drug discovery pipeline with the identification of novel chemical entities and promoting the development of those candidate drugs that are presently in clinical trials are therefore of outmost importance in order to shorten anti-TB treatment.

In this Special Issue of Applied Sciences dedicated to "Tuberculosis Drug Discovery and Development", we review the most recent achievements in drug and target identification and present an update on the clinical development of two candidate compounds (macozinone and delpazolid). An overview of technical advancements is included, together with a summary of the anti-TB vaccines which are either in the discovery or clinical phases.


**Abstract**

In this chapter we recall the well-known hysteresis Preisach model, widely employed in the area of magnetism. Some applications of this model in electrical engineering are also described, with a specific focus on the estimation of electromagnetic losses in electrical machines, the simulation of magnetizationdemagnetization processes arising in magnetic particle inspection, and the mathematical modeling of batteries for electric vehicles.

**Keywords:** magnetic hysteresis, Preisach model, electromagnetic losses, magnetic particle inspection, batteries for electric vehicles


**About the Editors**

#### **Grzegorz Mentel**

Grzegorz Mentel, DSc. PhD has been working at the Rzeszow University of Technology at the Faculty of Management since 2000 and is Head of the Department of Quantitative Methods. Between 2010–2012, he was the director of Bank Pocztowy S.A. in Rzeszow. As an author, he has published extensively on a wide variety of topics including the energy market, finance and capital markets, as well as having produced monographs on the risk of financial instruments. Furthermore, he is a specialist in security risk management, fundamental analysis, technical analysis, multidimensional analysis and forecasting.

#### **Sebastian Majewski**

Sebastian Majewski (Prof. Dr) currently works at the Institute of Economics and Finance, Department of Sustainable Finance and Capital Markets, University of Szczecin. His broad research interests lie in Sport Finance, Behavioral Finance, Sustainable Economics, Financial Economics and Econometrics. His current project is 'The football club valuation'.


**3. Results and discussion**

As a result of the performed analysis the variables pooled in factor groups **(F)** affecting the surgery prognosis were determined: **F1** – blood supply disturbance (HF, NYHA FC), **F2** – physical parameters (gender, age\*, weight\*, height\*, body surface area\*, Ketle index\*, CTI\*), **F3** – hemodynamic parameters (SBP\*, DBP\*, MBP\*, BSV, HR\*, BMV\*, TPR\*, SPR,HI\*, LV stroke work\*), **F4** – heart parameters (EDD\*,ESD\*, EDV\*, ESV\*, SV\*, EF\*, FS\*, RF\*, SVE\*, RV\*,LA\*, RA\*, PA\*), **F5** – myocardial parameters (IVS\*,LVPW\*, LVMM\*, sPLVWT and dPLVWT\*, 2HD\*), **F6** –valve morphology (calcification degree on AV, regurgitation degree on AV, MV, and TV), **F7** – valve parameters (FA and ascending aorta diameter\*, AV gradients\*, АО\* surface, МО\* surface, MV gradients\*,Еmv, Аmv, Е/А mv), **F8** – coronary blood supply parameters (blood supply type, percentage of coronary artery occlusion (LAD, DB, CA, RCA), number of planned bypass grafting). Indexed parameters, reverse values and second degree were considered in «\*» variables, it has been leading to increase in prognosis efficacy (see Table 2).

Revealing of Initial Factors Defining Results of Operation in

Patients with Aortic Valve Replacement and Coronary Artery Disease 23

coefficient (57)

septum thickness

LV wall thickness

LV wall thickness

LV wall thickness

7 LVEF\* % LVEF = 100\*(EDV-ESV)/EDV Ejection fraction 8 LVFS\* % LVSF = 100\*(EDD-ESD)/EDD Fractional shortening 9 RF % RF = ESV / EDV \* 100 Residual fraction (55) 10 SVE\* % SVE = EDV / ESV \*100 Systolic ventricular ejection (56) 11 TC\* TC = (EDV-ESV)/(EDD-ESD)\*1/ESV Ventricular wall tensility

12 RV\* сm Right ventricle 13 LA\* сm Left atrium 14 RA\* сm Right atrium 15 PA\* сm Pulmonary artery 16 PAP mmHg Pulmonary artery pressure 17 PA FAD mm PA fibrous annulus diameter **V Myocardial function parameters (F5)**

1 dIVST\* сm Diastolic interventricular

4 rsPLVWT\* U. rsPLVWT = dPLVWT / EDD Relative systolic posterior

5 rdPLVWT\* U. rdPLVWT = dPLVWT / ESD Relative diastolic posterior

6 2HD\* U. 2HD = (dIVST + dPLVWT)/EDD Relative double thickness

**VI Valve morphology (F 6)**  1 AVca score 1,2,3,4 AV calcification, degree 2 AVreg score 1,2,3,4 AV regurgitation, degree 3 MVreg score 1,2,3,4 MV regurgitation, degree 4 TVreg score 1,2,3,4 TV regurgitation, degree **VII Valve function parameters (F 7)**  1 ARD\* сm Aortic root diameter 2 AAD \* сm Ascending aorta diameter 3 AVppg\* mmHg AV peak pressure gradient 4 AVmpg\* mmHg AV mean pressure gradient 5 AVsfs m/s AV systolic flow speed 6 АО s\* cm2 Aortic orifice surface area

7 Е mv MV E peak 8 А mv MV А peak

2 dPLVWT\* сm Diastolic posterior

3 LVMM\* g LVMM = 1,04 \* ((EDD+VST+PLVWT)^3

**II Physical parameters (F 2)**

6 Ketle index\* U Ketle index = 10000\* Weight /Height^2 Ketle index (body weight index) 7 CTI\* % Cardiothoracic index **III Central hemodynamic parameters (F 3)**  1 SBP\* mmHg Systolic blood pressure 2 DBP\* mmHg Diastolic blood pressure 3 MBP\* mmHg MBP = DBP+[(SBP - DBP)/3] Mean blood pressure 4 PBP\* mmHg SBP-DBP Pulse blood pressure

0,61\*Age

7 CO\* l/min CO= SV \* HR / 1000 Cardiac output (blood supply)

9 RPR RPR = TPR /BSA Relative peripheral resistance

10 HI\* U HI =CO /BSA Heart index (109) 11 Asw\* U Asw(LV) = SV\*1,055\*(MBP-5)\*0,0136 LV stroke work (153) 12 LVMW U LVMW = 0,0136 \* 1,055 \*CO \* (MBP-5) LV minute work (157) 13 LVWI LVWI = 0,0136 \* 1,055 \* HI \* (MBP-5) LV work index (160) 14 LVWSI LVWSI = 0,0136 \* 1,055 \* SI \* (MBP-5) LV work stroke index (161) 15 HFi HFi= SBP\* HR /LVММ Heart functioning index **IV Heart parameters (F4)**  1 EDD\* сm End-diastolic dimension 2 ESD\* сm End-systolicdimension 3 EDV\* сm3 EDV= 7 \* EDD^3 / (2.4 + EDD) End-diastolic volume 4 ESV\* сm3 ESV = 7 \* ESD^3 / (2.4 + ESD) End-systolic volume 5 SV\* сm3 SV = EDV – ESV Stroke volume 6 SI\* u SI = SV / BSA Stroke index (108)

minute Heart rate

5TPR = 79,92\*MBP/CO Total peripheral resistance (59)

Height^0.725 Body surface areа

Blood stroke volume by Starr (39)

(110)

№ Variable Unit defenition Variable nomenclature **I Blood supply disturbance (F 1)**

1 HF I, IIА, IIB, III Heart failure 2 FC I , II, III, IV Functional class

1 Gender 1 - man, 2 – woman Patient gender 2 Age\* years Age 3 Weighr\* kg Weight 4 Height\* cm Height

5 BSA\* m2 BSA= 0.007184 \* Weight^0.423 \*

5 BSV BSV = 90,97 + 0,54 \* PBP - 0,57 \* DBP -

6 HR\* beat per

8 TPR\* dyne\*сm-

Revealing of Initial Factors Defining Results of Operation in

predictor for combined surgeries (Figure2).

0

Fig. 2. Correlation between prognosis and functional class

increase in afterload leads to decrease in LV work efficacy (Figure 3).

1

2

3

4

Patients with Aortic Valve Replacement and Coronary Artery Disease 25

valve functions (F7) (r=0.320 p<0.01), heart parameters (F4) (r=0.261 p<0.05), coronary blood supply parameters (F8) (r=0.046 p<0.05), hemodynamic parameters (F3) (r=0.284 p<0,05), and myocardial function parameters (F5)(r=0.589 p<0.001) have played greater role for peak systolic gradient (PSG). The parameters of the following factors affect changes in LV ejection fraction: heart parameters (F4) (r=0.381 p<0.01), hemodynamic parameters (F3) (r=0.332 p<0.01), coronary blood supply parameters (F8) (r=0.322 p<0.01), and valve function parameters (F7) (r=0.332 p<0.01). The positive surgery prognosis in patients with lower HF (r=-0.111) and lower NYHA FC (II, III) (r=-0.560) was higher than 80%. However, in operated patients with FC IV the surgery prognosis was less than 80%. It was noted that higher FC corresponded to lower LV EF values (r=-0.086). It means that FC IV is a high risk

0 20 40 60 80 100

Physical parameters (F2) suggested that PSG on AV had a trend to increase with age (r=0.264), i.e. compensated processes are progressing depending on age, although general biological and physiological processes are decreasing. However, age had no significant influence on surgery prognosis (r=-0.162). Moderate correlation between prognosis (r>0.31) and peak SPG (r>0,206) was observed when hemodynamic parameters were analyzed (F3). The correlation was direct for prognosis and reverse for SPG: e.g. in patients with CO more than 4.0 l/min surgery prognosis was higher. This parameter increased not due to HR, but due to minute volume (r=-0.215). Such pattern was observed between parameters of LV stroke work (Asw): surgery prognosis was higher if LV Asw was higher (r=0.468). But if SPG was increased, decrease in LV Asw was observed (r=-0.295). It may be concluded that

If peak SPG is more than 60 mmHg, LV Asw becomes less than 100 U, and favorable surgery prognosis does not exceed 80%. If stroke work was more than 100 U, positive surgery prognosis was 80-100%. It means that in patients with coronary artery lesions in combination with aortic defect SPG ≥ 60 mmHg is one of indications for aortic valve replacement. Heart parameters (F4) had the greatest influence on surgery prognosis. Thus,

NYHA FC Prognosis, %

Table 1. Risk factors and variables and their components

We determined that a percentage of complex factor influence on surgery prognosis – peak systolic gradient (PSG) and post-operation ejection fraction dynamics were different (Figure 1).

