**1. Introduction**

Lung cancer is one of the predominantly diagnosed cancers (11.6% of the total number of cases); it was the leading cause of cancer death in 2018 (18.4% of the total number of cancer deaths) [1,2]. Another common lung disease is a chronic obstructive pulmonary disease (COPD) [3]. Recently, COPD has become the focus of attention due to increased morbidity, mortality and increased risk of lung cancer [4]. COPD and lung cancer have common features: high mortality and risk factors such as smoking, some genetic background, environmental exposure and major common inflammatory processes [5]. Several studies have shown that COPD is a risk factor for lung cancer, regardless of exposure to smoking, with a four to six times increased risk of lung cancer [6]. This risk increases with a progressive decline in FEV1, regardless of smoking history [7]. In addition, COPD worsens the prognosis of lung cancer due to higher morbidity and mortality [8]. There are numerous studies confirming the role of chronic inflammation in the genesis of cancer in general and lung cancer in particular [9]. It was found that various dysregenerative changes in the pulmonary epithelium against

the background of chronic inflammatory lung disease have a malignant potential [10]. This explains the fact of frequent development of lung cancer against the background of COPD, in which dysplastic changes and metaplasia of the bronchial epithelium are clinical manifestations. Until now, no biomarker has been identified that would unequivocally determine the presence of lung cancer and/or COPD using both sputum and exhaled air and blood serum [11–13]. In previous studies, including ours, we have shown that the composition of saliva reflects metabolic changes occurring against the background of lung cancer [14–16]. However, a study of the effects of the combined pathology of lung cancer and COPD on the metabolic profile of saliva has not yet been conducted. The aim of the work was to study the characteristics of the biochemical composition of saliva in combined pathology, lung cancer and COPD of varying severity.

#### **2. Materials and Methods**

#### *2.1. Study Design and Group Description*

The study included 593 patients hospitalized in the thoracic department of the Clinical Oncological Dispensary in Omsk in the period 2014–2017. The inclusion criteria were: the age of the patients 30–75 years, the absence of any treatment, including surgery, chemotherapy or radiation. The collection of saliva samples was carried out strictly before the start of treatment.

A detailed description of the study group is given in Table 1. After histological verification, 168 people (28.3%) were diagnosed with non-malignant lung pathologies, including 51—hamartoma, 30—sarcoidosis, 28—tuberculoma, 39—fibrosis/pneumosclerosis, 13—inflammatory tumor, 4—pneumonia, 2—papilloma, 1—lipoma. These patients made up the comparison group. In 425 patients (71.7%), lung cancer of various histological types was confirmed, including 189—adenocarcinoma (ADC), 135—squamous cell carcinoma (SCC), 8—mixed (ADC + SCC), 68—neuroendocrine cancer (NEC) and 25—undifferentiated lung cancer. The NEC group included 16 patients with a diagnosis of typical and atypical carcinoid (low-grade G1 + G2) and 45 patients with small cell lung cancer, and 7 patients with large cell lung cancer (high-grade G3). Patient groups with mixed and undifferentiated cancers were subsequently excluded from the study. The control group consisted of 500 healthy patients, in whom no lung pathology was detected during routine clinical examination.

To describe the severity of COPD, a classification based on FEV1 as a percentage of predicted was used [17]. GOLD criteria were used to classify the severity of COPD (GOLD I; FEV1 > 80% of predicted, GOLD II; FEV1 = 50–79% of predicted, GOLD III; FEV1 = 30–49% of predicted, and GOLD IV; FEV1 < 30% of predicted). In this work, we examined groups of patients without COPD, with mild and moderate COPD (GOLD I, GOLD II), designated, respectively NO COPD, COPD I and COPD II.

The study was approved at a meeting of the Ethics Committee of the Omsk Regional Clinical Hospital "Clinical Oncology Center" on 21 July 2016 (Protocol No. 15). All of the volunteers provided written informed consent.

#### *2.2. Collection, Processing, Storage and Analysis of Saliva Samples*

Saliva (5 mL) was collected from all participants prior to treatment. Collection of saliva samples was carried out on an empty stomach after rinsing the mouth with water in the interval of 8–10 am by spitting into sterile polypropylene tubes; the salivation rate (mL/min) was calculated. Saliva samples were centrifuged (10,000× *g* for 10 min) (CLb-16, Moscow, Russia), after which biochemical analysis was immediately performed without storage and freezing using the StatFax 3300 semi-automatic biochemical analyzer [16].

#### *2.3. Statistical Analysis*

Statistical analysis was performed using Statistica 13.3 EN software (StatSoft, Tulsa, OK, USA); R version 3.6.3; RStudio Version 1.2.5033; FactoMineR version 2.3. (RStudio, version 3.2.3, Boston, MA, USA) by a nonparametric method using the Mann-Whitney *U*-test and the Kruskal-Wallis *H*-test. At the preliminary stage, the character of distribution and homogeneity of dispersions in groups was checked. According to the Shapiro-Wilk test, the content of all determined parameters does not correspond to the normal distribution (*p* < 0.05). The test for the homogeneity of variances in groups (Bartlett's test) allowed us to reject the hypothesis that variances are homogeneous across groups (*p* = 0.00017). Therefore, nonparametric statistical methods were used to process the experimental data. The description of the sample was made by calculating the median (Me) and the interquartile range as the 25th and 75th percentiles (LQ; UQ). Differences were considered statistically significant at *p* < 0.05.

The Kruskal-Wallis test is a nonparametric alternative to one-dimensional (intergroup) ANOVA. It is used to compare three or more samples. With a high significance of the Kruskal-Wallis test (*H*), the characteristics of different experimental groups significantly differ from each other (*p* < 0.05). Using the Kruskal-Wallis test, we compared several groups (3 groups in Tables 2 and 3; 5 groups in Table 4) and selected indicators whose change was significant at *p* < 0.05. These indicators were subsequently used for the principal component analysis (PCA). In addition, we included indicators in the PCA analysis, the values of which differ at the 0.10 significance level. In the case of 0.05 < *p* < 0.10, the limit of significance is slightly exceeded, which means that there is a tendency towards the manifestation of a pattern. If a significant pattern is identified, to identify groups that are significantly different from each other, it is necessary to test all groups in pairs. The Mann-Whitney test was used only for pairwise comparison of the differences between the COPD and NO COPD groups in different histological types of lung cancer; in all other cases, the Kruskal-Wallis test was used to compare the groups.

A principal component analysis (PCA) was performed using the PCA program in R [18]. The significance of the correlation is determined by the correlation coefficient (*r*): strong—*r* = ± 0.700 to ± 1.00, medium—*r* = ± 0.300 to ± 0.699, weak—*r* = 0.00 to ± 0.299. The rate of change of individual biochemical parameters was quantified using the ratio of natural logarithms (LnRR), with LnRR (indicator) = ln (COPD / NO COPD value). Medians and interquartile range presented in figures were calculated using nontransformed data.

The survival curve was calculated by the Kaplan-Meier method and compared using the Log-rank test for univariate analysis (Statistica 10.0, StatSoft). Prognostic factors were analyzed by multivariate analysis using Cox's proportional hazard model in a backward stepwise fashion to adjust for potential confounding factors. Overall survival (OS) was computed from the date of diagnosis to the date of death or the date of the last follow-up. Survival data were obtained until December 2019.

**Table 2.** Biochemical markers of saliva in patients with lung cancer, depending on the presence/absence and severity of cancer, chronic obstructive pulmonary disease (COPD).

\*—differences between 3 groups are statistically significant, *p* < 0.05; \*\*—differences between 3 groups are statistically significant, *p* < 0.10; the first arrow shows the change in the parameter when switching from the LC group to the LC + COPDI, the second—when changing from the LC + COPDI to LC + COPD II; ↑—the indicator value increases; ↓—the indicator value decreases; =—the indicator value does not change; SOD—superoxide dismutase; MDA—malondialdehyde; MM 280/254—middle molecular toxins; AST—aspartate aminotransferase; ALT—alanine aminotransferase; LDH—lactate dehydrogenase; GGT—gamma glutamyltransferase; ALP—alkaline phosphatase.

**Table 3.** Metabolic profile of saliva in patients with lung cancer of various histological types depending on the presence/absence of COPD.

**Table 3.** *Cont.*

HT—histology type; ADC—adenocarcinoma; SCC—squamous cell carcinoma; NEC—neuroendocrine cancer; AST—aspartate aminotransferase; ALT—alanine aminotransferase; LDH—lactate dehydrogenase; \*—differences between 3 groups are statistically significant, *p* < 0.05; \*\*—differences between 3 groups are statistically significant, *p* < 0.10.

**Table 4.** *Cont.*

1—Groups without COPD, 2—groups with COPD; \*—differences between the 5 groups are statistically significant (*p* < 0.05); SOD—superoxide dismutase; MDA—malondialdehyde; MM 280/254—middle molecular toxins; AST—aspartate aminotransferase; ALT—alanine aminotransferase; LDH—lactate dehydrogenase; GGT—gamma glutamyltransferase; ALP—alkaline phosphatase.

#### **3. Results**

#### *3.1. Metabolic Features of Saliva in Lung Cancer Depending on the Presence*/*Absence of COPD*

At the first stage, the biochemical composition of the saliva of patients with lung cancer was studied to identify metabolic changes (Table 2). It was found that the differences between the three groups are significant in the following indicators: pH, calcium concentration, the level of diene conjugates, sialic acids, and lactate dehydrogenase (LDH) activity (Table 2).

High values of the Kruskal-Wallis criterion were also noted for the Ca/P-ratio, magnesium concentration, catalase activity, alanine aminotransferase (ALT)/AST (aspartate aminotransferase)-ratio, and the level of Schiff bases (*p* < 0.10). All these indicators were used further for analysis by the PCA method (Figure 1). The first two dimensions of the analysis represent 34.25% of the total inertia of the dataset; this means that 34.25% of the total cloud variability is explained by the plane (Figure 1B). Estimating the correct number of axes for interpretation suggests limiting the analysis to describing the first five axes (principal components) (Figure 1B,D,F).

