3.2.2. Antibacterial Activity

Figure 10 illustrates the antibacterial activity of the gelatin/ZnO fibers against *E. coli* and *S. aureus* with and without UV light. The bacteria were determined by using the viable colony count method. Figure 11 represents photographs showing the antibacterial activity of gelatin/ZnO fibers against *S. aureus* and *E. coli*. Obviously, pure gelatin fibers (GZ0) have no toxic effect on the bacterial strains, and pure ZnO, GZ1 and GZ2 have excellent bacteriostatic effects. In addition, GZ2 containing a higher concentration of ZnO has stronger antibacterial activity than GZ1 containing a low concentration of ZnO. According to the reported literature [39–41], the reason for the antibacterial activity of gelatin/ZnO fibers is the superoxide radicals (·O2 −) produced by ZnO particles. The superoxide radicals can attack the bacterial cell wall and lead to cell wall leakage, resulting in the death of bacteria. In addition, under UV irradiation, ZnO particles can produce a large number of superoxide radicals. The obtained data (Figure 10) show that the antibacterial effect of gelatin/ZnO fibers irradiated by UV light is stronger than that of non-irradiated. This also proves the antibacterial mechanism of gelatin/ZnO fibers.

**Figure 10.** The antibacterial activity of the gelatin/ZnO fibers against (**a**) *Staphylococcus aureus* and (**b**) *Escherichia coli* with and without UV light. (\* represents *p* < 0.05, \*\* represents *p* < 0.01 and \*\*\* represents *p* < 0.001.).

**Figure 11.** The photographs showing the antibacterial activity of (**a**,**b**) negative control, (**c**,**d**) ZnO particles, (**e**,**f**) GZ0, (**g**,**h**) GZ1 and (**i**,**j**) GZ2 against *S. aureus* and *E. coli*, respectively.


*2.4. The Diversity of Virulence and Resistance Genes across Microbiomes*

We have argued above that non-housekeeping genes are somewhat free to spread within and between microbiomes (namely between people). Some people may use antibiotics, which select virulence genes that are present in resistant cells and resistance genes but counter-select other genes, including those that confer resistance to unrelated antibiotics and virulence genes encoded in bacterial cells susceptible to the used drug.

The use of antibiotics by sick people may select antibiotic-resistant pathogens, which, by definition, encode virulence genes. Perhaps this is why several studies have shown antibiotic and virulence genes co-occurrence in bacterial genomes [73–75]. In general, one could predict that the continuing use of tons of antibiotics through the decades would co-select virulence and resistance genes. However, it is also manifest that the administration of antibiotics hits several other bacteria, namely commensal and mutualistic bacteria, in the human microbiome [76–78]. Moreover, the use of antibiotics as growth promoters in livestock and agriculture or with prophylactic purposes may select resistant cells independently on whether or not they encode virulence genes [79].

Paradoxically, however, the diversity of resistance genes correlates positively with the diversity of virulence genes across human and environmental microbiomes [72]: microbiomes with a high diversity of one class of genes tend to have a high diversity of the other class. However, Darmancier et al. studied 16,632 bacterial genomes and concluded that no such positive correlation exists at the level of genomes, neither across chromosomes nor across plasmids [80]. The results of these two studies (references [72,80]) are compatible if, and only if, metagenomes with a high diversity of both gene types have those genes mostly located in different genomes. Why should there be a positive correlation at the level of metagenomes but not at the genome level?

In the previous two sections, we have also argued that virulence and resistance genes have conditions to remain in metagenomes for a long time. Each person receives diverse genes of both types during this period, accumulating them in their microbiomes, although not necessarily in the same bacterial cells. Meanwhile, pathogenic bacteria circulate through the human population. Some of those ill people take an antibiotic, which selects cells resistant to it but kills susceptible cells, including those resistant to other drugs. Therefore, a consequence of taking the antibiotic is a decrease in the diversity of drug-resistance genes [81]. Moreover, virulence genes are present in dead cells, decreasing their diversity. This process implies that people who took antibiotics recently are those with the lowest diversity of both resistant and virulence cells [82]. On the other hand, those that took antibiotics a long time ago have the highest diversity of both gene types [82]. The overall result is a positive correlation between the diversities of both gene types across human microbiomes [72,82].