% influence on the prognosis % influence on the ppgAV % infl

Fig. 1. Percentage of complex factor influence on prognosis, PSG, LVEF in patients suffered from valve defect combined with coronary artery lesions

Thus, heart parameters (F4) (r=0.320 p<0.01),coronary blood supply parameters (F8) (r=0.165 p<0.05), F3 (r=0.330 p<0.01), valve function parameters (F7) (r=0.183 p<0.05), and physical parameters (F2) (r=0.223 p<0.05) had greater influence on prognosis. However,

10 MО s\* cm2 Mitral orifice surface area 11 MV ppg mmHg MV peak pressure gradient 12 MV mpg mmHg MV mean pressure gradient **VIII Coronary blood supply parameters (F8)** 1 CVG 1-right, 2- balanced, 3- left Blood supply type by CVG 2 LAD % Left anterior descending,

3 DB % Diagonal branch, lesion % 4 CA % Circumflex artery, lesion % 5 RCA % Right coronary artery, lesion % 6 IA % Intermediate artery, lesion % 7 No.of grafts pcs Number of grafts

We determined that a percentage of complex factor influence on surgery prognosis – peak systolic gradient (PSG) and post-operation ejection fraction dynamics were different (Figure 1).

**21.6**

**19.1**

% influence on the prognosis % influence on the ppgAV % infl

Fig. 1. Percentage of complex factor influence on prognosis, PSG, LVEF in patients suffered

Thus, heart parameters (F4) (r=0.320 p<0.01),coronary blood supply parameters (F8) (r=0.165 p<0.05), F3 (r=0.330 p<0.01), valve function parameters (F7) (r=0.183 p<0.05), and physical parameters (F2) (r=0.223 p<0.05) had greater influence on prognosis. However,

**16.5**

**16.4**

Table 1. Risk factors and variables and their components

**24.6**

**9.4**

**13.8**

from valve defect combined with coronary artery lesions

**16.5**

**23.9**

**18.9**

**6.1**

**6.1**

**11.2**

**6.6**

**3.1**

**3.4**

**F1**

**F2**

**F3**

**F4**

**F5**

**F6**

**F7**

**F8**

**14.3**

**7**

**10.2**

**19.7**

**5.2**

lesion %

**26.5**

9 Е/А mv U. Е/А mv = Е mv / А mv E/A ratio

valve functions (F7) (r=0.320 p<0.01), heart parameters (F4) (r=0.261 p<0.05), coronary blood supply parameters (F8) (r=0.046 p<0.05), hemodynamic parameters (F3) (r=0.284 p<0,05), and myocardial function parameters (F5)(r=0.589 p<0.001) have played greater role for peak systolic gradient (PSG). The parameters of the following factors affect changes in LV ejection fraction: heart parameters (F4) (r=0.381 p<0.01), hemodynamic parameters (F3) (r=0.332 p<0.01), coronary blood supply parameters (F8) (r=0.322 p<0.01), and valve function parameters (F7) (r=0.332 p<0.01). The positive surgery prognosis in patients with lower HF (r=-0.111) and lower NYHA FC (II, III) (r=-0.560) was higher than 80%. However, in operated patients with FC IV the surgery prognosis was less than 80%. It was noted that higher FC corresponded to lower LV EF values (r=-0.086). It means that FC IV is a high risk predictor for combined surgeries (Figure2).

Fig. 2. Correlation between prognosis and functional class

Physical parameters (F2) suggested that PSG on AV had a trend to increase with age (r=0.264), i.e. compensated processes are progressing depending on age, although general biological and physiological processes are decreasing. However, age had no significant influence on surgery prognosis (r=-0.162). Moderate correlation between prognosis (r>0.31) and peak SPG (r>0,206) was observed when hemodynamic parameters were analyzed (F3). The correlation was direct for prognosis and reverse for SPG: e.g. in patients with CO more than 4.0 l/min surgery prognosis was higher. This parameter increased not due to HR, but due to minute volume (r=-0.215). Such pattern was observed between parameters of LV stroke work (Asw): surgery prognosis was higher if LV Asw was higher (r=0.468). But if SPG was increased, decrease in LV Asw was observed (r=-0.295). It may be concluded that increase in afterload leads to decrease in LV work efficacy (Figure 3).

If peak SPG is more than 60 mmHg, LV Asw becomes less than 100 U, and favorable surgery prognosis does not exceed 80%. If stroke work was more than 100 U, positive surgery prognosis was 80-100%. It means that in patients with coronary artery lesions in combination with aortic defect SPG ≥ 60 mmHg is one of indications for aortic valve replacement. Heart parameters (F4) had the greatest influence on surgery prognosis. Thus,

Revealing of Initial Factors Defining Results of Operation in

Fig. 5. Influence of EDV and ESV on LV ejection fraction

Patients with Aortic Valve Replacement and Coronary Artery Disease 27

20 30 40 50 60 70 80 EDV ESV p/o LV EF,%

30 40 50 60 70 80 EDV p/o ESV p/o p/o LV EF,%

Fig. 6. Influence of p/o EDV and p/o ESV on p/o LV ejection fraction

Fig. 3. Correlation between prognosis with SPG and LV stroke work

Fig. 4. Correlation between SV and SI with surgery outcome

0 20 40 60 80 100

SV SI Prognosis, %

0 20 40 60 80 100 120 140 160 Asw Peak SPG

0 20 40 60 80 100

Fig. 4. Correlation between SV and SI with surgery outcome

A sw Prognosis, %

Fig. 3. Correlation between prognosis with SPG and LV stroke work

Fig. 5. Influence of EDV and ESV on LV ejection fraction

Fig. 6. Influence of p/o EDV and p/o ESV on p/o LV ejection fraction

Revealing of Initial Factors Defining Results of Operation in

numbers of baseline and postoperative LV EF (Figure 8).

30

hibernated myocyte.

40

50

60

70

80

Patients with Aortic Valve Replacement and Coronary Artery Disease 29

LV EF calculated using the program for prognosis significantly correlated with true

30 40 50 60 70

Fig. 8. Correlation of calculated LV EF with pre- and postoperative LV EF

baseline EF p/o EF calculated LV EF,%

Assessment of correlation between postoperative LV EF parameters and calculated ones using the program for surgery prognosis revealed a common pattern (trend lines had

Decrease in postoperative LV EF is caused by cardiopulmonary bypass, aortic occlusion, and cardioplegia through unfavorable influence on myocardial contractility in spite of coronary artery bypass grafting, procedure improving coronary blood supply, activation of

Analysis of myocardial function parameters (F5) showed that surgery prognosis is highly affected by posterior left ventricular wall thickness (PLVWT) (r=-0.306) and to lesser extent by interventricular septum thickness (IVST) (r=-0.072). Increase in IVST leads to greater increase in peak SPG rather than PLVWT (r=0.679 and r=0.526, respectively). It can be possibly explained by appearance of additional component of LV outflow tract obstruction as a hypertrophied IVS. When thickness of IVC and PLVW ranges from 1.5 to 2.0 cm, SPG is equal to 80-120 mmHg, and positive surgery prognosis is 80-100%. However, increased dimensions of IVS and PLVW lead to decrease in percentage of favorable prognosis. Degree of ejection fraction increase was mostly related to PLVWT (r=0.433) than to IVST (r=0.265), had no relation with LV myocardial mass (r=-0.113), although increase in myocardial mass improved surgery prognosis. Thus, optimal left ventricle myocardial mass (LVMM) value

similar direction of dynamics and were approximately at the same level) (Figure 9).

LV parameters had direct correlation with prognosis (r>0.224) and LV EF dynamics (r> 0.598) and reverse correlation with SPG (r<-0.343). LV end-diastolic dimension (EDD) and end-diastolic volume (EDV) had a greater influence on prognosis (r=0.349 and r=0.429, respectively), than LV end-systolic dimension (ESD) and end-systolic volume (ESV) (r=0.303 and r=0.352, respectively). Even in cases when increase in LV EDD (EDV) was observed after surgery and LV ESD (ESV) was constant (or decreased), possibility of favorable surgery prognosis was increased. This relationship between EDV and ESV contributes to increase in stroke volume (SV) and suggests preservation of LV myocardial contraction. The analysis showed that increased SV (r=0.458) and stroke index (SI) (r=0.385) was associated with increased percentage of favorable prognosis. We have found that if SI was >40 ml/m2 (SV=80 ml), positive surgery prognosis was more than 80% (Figure 4).

Analysis of influence of baseline EDV and ESV on postoperative LV EF has shown that this value was greater in patients with preserved LV parameters (Figure 5), and in patients with significant reduction of LV EDV and ESV (Figure 6).