**Figure 1.** *Cont.*

**Figure 1.** Principal component analysis (PCA)—Factor plane (**B**,**D**,**F**) and the corresponding correlation circle (**A**,**C**,**E**) in the first five dimensions for three groups (lung cancer without COPD, lung cancer with COPD I, lung cancer with COPD II). The color of the arrows on the correlation circle changes from blue (weak correlation) to red (strong correlation), as shown on the color bar. The orientation of the arrows characterizes positive and negative correlations (for the first principal component, we analyze the location of the arrows relative to the vertical axis, for the second principal component—relative to the horizontal axis). LDH—lactate dehydrogenase; CAT—catalase; SB—Schiff bases; DC—diene conjugates; SA—sialic acids; Ca—calcium; Mg—magnesium; AST/ALT—aspartate aminotransferase to alanine aminotransferase ratio.

For the first principal component (PC1), high correlation coefficients (*p* = 0.001) were obtained only for LDH (*r* = 0.71), average strength for catalase (*r* = 0.61), magnesium (*r* = 0.53), calcium (*r* = 0.44) and pH (*r* = −0.47). For PC2, high correlation coefficients were not revealed, correlations of average strength were confirmed for catalase (*r* = 0.57), pH (*r* = 0.52), LDH (*r* = 0.41), Schiff bases (*r* = 0.35), calcium (*r* = −0.38) and sialic acids (*r* = −0.57). For PC3, correlations of average strength were established with Schiff bases (*r* = 0.68), calcium (*r* = 0.50), magnesium (*r* = 0.37), sialic acids (*r* = −0.35) and the AST/ALT-ratio (*r* = −0.33). For PC4 and PC5, only one strong correlation was revealed: with diene conjugates (*r* = 0.84) and the AST/ALT-ratio (*r* = 0.90), respectively (Figure 1). Thus, the given factor planes make it possible to divide the studied groups among themselves, but it should be borne in mind that the ellipses in the diagrams cover only 60% of the values that make the greatest contribution (Figure 1B,D,F). Thus, the most significant indicators affecting the composition of saliva in COPD are the activity of enzymes (LDH, catalase) (Figure 1B), then the level of lipid peroxidation products (diene conjugates, Schiff bases) (Figure 1D) and the value of AST/ALT-ratio are significant (Figure 1F).

### *3.2. Influence of Histological Type of Lung Cancer on Metabolic Indicators of Saliva in Patients with COPD*

Since the group of patients with lung cancer is heterogeneous in its composition, at the next stage, each of the groups under consideration was divided into subgroups in accordance with the histological type of lung cancer (Table 3).

It was found that some of the indicators change unidirectionally for different histological types of lung cancer (Table 3). Regardless of the histological type of lung cancer, the pH increases in the presence of COPD. The maximum increase in pH was observed in COPD II. For adenocarcinoma, there is the smallest increase in pH (+2.0%), the largest for neuroendocrine lung cancer (+6.5%). For squamous cell lung cancer, the pH increases by 4.8%; it is for this group that the difference between the subgroups is statistically significant (*p* = 0.0106). The concentration of calcium and magnesium decreases in the presence of COPD. The decrease in calcium concentration is more pronounced for squamous cell carcinoma (−35.4%, *p* = 0.0053), while magnesium for adenocarcinoma (−23.4%, *p* = 0.0658). For diene conjugates and Schiff bases, a pronounced minimum is observed in COPD I, and then a sharp increase in their content. For LDH, on the contrary, there is a different nature of the change in activity depending on the histological type of lung cancer. Thus, for adenocarcinoma, LDH activity decreases (−5.4% for groups NO COPD vs. COPD I; −51.7% for COPD I vs. COPD II). For squamous cell and neuroendocrine cancers, LDH activity first increases (+1.0 and +18.1%, respectively) and then decreases (−34.0 and −1.5%, respectively). Lactate levels increase for adenocarcinoma and squamous cell carcinoma (+45.8 and +85.7%, respectively) but decrease for neuroendocrine lung cancer (−52.9%). The α-amylase activity increases for adenocarcinoma and neuroendocrine cancer (+19.1 and +127.9%, respectively) but decreases for squamous cell lung cancer (−14.0%).

For a visual representation of how the indicators change while taking into account the histological type of lung cancer and the severity of COPD, diagrams of the intensity of changes are plotted in comparison with the corresponding groups without COPD (Figure 2). To plot the diagram, only those biochemical indicators of saliva were selected, the change in which is statistically significant according to the Kruskal-Wallis test (Table 3). As can be seen from Figure 2, for all indicators except LDH and α-amylase, a wide scatter of data is observed. Changes in these indicators against the background of COPD of varying severity most reliably show the differences between histological types of lung cancer.

**Figure 2.** The intensity of the difference (LnRR = Ln (COPD/NO COPD)) in saliva biochemical parameters for different histological types of lung cancer, depending on the presence/absence of COPD. The rectangles show the mean (line) and median (cross); individual points show values outside the interquartile range. The same letters (a, b, c, d, e, f) denote groups, the differences between which were not revealed (α = 0.05). The dotted line represents the zero level (LnRR = 0). Significance of difference from 0 was checked using the t-test of one sample and indicated by numbers ("0": not significant; "1": *p* < 0.05; "2": *p* < 0.01; "3": *p* < 0.001). AC—adenocarcinoma; SCC—squamous cell carcinoma; NEC—neuroendocrine lung cancer.

### *3.3. Biochemical Markers of Saliva in Patients with Lung Cancer, Noncancerous Pathologies of the Lung and Control Group Depending on the Presence*/*Absence of COPD*

The differences between the groups with and without COPD are significant even within the group of patients with lung cancer; nevertheless, at the next stage of the study, the results obtained were compared with the control group and the comparison group. Table 4 shows the values of the indicators for each of the five groups and the corresponding value of the Kruskal-Wallis test. It was shown that the number of indicators by which the studied groups differ is significantly greater than for differences within the lung cancer group (Table 2).

The indicators, which turned out to be statistically significant according to the Kruskal-Wallis test (Table 4), were used for the analysis by the PCA method (Figure 3). It is shown that the first two dimensions of the analysis express 25.23% of the total inertia of the data set; therefore, for correct interpretation of the data, it is necessary to limit the analysis to the description of the first four axes (Figure 3).

For PC1, strong correlations were found only with protein content (*r* = 0.71), correlations of average strength with the activity of the enzymes LDH (*r* = 0.64), catalase (*r* = 0.58), GGT (*r* = 0.52) and ALP (*r* = 0.42), as well as with the content of α-amino acids (*r* = 0.49) and sialic acids (*r* = 0.31). For the other axes, high correlation coefficients were not found. For PC2, correlations of average strength were found for pH (*r* = 0.52), LDH enzymes (*r* = 0.36) and catalase (*r* = 0.44), diene conjugates (*r* = 0.33), and Schiff bases (*r* = 0.43), negative correlations were noted for protein metabolites: imidazole compounds (*r* = −0.41) and sialic acids (*r* = −0.45). For PC3, correlations of medium strength with imidazole compounds (*r* = 0.51) remain, correlations with diene conjugates (*r* = 0.56) and Schiff bases (*r* = 0.46) become more significant, and a correlation with the level of middle molecular toxins MM 280/254 (*r* = 0.37) is added. For PC4, the indicators of mineral metabolism are significant: pH (*r* = 0.34), calcium (*r* = 0.41), Ca/P-ratio (*r* = 0.56), as well as the level of α-amino acids (*r* = 0.32), diene conjugates (*r* = −0.35) and AST/ALT-ratio (*r* = −0.33).

As seen in Figure 3, the horizontal axis divides the study groups into a control group and a comparison group without COPD (above the axis), and patients with lung cancer, regardless of the presence/absence of COPD (below the axis) and a comparison group with COPD (below the axis). At the same time, the differences between the control group, the comparison group without COPD, and patients with lung cancer without COPD are less pronounced. In the diagram, these groups are located to the right of the vertical axis, while both groups of patients with COPD (lung cancer and the comparison group) are located to the left of the vertical axis (Figure 3B). The division into the listed groups provides differences in the content of protein metabolites (protein, α-amino acids, sialic acids and imidazole compounds) and enzymes (LDH, catalase, GGT, ALP and AST/ALT-ratio) in saliva. According to the severity of intoxication (indicators of lipid peroxidation and the level of medium molecular weight toxins), a control group can be distinguished both relative to the vertical and relative to the horizontal axes (Figure 3D).

**Figure 3.** PCA—Factor plane (**B**,**D**) and the corresponding correlation circle (**A**,**C**) in the first four dimensions for five groups (control, lung cancer without COPD, lung cancer with COPD, comparison group without COPD, comparison group with COPD). The color of the arrows on the correlation circle changes from blue (weak correlation) to red (strong correlation), as shown on the color bar. The orientation of the arrows characterizes positive and negative correlations (for the first principal component, we analyze the location of the arrows relative to the vertical axis, for the second principal component-relative to the horizontal axis). LDH—lactate dehydrogenase; CAT—catalase; SB—Schiff bases; DC—diene conjugates; SA—sialic acids; Ca—calcium; Mg—magnesium; Ca/P—calcium to phosphorus ratio; IC—imidazole compounds; GGT—gamma glutamyltransferase; MM 280/254—middle molecular toxins; AST/ALT—aspartate aminotransferase to alanine aminotransferase ratio; ALP—alkaline phosphatase.

#### *3.4. Predictive Value of Saliva Biochemical Parameters Taking into Account the Presence*/*Absence of COPD*

At the first stage of the study, we compared the overall survival rates (OS) of patients with lung cancer, as well as patients with concomitant pathology: lung cancer and COPD I and II (Figure 4A,B).