What if there is a misuse (and overuse) of antibiotics, where some people use them even if they are not infected by a bacterial pathogen (or are randomly in contact with antibiotics from environmental contamination)? The process is very similar to the one described above, and the overall result is the same: a positive correlation of both genes' diversities. These are the predictions if the probability that a metagenome loses resistance determinants is lower than the transmission probability between people [82]. We have seen above how unlikely it is that metagenomes lose resistance determinants, so the transmission probability between people must prevail or even the contamination of people from the environment or, for example, non-cooked vegetables [83].

A computer model enabled the analysis and corroboration of the above predictions [82]. The model simulated the transfer of pathogenic bacteria and one hundred categories of resistance and virulence genes. Simulations ran with different network types (regular, smallworld, and random), and the results were always similar (the only difference being that the simulations reached stable results much sooner in small-world and random networks than in the regular network as expected, and according to explanations given above). Control simulations included changing the number of interacting people, the number of virulence and resistance genes, the relative probabilities of losing genes and acquiring them through contacts, or even changing the initial number of people already containing virulence and resistant genes, providing similar results [82].

Until this point, we have shown why the arguments (i) to (iii) presented in the Introduction section are wrong. About the fourth one, our discussion above does not predict a co-location of virulence and resistance genes in bacterial genomes; moreover, the computer model did not need to postulate their co-location to explain the positive correlation between the resistance and virulence genes' diversities in microbiomes observed over human microbiomes. However, one must ask whether the last eighty years of intensive antibiotic use have put virulence and resistance genes together. As mentioned above, there is no correlation between resistance and virulence genes' diversities throughout genomes [80]. Nevertheless, there are indirect signs of the selection of antibiotic-resistant pathogens, given that some categories of resistance and virulence genes preferentially occur in the same genome [80]. For example, Darmancier et al. observed the co-occurrence between type VII secretion systems and fusidic acid resistance genes [80]: fusidic acid is used, e.g., to treat methicillin-resistant *S. aureus* infections, and is also active against tuberculosis [84,85].


**4. Preceding pandemics and mental health**

Previous researches reveal profound and wide range of impact on mental health of individuals, communities and countries, during past outbreak of infectious diseases primarily on disease survivors (Ebola, SARS) [13]. Studies report adverse psychological symptoms in the form of mood alteration, insomnia, anger and emotional exhaustion. The psychological trauma of bereavement during Middle East Respiratory Syndrome (MERS) outbreak showed that surviving individuals were stigmatized, marginalized and socially isolated even after successful treatment [11]. Literature published during Severe Acute Respiratory Syndrome (SARS) outbreak a decade ago suggested 50% of health care workers (HCW) were at an increased risk of acute

distress syndrome (ADS) during these periods [14]. Moreover, long term behavioral changes in the form of vigilant hand washing and avoidance of crowds many months even after quarantine have been reported, depicting that pandemic and isolation has a definite long-term impact on the mental health of humans [15].


**Author details**

William M. Novick Department of Surgery, University of Tennessee Health Science Center, Global Surgery Institute, Memphis, TN, USA

\*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.


**2.1 Endogenous antioxidants**

Endogenous antioxidants are divided into two classes, enzyme and non-enzymatic.

Endogenous antioxidants that are enzymes are: (1) Superoxide dismutase (SOD), (2) Glutathione peroxidase (GSH-PX), (3) Glutathione S-Transferases (GST), (4) Catalase (CAT), (5) Mitochondrial cytochrome oxidase system, (6) Hydroperoxidase [25, 26].