The performed analysis revealed that in patients with normal LV myocardial contractility at baseline we had good prognosis and increased LV EF after surgery. It was determined that if LV EF is higher than 50% at baseline, the positive surgery prognosis exceeds 80%. Such pattern of baseline EDV and ESV influence on LV EF dynamics was observed, if LV EF parameters obtained from calculation using the program for prognosis were analyzed. (Figure 7).

Fig. 7. Influence of baseline EDV and ESV on calculated LV EF

LV parameters had direct correlation with prognosis (r>0.224) and LV EF dynamics (r> 0.598) and reverse correlation with SPG (r<-0.343). LV end-diastolic dimension (EDD) and end-diastolic volume (EDV) had a greater influence on prognosis (r=0.349 and r=0.429, respectively), than LV end-systolic dimension (ESD) and end-systolic volume (ESV) (r=0.303 and r=0.352, respectively). Even in cases when increase in LV EDD (EDV) was observed after surgery and LV ESD (ESV) was constant (or decreased), possibility of favorable surgery prognosis was increased. This relationship between EDV and ESV contributes to increase in stroke volume (SV) and suggests preservation of LV myocardial contraction. The analysis showed that increased SV (r=0.458) and stroke index (SI) (r=0.385) was associated with increased percentage of favorable prognosis. We have found that if SI was >40 ml/m2 (SV=80 ml), positive surgery prognosis was more than

Analysis of influence of baseline EDV and ESV on postoperative LV EF has shown that this value was greater in patients with preserved LV parameters (Figure 5), and in patients with

The performed analysis revealed that in patients with normal LV myocardial contractility at baseline we had good prognosis and increased LV EF after surgery. It was determined that if LV EF is higher than 50% at baseline, the positive surgery prognosis exceeds 80%. Such pattern of baseline EDV and ESV influence on LV EF dynamics was observed, if LV EF parameters obtained from calculation using the program for prognosis were analyzed.

> 30 40 50 60 70 80 EDV ESV calculated LV EF, %

80% (Figure 4).

(Figure 7).

significant reduction of LV EDV and ESV (Figure 6).

0

Fig. 7. Influence of baseline EDV and ESV on calculated LV EF

40

80

120

160

200

240

280

LV EF calculated using the program for prognosis significantly correlated with true numbers of baseline and postoperative LV EF (Figure 8).

Fig. 8. Correlation of calculated LV EF with pre- and postoperative LV EF

Assessment of correlation between postoperative LV EF parameters and calculated ones using the program for surgery prognosis revealed a common pattern (trend lines had similar direction of dynamics and were approximately at the same level) (Figure 9).

Decrease in postoperative LV EF is caused by cardiopulmonary bypass, aortic occlusion, and cardioplegia through unfavorable influence on myocardial contractility in spite of coronary artery bypass grafting, procedure improving coronary blood supply, activation of hibernated myocyte.

Analysis of myocardial function parameters (F5) showed that surgery prognosis is highly affected by posterior left ventricular wall thickness (PLVWT) (r=-0.306) and to lesser extent by interventricular septum thickness (IVST) (r=-0.072). Increase in IVST leads to greater increase in peak SPG rather than PLVWT (r=0.679 and r=0.526, respectively). It can be possibly explained by appearance of additional component of LV outflow tract obstruction as a hypertrophied IVS. When thickness of IVC and PLVW ranges from 1.5 to 2.0 cm, SPG is equal to 80-120 mmHg, and positive surgery prognosis is 80-100%. However, increased dimensions of IVS and PLVW lead to decrease in percentage of favorable prognosis. Degree of ejection fraction increase was mostly related to PLVWT (r=0.433) than to IVST (r=0.265), had no relation with LV myocardial mass (r=-0.113), although increase in myocardial mass improved surgery prognosis. Thus, optimal left ventricle myocardial mass (LVMM) value

Revealing of Initial Factors Defining Results of Operation in

and anterior myocardial infarction prior to surgery.

70%. It allows increasing the favorable surgery percentage.

duration of hospitalization.

**4. Conclusion**

Patients with Aortic Valve Replacement and Coronary Artery Disease 31

performed in patients with right dominance. Thus, greater number of grafts required corresponds to worse surgery prognosis (r=-0.312). Analysis of coronary artery lesions showed that significance of left descending artery (LAD) lesions, i.e. necessity of its grafting makes worse surgery prognosis (r=-0.303). It was also revealed that there is a direct correlation between grade of LAD lesion and value of mitral regurgitation (r=0.283). This suggests a significant role of LAD in coronary blood supply and it should be grafted if affected, especially in patients with combined lesion of aortic valve and coronary arteries. Our conclusions generally support the literature data. Analysis of the huge body of materials (108 687 aortic valve replacements) performed by Brown et al. in 2009 demonstrated that female gender, age above 70 years and ejection fraction less than 30% led to higher postoperative mortality, higher percentage of postoperative stroke, and prolonged

The authors confirmed the data published by Doenst et al. in 2006 on higher incidence of stroke in women during immediate postoperative period, and did not confirmed the data on a similar percentage of mortality. Although, Doenst et al. (2006) analyzed cases of combined CABG and valve replacement (1567 patients). But this also cannot be a final conclusion (combined interventions have worse results than that of one-organ surgeries). However, Thulin and Sjogren (2000) did not demonstrate any differences in the results of simple aortic valve replacement (121 patients) and valve replacement in combination with CABG (98 patients). Some investigators apart from hemodynamic parameters pay attention on the values of laboratory tests. Thus, Florath et al. (2006) showed that elevated blood levels of glucose, creatine kinase, lactate dehydrogenase, sodium, and proteins in patients prior to aortic valve replacement and CABG (908 patients) resulted in increased postoperative mortality. Jamieson et al. demonstrated results similar to our ones (2003). Bioprosthetic valve replacement and CABG was performed in 1388 patients. The mortality rate in NYHA I-II and NYHA IV was 2% and 16%, respectively. The mortality rate in men and women was 4.6% and 13.8%, respectively. Older patients more often required repeated interventions (59 versus 52 years). Nardi et al (2009) showed that surgery prognosis was worse in patients with low ejection fraction, history of paroxysmal ventricular tachycardia, renal insufficiency,

Patients with aortic valve lesion combined with coronary artery lesion are a severe group for surgical treatment and require intervention at early stages of the disease. NYHA FC IV is a high-risk predictor for combined surgeries CHD + CABG. We believe that systolic gradient ≥60 mmHg in patients assigned to CABG is an indication for combined aortic valve surgery. Analysis of LV linear and volume parameters revealed that LV diastolic dimension and diastolic volume had the greatest influence on prognosis in this patient group. iEDV/iESV ratio with SI>40 ml/ m2 (SV=80 ml) is a good prognostic sign allowing to predict a prognosis of more than 80%. The optimal LVMM value was 350-600 g (200-400 g/m2) in the presence of corresponding linear parameters of LV and IVS, when a surgery prognosis was higher than 80%, and baseline LVEF was more than 50%. Appearance of functional changes in MV (regurgitation grade >1) and TV (regurgitation grade >1) is a poor prognostic factor. LAD grafting in these patients is a required intervention, even is a lesion degree is less than

was 350-600 g (200-400 g/m2) in the presence of corresponding linear parameters of LV and IVS. In these cases, positive surgery prognosis was more than 80%. Increase in ejection fraction more than 50% was postoperatively observed especially in patients with such characteristics. Analysis of valve morphology parameters (F6) revealed that significance of aortic valve calcification increases in peak SPG (r=0.448), but not affecting surgery prognosis (r=0.172). Baseline AV regurgitation also does not influence on surgery outcome (r=0.263). We can see the possible explanation of this fact is that AV calcification in the patients was mostly caused by age-related sclerosis and rheumatoid degeneration with no elements of myocardial inflammation (myocarditis) and inflammation of conduction system.

Fig. 9. Correlation between postoperative EF and calculated LV EF

Decreased ejection fraction was observed in patients who had regurgitation on MV (r=-0.377) and TV (r=-0.313) exceeding Grade I, this also resulted in impairment of surgery prognosis. Analysis of valve function parameters (F7) demonstrated that lower baseline SBG value was associated with more favorable surgery prognosis (r=-0.284). When peak SPG was less than 80 mmHg, favorable surgery prognosis ranged from 90 to 100%. Therefore, in the patients with coronary artery lesions aortic valve replacement should be performed at the early stages of defect manifestations when a systolic gradient is 60-80 mmHg. Analysis of coronary blood supply factor (F8) showed that patients with right dominance had worse surgery prognosis than patients with left dominance. Analysis demonstrated that among patients with right dominance only one artery was grafted in 41.9% patients, and 58.1% patients had two grafted arteries (35.5%) or more (22.6%). However, among patients with left dominance, one artery was grafted in 66.7% patients and only 33.3% patients had two (22.2%) or more (11.1%) grafted arteries, i.e. we see that the larger grafting volume was performed in patients with right dominance. Thus, greater number of grafts required corresponds to worse surgery prognosis (r=-0.312). Analysis of coronary artery lesions showed that significance of left descending artery (LAD) lesions, i.e. necessity of its grafting makes worse surgery prognosis (r=-0.303). It was also revealed that there is a direct correlation between grade of LAD lesion and value of mitral regurgitation (r=0.283). This suggests a significant role of LAD in coronary blood supply and it should be grafted if affected, especially in patients with combined lesion of aortic valve and coronary arteries. Our conclusions generally support the literature data. Analysis of the huge body of materials (108 687 aortic valve replacements) performed by Brown et al. in 2009 demonstrated that female gender, age above 70 years and ejection fraction less than 30% led to higher postoperative mortality, higher percentage of postoperative stroke, and prolonged duration of hospitalization.