**Figure 4.** Overall survival of patients with lung cancer: (**A**) without COPD (curve 1) and in the presence of COPD (curve 2); (**B**) without COPD (curve 1), lung cancer with COPD I (curve 2) and lung cancer with COPD II (curve 3). *n* is the number of patients in each group.

It was shown that, without taking into account the severity of COPD, the differences between OS indicators are insignificant HR = 1.17, 95% CI 0.79–1.74, *p* = 0.34185 (Figure 4A); in the case of combined pathology, the indicators of 1-year-old (63.9 vs. 60.8%), 3-year (32.3 vs. 31.1%) and 5-year survival rates (20.2 vs. 12.6%) decrease slightly. The median OS was 18.1 and 17.4 months, respectively. Taking into account the severity of COPD, it was found that the differences between patients with only lung cancer and those with COPD I are not significantly expressed HR = 0.86, 95% CI 0.56–1.32, while the differences between the groups of patients with lung cancer and COPD II statistically significant HR = 2.52, 95% CI 1.22–5.13, *p* = 0.05250 (Figure 4B). Differences between groups with COPD I and COPD II are also significant; HR = 2.94, 95% CI 1.37–6.20, *p* = 0.03878 (Figure 4B). It was found that the 1-year survival rates for patients with lung cancer, as well as patients with a combination of lung cancer with COPD I and COPD II, were 63.9, 62.3 and 56.3%, while the 3-year survival rates were 32.3, 38 and 13.7% and 5-year-old were 20.2, 19.7 and 13.7%, respectively (Figure 4B). The median of OS for the group of patients with COPD I increased slightly and amounted to 20.5 months, while against the background of COPD II, it decreased to 16.2 months.

For groups of lung cancer patients without COPD and with COPD (COPD I, II), the prognostic value of saliva biochemical indicators was determined by the method of multivariate regression. For the group of patients with lung cancer without COPD, such indicators were the level of imidazole compounds (ICs) and the activity of salivary LDH (Figure 5A).

It has been shown that the level of ICs less than 0.478 mmol/L and the LDH activity more than 1248 U/L are prognostically favorable signs. The selected values correspond to the median for LDH and the upper value of the interquartile range for ICs (Table 2). When combining indicators ICs < 0.478 and LDH >1248, a group of patients with a favorable prognosis can be distinguished (HR = 1.56, 95% CI 0.40–6.07, *p* = 0.03891), for which the median of OS is 11.8 months higher than for the group with a poor prognosis (Figure 5A). The differences between the other groups are not statistically significant.

For the group of patients with COPD of varying severity, the indices were divided: for COPD I, only the ICs level is significant, while for COPD II, the LDH activity and pH are significant (Figure 5B,C). For COPD I, the prognostically favorable ICs level is less than 0.182 mmol/L (HR = 1.74, 95% CI 0.71–4.21 and HR = 1.87, 95% CI 0.65–5.28, *p* = 0.07270, Figure 5B), while the median of OS with a favorable prognosis reaches 34.5 months. For COPD II, pH values less than 6.74 and LDH activity less than 1006 U/L are predictively favorable. In this case, we identified a group of patients with a poor

prognosis HR = 2.17, 95% CI 0.43–10.79, *p* = 0.08977 (pH > 6.74 and LDH > 1006 U/L), for which the median of OS was 7.3 months; this is 2.2 times lower than the average for the group of patients with COPD II (Figure 5C). Some groups contain a small number of patients; therefore, the differences are not statistically significant and can be considered as preliminary data requiring verification on more representative samples of patients.

**Figure 5.** Multivariate regression analysis of overall survival: (**A**) ICs < 0.478 mmol/L and LDH > 1248 U/L (curve 1, favorable prognosis, median 26.3 months), ICs > 0.478 mmol/L and LDH < 1248 U/L (curve 2, poor prognosis, median 16.9 months), other combinations (curve 3, median 14.5 months); (**B**) ICs < 0.182 mmol/L (curve 1, median 34.5 months), 0.182 < ICs < 0.455 mmol/L (curve 2, median 14.3 months), ICs > 0.455 mmol/L (curve 3, median 16.7 months); (**C**) pH < 6.74 and LDH < 1006 U/L (curve 1, favorable prognosis, median 21.9 months), pH > 6.74 and LDH > 1006 U/L (curve 3, poor prognosis, median 7.3 months), other combinations (curve 2, median 16.0 months). *n* is the number of patients in each group.

#### **4. Discussion**

It has long been assumed that there is a link between persistent chronic inflammation and lung cancer. Complex inflammatory processes involving many types of immune cells can cause tissue damage, leading to the development of COPD and lung cancer [19]. Over the past decade, there has been an increase in interest in the identification of biomarkers, in particular in COPD [20]. Some studies have observed a reduction in the risk of lung cancer in patients using inhaled corticosteroids [21]. Consequently, the likelihood of lung cancer due to COPD is increased, especially in cases where chronic inflammation is present along with the action of carcinogenic compounds of tobacco.

We have shown that in the group of patients with lung cancer, 43.1% have COPD of varying severity as a concomitant pathology, while in the comparison group, this percentage is significantly lower than 16.1%. In accordance with the histological type of lung cancer, COPD was detected in 40.2% of patients with adenocarcinoma, 48.9% with SCC, and 39.7% with neuroendocrine lung cancer. It is known that inflammatory processes in squamous cell lung cancer are more pronounced, which may explain the higher proportion of patients with concomitant COPD. Our data, however, somewhat contradict the literature, according to which 98% of lung cancer cases in patients with COPD are non-small cell lung cancer [22]. In our case, the share of patients with COPD accounts for 84% of non-small cell lung cancer, but this may be due to the characteristics of the sample.

The saliva composition of patients with lung cancer and COPD of varying severity has its own characteristics. Thus, the most pronounced differences between the groups in terms of electrolyte balance (pH, calcium and magnesium) and enzyme activity (catalase, LDH). The same parameters remain important when considering the histological type of lung cancer. It is known that hypercalcemia is relatively common in patients with lung cancer [23,24]; however, we have shown that, against the background of combined pathology of lung cancer and COPD, the level of calcium in saliva decreases. Some studies have shown that oxidative stress, defined as an imbalance between antioxidant defenses and the production of reactive oxygen species, is exacerbated in lung cancer in combination with COPD and can cause cellular dysfunction, DNA damage, and protein and lipid peroxidation [25–28]. This fact is confirmed by the high correlation coefficients for diene conjugates and Schiff bases (Figure 1C), as well as by a decrease in the activity of the main antioxidant enzyme in saliva, catalase [29]. Sialic acids play an important role in the differentiation of the studied groups; against the background of COPD, their content significantly decreases, which is consistent with our earlier data [30]. The literature describes the use of 13 glycans for differentiating lung cancer, COPD and their comorbidity from control and lung cancer from COPD [31]. In the cited work, it was shown that changes in the subclasses of N-glycans, in particular sialylation, can carry interesting diagnostic information.

It should be noted that when taking into account the histological type of tumor, two additional parameters appear, the activity of α-amylase and the level of lactate in saliva. Earlier, we showed that the activity of salivary α-amylase significantly increases against the background of lung adenocarcinoma, which coincides with the literature data, and the level of salivary amylase is increasing [32]. For neuroendocrine lung cancer, we have shown an increase in salivary amylase activity for the first time. In this case, the revealed patterns persist against the background of COPD. Hyperlactatemia can be considered as part of the stress response, which includes an increase in metabolic rate, activation of the sympathetic nervous system, accelerated glycolysis, and modified bioenergy reserves [33]. An increase in lactate levels may also be due to a corresponding decrease in LDH activity in the presence of COPD.

Despite the differences within the lung cancer group, when trying to compare the metabolic saliva profile of this group with the control group and the comparison group, it was likely that the division of groups with COPD of different severity would not occur. Nevertheless, it has been shown that, first, groups of patients without COPD are distinguished (control group and comparison group), and indicators of protein metabolism (protein, α-amino acids, sialic acids and imidazole compounds), as well as metabolic enzymes. All this indicates significant metabolic changes that are observed in lung cancer, including in combination with COPD. Against the background of COPD, there is a more

pronounced change in the content of individual components in saliva (Table 2). Thus, it has been shown that it is possible to monitor metabolic changes in lung cancer by saliva, while the presence of COPD as a concomitant disease does not radically change the metabolic profile but increases the range of changes in the corresponding biochemical parameters.

We have found that some of the studied biochemical parameters may have a prognostic value in lung cancer. Therefore, for the group of patients without COPD, these indicators include imidazole compounds and LDH, with COPD I—only imidazole compounds, with COPD II—pH and LDH. In the first two cases, groups with a favorable prognosis were identified, for which the median overall survival was significantly higher (26.3 vs. 18.1 months and 34.5 vs. 20.5 months, respectively). In the case of COPD II, we managed to identify a group with a poor prognosis: the median was 7.3 vs. 16.2 months. Such prognostic results using saliva were obtained for the first time. Earlier, a comparison was made of the mortality rate in the groups of patients with COPD and patients with combined pathology of COPD and NSCLC [34,35]. NSCLC has been shown to have a higher mortality rate in patients with mild to moderate COPD (HR = 2.62, 95% CI 1.47–4.68), while in patients with severe/very severe COPD, mortality rates in lung cancer increased insignificantly (HR = 1.22, 95% CI 0.71–2.08) [36]. In general, according to the literature, COPD is associated with a poor prognosis; for example, 3-year mortality among patients with COPD in the GOLD 4 group was 25.8% compared to 4% in the group of patients with COPD in the GOLD 1 group [37].