Non-enzyme endogenous antioxidants are: (1) Melatonin, (2) Ceruloplasmin, (3) Transferrin, (4) Myoglobin, (5) Hemoglobin, (6) Ferritin, (7) Bilirubin, (8) Glutathione, (9) Cysteine, (10) Methionine, (11) Urate, (12) Lactoferrin, (13) Albumin [26].


**9. Need for certification**

Certification ensures quality produce. "Certified Organic" serves as a product assurance to consumers. Certification necessarily aims to regulate and facilitate the sale of organic vegetables to consumers.


**5. Satellite imagery for the purpose of land degradation mapping**

As it is obvious, land degradation phenomena is of high priority to be researched and different techniques are to be considered for that. The solution to this problem requires not only a detailed study of the land degradation causes, but also involves identifying the risk of degradation. Satellite imagery has its advantages and widely used for investigation of landscape changes dynamics as a result of anthropogenic impact since it is able to cover areas at different scales. Processing of time series of geoinformation products allows reliable detecting and mapping of landscape changes at local, regional and even global level.

As it was mentioned above, large scale or inaccessibility of degraded areas, insufficient funding for research on soil and vegetation cover condition, as well as unsatisfactory quality of the relevant archival materials, make multispectral satellite imagery a reliable information basis for the assessment of potential land deterioration. At the same time, the main task of multispectral satellite imagery processing is the selection of land degradation indicators. In this study, vegetation change and soil erosion dynamics are defined as such indicators. To map them, besides medium spatial resolution multispectral satellite images, auxiliary geospatial data—digital terrain, soil maps and climatic parameters of the study area—are required.

The authors used the land degradation mapping technique to investigate land degradation within relatively pristine (Oleshki sands) and human-inspired mining and industrial areas (central part of Ukraine) [19]. In each particular case, the authors offered thematic land degradation maps obtained as a result of multispectral images processing, allowed assessing the state and tendencies in land degradation processes within the study areas. The research visually emphasizes the level of anthropogenic stress within investigated territories.


**2.4 Treatment**

*Inflammatory Heart Diseases*

ECG [2, 3, 11].

usually associated with sinus tachycardia.

assess the hemodynamic impact; and (3) effort to identify the underlying etiology. Transthoracic echocardiography (TTE) is recommended in all patients with suspected effusion as a class I, level C recommendation. Further imaging modalities such as computed tomography (CT) scan, cardiac magnetic resonance imaging (CMRI), pericardial fluid analysis, or biopsy can be considered in cases where loculated effusion, masses, or thickening of the pericardium are suspected. Basic diagnostic work up, including blood counts, chemistry, thyroid function tests, cardiac biomarkers, inflammatory markers such as C-reactive protein (CRP) and sedimentation rate (ESR), electrocardiogram (ECG), and chest X-ray, should be done [2, 10]. ECG findings in pericardial effusion include low QRS voltage and electrical alternans, a finding of large pericardial effusion or tamponade that is

TTE is recommended as the first modality to determine the hemodynamic significance of pericardial effusion and is highly sensitive and specific. The pericardial fluid appears as echo-lucent space between the pericardium and epicardium on TTE. The semi-quantitative assessment for largest echo-free space in echocardiographic views provides an assessment of severity. Mild pericardial effusion is considered <10 mm, moderate between 10 and 20 mm, and large effusion is any collection >20 mm. The collection of effusion follows gravity initially in the inferolateral position close to right atrium in the apical four chamber view with the patient in a supine-left lateral position. The pattern of collection changes to circumferential in the pericardium with increasing amount of fluid (**Figure 1**). After the development of a large amount of effusion, the heart can be seen swinging in the pericardial cavity, a finding that correlates with electrical alternans on

Cardiac CT and CMRI are useful imaging modalities for the evaluation of pericardial effusion and tamponade especially for more detailed assessment and the localization of the effusion and associated abnormalities in the mediastinum, lungs,