The authors confirmed the data published by Doenst et al. in 2006 on higher incidence of stroke in women during immediate postoperative period, and did not confirmed the data on a similar percentage of mortality. Although, Doenst et al. (2006) analyzed cases of combined CABG and valve replacement (1567 patients). But this also cannot be a final conclusion (combined interventions have worse results than that of one-organ surgeries). However, Thulin and Sjogren (2000) did not demonstrate any differences in the results of simple aortic valve replacement (121 patients) and valve replacement in combination with CABG (98 patients). Some investigators apart from hemodynamic parameters pay attention on the values of laboratory tests. Thus, Florath et al. (2006) showed that elevated blood levels of glucose, creatine kinase, lactate dehydrogenase, sodium, and proteins in patients prior to aortic valve replacement and CABG (908 patients) resulted in increased postoperative mortality. Jamieson et al. demonstrated results similar to our ones (2003). Bioprosthetic valve replacement and CABG was performed in 1388 patients. The mortality rate in NYHA I-II and NYHA IV was 2% and 16%, respectively. The mortality rate in men and women was 4.6% and 13.8%, respectively. Older patients more often required repeated interventions (59 versus 52 years). Nardi et al (2009) showed that surgery prognosis was worse in patients with low ejection fraction, history of paroxysmal ventricular tachycardia, renal insufficiency, and anterior myocardial infarction prior to surgery.


**5. Pharmacology**

Given the affiliation of cathinone derivatives to beta-keto amphetamines, mephedrone would be expected to act as a Central Nervous System stimulant. In vitro studies on the effects of the cathinone derivatives methcathinone and methylone confirm that the main mechanism of action is very similar to that of amphetamine, being characterised by a predominant action on plasma membrane catecholamine transporters (Cozzi et al., 1999). The presence of the ring substituent on the phenethylamine core modifies the

Mephedrone-Related Fatalities in the United Kingdom: Contextual, Clinical and Practical Issues 359

The effects of mephedrone have been compared by users variously to those of cocaine, amphetamine and MDMA. Self-reported subjective effects may include (Winstock et al., 2011; Deluca et al., 2009): intense stimulation and alertness, euphoria; empathy/feelings of closeness, sociability and talkativeness; intensification of sensory experiences; moderate

Dargan et al. (2010) report that some 56% of those who had used mephedrone may complain of at least one unwanted effect associated with mephedrone use. These may include (ACMD, 2010; Deluca et al., 2009; James et al., 2010; Wood et al., 2009, 2010b): loss of appetite, nausea, vomiting and stomach discomfort; tremors, headache (very common), dizziness/light-headedness, seizures, nystagmus, pupil dilation, blurred vision, numbness of tactile sensitivity (reported at higher dosages); anxiety, confusion, dysphoria, aggression, depression, long-lasting hallucinations, paranoid delusions, short-term psychosis, shortterm mania, insomnia and nightmares, impaired short-term memory, poor concentration, tachycardia, elevated blood pressure, respiratory difficulties, chest pain. Possibly due to vasoconstriction, users have anecdotally described cold/blue fingers. Of particular interest are recent reports of clinical significance: severe refractory left ventricular failure (Chhabra et al., 2010); and acute myocarditis (Nicholson et al., 2010). Further unwanted effects may include: difficulties in urination, possible nephrotoxicity, anorgasmia; changes in body temperature regulation, with hot flushes and sweating; immunological toxicity (vasculitis, infections and ulcerations); posterior reversible encephalopathy syndrome (Omer &

Most of the above untoward effects seem to be similar to those already documented for amphetamine, methylamphetamine and MDMA (Schifano et al., 2010), implicitly supporting a sympathomimetic activity of mephedrone. Conversely, symptoms of depression and anhedonia could be tentatively associated to a putative depletion of serotonin and dopamine as a consequence of drug use (ACMD, 2010), similarly to what may occur with other stimulants (Schifano, 1996). It is impossible to determine a 'safe' dose for mephedrone since negative side-effects may present in association with any dosage taken. Furthermore, similar dosages may have dramatically different consequences in different

During the last few months of 2009 and the first few months of 2010, the UK media were constantly reporting fatalities allegedly related to mephedrone consumption, but only a proportion of them had by that time been formally confirmed. A report on a mephedronerelated fatality first appeared in Sweden, referring to an 18-year-old female death which occurred in December 2008. No other drugs, apart from mephedrone, were identified by the toxicological screenings (Gustaffson & Escher, 2009). Previously, a Danish teenager found in possession of mephedrone died in May 2008, although toxicology reports were inconclusive (Campbell, 2009). The first mephedrone-related death in the USA involved the combined use of mephedrone and heroin (Dickson et al., 2010).

Doherty, 2010); and finally serotonin syndrome (Garrett & Sweeney, 2011).

**7. Adverse effects**

individuals (Dickson et al., 2010).

**8. Fatalities**

been reported.

pharmacological properties by giving the compound some MDMA-like effects (Europol-EMCDDA, 2010). Cathinones' potencies are mostly lower than those of amphetamines as beta-keto amphetamines show a reduced ability to cross the blood–brain barrier due to the presence of the beta group (Nagai et al., 2007; Gygi et al., 1996).

N-demethylation to the primary amine, reduction of the keto moiety to the respective alcohol, and oxidation of the tolyl moiety to the corresponding alcohols and carboxylic acid is the major metabolic pathway for mephedrone, followed by N-dealkylation.

#### **6. Routes of administration, dosage, use in combination with other drugs, effects**

The most common routes for recreational use include insufflation (snorting) and oral ingestion. Because of its solubility in water, mephedrone is reportedly used by rectal administration or injected intravenously. Other typical methods of intake include oral ingestion as capsules or tablets; swallowing mephedrone powder wrapped up in cigarette paper (bombing); or mixed with water. Insufflation is likely to be the most common modality as, when snorted, mephedrone elicits its effects within a few minutes, with the peak being reached in less than 30 min followed by a rapid comedown. According to online users, the mephedrone dosage for snorting may range between 25 and 75 mg, with a lower threshold at 5–15 mg and levels in excess of 90 mg considered a high dosage (Sumnall and Wooding, 2009). Dosing is more frequent when taken intranasally; this route is allegedly associated with greater abuse liability than the oral route (Winstock et al., 2010, 2011). On average, the most common oral dosages are higher than the snorting ones (Sumnall & Wooding, 2009), in the range 150 to 250 mg.

Time of onset may be from 45 min to 2 h and may vary in association with the amount of food in the stomach. Because of this, users suggest taking mephedrone on an empty stomach. Psychoactive effects may last longer (up to 2–4 h) with oral ingestion; side-effects might be milder and the need to re-dose less urgent. Some users employ both insufflation and oral ingestion in combination to obtain faster onset and long-lasting effects (Deluca et al., 2009). Users report that rectal administration is characterised by faster onset of the effects and requires lower doses, e.g. 100 mg on average than oral ingestion (Deluca et al., 2009). Although not typically advised, because this may increase the drug's addictive liability levels (Deluca et al., 2009), mephedrone may also be injected either intramuscularly (Wood et al., 2010a) or intravenously, at one half or two-thirds of the oral dose (Deluca et al., 2009). According to online user fora, mephedrone may be taken in combination with a number of stimulants, sedatives and psychedelics (Deluca et al., 2009; Schifano et al., 2011).

As mephedrone has the capacity to induce tolerance on repeated dosing, an increasing number of user reports have stated a quick progression to either regular drug use and/or uncontrolled bingeing behaviour (known as 'fiending'), with 1–4 g of mephedrone consumed in a session to prolong the duration of its effects (Deluca et al., 2009; Europol-EMCDDA, 2010; Dargan et al., 2010). A recent survey carried out by a drug-related web site has unveiled an average monthly use of 11.16 g for each mephedrone consumer (Drugsforum, 2010). Although withdrawal symptoms are not commonly reported, users often display strong cravings for mephedrone (Newcombe, 2009).

The effects of mephedrone have been compared by users variously to those of cocaine, amphetamine and MDMA. Self-reported subjective effects may include (Winstock et al., 2011; Deluca et al., 2009): intense stimulation and alertness, euphoria; empathy/feelings of closeness, sociability and talkativeness; intensification of sensory experiences; moderate sexual arousal; and perceptual distortions (reported with higher dosages only).


*Edited by Mithun Rudrapal*

This book, *Drug Metabolism and Pharmacokinetics*, delivers the latest information on biotransformation and other pharmacokinetic parameters required to assess drug safety in drug discovery and development. Drug metabolism and pharmacokinetics (DMPK) is considered a rational strategy for the design, optimization, and selection of successful drug candidates in the pre-clinical stages of drug discovery. The evaluation of various pharmacokinetic parameters such as aqueous solubility, lipophilicity, cell permeability, bioavailability, protein binding, and metabolism and elimination half-life, drug–drug interactions (DDIs), and toxicokinetics is essential for a candidate drug to become a successful therapeutic agent. This book discusses recent advances in in-silico tools and in vitro/in vivo experimental techniques applied in pharmacokinetic, metabolomic, and bioanalytical studies. Key Features: • Presents pharmacokinetic and biotransformation parameters used for pre-clinical safety evaluations • Depicts experimental and biophysical techniques used in bioanalytical and metabolomic studies • Summarizes in silico, in vitro, and in vivo methods used in drug–enzyme interactions, drug– drug interactions, and toxicokinetics Written by a global team of experts, this book is a valuable resource for drug discovery scientists, drug developers, medicinal chemists, pharmacologists, biochemists, clinicians, biomedical scientists, healthcare professionals, researchers, teaching faculty, and students.