In our study, only four cases of severe COPD were identified, so they were not taken into account in statistical processing. We obtained data according to which the survival rates of patients with mild COPD were not only not worse but also even better than those of the group with lung cancer without COPD. One of the probable reasons is the uneven distribution of different histological types of lung cancer in groups. Thus, the group without COPD includes 50.7% adenocarcinomas, 30.9% squamous cell and 18.4% neuroendocrine cancers, while the corresponding values for COPD I group are 50.0, 35.1 and 14.9%, for COPD II group—35.3, 45.1 and 19.6%. The median survival for adenocarcinoma calculated for the study group is 23.2 months, for squamous cell carcinoma 15.2 months, for neuroendocrine carcinoma—11.1 months. Thus, HR = 1.81 (95% CI 1.10–2.95) was for SSC and HR = 2.69 (95% CI 1.23–5.77) for neuroendocrine cancer (*p* < 0.00001). Therefore, it can be assumed that the prevalence of patients with adenocarcinoma in the COPD I group improves the survival rates for this group, while in the COPD II group, the proportion of patients with squamous cell and neuroendocrine cancers with a less favorable prognosis significantly increases. It is known that associations between COPD and worse survival outcomes were stronger in squamous cell carcinoma [38]. The group of patients with COPD II accounts for the maximum proportion of cancer with central growth (42.6%), while for the COPD I group and without COPD, the corresponding values were 33.8 and 27.3%. The share of peripheral cancer was 51.5, 62.5 and 69.2% for the groups with COPD II, COPD I and without COPD. It is also one of the factors in the less favorable prognosis for lung cancer combined with COPD II. By the nature of the treatment carried out, the study groups are comparable. Complete radical treatment was performed in 22.1, 28.1, and 26.9%, combined—in 19.1, 28.8 and 28.0%, palliative—in 39.7, 32.3 and 34.8% of patients with COPD II, COPD I and without COPD. For the group of patients with COPD II, the proportion of patients who are not indicated for special treatment methods (19.1%) is higher; for the groups with COPD I and without COPD, the corresponding values were 10.8 and 10.2%. These data show that the group with lung cancer in combination with COPD I includes a larger number of patients who are indicated for radical and combined treatment, and therefore higher survival rates are quite justified. Nevertheless, we have shown that when comparing the metabolic parameters of saliva for the group of patients with lung cancer and COPD I, the values are closer to the control than those for the group of patients with lung cancer without COPD. This may be due to the formation of compensatory mechanisms in the early stages of COPD, which requires more detailed research in the course of further work.

The limitations of the study are associated with the absence of patients with severe COPD (GOLD III and GOLD IV) and with the absence of patients with COPD without other pulmonary pathologies (control group). The small sample size of patients with lung cancer and COPD II limits the possibility of dividing this group into subgroups and reduces the statistical significance of the results.

## **5. Conclusions**

Thus, saliva adequately reflects metabolic characteristics in lung cancer, as well as lung cancer in combination with COPD of varying severity. It has been shown that the presence of COPD as a concomitant pathology does not fundamentally change the metabolic profile of saliva but increases the range of changes in the corresponding biochemical parameters. A complex of biochemical parameters of saliva, which have prognostic value in lung cancer, was revealed. It should be noted that in the presence of COPD, the list of parameters differs from that without COPD, which must be taken into account when planning treatment tactics and assessing the prognosis of lung cancer. In general, for patients with lung cancer in combination with mild COPD, the prognosis is more favorable than without COPD, which is due to compensatory mechanisms. The revealed biochemical parameters (catalase, imidazole compounds, sialic acids, LDH) can be used to monitor patients from risk groups, for example, groups of patients with initial stages of COPD, in particular for the timely diagnosis of lung cancer.

**Author Contributions:** Conceptualization, L.V.B. and V.K.K.; methodology, L.V.B.; validation, L.V.B., E.A.S. and D.V.S.; formal analysis, D.V.S.; investigation, L.V.B. and E.A.S.; resources, V.K.K.; data curation, D.V.S.; writing—original draft preparation, L.V.B. and D.V.S.; writing—review and editing, V.K.K.; visualization, D.V.S.; supervision, L.V.B.; project administration, V.K.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.


Preface

When I was invited to edit and put together this book, as part of the Women in Science Competition in 2018 proposed by IntechOpen, I was very excited to get the opportunity to put together a collection of works in the field of communication that would resonate with the idea of pursuing a fundamental thread of how communication has evolved on life on Earth, from the very building blocks of life such as DNA or cells, to supra-human organisms such as institutions and computer systems.

The idea of finding universal, fundamental patterns of communication arose while I was working on a computational model of information exchange that could find its applications in molecular biology as well as in artificial intelligence. While researching this idea, I came across a wide variety of research from most fields of study, such as biology, anthropology, history, marketing, economics, information sciences, psychology, social sciences, to name only a few. Particularly in biology, psychology, and marketing or business, topics such as animal communication or communication with children or effective communication in business seemed very large, broad, and predominant, but without much reference between each other or across disciplines. This gap in the current literature and the siloed research within fields spurred the idea of trying to find what is common and fundamental in communication between these seemingly different subfields.

A first step towards opening the dialogue and research on common threads of communication in various and different systems was done by the publication of the book "Emergence of Communication in Socio-Biological Networks" at Springer in 2018. But this has only scratched the surface and, given the wide diversity of the fields and researchers involved, the next logical step was to gather into a collection the works of worldwide researchers that are intimately familiar with specific aspects of communication.

This book is highly curated, as the chapters published here are not the typical research on communication specific to each field, but research on communication as a complex system that can be read and understood across disciplines and that can address a wider audience than within one's specialty. This effort shows again how big the gap is in the cross-disciplinary approach of communication, how open and wide the field is and how important it is to keep on pursuing in this search for knowledge.

As this is another important step in the discovery of fundamental communication patterns, I am confident that it is also only another open door to an even wider and broader body of research and therefore I see this book as an invitation for the current and future scientists and scholars to look at this phenomenon through the lenses of complex systems, multidisciplinary and universality.

I would like to express my deepest gratitude to the authors that put in a great deal of effort into their papers and for submitting their work to this edition, to the

publishers at InTech Open for working with me and supporting me in this effort, as well as to the Ronin scholars that gave me invaluable advice at their seminars on how to conceptualize and approach this idea in depth.

**Anamaria Berea, PhD**

**1**

Section 1

Introduction

Complex Adaptive Systems Laboratory, University of Central Florida, Orlando, Florida, USA, Ronin Institute, USA

Section 1 Introduction

**3**

**Chapter 1**

*Anamaria Berea*

living systems as well [2].

exchanged [2].

Introductory Chapter: The Role of

Communication as a Fundamental

The area of research of communication is one of the largest and vastest one can find oneself immersed in. Without communication, none of the living systems on Earth could exist, as life itself is an emergent process of interactions between differ-

new organisms or to the death of one or both of the organisms engaged in the process of communication [1]. This is true both in the natural and the social worlds, where organisms are either single celled or human institutions. In the largest sense, communication can be viewed as peer to peer interactions. In the narrowest sense, communication can be viewed as specialized language. Either way, communication implies a process of information exchange and the latest developments in information and computer science can tell us a little bit more about the process of communication in

Unlike interactions, which are common in physical nonliving systems as well, communications are about intent and outcomes that are not necessarily deterministic in nature, such as chemical reactions are, for instance. In living systems, communication is intimately linked to the action, either to initiate or to receive information, or to take some action post-communication, based on the information

While each field of study has researched communication within its own framework, I was not able to find a broader view of communication that would encompass this larger view of the phenomenon. Surely, biologists have an in-depth view of animal communication with the specifics of each species, marketing specialists have an in-depth view of communication between institutions and customers, psychologists have an in-depth view of communication between humans in various contexts, and social scientists have an in-depth view of communication between groups or cultures, while linguists have an in-depth view of communication we call language. Yet, there is little attempt to cross borders between disciplines and to try to understand this phenomenon at large, in its universality, and not inside different contexts and scenarios. Perhaps because such a view is too large to be approached in one research endeavor, or perhaps because the phenomenon itself is too broad, or perhaps because attempts have been made but failed; nonetheless, as scientists, when faced with fundamental and ubiquitous phenomena, we have the duty to try to understand them in these terms and aim toward grasping their universality [3].

Nonetheless, given some ubiquitous and fundamental understandings of what communication is about, we must at least attempt to discover common and universal features

that run across disciplines, or characteristics that are similar in all living systems.

ent organisms, whether these interactions ultimately lead to the creation of

Process for Life and Society

**1. Communication as a fundamental process for life**

### **Chapter 1**


**Preface to "Natural and Artificial Unsaturated Soil Slopes"**

Recently, there have been extensive studies on engineering problems involving soils whose mechanical and hydraulic properties are strongly influenced by the degree of saturation. Earthen embankments, soil–vegetation–atmosphere interactions, geoenvironmental applications, and risk mitigation are just a few examples of the constant interest of the scientific community to the subject. The presence of a sloping ground surface is common to many of these problems. In slightly inclined natural slopes, susceptible to deep landslides, the unsaturated condition of shallow soil horizons affects deep pore water pressure distribution and, consequently, the stability of the entire slope. The stability of steep mountain areas covered by shallow deposits is often guaranteed by a shear strength contribution related to the unsaturated nature of the soil. In this case, the degree of saturation plays a key role in determining which rainfall events can act as landslide triggers, therefore controlling the post-failure evolution. Partial saturation is the basic characteristic of soils adopted as construction materials of geo-structures such as levees, dikes, and dams. This condition governs the structure behavior during construction phases, in serviceability, and in extreme scenarios. Hoping to provide a bridge between theoretical research and practical applications, this Special Issue collects quality contributions related to natural and artificial slopes under unsaturated conditions, and focuses on several aspects, including: water retention and transport properties, mechanical behavior, advances in experimental methods, laboratory and in situ characterization, soil improvement, field monitoring, geotechnical and geophysical field tests, landslide investigation and prevention, the design and maintenance of engineered slopes, analysis at different spatial scales, and the constitutive and numerical modeling of chemo-hydro-mechanical behavior.