**14**

**Figure 1.**

*Large circumferential pericardial effusion.*

For small-to-medium sized, asymptomatic pericardial effusion without signs of hemodynamic instability, regular outpatient follow-up with clinical examination and/or echocardiography should be preferred. Management of pericardial effusion with signs of inflammation (pericarditis) should follow the standard or treatment for pericarditis; however, in the absence of any inflammation, antiinflammatory drugs, such as nonsteroidal anti-inflammatory drugs (NSAIDs), colchicine, and corticosteroids, are generally not effective. Such cases as well as cases with large effusion that failed empiric anti-inflammatory therapy would require pericardiocentesis. Recurrence of effusion is fairly common and further management options include pericardial window formation or pericardiectomy. A study comparing patients age 15–65 to a group of patients age 66–88 years showed elderly people had more persistence of effusion (6.3 vs. 14%; P < 0.05) but no statistically significant difference in mortality (24 vs. 30%) or evolution of cardiac constriction (4 vs. 2%) during median follow up time of 11 months [6]. There is no standard guideline available for elderly patients regarding pleural effusions; however, the expert consensus suggests adjusting the type and dosages of medications with special attention to drug interactions and renal function given the prevalence of polypharmacy and renal dysfunction in the geriatric population [2]. A proposed management algorithm for pericardial effusion of unknown origin is depicted in **Figure 2** [2, 3].


*3.1. Sorghum Growth*

Except for the panicle diameter under the effect of the *Leucaena* alleys in the double crop (in which no alteration was verified), the components related to sorghum growth (plant height, stem diameter, panicle diameter and panicle length) were higher in the presence of fertilization when compared to the non-fertilized treatments (Figure 3).

**Figure 3.** Plant height (**A**), stern diameter (**B**), panicle length (**C**) and panicle diameter (**D**) of forage sorghum submitted to the combination of leguminous alleys and mineral fertilization. Averages followed by the same lowercase letter for fertilization and uppercase letter for cropping systems were found by the Tukey test to not differ from each other at 5%.

In the crop, the presence of *Leucaena* alleys increased the diameters of the stem and the panicle when compared to the single sorghum system. In the double crop, *Leucaena* cultivation caused more positive impacts on plant height, stem diameter, panicle diameter, and panicle length (up to 72% more than single sorghum) than in the experimental units with the presence of mineral fertilization. In the experimental units without mineral fertilization, the cultivation in *Leucaena* alleys was 30% superior to the single sorghum cultivation plots in relation to plant height, stem diameter, panicle diameter and panicle length.

#### *3.2. Morphological Components and Biomass Production of Forage Sorghum*

In the absence of mineral fertilization, the addition of the plant residues increased the leaf dry mass (LDM) and the stem dry mass (SDM) of sorghum when compared to the single crop cultivation. This beneficial effect was verified both in the double crop and in the crop. However, there were no alterations between the cultivation systems in the presence of mineral fertilization (Figure 4). On average, leaf dry mass in alley crops exceeded the single crop by 28% and 66% in the double crop and crop, respectively. The potential of SDM production under the effect of the leguminous alleys was, on average, 100% higher than single sorghum in the double crop and crop (Figure 4). Except for sorghum in the double crop cultivated in *Leucaena* alleys, mineral fertilization increased the LDM and the SDM in relation to the treatments without fertilization.

**Figure 4.** Leaf dry mass (**A**), stem dry mass (**B**), panicle dry mass (**C**), and dry mass of dead material (**D**) of forage sorghum submitted to the combination of leguminous alleys and mineral fertilization. Averages followed by the same lowercase letter for fertilization and uppercase letter for cropping systems were found by the Tukey test to not differ from each other at 5%.

Regarding the production of the PDM in the double crop, sorghum cultivated in *Leucaena* alleys was much higher than single sorghum (up to 500%). *Leucaena* alleys influenced the increase of the dry mass of dead material (DMDM) of sorghum cultivated without fertilization in the double crop. However, with the application of mineral fertilization, the presence of *Gliricidia* alleys caused an increase in the double crop and crop of 17% and 30%, respectively, when compared to the single crop.