*Rosario Pignatello, Pharmaceutical Science Series Editor*

ISBN 978-0-85014-147-4

Drug Metabolism and Pharmacokinetics

ISSN 3033-3318

Published in London, UK © 2024 IntechOpen © LucaDAddezio / iStock


**Abstract**

The gut virome consists of a large population of eukaryotic and prokaryotic viruses that have an emerging role in human health and disease. Growing evidence for the importance of the virome includes recent findings on fecal virome transplantation (FVT) that suggest FVT may have therapeutic potential for the resolution of dysbiosis and treatment of dysbiosis-related disorders. Most viruses in the gut virome are bacteriophages (phages), which have a well-established role in regulating bacterial communities across environments. Phages also influence health and disease by interacting directly with the host immune system. The full extent to which gut phages should be considered as both a target and a tool for microbiome modulation remains to be seen. This chapter will explore the current understanding of the gut virome and the therapeutic potential for FVT.

**Keywords:** dysbiosis, virome, bacteriophage, phage therapy, microbial therapeutics


**2. Materials and Methods**

Bi1-xSmxFe0.98Mn0.02O3 thin films (x = 0, 0.02, 0.04, 0.06) were prepared by the sol-gel method on ITO/glass substrate. Fe(NO3)3·9H2O (purity of 98.5%), Bi(NO3)3·5H2O (purity of 98.5%), Sm(NO3)3·6H2O (purity of 98.5%), and MnC4H6O4·4H2O (purity of 98.5%) were taken as solutes, according to stoichiometric ratio. Bi excess of 5% compensated for bismuth volatilization during high-temperature annealing. The solutes were successively added to a solvent mixture of CH3COOH and C2H6O, with a volume ratio of 3:1, and stirred at room temperature at a uniform speed until completely dissolved. Then, C5H8O2 was added to the solution as a chelating agent and stirred at room temperature for 12 h at a constant speed to obtain a red-brown and transparent precursor solution. Finally, the stable precursor solution of 0.3 mol/L was obtained by allowing the precursor solution to rest for 24 h. Then, the BSFM precursor solution was rotated onto ITO/glass substrate, and the film was coated at 3500 r/min. The wet film was dried on an electric heating plate at 250 ◦C to remove excess organic solvents and water. It was then placed in an annealing furnace and annealed at 550 ◦C. The coating was repeated, and the film was dried and annealed 10 times to obtain the desired samples.

Before testing the electrical properties of the sample, Au was sputtered on the surface of the sample to achieve the effect of conduction. We used a small-ion sputtering instrument (JS-1600, Beijing Hetong Venture Technology Co., Ltd., Beijing, China) to complete this process. The samples were characterized by an X-ray diffractometer (D8-Advance, Drudy, Germany) recorded in the 2-theta range of 20–60◦ with a step of 0.02◦, and by a microconfocal Raman spectrometer (HR800, LabRAM, Horiba Co., Palaiseau, France) to measure in the shift range of 50–650 cm−1. The Fe and O elements in the samples were analyzed by a Wscabb X-ray photoelectron spectrometer. A dielectric tester (TH2828, Xintonghui Electronics Co., Ltd., Suzhou, China) was used to test the dielectric properties of the samples in the range of 1 kHz–1 MHz with an oscillation voltage of 1 V. The ferroelectric properties at 1 kHz and leakage properties of the samples were measured via a multiferroic tester (Radiant Co., Albuquerque, NM, USA).


**1. Introduction**

Maize is an important food crop, feed crop, and cash crop [1]. Compared with wheat and rice, maize has larger embryos, and its moisture content at harvest reaches about 30%, higher than the 25% and 22.5% of wheat and rice, respectively, which makes it more susceptible to mold infection during growth and storage [2]. Among them, *Aspergillus flavus* is the strain that most easily and commonly infects maize; aflatoxin (AFB1), produced by *Aspergillus flavus*, is extremely carcinogenic and toxic, and is the most toxic mold secondary metabolite in contaminated food [3,4]. Humans and animals eating food contaminated with AFB1 is a serious threat to life and health safety. Therefore, the early real-time detection of moldy maize has very important research significance.

Traditional detection methods for moldy maize include sensory evaluation and physical and chemical component detection [5]. Sensory evaluation is simple, time-saving, and low cost, but the evaluation results are easily disrupted by the external environment and the subjective emotions of the inspectors. Additionally, the toxic substances will also pose a threat to the health of the inspectors. The physical and chemical component determination is generally detected by high-performance liquid chromatography (HPLC), polymerase chain reaction (PCR), and enzyme-linked immunosorbent assay (ELISA) [6–8]. Although these methods can achieve more accurate measurement and qualitative analysis, they require expensive testing equipment and professional technicians, and the testing process

**Citation:** Wang, W.; Huang, W.; Yu, H.; Tian, X. Identification of Maize with Different Moldy Levels Based on Catalase Activity and Data Fusion of Hyperspectral Images. *Foods* **2022**, *11*, 1727. https://doi.org/10.3390/ foods11121727

Academic Editor: Corrado Costa

Received: 6 May 2022 Accepted: 10 June 2022 Published: 13 June 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/).

is complex and time-consuming [9]. Hence, using traditional methods, it is difficult to achieve simple, rapid, and non-destructive detection, which cannot meet the actual needs for identification of kernel maize.

In recent years, non-destructive detection technologies, such as electronic nose, machine vision, near-infrared, and hyperspectral imaging, have been successfully applied to the classification of moldy maize [10,11]. Electronic nose technology [12] is mainly used specific sensors to identify the level of mold based on the change of volatile organic compounds (VOCs) information. Leggieri et al. [13] used electronic nose technology to determine the concentration of AFB1 and fumonisins (FBs) in maize; the prediction model of AFB1 and FBs built by the artificial neural networks were 78% and 77%, respectively. However, the concentration of gas is easy to change in the flow state, which affected the discrimination accuracy. Machine vision detection mainly adopts machine learning algorithms to extract features from kernel images and then establish the classification model based on extracted features [14]. Shi Ying [15] extracted R-channel eigenvalues of RGB images (red, green and blue three-channel color image) and classified maize kernel samples with different levels of mold using the Back Propagation (BP) neural network. Visible-near infrared (Vis-NIR) spectroscopy technology connects the spectral information with the internal content of substances and uses the spectral curve to analyze the changes of internal components of seeds in the process of mold growth [16]. Therefore, the machine vision and Vis-NIR spectroscopy can express the external and internal changes of the target samples respectively. However, both internal quality and external characteristics of maize will change during the moldy process; neither machine vision nor Vis-NIR spectroscopy can obtain internal and external quality information at the same time. Hyperspectral imaging technology combines spectral analysis technology with image processing technology, which can simultaneously obtain the spectral data with internal component information and the image data with appearance feature information, realizing the rapid, pollution-free and non-destructive detection [17–19].

In terms of using hyperspectral imaging technology to identify moldy maize, Tao et al. [20] used random frog (RF) combined with partial least-squares discriminant analysis (PLS-DA) to qualitatively analyze the healthy maize and polluted maize inoculated with aflatoxigenic fungus at different culture days based on long wave near-infrared (LWNIR) hyperspectral images; the classification accuracy of the calibration set and verification set was 82.3% and 94.9%, respectively. Williams et al. [21] evaluated the fungal development in maize kernels using LWNIR; principal component analysis (PCA) was firstly used to remove the interference of noise, such as background, bad pixels, and shadows, from the hyperspectral images. Three distinct clusters related to the degree of infection were found in the scoring plots of PC4 and PC5. Dai et al. [22] established a classification model of moldy maize with different culture days (0 days, 2 days, 4 days, 6 days, and 8 days) based on 9 characteristic wavelengths selected from visible and short wave near-infrared (Vis-SWNIR) hyperspectral imaging using fisher discriminant analysis (FDA); the classification accuracy of the calibration set and validation set were 100% and 98.67%, respectively, illustrating that the characteristic wavelengths could represent the main information about moldy levels of maize samples. Del Fiore et al. [23] used Vis-SWNIR hyperspectral imaging combined with multivariate statistical analysis to identify maize kernels infected with fungi under different growths. The results showed that hyperspectral imaging was able to quickly distinguish between healthy and infected maize, i.e., 48 h after inoculation with mycorrhizal fungi. Previous studies have shown that both the spectral ranges of Vis-SWNIR and LWNIR can be used to distinguish the moldy level of maize; however, it has not been found that fusing the spectral information of different hyperspectral systems can construct a classification model of maize with different moldy levels. Yu et al. [24] studied the influence of Vis-SWNIR and LWNIR hyperspectral imaging systems on the prediction ability of total volatile basic nitrogen (TVB-N) content in tilapia fillets during refrigeration; the results showed that the fused spectral data of both sensors achieved a better prediction result than that of individual sensor. Meanwhile, the study of fusing

spectral data with texture data to discriminate the moldy level of maize is less extensive. Ma et al. [25] developed the classification model of fresh and frozen meats based on the spectral and texture information extracted from Vis-SWNIR hyperspectral images; the research showed that the classification model built by the feature fusion of spectra and texture was better than that of spectra and texture alone. Therefore, fusing the information of spectra and texture obtained from different hyperspectral image systems would be a new idea for constructing an accurate classification model of maize with different moldy levels.