> **Roberto Vassallo, Luca Comegna, Roberto Valentino** *Editors*

**Roberto Vassallo 1,\*, Luca Comegna 2 and Roberto Valentino 3**

Natural and artificial slopes are frequently constituted, at least in part, by soils in unsaturated conditions. Their degree of saturation strongly influences the hydraulic and mechanical properties and, therefore, pore water pressures and stability. This Special Issue collects six papers focused on unsaturated soil behavior in engineering applications involving natural or artificial slopes. All the papers hold a significant laboratory/site experimental characterization and/or numerical analysis content for the interpretation of the considered phenomena.

Ponzoni et al. [1] report water retention data for a sandy silt and a silty sand coming from two different climatic European environments. The soils were tested in the laboratory by using two commercial devices, and the results were modeled with van Genuchten's law. The authors determined a correlation of the fitted parameters with the amount of fines and discussed the advantages and limitations of the proposed approach on the basis of drying–wetting test results. The study demonstrated the feasibility of the approach, aiming for a rather inexpensive characterization of retention properties for the assessment of climate impact on slope stability, and providing a careful evaluation and an elaboration of data.

Bovolenta et al. [2] discuss the reliability of soil moisture capacitive sensors, the operations related to their calibration and the installation phases for several European monitoring networks designed to assess the susceptibility to rainfall-induced debris and earth slides. The aim of such networks is to establish a cause–effect relationship between rainfall and landslides by measuring near-surface water content variations over time. After appropriate calibration, the systems are revealed to be able to provide consistent soil moisture data. The sensors are rather cheap, easily relocatable and replaceable, which makes a remotely managed network relatively inexpensive and rapid to install.

Castelli et al. [3] report experimental data derived from laboratory and site tests, carried out with the aim of evaluating soil parameters for debris-flow run-out simulations. The experimental activities included triaxial tests in unsaturated conditions and geophysical investigations. Starting from a study of the geological framework and the historical background, this research focuses on the causes that triggered a recent debris-flow event in Sicily, Italy. The authors discuss possible strategies to determine the hydromechanical parameters that can be used for a numerical analysis of such phenomena and the role of matric suction, which reflects soil infiltration, in the investigation of rainfallinduced landslides.

Sitarenios and Casini [4] present a three-dimensional, limit equilibrium, slope stability solution for translational planar failure modes. The solution uses Bishop's average skeleton stress combined with the extended Mohr–Coulomb failure criterion. The performance of the proposed theoretical solution is evaluated by comparing its predictions with the results of a full-scale slope failure experiment carried out in Switzerland. The 3D solution can

**Citation:** Vassallo, R.; Comegna, L.; Valentino, R. Editorial of Special Issue "Natural and Artificial Unsaturated Soil Slopes". *Geosciences* **2021**, *11*, 334. https://doi.org/10.3390/ geosciences11080334

Received: 5 August 2021 Accepted: 6 August 2021 Published: 7 August 2021

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

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

satisfactorily capture the experimentally observed behavior. Furthermore, its simplicity and very limited calculation costs allow for an easy evaluation of different scenarios with respect to slope geometries and soil parameters, which can be very useful in a landslide back-analysis.

Ventini et al. [5] discuss the assessment of the hydraulic and mechanical behavior of river embankments, by considering transient seepage under unsaturated soil conditions. This is a more accurate approach than assuming, as frequently occurs in routine engineering practice, steady-state seepage and either fully saturated or "dry" conditions. A comprehensive procedure for the definition of the key aspects of the problem is presented. The soil characterization in unsaturated conditions of a compacted mixture of sand and fines, typical of flood embankments of the main tributaries of the Po River in Italy, is reported. The laboratory results are used for parameter estimation, aiming to model the embankment performance under transient conditions and following a set of possible hydrometric peaks. The importance of carrying out such an analysis is highlighted in a comparison with steady-state seepage analyses performed at the maximum hydrometric height recorded, which frequently provide excessively conservative stability assessments. Safety factor variations over time are obtained by limit equilibrium analyses as the effect of river level fluctuations, providing useful information for further detailed experimental and numerical investigations on the river embankment response.

Sepe et al. [6] analyze the capillary barrier formation at the interface between shallow soil layers characterized by textural discontinuities. The investigation is focused on the effects of rainfall events in pyroclastic deposits of the Vesuvian Area, in Campania region, Italy, undergoing shallow instability phenomena caused by progressive soil saturation. Based on a detailed field survey, the failure surface location was identified at the interface between grey pumice and black scoriae layers, likely due to their contrasting hydraulic properties. One-dimensional infiltration into the stratified soil is numerically simulated, considering rainfall events of increasing intensity and duration. The changes in the degree of saturation and suction over time are used for the evaluation of stability condition variations. By analyzing the time history of the infiltration process, the authors highlight the capillary barrier water break-through as a consequence of the progressive suction equalization between the two layers, as well as the gravity-driven downward water flux over time. The simulated failure conditions due to a medium intensity, long duration rainfall event are consistent with the field observations.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

## *Article* **Determination of Water Retention Properties of Silty Sands by Means of Combined Commercial Techniques**

**Elisa Ponzoni 1 , Alessandra Nocilla 2,\* and Cristina Jommi 3,4**

**Abstract:** A recent increase in frequency and severity of exceptional climatic events is of concern for the stability of natural and artificial slopes. These undergo continuous evaporation and infiltration cycles, which change the suction distribution and trigger shrinkage, swelling, cracking, and surficial erosion, overall decreasing the soil strength. To assess the impact of these climatic stresses, the determination of water retention properties is a priority. Although advanced techniques have been proposed in the last few decades to this end, simpler commercially available techniques allow collecting data for a larger number of samples in a shorter time, thus enabling a basic description of the water retention properties for a larger database of soils. Data on two silty sands, coming from very different climatic environments in Europe, were collected with a combination of two simple commercial devices, and the results were modelled with a van Genuchten's law. The fitted parameters were found to correlate well with the amount of fines, irrespective of the different origin and composition of the two soils. Eventually, the limitation of the approach is discussed based on the results of cyclic drying–wetting tests.

**Keywords:** water retention; suction; silty sands; commercial experimental techniques

#### **1. Introduction**

Slope stability analyses involve the identification of the different mechanisms that might trigger failure no matter whether an artificial or a natural slope is involved. A recent increase in the severity of climatic events suggests that the influence of evaporation and infiltration on the mechanical behaviour of soils must be considered carefully among other factors. Two recent case histories, very different from each other, promoted the investigation presented in the following. The first one refers to the stability of regional dykes in The Netherlands, which are experiencing droughts due to substantial increase in summer temperatures in the recent years [1]. The second case refers to natural cliffs made of carbonate sand at Agrigento in Italy, where local collapses have been observed following heavy rainfalls [2–5]. Both cases involve large areas, for which management decisions have to be taken based on a relatively small amount of data, as advanced lengthy laboratory investigation cannot be afforded.

The hydromechanical response of dykes under unsaturated conditions has been tackled by many researchers, who highlighted the role of unsaturated conditions on their response. Among others, Schmertmann (2006) and Calabresi et al. (2013) [6,7] highlighted that partial saturation is largely prevailing in silty dykes. As a consequence, the current guidelines, which suggest assessing slope stability assuming a steady state pore pressure distribution following the design high-water event, are largely over conservative. As

**Citation:** Ponzoni, E.; Nocilla, A.; Jommi, C. Determination of Water Retention Properties of Silty Sands by Means of Combined Commercial Techniques. *Geosciences* **2021**, *11*, 315. https://doi.org/10.3390/ geosciences11080315

Academic Editors: Roberto Vassallo, Luca Comegna, Roberto Valentino and Jesus Martinez-Frias

Received: 7 June 2021 Accepted: 20 July 2021 Published: 27 July 2021

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

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

pointed out by Jommi and Della Vecchia (2013) [8], the steady state assumption leads to an overestimation of the dimensions of the embankment, hence of the amount of soil, needed to guarantee a sufficient factor of safety during the working life of the structure. As is well-known from the literature, modelling time dependent unsaturated flow better allows assessing the change in the pore water pressure and in the soil shear strength [9].

Things are not less complex for natural slopes. Of interest for the following, it is worth noting that Ercoli et al. (2015) [3] suggested that monitoring the change in water content in the sand is fundamental to appreciate its main role in the retrogression collapse mechanisms of the Valle dei Templi cliffs at Agrigento.

The water retention properties are fundamental in this respect, not only because they govern the hydraulic conductivity and seepage, but eventually for their role in governing changes in stiffness and strength of the soil. Many factors affect the water retention, including the void ratio, the activity of the clay fraction, the fabric of the soil [10–18]. Among them, the particle size distribution may become the dominant one for non-plastic mixtures of silty sands [19,20].

Although advanced techniques have been proposed in the last few decades to study the water retention behaviour of soils, simpler commercially available techniques allow collecting data for a larger number of samples in a shorter time, thus enabling a basic description of the water retention properties for a larger database of soils. Following this consideration, two non-plastic soils were investigated over drying and wetting combining the data from two commercially available devices: the Hyprop® and the Dew Point Potentiometer WP4C®. The results are modelled with a simple van Genuchten's model (1980) [21], in an attempt to derive correlations to be used as a first approximation in the assessment of natural and artificial slopes.

#### **2. Materials and Methods**

Two different soils were investigated: a sandy silt from a dyke at the Leendert de Boerspolder (South Holland, The Netherlands) and a silty carbonate sand from a natural cliff in the "Valle dei Templi" at Agrigento (Sicily, Italy). Relevant information on the samples tested are collected in Table 1. The dyke silty material goes from a silty sand with traces of gravel and clay to a clayey silt with traces of sand. The void ratio, e, of the samples investigated is almost constant, to better highlight the influence of grain size distribution on the water retention properties. Both natural and reconstituted samples were investigated to assess the influence of fabric on the response. The material from the Valle dei Templi is a carbonate fine and medium sand with a clay fraction up to 10% and a silt content slightly less than 20%, characterised by a quite high uniformity coefficient, U, of about 5 [22]. For the Agrigento sand the values reported are the average of the three samples. For both materials the amount of clay is below 8%.