As for the total green mass (TGM) produced by sorghum in the experiment conducted during the crop, *Gliricidia* and *Leucaena* alleys without fertilization and with fertilization were 78% and 11% higher than single sorghum, respectively. In the double crop in the absence of mineral fertilization, the greater green mass production of sorghum cultivated between the alleys (up to 116% when compared to single sorghum cultivation) was also verified. Nevertheless, there was no effect of the cultivation system when mineral fertilization was applied (Figure 5). In general, the total dry mass (TDM) followed the same patterns of green mass; however, in the double crop without the application of mineral fertilization, *Leucaena* alleys caused more benefits to sorghum development than *Gliricidia* alleys.

**Figure 5.** Total green mass (**A**), total dry mass (**B**), root dry mass (**C**) and forage sorghum leaf stem ratio (**D**) of forage sorghum submitted to the combination of leguminous alleys and mineral fertilization. Averages followed by the same lowercase letter for fertilization and uppercase letter for cropping systems were found by the Tukey test to not differ from each other at 5%.

Both in the crop and in the double crop, the root dry mass (RDM) was strongly influenced by the application of mineral fertilization with an increase in root production, regardless of the cultivation system. However, in the absence of mineral fertilization in the double crop, the sorghum RDM in *Gliricidia* alleys exceeded single sorghum by up to 142%.

The absence of mineral fertilization increased the leaf steam ratio regardless of the cultivation system. The cultivation in alleys without fertilization presented leaf stem ratios 38% and 20% lower than the single crop in the double crop and crop, respectively.

#### *3.3. Leaf Macronutrient Contents*

In both the double crop and in the crop, fertilization did not alter the contents of N and P in sorghum plants. However, in the crop, the cultivation of *Leucaena* alleys caused an increase in the contents of these nutrients when compared to the single crop (up to 28% and 26% for N and P, respectively). However, in the double crop with the presence of mineral fertilization, a lower N content was observed under the effect of *Leucaena* alleys (Table 5). A lower leaf content of P also predominated under the effect of the alleys when compared to the single crop.

As for the leaf content of K, the cultivations of the alleys without the presence of mineral fertilization in the double crop were benefited by 50% under the effect of *Gliricidia* and by 100% under the effect of *Leucaena*. However, in the crop, mineral fertilization was responsible to increase the content of K, regardless of the cultivation system.

*Gliricidia* and *Leucaena* alley cultivation predominantly provided the lowest leaf contents of Ca and Mg in both the double crop and the crop when compared to the single crop.

Averages followed by the same lowercase letter within the line and uppercase letter within the column were found by the Tukey test to not differ from each other at 5%.

#### *Agronomy* **2019**, *9*, 636

## *3.4. Sorghum Yield*

The forage sorghum yield was improved in the crop by the mineral fertilization, regardless of the cultivation system. However, single sorghum cultivation was more dependent on fertilization than sorghum cultivated in the presence of the alleys. Single sorghum without fertilization had its productivity decreased by 62% when compared to the cropping systems with mineral fertilization in the double crop, while in the presence of the alleys, the decrease was 30%. Regarding the cultivation of the crop the productivity decreased by 69% for single sorghum and by 48% for alley cropping.

In the crop without mineral fertilization, the crop systems presented similar productivities. However, with the presence of fertilization, single sorghum productivity increased by an average of 5500 kg ha−1 when compared to cultivation in *Gliricidia* and *Leucaena* alleys (Figure 6).

**Figure 6.** Productivity of forage sorghum cultivated with the combination of alleys and mineral fertilization. Averages followed by the same lowercase letter for fertilization and uppercase letter for cropping systems were found by the Tukey test to not differ from each other at 5%.

In the double crop without mineral fertilization, sorghum cultivation in *Leucaena* alleys presented a higher productivity than the single sorghum cultivation (up to a 67% increase). With the presence of mineral fertilization, sorghum productivity in *Gliricidia* alleys was similar to single sorghum. Nevertheless, the cultivation in *Leucaena* alleys was smaller than single sorghum.