Mold growth is uncontrollable. Hence, moldy levels may not be uniform among different samples at the same culture time and a small number of samples were not consistent with the designed moldy levels. Catalase (CAT) is a metabolite in the growth of *Aspergillus flavus* and other microorganisms [26]. Zhang et al. [27] found that the correlation coefficient between the number of mold colonies and the activity value of CAT reached more than 0.9 in various grains such as wheat, rice, and maize. In addition, CAT is the precursor product of AFB1 produced by *Aspergillus flavus*. Zhang et al. [28] analyzed the correlation between CAT activity value and AFB1 content of moldy maize and found that both of them had the same change curves. The above research showed that CAT activity value could reflect the moldy levels of maize. However, at present, it is not found that the model for discrimination of maize with different moldy levels was established based on the feature fusion of hyperspectral imaging information and CAT activity value of moldy maize.

In this study, we proposed a new method to better divide the maize with different moldy levels by monitoring the CAT activity value of maize samples infected with *Aspergillus flavus* under different culture days. The objective of this study is to examine the potential of using multi-levels data fusion of hyperspectral images to identify the maize with different moldy levels. The specific objectives of this study were to: (1) analyze the difference of spectra and texture of Vis-SWNIR and LWNIR hyperspectral images of maize samples with different moldy levels; (2) examine the ability of different pretreatment methods and classifier for identification of maize samples with different moldy levels; (3) compare the classification ability of the models based on the pixel-level fusion of spectra and different texture parameters; (4) evaluate the effects of features selected by different variable selection methods on the classification models of feature-level fusion; and (5) establish the best classification model of maize with different moldy levels by integrating the spectrum and texture data with Vis-SWNIR and LWNIR regions.


**4. Conclusions**

*Novel Nanomaterials*

**72**

**Figure 7.**

**Figure 6.**

*1 m2*

*requirements.*

*Properties of recycled cardboard with different nanocellulose consumption; asterisk: Line of standard*

high-performance loudspeakers, and lightweight actuators [130, 131].

improve barrier properties [132–134].

in energy storage systems [126], biosensors [127], as well as in various electronic and optoelectronic devices [128, 129]. Among them, transparent transistors, light emitting diodes, solar cells, antennas and radiofrequency identification devices,

*Properties of cardboard for flat layers of corrugating cardboard with different consumption of sizing agents on*

*(4); 7 g of nanocellulose (5); asterisk: Line of standard requirements.*

*: Without application (1); 7 g of glue (2); 3.5 g of nanocellulose (3); 3.5 g of glue and 3.5 g of nanocellulose*

Nano-sized cellulose fibers are considered as promising candidates for the production of nanocomposites. NC was added to polymer matrices to obtain reinforced composites with mechanical strengths from ten to one hundred times and to

Loading of structural materials with NC particles makes it possible to reduce their weight while maintaining the strength of the composites [135, 136]. For instance, addition of 6.5% nanofibrils improved the tensile strength and elongation at the break of the nanocomposite from cassava starch and polyvinyl alcohol by 24% and 51%, respectively. At the same time, the water vapor permeability and water solubility of the nanocomposite containing high contents of nanofibrils decreased up to 20% and 30%, respectively, in relation to the control blend [137]. A high effect of reinforcement was observed even at a low content of CNC when used to obtain nanocomposites with a matrix of natural rubber. With the addition of only 2.5 wt % CNC, which were isolated from soybean husks by acid sulfur hydrolysis, the elastic modulus of the composite was about 21 times higher than that of a pure rubber matrix [138]. In [139] it was shown that the addition of 10% NC from miscanthus to a composite based on epoxy resin Eposir-7120 with a polyethylene polyamine

With the increasing requirements for environmental protection, there is a need to replace exhaustible sources - oil, gas, coal, and existing forest resources with biodegradable and renewable, including non-wood plant raw materials (NWPM). NWPM have the necessary reserves and properties to make up for a possible shortage of wood fiber for pulp production. To obtain pulp suitable for the production of nanocellulose (NC), a two-stage technology for delignification of NWPM with reagents that does not contain sulfur and chlorine has been proposed. NC has unique physical and mechanical properties and can replace well-known materials such as glass and some polymers, which are not biodegradable under ambient conditions. Methods for preparing nanocellulose are described. The influence of the main technological parameters of the cellulose hydrolysis process on the properties of nanocellulose is discussed. It is proposed to carry out the hydrolysis of cellulose using 43% concentration of sulfuric acid. Examples of the use of nanocellulose in various industries are given.


**Abstract**

Payment for forest environmental services (PFES) has become an effective management tool for forest resources in the world, especially in developing countries such as Vietnam. The Vietnamese PFES policy has been institutionalized and implemented since 2010, contributing to forest protection and improving the livelihoods of people who depend on forests. In this chapter, the PFES policy was analyzed, followed by the evaluation of the effectiveness of a PFES program applied in the Ba Be district, Bac Kan province, Vietnam in three aspects: environment, economy, and society. Finally, we synthesize the combined effectiveness of the PFES program in Vietnam and offer solutions to improve and promote the PFES policy in Vietnam and developing countries around the world.

**Keywords:** babe district, evaluation, integrated effect, payment for forest environmental services (PFES), policy


Psychological Factors Influencing Protective Behaviours during the COVID-19 Pandemic: Capability, Opportunity and Motivation

*Jilly Gibson-Miller, Orestis Zavlis, Todd Hartman, Orla McBride, Kate Bennett, Sarah Butter, Liat Levita, Liam Mason, Anton P. Martinez, Ryan McKay, Jamie Murphy, Mark Shevlin, Thomas V.A. Stocks and Richard P. Bentall*

#### **Abstract**

This chapter will explore psychological and demographic influences on citizens' ability to enact protective health behaviours during the COVID-19 pandemic. Such behaviours include social distancing and hygienic practices that have been recommended across the globe to reduce the spread of infection from the coronavirus. Such behaviours represent a seismic change in usual social behaviour and have been particularly difficult to adopt under urgent circumstances. However, human behaviour is the essential driver of the rate and spread of infection. Using evidence from a large-scale longitudinal survey conducted throughout the pandemic in the UK, this chapter explores protective behaviours in relation to the Capability, Opportunity, Motivation-Behaviour (COM-B) model of behaviour change, which presents a framework for understanding the influences on behaviour. We will illustrate how the components of the COM-B model can inform behaviour change interventions and the importance of the role of anxiety in shaping behavioural responses to the pandemic.

**Keywords:** social distancing, hygienic practices, COM-B model, behaviour change intervention

#### **1. Introduction**

The COVID-19 pandemic began in China in late 2019 and is perhaps one of the biggest health threats the world has faced this century. This highly infectious disease spread quickly across the globe, mutating into a number of variants that have made containment extremely difficult. It is clear that this global pandemic will leave in its wake extensive social, economic and health impacts for many years to come and we are only just beginning to recognise the extent of its legacy.

During the outbreak, citizens around the world experienced significant restrictions in terms of their social and economic activities in the form of quarantining at home for prolonged periods of time so that social interaction (and thus, the ability of the virus to transmit between people) is limited. Behavioural guidelines to help prevent infection and slow the spread of disease have mandated the wearing of face coverings in confined spaces and recommended the adoption of a wide range of hygienic practices (for example frequent hand washing, cleansing surfaces more regularly and using hand sanitizer when hand washing was not possible). These measures have signified large-scale changes in behaviour that are psychologically burdensome for individuals to successfully achieve [1]. However, human behaviour plays a decisive role in in shaping the progression and spread of COVID-19 [2] and therefore it is a matter of urgency that behavioural scientists understand the psychological drivers that underpin such behaviour to help swiftly implement interventions to promote behavioural changes on a population level that are necessary to stem the spread of the virus and protect vulnerable groups from contagion [1, 3].

The Capability, Opportunity, Motivation-Behaviour (COM-B) model of behaviour change (**Figure 1**) [4] is widely used in behavioural science research to explore influences on behaviour. This model proposes that a person must have sufficient psychological and physical capability (strength, knowledge, skills, etc.), physical and social opportunity (time, social cues, etc.) as well as reflective and automatic motivation (intentions, planning, emotion regulation, etc.) to enact a given behaviour. Michie, West and Harvey [5] argue that each of these factors could contribute to lower levels of adherence than are needed to enact behaviours that prevent the spread of the COVID-19 virus. The COM-B model is at the centre of the Behaviour Change Wheel (BCW), which is a tool kit for designing tailored behaviour change interventions (BCIs) [6]. Thus, once a behavioural 'diagnosis' has been conducted utilising the components of the COM-B model, suitable targets for intervention can then be identified [1]. These targets will be the components of the COM-B that are most likely to influence a particular behaviour and can be developed into BCIs to improve adherence to protective health behaviours.

In this chapter, we apply the COM-B model to two key sets of COVID-19 transmission-related protective behaviours: 'hygienic practices' (including frequent hand washing and wearing a face covering) and 'social distancing practices' (involving staying at home where possible, keeping a 2-metre distance from others in public and not gathering in large groups). These behaviours are key in reducing transmission of the virus and it is likely that such measures will remain in place for some time in most countries, to some extent [7, 8]. Indeed, despite the inception of widespread vaccination programmes across the globe, maintaining protective behaviours

**Figure 1.** *The COM-B model.*

*Psychological Factors Influencing Protective Behaviours during the COVID-19 Pandemic… DOI: http://dx.doi.org/10.5772/intechopen.98237*

will ensure the continued reduction in the spread of infection to mitigate low vaccination uptake rates, difficulties in vaccine supply and variants immune to the vaccine. It is vital therefore that behavioural scientists understand the psychological factors influencing such behaviours in the context of the COVID-19 pandemic within a theoretical framework to feed into efforts to promote continued adherence to essential protective behaviours.