**Table 1.** Samples investigated for water retention. In the table, r stands for reconstituted.

The water retention behaviour was investigated by combining data from two commercial apparatuses (Figure 1) available at TU Delft (NL), namely the Hyprop® and the Dew Point Potentiometer WP4C®. The HYPROP® device (HYdraulic PROPerty analyser, constructed in Munich, Germany) was developed by UMS to determine the water retention curve and the unsaturated conductivity over drying from the top surface of the sample at room temperature and relative humidity. Over the testing period the total mass of the

sample is measured, as well as the temperature at the bottom of the sample. The sensor placed at the bottom of the sample can measure temperature between −30 and 70 ◦C, with a tolerance of ±0.2 ◦C in the range of −10 to 30 ◦C. Two tensiometers, having a porous stone with a high entry value (880 kPa), allow direct measurement of suction at two different heights, with an accuracy of 0.15 kPa. The range of suctions typically covered by the tensiometers goes from zero to about 100 kPa, depending on the saturation achieved by the suction measurement system before the start of the test. Actually, although the porous stone in contact with the soil guarantees no cavitation of the tip, the longer the tests the more likely is cavitation of the measuring system behind the tip. Water mass changes are measured by a scale (accuracy of 0.1 g) at the bottom of the system, which allows estimating the average water ratio of the sample at any time. The water content at the end of each test was double checked by oven drying.

**Figure 1.** Geometry and cross section of (**a**) the Hyprop device edited from the User Manual Hyprop, 2012 [23], and (**b**) The Decagon dew point potentiometer WP4C ® Cross Section [24].

The Dew Point Potentiometer, constructed in Munich, Germany, uses the chilled mirror dew point technique to measure the water potential of the soil, through the vapour pressure of the air in equilibrium with the sample in a sealed measurement chamber. Although the measurement range theoretically goes from 0 to 300 MPa, the accuracy of the device below 1MPa is extremely low, which does not guarantee the reliability of the measurement. From 10 to 300 MPa the accuracy is ±1%.

Although both techniques have been designed to investigate the water retention properties over drying, an attempt was made to conduct the tests in order to investigate also the response of the samples over wetting. However, it is worth noting that the data obtained from the Dew Point Potentiometer after wetting the samples cannot be interpreted as lying on a main wetting curve, as equilibrium is achieved in the chamber due to an evaporation process [25]. In the Hyprop, wetting stages were controlled by protecting the samples from evaporation after adding a controlled amount of water on the top. The recorded data over drying and wetting will be commented later in Section 4.

#### **3. Results**

The mass of water contained in a soil samples allows determining directly the water content of the soil, w

$$\mathbf{w} = \frac{M\_{\mathbf{w}}}{M\_{\mathbf{s}}} \tag{1}$$

where *Mw* and *Ms* are the mass of water and solid particles, respectively.

Following the discussion by Romero and Vaunat (2000) [26], water ratio, e*w*, is used instead of water content in the presentation of the results, as it better normalises the results for soils having different specific gravity, *Gs* :

$$\mathbf{e}\_w = \mathbf{G}\_\mathbf{s} \cdot w \tag{2}$$

For soils having low deformability like those tested, volumetric strains over drying in the range of suctions investigated in the Hyprop can be disregarded as a first approximation, thereby allowing a sufficiently reliable estimate of the current average degree of saturation of the sample assuming constant void ratio over the test (e = e0).

$$\mathbf{S}\_{\mathbf{r}} = \frac{\mathbf{e}\_w}{\mathbf{e}\_0} \tag{3}$$

Typical data for the two materials over main drying are reported in Figure 2. During the Hyprop test water tension readings (suction) are taken continuously by the two tensiometers. The suction in the upper part of the sample is measured by the tensiometers having the longer shaft and that in the lower part of the sample by the tensiometers mounted on the shorter shaft. Both shafts are schematized in Figure 1a. By contrast, the discrete measurements taken with the WP4C are represented by symbols in the figures.

**Figure 2.** Water retention data of (**a**) Agrigento sand and (**b**) Leendert de Boerspolder silt.

Figure 2 highlights that combining the two techniques allows covering the entire range of suction expected for these soils, although the suction range from about 100 kPa to 1 MPa is not covered by any of the two techniques. Nonetheless, the lower suction and higher suction ranges are accurately described, which allows including the entire suction range in a model.

It is worth noting that the suction at the bottom and at the top of the samples are different, as the measurements are taken over a time-dependent process characterised by vertical flow. Moreover, the two measurement are related to a water ratio, which actually measures the average water mass on the entire sample coming from a non-uniform distribution over sample height. Nevertheless, for rather pervious almost undeformable soils the average suction over height and the average water ratio over the volume of the sample are well correlated to each other because their gradients are not high. This is not the case for more impervious and deformable clay samples, which present steep gradients of water content and suction over height due to the preferential flow direction in time [27]. Assuming that the average values are representative of the "material" behaviour, the water retention data can be elaborated to derive a degree of saturation–suction relationship for each sample, as shown in Figure 3.

**Figure 3.** Elaboration of water retention data of (**a**) Agrigento sand and (**b**) Leendert de Boerspolder silt.

The elaborated data are interpolated with a simple van Genuchten's model (1980):

$$\mathcal{S}\_{r} = \left(1 + \left(\frac{\mathcal{S}}{P}\right)^{1/1-\lambda}\right)^{-\lambda} \cdot (\mathcal{S}\_{rmax} - \mathcal{S}\_{rres}) + \mathcal{S}\_{rres} \tag{4}$$

drawn as a dotted line in the figure. In the previous expression, *S* is the suction, *Sr* is the degree of saturation, *Srmax* is the maximum attainable degree of saturation over wetting at the atmospheric pressure, and *Srres* is the residual degree of saturation at high suctions. The curve depends on two parameters, *p* linked to the air entry value and *λ*, ruling the average slope of the curve. For the samples investigated, the residual saturation is almost null, while the maximum attainable saturation at the atmospheric pressure corresponds with the air occlusion value, as the samples did not undergo saturation under back pressure. Strictly speaking, the initial part of the plotted drying curve for Agrigento's soil (until the air entry value) is not the "main drying" because the soil sample did not start from full saturation. However, it is equally representative of the response of the soil. The parameters of the fitted van Genuchten's curve for the different samples are summarised in Table 2, where *p* has been normalised to the atmospheric pressure.

**Table 2.** Parameters of the van Genuchten's model for the samples investigated.

The calibrated parameters are very well correlated with the fine content, including the silt and the clay fraction, of each sample. The correlations are reported in Figure 4, and the curves fitting the available experimental data, starting from the experimental *Srmax*, are depicted in Figure 5.

**Figure 4.** Fitting curves of the water retention model parameters as a function of the fine content, FC: (**a**) *p*/patm, and (**b**) *λ*.

**Figure 5.** Fitting curves of the samples investigated.

Although the amount of data is limited, the correlations seem to suggest few conclusions: (i) the fabric does not play a relevant role for these samples, as both natural and reconstituted samples follow the same trend; (ii) as expected, the air entry value increases with the amount of fine, irrespective of the mineralogical composition of the two soils, which in both case is non-active; (iii) the slope of the water retention curves also depends on the amount of fines, as it rules the coefficient of uniformity. Given the very different origin of the two soils, it is expected that the conclusions may be valid for non-active silty sands in general, although validation is still needed.

#### **4. Time-Dependent Data and Hysteresis**

Although the Hyprop system is designed to determine the water retention curve over drying at atmospheric boundary conditions, an attempt was made to investigate the soil water retention branch. As suggested by Schindler et al. (2015) [28], water can be added to the surface of the Hyprop sample simulating a precipitation event in order to quantify hysteresis due to cycles of drying and wetting.

The data recorded over time over a number of drying and wetting cycles on a sample of the dyke silty material are reported in Figure 6, as a function of the current degree of

saturation. The shape of the curves obtained by averaging the negative pore pressure recorded by the two tensiometers clearly highlight the transient response of the system, especially over the first cycles, and hardly allows determining the separate main drying and main wetting branches of the water retention domain. Actually, whenever forced infiltration is stopped, the upper part of the samples undergoes a new drying stage due to evaporation from the top, while the bottom part of the sample is still wetting due to previous infiltration.

**Figure 6.** Suction measurements during drying–wetting cycles in Hyprop on a sample of the dyke silty material as a function of the average degree of saturation of the sample.

Therefore, it was decided to adopt a slightly different strategy, which could help in the interpretation of the data. After adding water on the top of the sample, an impervious plastic cover was placed, and kept until the tensiometers measurement reached an almost asymptotic value of suction, indicating equalisation, before letting the sample evaporate again. An example of the entire data set logged during evaporation and infiltration cycles is provided in Figure 7 for another sample of the dyke silty sand. In Figure 7a the inner and outer temperature are plotted against time. The term "inner" indicates the temperature recorded at the base of the sample from the sensor placed in the Hyprop unit, whereas "outer" indicates the ambient temperature of the laboratory. Figure 7b shows the mass change of the sample over time. Its rate is plotted as a function of the two variables ruling the exchanges, temperature and relative humidity, in Figure 7c.

**Figure 7.** Data logged during drying and wetting cycles on a sample of the dyke silty material: (**a**) inner and outer temperature, (**b**) temperature, Rh and mass loss rate, (**c**) net sample mass and (**d**) suction readings as a function of time.

Of relevance for the present discussion, the suction measured on the upper part of the soil sample (long shaft) and in the lower one (short shaft) are reported in Figure 7d. The measurements clearly show the substantial difference between the suction at the two heights over time during drying, when equalisation was not forced. The maximum suction over drying is reached in the lower part of the sample shortly after the peak value at the top. If equalisation is forced, as it was done over the wetting stages here, the two tensiometers reach almost the same value (with a difference due to the gravitational potential, which can be disregarded) and allow for the determination of a clear value for the suction in equilibrium with a given water content, to be used in a water retention model. The decrease in the measured suction on the long shaft tensiometer during the 8th drying stage is due to cavitation of the instrument.