#### **2. Protective behaviours in the COVID-19 pandemic**

To inform BCIs, an understanding of the drivers that underpin protective behaviours are required, along with a deeper exploration that addresses the nuances in how people might understand, accept and adhere to such a set of behaviours. As yet, there is a dearth of evidence relating to how protective behavioural practices could be adopted on a population-wide level [4] and so it is important to assess behavior under the current adverse circumstances. Protective behaviours are largely under the volitional control of individuals, in that one can choose whether or not to follow the suggested practices. Further, whilst wearing a face covering and washing or sanitising hands in specified situations represents a fairly clear set of actions, the actions required to achieve 'social distancing' successfully are arguably more complex and nuanced. Some social distancing behaviours rely on the individual themselves committing to and enacting the behaviour (e.g., staying at home) and others require the reciprocal observance of others (e.g., gathering in groups, close contact greetings). We also know that social isolation could have a negative impact on health and well-being, which impacts upon decisions about adherence to behaviours [9].

Whilst there is a wide and good-quality literature on the enactment of hygiene behaviour, especially handwashing [10], we know little about these behaviours in the current context where the drivers of behaviour and nature of the threat may be entirely different from usual circumstances.

The term 'social distancing' has been coined during the pandemic and is complex and nuanced. Although large-scale population surveys have shown that social distancing practices have been sustained as the pandemic unfolded and citizens generally support these measures (e.g., [11–13]), there is evidence that motivation to comply over time may be threatened by other psychological factors. For example, as psychological resources are cumulatively depleted over time with lengthy and repeated lockdowns [11]; as competing drivers of behaviour begin to take priority (e.g., the inherent drive for social connection) [14]; as confidence in the government reduces [15]; and 'moral' judgements impact upon decision making [16] adherence to social distancing practices may diminish.

Indeed, evidence suggests that the extent to which different groups of individuals have been willing and able to comply with these important protective behaviours is mixed. Population surveys have found that 1 in 4 individuals struggle to follow social distancing guidelines, due to difficulties in meeting up with family or friends outside because of bad weather or feeling worn out by the pandemic [11, 17]. For other groups in society, it is likely that enacting social distancing behaviours is difficult for other, more practical, reasons. For example, individuals who do not have access to a garden, those who share private spaces with other families, or those who are required to work outside the home may not have the opportunity to comply and are inevitably at increased risk of exposure and infection [18]. These 'structural' factors are likely to be more impactful on the ability to comply with social distancing in groups who are already disadvantaged and who are faring worse due to the pandemic – reflecting the 'slow burn of inequality' exposed by epidemics, described by Marmot [19].

#### **3. The COM-B and protective behaviours**

Exploring protective behaviours in relation to the COM-B is useful for understanding the conditions that must be in place for these behaviours to be successfully enacted and therefore developing BCIs that promote adherence. We conducted this investigation using data from a large-scale survey of UK citizens.

The COVID-19 Psychological Research Consortium (C19PRC) Study (www. sheffield.ac.uk/psychology-consortium-covid19) is a longitudinal study mapping changes in behaviour and mental health over time from the very early days of the COVID-19 outbreak. The C19PRC study has collected data from 2025 participants in five waves over 12 months (March 2020–March 2021) from the four UK Nations, with comparable data sets from Ireland, Italy, Spain, and Saudi Arabia. A multitude of detailed demographic, health, behavioural and psychosocial measures have been collected, including socio-demographic characteristics, health status, depression, anxiety, traumatic stress, somatic symptoms, loneliness, resilience as well as health behaviours and lifestyle habits (see McBride et al. for full methodology [20, 21]). We modelled the complex relationships between the social, physical and mental health of our sample and conducted extended behavioural analyses on protective behaviours and the COM-B model [17, 22–26].

Participants self-reported motivation, capability and opportunity to enact protective behaviours in the C19PRC survey. Items were adapted from a preliminary version of the COM-B self-evaluation questionnaire and other guidelines (COM-B-Qv1) [4, 6] and respondents indicated the extent to which seventeen statements were true for them during the COVID-19 pandemic on a 5–point scale (labelled: strongly agree, agree, neither agree nor disagree, disagree, strongly disagree). Three items measured psychological capability: e.g., "I knew about why it was important and had a clear idea about how the virus was transmitted". Two items measured physical opportunity: e.g., "It was easy for me to do it" and four items measured social opportunity: e.g., "I had support from others". Five items measured reflective motivation: e.g., "I intended to do it" and three items measured automatic motivation: e.g., "I would feel bad if I didn't do it".

Analysis of the C19PRC data revealed three main themes in relation to protective behaviours. First, we identified specific components of the COM-B model that drive different types of protective behaviours. Second, we identified specific demographic groups that have particular difficulties with such behaviours. And third, there are significant emotional drivers that influence adherence to protective behaviours.

The first set of behaviours explored in Wave 1 during the first lockdown in the UK (March 2020) were five self-reported hygienic practices: Touching eyes or mouth, washing hands with soap and water more often, using hand sanitising gel if soap and water were not available, using disinfectants to wash surfaces in the home more frequently and covering nose and mouth with a tissue or sleeve when coughing or sneezing. Response scales were 'No', 'Occasionally' and 'Whenever possible'.

After controlling for demographic variables (age, gender, ethnicity, income, etc.), psychological capability, social opportunity and reflective motivation predicted hygienic practices most and reflective motivation had the largest influence [20]. This means that adults who knew why hygienic practices were effective in reducing the transmission of the virus, who had social support, and had made plans to carry out hygienic practices were more likely to successfully carry out these protective health behaviours. Notably, we observed that older age and higher levels of household income were associated with more engagement with hygienic practices. Hygienic practices were practiced less by males (compared to females) and those living in suburban areas (compared to those living in more rural areas).

#### *Psychological Factors Influencing Protective Behaviours during the COVID-19 Pandemic… DOI: http://dx.doi.org/10.5772/intechopen.98237*

For social distancing behaviours, participants in Wave 2 (April 2020) selfreported which behaviours in the past week they had engaged in, out of seven social distancing practices; e.g., "Stayed at least 2 metres (6ft) away from other people when in", "Met up with friends or extended family (outside of your home)"; "Engaged in close contact greetings with people outside of your family (e.g., shaking hands, hugging)"; "Gathered in a group of more than two people in a park and other public space". These behaviours represented clear violations of or adherence to social distancing guidelines in the first UK lockdown (responses were: Not at all, 1–2 days a week, 3–4 days a week, Most days, Every day).

Here, a different picture emerged. Of the COM-B components, only Psychological Capability exhibited a direct and positive association with adherence to social distancing [21]. Older adults and city dwellers were more likely to report higher levels of psychological capability and women were more likely to report increased motivation for social distancing. As with hygienic practices, those with higher levels of education and income were more likely to practice social distancing.

We explored adherence to social distancing further using a list experiment, embedded in Wave 4 of the C-19PRC survey (December 2020). This method allows researchers to measure responses to sensitive items that may normally invoke untrue or inaccurate answers due to social desirability concerns. The C19PRC survey list experiment used four control states and included a fifth sensitive item, as follows:

"We would now like to ask you how willing you are to break rules or conventions. Please look at the following list of common rules and indicate how many of these you have done in the last 6 months:

One-quarter of our sample revealed that they had violated government guidelines by socialising in another household during lockdown. An examination of whether any particular social or psychological factors were associated with agreement to the sensitive item, we found that the only statistically significant predictor was anxiety related to COVID-19. This anxiety was in response to the question 'How anxious are you about the coronavirus COVID-19 pandemic?'; participants were provided with a 'slider' (electronic visual analogue scale) to indicate their degree of anxiety with '0' and '100' at the left- and right-hand extremes, respectively, and 10-point increments. This produced continuous scores ranging from 0 to 100 with higher scores reflecting higher levels of COVID-19-related anxiety. This factor was negatively correlated with agreement to the sensitive item - indicating that experience of COVID-related anxiety was strongly associated with a tendency to follow the lockdown rules.

Previous research has found that emotions are an important influencing factor in the behavioural responses to pandemics; in particular, worry has been found to motivate action to control danger [27]. Liao et al. [28] conducted a multi-wave longitudinal survey study in Hong Kong during the influenza A(H7N9) pandemic and reported that worry about infection from the virus was positively associated with the enactment of protective behaviours (e.g., avoiding crowds, rescheduling travel plans). The authors reported that, as worry about the virus changed over time,

so did protective behaviours, implying a causal link between worry and engaging in protective behaviours. Other evidence from the Swine Flu pandemic also illustrates how emotional status mediates behavioural responses; Jones and Salathe [29] reported that self-reported anxiety over the epidemic mediated the likelihood that US citizens engaged in protective behaviours such as social distancing. Exploring emotional factors that might mediate protective behavioural responses during the current pandemic, may help enormously with the design of BCIs to promote the enactment of essential protective behaviours such as social distancing.

#### **4. Behaviour change interventions to promote protective behaviours**

The findings of the C19PRC Study in relation to the COM-B have clear implications for the design of BCIs to promote protective behaviours at a population level. For hygienic practices, interventions should focus on increasing and maintaining motivation to act and should contain behaviour change techniques (BCTs) that focus on selfregulatory processes involving planning and goal setting. We have suggested utilising implementation intentions, a specific planning technique found to help successfully bridge the 'intention-behaviour' gap [30, 31]. Further, to make it feasible that individuals are able to enact such techniques independently (e.g., during the lockdown), we suggest utilising the compendium of self-enactment BCTs [32] in intervention design (self-regulatory techniques #5 - #18 are especially relevant for hygienic practices). Our data show that groups in particular need of targeting for interventions to increase hygienic practices are males and those living in cities and suburbs.

For social distancing, interventions should focus on increasing psychological capability and include BCTs that bolster knowledge around social distancing and why it is important, to enable citizens to develop psychological skills in enacting and maintaining these behaviours. For increasing psychological capability, it is important that it is clear why social distancing is important and how social contact transmits the virus; as well as specifying the situations in which social distancing should be enacted and exactly how to do that. BCIs would help people to overcome physical or psychological barriers to action (or inaction) and should be specifically tailored to those sociodemographic groups who display particular difficulties in enacting social distancing, namely, younger people and those living in cities. For those with lower incomes and lower levels of education, who may struggle with social distancing for more practical reasons, wider functions of intervention from the BCW would need to be employed, whereby economic and social policy would assist in overcoming practical or structural barriers to enable these groups to follow guidelines (e.g., if working from home is not possible, ensuring COVID-safe workspaces where social distancing is achievable and implementing paid time off for isolation). It is important that individuals who feel anxious about COVID-19 are supported in managing their anxiety levels.

#### **5. Conclusion**

This chapter has explored psychological and demographic influences on citizens' ability to enact protective behaviours during the COVID-19 pandemic. We have discussed how enacting social distancing and hygienic practices are influenced by different components of the COM-B model and made recommendations for intervention. Behavioural scientists face the challenge of urgently developing interventions that help citizens to maintain adherence to protective behaviours to control the spread of the COVID-19 virus.

*Psychological Factors Influencing Protective Behaviours during the COVID-19 Pandemic… DOI: http://dx.doi.org/10.5772/intechopen.98237*

#### **Author details**

Jilly Gibson-Miller1 \*, Orestis Zavlis2 , Todd Hartman1 , Orla McBride3 , Kate Bennett4 , Sarah Butter1 , Liat Levita1 , Liam Mason5 , Anton P. Martinez1 , Ryan McKay6 , Jamie Murphy3 , Mark Shevlin3 , Thomas V.A. Stocks1 and Richard P. Bentall1

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

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#### **Chapter 10**


**Abstract**

Macrophage polarization is a spectrum of phenotypes and generally can be classified into two states: (1) classically activated or M1 macrophages, which can be driven by lipopolysaccharide (LPS) alone or in association with Th1 cytokines and produce pro-inflammatory cytokines such as TNF-α, IL-6 and, IL-12, and (2) alternatively activated M2 macrophages, which can be promoted by Th2 mediators IL-4 and IL-13 and produce anti-inflammatory cytokines such as TGF-β and IL-10. Current studies have found that the phenotypic switch between M1 and M2 macrophages governs the fate of an organ in inflammation or injury. The imbalance of M1/M2 polarization is closely involved in various pathological processes and is becoming a potential target for therapeutic strategies. Traditional Chinese medicine is an integrated healthcare system composed of many practices and is characterized by multi-target, multi-level, and coordinated intervention effects. Chinese medicines nowadays are applied to regulate phenotype polarization of macrophages to improve the microenvironment, thus ameliorating or even eliminating the symptoms. In this chapter, we will discuss the molecular mechanisms of macrophage polarization, their roles in health and disease, and the intervention with Chinese medicines to modulate the polarization of macrophages in tumor microenvironment (TME) for therapeutic purpose.

**Keywords:** tumor microenvironment, tumor-associated macrophage, polarization, Chinese medicine

## **1. Introduction**

Primary and metastatic tumors are generally known as a complex ecosystem containing tumor cells and the surrounding environment, called tumor microenvironment (TME). Apart from autonomous changes by genetic alteration of tumor cells, the dynamic changes of TME progress the tumor progression [1]. TME is a multifaceted pool that consists of various cell types including neoplastic cells, stromal cells, and immune cells that interact with one another via numerous secreted cytokines, growth factors, and chemokines. Tumor-associated macrophages (TAMs) take up a large portion of recruited immune cells and constitute up to 50% of the tumor mass. It was reported that the high level of TAMs is associated with poor prognosis and decreasing overall survival in many cancers, such as liver, breast, gastric, and thyroid cancers, suggesting that TAMs certainly play essential roles during tumor development [2–6].

TAM recruitment and accumulation are regulated by various cytokines and chemokines, such as CCL2, CCL5, CCL7, CXCL12, etc., and growth factors including VEGF, PDGF, and CSF1, as well as other factors such as fibronectin and fibrinogen [7–10]. CSF1 is the major regulator for monocyte proliferation and differentiation. CCL2 is a dominant attractant in many tumors. Since monocytes highly expressed the receptor of CCL2 (CCR2), most of tumors produced a high level of CCL2 that can intensely attract monocytes migrating toward CCL2-CCR2 axis [11–17]. However, CCL2 inhibition studies show that it could not completely suppress TAM accumulation, indicating that other factors affect this process [7, 17–21]. The CCL12-CXCR4 axis is reported to promote TAM regional accumulation under therapeutic treatments. In mice model, breast cancer highly expressed CCL20 and CCL5; Either inhibited CCL20 expression or treated with CCR5 antagonist, the number of TAMs was significantly reduced within tumors. These studies have shown that in breast cancer, CCL20-CCR6 and CCL5-CCR5 axes contribute to TAM accumulation. Another chemokine CCL11 can be induced under hypoxia condition and subsequently recruit TAMs to the hypoxic region.

In turn, TAMs can produce different molecules to remodel TME and influence fundamental aspects of tumor pathology. For instance, TAMs secrete endothelial growth factor (EGF) to increase neoplastic proliferation directly [22]; TAMs release vascular endothelial growth factors (VEGF) [23], angiogenic factor thymidine phosphorylase, and other chemokines including CCL2 and CXCL8 to enhance angiogenesis; TAMs produce metalloproteases (MMPs) to change TME matrix architecture for tumor metastasis [24]; and TAMs express immune regulatory molecules such as arginase-1 (ARG1), IL-10, and IL12 to modulate immune response [2]. The role of TAMs is accomplished by their phenotypic plasticity, either proinflammatory or anti-inflammatory phenotype, in response to the complex stimuli in TME. The double-edged sword feature of TAM polarization makes them as a novel and potent target for cancer prevention and treatments.

Traditional Chinese medicine is an integrated healthcare system composed of many practices that were rooted in China for over 5000 years. Due to its multitarget, multi-level, and coordinated intervention effects, Chinese medicine is widely used for therapeutic strategies. Recent studies reported that some of the Chinese herbal medicines have beneficial effects on cancer therapy via modulating TAM polarization, indicating a new mechanism for Chinese medicine treatment. In this chapter, we will explore the molecular mechanisms of TAM polarization and their roles in health and disease, and we will review the intervention by some of the Chinese herbal medicines on TAM polarization.

#### **2. TAM polarization and molecular mechanisms**

#### **2.1 TAM polarization**

It is widely accepted that the majority of TAMs are derived from circulating monocytes via cytokine recruitment and then differentiate to macrophages. And those at the metastatic sites are called metastatic-associated macrophages (MAMs) according to their location [25]. While recent studies have shown that the tissue-resident macrophages also contribute to TAM population [26, 27], these

#### *Polarization of Tumor-Associated Macrophages by Chinese Medicine Intervention: Mechanisms… DOI: http://dx.doi.org/10.5772/intechopen.86484*

progenitors, also called embryonic macrophages, are derived from the yolk sac or fetal liver-derived progenitors, and they can maintain themselves by local proliferation in a hematopoietic system-independent way [28]. The selective depletion studies found that only the tissue-resident macrophages support the established tumor growth. Therefore, TAMs are heterogeneous cell populations from both tissue-resident macrophages and monocyte-derived macrophages and assist TME remodeling.

Besides their heterogeneity, TAMs are also characterized by high plasticity. In the general regard, macrophages can be overgeneralized to two extreme subsets based on the stimuli, surface markers, and secreted molecules, as well as functional properties: the classically activated M1 and alternatively activated M2 macrophages. The M1 phenotype is induced by the Th1 cytokine interferon-γ (IFN-γ), bacterial moieties such as lipopolysaccharide (LPS), and Toll-like receptor (TLR) agonists. The M1 macrophages are characterized by their capacity to produce inflammatory cytokines (e.g., IL-6, IL-1, IL-12, IL-23, and TNF-α) and stimulate immune response, express reactive oxygen species (ROS) and inducible nitric oxide synthase (iNOS), and have a cytotoxic effect toward neoplastic cells and phagocytic microorganisms [29–34]. Generally, the M1-like macrophages act as sentries and display tumoricidal function, antimicrobial activity, and tissue destruction effect [33, 35].

In contrast, the M2 phenotype is promoted by Th2 mediators and produces immunosuppressive factors (e.g., IL-10, TGF-β) and growth factors (e.g., VEGF) and exerts anti-inflammatory and pro-tumorigenic activities [34, 36, 37]. Moreover, the M2-like macrophages can be further subdivided into three categories, M2a, M2b, and M2c, based on the type of stimuli. The M2a macrophages are driven by type II cytokines including IL4 and IL13 and expressed a high level of arginase-1; M2b macrophages are activated by immune complexes/TLR, while M2c macrophages by anti-inflammatory cytokines (e.g., IL-10) and glucocorticoids [38]. The M2-like macrophages promote angiogenesis, wound repair, and tumor growth, as well as resistance to parasitic infection. Many studies reported that TAMs mostly represent M2-like macrophages and play pro-tumoral roles.

### **2.2 Molecular mechanisms in regulating TAM polarization**


Acknowledgements

This paper presents some results of the research project: Informatics solutions for optimizing the operation of photovoltaic power plants (OPTIM-PV), project code: PN-III-P2-2.1-PTE-2016- 0032, 4PTE/06/10/2016, PNIII - PTE 2016.