The latter data were elaborated to identify the wetting branch of the water retention curve of the sample, which is plotted in Figure 8, together with the drying data, which were already used as example in Figure 2b. The hysteretic cycles could be well identified, as shown in Figure 8b, with the procedure adopted, with the scanning paths that tend to overlap to each other after the first cycle, as expected.

**Figure 8.** Drying and wetting branches of the water retention domain, as elaborated from the Hyprop data in Figure 7 on a sample of dyke silty material: (**a**) full suction range, and (**b**) 0 < S < 100 kPa.

#### **5. Conclusions**

Soil water retention properties over drying were investigated on a sandy silt from a dyke at the Leendert de Boerspolder (NL) and for a carbonate silty sand from the Valle dei Templi in Agrigento (IT), with a combination of data obtained from two commercially available devices, namely the Hyprop ® and the Dew Point Potentiometer WP4C ®. The scope of the work was to investigate the possibility for a rather cheap characterisation of the retention properties of soils of concern for the assessment of climate impact on the stability of slopes, even in very different contexts.

The results demonstrate the feasibility of the approach, and provided careful evaluation and elaboration of the results. Classical water retention models are conceived to describe steady equilibrium states, while in the popular devices chosen for the investigation, data are continuously recorded over time during time-dependent dynamic processes. Using the average over a given time interval of the suction measurements taken at two different heights in the sample may work for the first drying stage of non-active silty sands and sandy silts. However, to obtain reliable information on the hysteretic response over the drying–wetting cycles expected on site, the standard procedures must be amended.

An advantage of the technique is that it reproduces evaporation and infiltration naturally occurring in the soil at the environmental conditions of the laboratory, which can be controlled if necessary. However, the way in which the Hyprop device was designed implies measuring suction during a transient process, which might even be non-monotonic over the sample height in case wetting stages were added during the test. With minor changes to the set up and the test protocol of the Hyprop device, data at suction equalisation can be obtained, creating the possibility to investigate both the drying and the wetting branches of the water retention domain of non-active soils. Soils experiencing relevant volume changes over drying and wetting are much less suitable for testing with the technique and the coupled hydromechanical response of the soil requires careful investigation.

**Author Contributions:** Conceptualization, A.N.; Investigation, E.P.; Methodology, C.J.; Writing original draft, A.N., E.P.; Writing—review & editing, A.N., C.J. All authors have read and agreed to the published version of the manuscript.

**Funding:** The financial support on the experimental equipment of the perspective program BioGeoCivil (grant 11344) and CEAMaS (Civil Engineering Applications for Marine Sediments), supported by the European Regional Development Funding through INTERREG IV B, is gratefully acknowledged.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The authors acknowledge Margherita Zimbardo of the University of Palermo and the "Ente Parco Archeologico della Valle dei Templi" at Agrigento (Sicily, Italy) for providing the Agrigento sand specimens.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


3.3.2. Exploratory Spatial Data Analysis

An exploratory spatial data analysis (ESDA) can perform a correlation and aggregation analysis of neighborhood spatial data, which can effectively verify the spatial clustering characteristics of regional CLUTs. Two types of autocorrelation coefficients are usually used for this measurement. The first is the global spatial autocorrelation coefficient: the distribution of the Moran scatter plot is used to show the spatial correlation of the CLUT in the study area. The expression is

$$I = \frac{n\sum\_{i=1}^{n}\sum\_{j=1}^{n}\mathcal{W}\_{ij}(\boldsymbol{\chi}\_{i}-\overline{\boldsymbol{\pi}})\left(\boldsymbol{\chi}\_{j}-\overline{\boldsymbol{\pi}}\right)}{\sum\_{i=1}^{n}\sum\_{j=1}^{n}\mathcal{W}\_{ij}\sum\_{i=1}^{n}\left(\boldsymbol{\chi}\_{i}-\overline{\boldsymbol{\pi}}\right)^{2}}\tag{1}$$

where *I* is the global Moran index, and *xi* and *xj* are the CLUT index in cities *i* and *j*, respectively, and *x* represents the average of the CLUT indices, and *Wij* is the spatial weight matrix. In this study, a spatial adjacency matrix was used, which was constructed by GeoDa software. The value of *I* is [−1,1]. When *I* = 0, this indicates that the space is not autocorrelated; when *I* > 0, this means that there is a positive correlation, and when *I* < 0, this indicates that there is a negative correlation. The closer the absolute value of *I* is to 1, the greater the degree of clustering and the spatial correlation.

The second type is the local spatial autocorrelation coefficient: it can use an LISA graph to check the heterogeneity of the data calculation and reveal the correlation degree of the attribute values between spatial units and adjacent units. The formula is as follows:

$$I\_i = \frac{n(\mathbf{x}\_i - \overline{\mathbf{x}}) \sum\_{j=1}^{n} \mathcal{W} i\_j (\mathbf{x}\_j - \overline{\mathbf{x}})}{\sum\_{i=1}^{n} (\mathbf{x}\_i - \overline{\mathbf{x}})^2} \tag{2}$$

When *Ii* > 0, high-high/low-low means that the spatial unit value is higher/lower than all the surrounding units and that the integrated spatial difference is smaller. When *Ii* < 0, then low-high/high-low means that the lower/higher spatial unit value is higher/lower than the surrounding units and that the integrated spatial difference is smaller.

#### 3.3.3. Standard Deviation Ellipse

The standard deviation ellipse (SDE) is used to quantitatively describe the spatial characteristics of the elements. The azimuth of the ellipse represents the main trend direction, the long axis represents the dispersion of the geospatial elements in its direction, and the center of gravity represents the relative position. The results of the SDE calculation can reflect the spatial change in the CLUT (Equations (3)–(5)).

$$\tan \theta = \frac{\left(\sum\_{i=1}^{n} w\_i^2 \mathbf{x}\_i'^2 - \sum\_{i=1}^{n} w\_i^2 \mathbf{y}\_i'^2\right) + \sqrt{\left(\sum\_{i=1}^{n} w\_i^2 \mathbf{x}\_i'^2 - \sum\_{i=1}^{n} w\_i^2 \mathbf{y}\_i'^2\right)^2 + 4 \sum\_{i=1}^{n} \left(w\_i^2 \mathbf{x}\_i'^2 \mathbf{y}\_i'^2\right)}}{2 \sum\_{i=1}^{n} w\_i^2 \mathbf{x}\_i' \mathbf{y}\_i'} \tag{3}$$

$$\overline{X}\_{\overline{w}} = \frac{\sum\_{i=1}^{n} w\_i \mathbf{x}\_i}{\sum\_{i=1}^{n} w\_i} ; \overline{Y}\_{\overline{w}} = \frac{\sum\_{i=1}^{n} w\_i y\_i}{\sum\_{i=1}^{n} w\_i} \tag{4}$$

$$\sigma\_{\mathbf{x}} = \sqrt{\frac{\sum\_{i=1}^{n} (w\_i \mathbf{x}\_i^\prime \cos \theta - w\_i y\_i^\prime \sin \theta)^2}{\sum\_{i=1}^{n} w\_i^2}};\\ \sigma\_{\mathbf{y}} = \sqrt{\frac{\sum\_{i=1}^{n} (w\_i \mathbf{x}\_i^\prime \sin \theta - w\_i y\_i^\prime \cos \theta)^2}{\sum\_{i=1}^{n} w\_i^2}}\tag{5}$$

where tan *θ* is the azimuth angle of the ellipse, i.e., the angle formed by the clockwise rotation from due north to the long axis of the ellipse; (*Xw*,*Yw*) are the center of gravity coordinates, and *Xi* and *Yi* are the spatial location elements, *Wi* represents the weight, *x* 0 *i* , *y* 0 *i* represents the deviation of the coordinates of the elements at different points from the mean center, and *σx* and *σy* are the standard deviations along the x- and y-axes, respectively.

#### 3.3.4. Data Collection

The socioeconomic data come from the provincial and municipal statistical yearbooks of Hubei, Hunan, Jiangxi, Anhui, and Jiangsu in 2002, 2008, 2014, and 2020. Since ESA's land-use cover data has the advantages of authority, continuity, openness, and includes research time-point data, the land-use data in this research are derived from the ESA's global 300-m land cover data in 2001, 2007, 2013, and 2019. The administrative zoning data were obtained from the 1:4 million dataset of the China National Basic Geographic Information Center. To preprocess the land-use data, we first classified the land-use types into six types: namely, cultivated land, forestland, grassland, water bodies, construction land, and unused land. Afterward, the different land-use type data were recoded and assigned CLT1 to CLT6. On this basis, Fragstats software was then used to measure the fragmentation, aggregation, and landscape morphology indices of the cultivated land.

### **4. Results**


**6. Conclusion**

Potential for conflict that can arise from technology-driven cultural change is significant. Undoubtfully technology is changing every aspect of our lives. And the benefits provided by new digital approaches are showing a huge improved impact on our societies. However, one of the greatest business challenges is not about the devices, software or solutions – it is about how we positively manage the process of cultural change. It's important to remember that technology is not inherently bad as Technology and culture directly influence each other. As cultures change, so does the technology it innovates Structure. Much of this is for the greater good. It can be used to improve the quality of life and living Standards, but it can also be used to harm people. So it's important to be aware of the potential for conflict that can arise from technology-driven cultural change, so that we can take steps to mitigate this conflict.

One way to mitigate the conflict that can arise from technology-driven cultural change is through education and sharing knowledge about not only our customs, traditions and norms but also to respect each and every culture in the same way as we do ours. It is important to educate people about the potential impact of new technologies; so that they can make informed decisions about how to use these technologies. Another way to mitigate the conflict that can arise from technology-driven cultural change is through dialog. It is important to create spaces where people can come together to discuss the impact of new technologies, and to find ways to bridge the gaps between people who hold different values and norms. Through taking these corrective measures we can help to create a more all-encompassing and harmonious societal face of technology-driven cultural change.


**2.3 Arteriovenous fistula creation**

Appropriate and efficient vascular access is necessary for a successful HD. According to the National Kidney Foundation Kidney Disease Outcomes Quality Initiative (NKF-KDOQI) guidelines, the ideal vascular access is described as one which can deliver an adequate flow rate along with durability and a low complication rate. An arteriovenous fistula (AVF) is usually considered to be the best access for HD in adults and, as commented on by an emerging body of evidence, that it is the same also in children [12].

It has been seen that children's vascular biology is not the same as that of adults; henceforth, the ideal size of the vein and artery, anastomosis maturation time, and volume flow rates for a functional fistula in children on HD are not known. Since its inception, advances in AVF creation, especially with improved surgical experience, primary failure rates have been gone down to as low as 5%. For an AVF creation, the preferred sites include, in order, the radial artery to cephalic vein (radiocephalic), brachial artery to cephalic vein (brachiocephalic), and brachial artery to basilic vein (brachiobasilic, with or without transposition). Alternatively, an ulnar artery to basilic vein AVF can be created. Though rarely utilized, an AVF between femoral artery to saphenous vein has also been described. Although there are no guidelines regarding ideal/minimum vessel size in the literature, the general consensus is a minimum venous diameter of 2.5 mm. The essential information before AVF creation includes adequate vessel size; venous stenosis/occlusion can be obtained by duplex ultrasound scanning or venography and is necessary to be carried out in children to decide on the best vessels for AVF creation [13]. Complications of AVF creation include stenosis/ occlusion, thrombosis, steal syndrome, and possible discrepancy in limb length if the AVF is placed in the lower extremity. Time for anastomosis maturation may be prolonged, with reports of up to 6 months.


*1.1.1 Sword beans (*C. ensiformis*)*

*C. ensiformis* (Sword beans) is a leguminous crop that could potentially be used as a biocontrol [11]. The article provides valuable insight into this leguminous vine. It is native to South and Central America however; it has adapted well to thrive in depleted soils of the tropics and subtropics worldwide. *C. ensiformis* is beneficial in curbing erosion, sandbank stabilization, soil rejuvenation, conservation, improvement and general soil health. It is grown for food, fodder, as a cover crop or green manure. *C. ensiformis* is resistant to drought and pests but does not grow well in excessively wet soil and has certain compounds such as Canavaine that increase its resistance to pest and herbivory due to the presence Concanavalin A—a lectin that guards against diseases caused by microorganism infections Canavanine is lethal as, when consumed, it could interchange l-arginine during protein synthesis and cause proteins to become malformed [11]. The beetle *Caryedes brasiliensis* thrives on *C. ensiformis* seeds as it contains an arginyl-T-RNA synthase that can discriminate between canavanine and l-arginine, countering the toxic effects.

#### *1.1.2 Bambara groundnut (*V. subterranean*)*

Bambara groundnut (*V. subterranea* (L.) Verdc.) is an indigenous legume crop, cultivated by subsistence farmers throughout sub-Saharan countries [12]. Neglected and underutilized legumes have for long been a question of great interest in a wide range of fields. In recent years, researchers have shown an increased interest in this neglected legume and considerable literature has grown up around the theme. It serves as the main source of income generation to subsistence farmers in Zimbabwe, Swaziland, Uganda and Zambia with a suitable land area (84, 100, 98 and 95%) respectively for cultivating this legume. A pilot study was initiated in seven rural districts of Zimbabwe aimed at improving nutrition, food security and maximizing its production. Follow-up studies according to Mubaiwa [12] report revealed a decline in production due to increasing climate and weather variability which in turns negatively affected food security. In an effort to keep this in check, agriculture and policy-makers in Zimbabwe have recognized the hidden potentials of NUS in improving food security by adapting to current climate change.

One major finding on Bambara groundnut in Nigeria is by Onuche et al. [13] who referred to it as "special" legume as it can adapt to high temperature and resistant to drought and suitable also for marginal soils where other leguminous crops are unlikely to thrive as it requires little resource from the soil in general. It is regarded also as special because it is not prone to total harvest failure even in low and uncertain rainfall regions. It increases soil fertility and boost yield of other crops cultivated around it. Therefore, it could potentially serve as great source of bio-fertilizer which is safe and free from high cost of synthetic chemicals commonly used as fertilizers which are indeed detrimental to health.

Bambara groundnut (*V. subterranea*) is a grain legume indigenous to sub-Saharan Africa where it is widely cultivated by subsistence farmers. The center of origin is most likely North-Eastern Nigeria and Northern Cameroon, in West Africa. The species is also grown to a lesser extent in some Asian countries such as India, Malaysia, Philippines and Thailand [4]. It is cultivated for its subterranean pods, is extremely hardy and produces reasonable yields even under conditions of drought

and low soil fertility. There is notable paucity of empirical research focusing specifically on neglected underutilized legumes in relation to soil microbial interaction.

To date, very little attention has been paid to the role of NUS such as Bambara nut in sustainable crop production. Previous studies have failed to find any consistent association between neglected underutilized legumes and rhizosphere and rhizoplane microbial relations. Research studies in these areas should contribute to an increase in the utilization of African legumes. It has been proposed that African legumes are drought-tolerant crops, thus excellent candidates utilized as climate friendly food crops. Global warming and climate change with the resultant effects of low agricultural productivity and food insecurity are important global issues. African legumes therefore have great global potential as sustainable food sources [3].

Miscellaneous underutilized grain legumes play significant roles in the food cultures around the world as absolute sources of quality protein, natural medicine, animal fodder, natural fertilizers, and environmental restoration products, alongside the well-established soil enrichment property of symbiosis with nitrogenfixing bacteria also contain high-quality proteins and micronutrients which are comparable to those found in conventional legumes. They are also indispensable in crop rotation strategies to fertilize agricultural soils [5].

#### *1.1.3 Lima beans (*P. lunatus*)*

*P. lunatus* L. was similarly reported to have tolerance and resistant traits against insects' attack [14]. Lima Beans roles in sustainable crop production have been investigated by Ruiz-Santiago et al. identifying plant-insect interactions as a determining factor for sustainable food and nutritional security. This could be utilized to influence the impact of herbivorous insects and their expression under varying climatic condition to achieve sustainable environmentally safe crop production. *P. lunatus* was similarly reported is an important legume for the poor population of the Brazilian northeast region [15]. The legume is able to take advantage of the nitrogen fixation process, but research to date has not been able to explore the diversity of indigenous microsymbionts.

#### *1.1.4 Pigeon peas (*C. cajan*)*

The study by Ayenan et al. [16] offers probably the most comprehensive empirical analysis. It was established from the report findings that in terms of production and quantity, pigeon pea is the 5th legume after cowpea (*Vigna ungulata*), Bambara groundnut (*V. subterranean*), Soy bean (*Glycine max*) and groundnut (*Arachis hypoaea*). Pigeon pea is a perennial shrub that has many advantages over annual legumes as it ensures several harvests and capable of enhancing soil fertility. The legume can withstand harsh environmental stress and has high drought tolerance which are the major challenges in this present-day climatic variability constraints.

Sinan et al. [1] in a recent study carried out the enzymatic assays, revealed that *C. cajan* has high amount of essential amino acids such as methionine, lysine and tryptophan. The study similarly was able to determine the total phenolic (TPC) content and total antioxidant capacity of the legume. Their study therefore serves as a baseline which gives opportunity for further investigation as the methanolic extract among other extract (hexane, ethyl acetate, aqueous) of prepared from *C. cajan* stem bark shows prominent antioxidant ability. This suggest that *C. cajan* could serve as a new source of antioxidant and key enzymes inhibitor (α-amylase and α-glucosidase enzymes) among others which could be exploited for future bioproduct development.

#### *Enzymatic Processing of Pigeon Pea Seed Increased Their Techno-Functional Properties DOI: http://dx.doi.org/10.5772/intechopen.101406*

From the archives of the ECHO's Seed Bank [16], in a paper titled "factors to consider when selecting a pigeon pea variety" highlighted how pigeon pea requires lesser amount of nitrogen (N2) into a form useful to the plant. Interestingly, *C. cajan* are reported to require lesser amount of N2 compared to other crops like maize. It obtains N2 from the atmosphere, assimilate it and are released to the soil as the plant die provided that this legume residues are kept in the field. Motis [17] also highlighted that pigeon pea has extensive roots which could grow through hard layers of soil aggregates, increasing soil porosity and aeration which in turn improves soil fertility. This report therefore is to infer that mixed cropping could be beneficial to other crops when pigeon pea is incorporated.

#### *1.1.5 Common bean (*Phaseolus vulgaris *L.)*

Common bean is one of the most familiarized beans worldwide. The developing countries especially in the sub-saharan regions mainly rely on common bean as a protein source; an alternative to animal protein, which are highly costly and beyond the reach of many low-income earners especially among the rural population.

Ismail et al. [18] recently identified six bacterial and four fungal strains which were isolated from the common bean (*P. vulgaris*) root plant for their growth promoting properties using molecular techniques and all the microbial isolates showed varying activities to produce indole-3-acetic acid and different hydrolytic enzymes such as amylase, cellulase, protease, pectinase and xylase. All the six bacterial endophytes isolates were reported to display phosphate solubilizing capacity. To increase the reliability of measures, each bacterial and fungal were tested twice by conducting a field experiment to evaluate the promotion activity of the metabolites of the most potent endophytic bacterial and fungal strains, in comparison to two exogenous applied hormones; IAA and benzyl adenine (BA) on the growth and biochemical characteristics of the *P. vulgaris*. Base on the results findings of Ismail et al. [18] it was established that both bacterial and fungal endophytic metabolites surpassed the exogenously applied hormone in increasing the plant biomass, photosynthetic pigments, carbohydrates, and protein contents, antioxidants, enzyme activity, endogenous hormones and yield traits. Therefore, it could be deducted that endophytic bacteria *Brevibacillus agri* (PB5) shows promising traits as a stimulator for growth and productivity of the common bean.