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measurement of bladder wall thickness is associated with the overactive bladder syndrome. *Neurourol Urodyn*, Vol.29, No.7, (Sep 2010), pp. 1295-1298, ISSN 1520-

**Part 3**

**Surgical Options**

**Part 3**

**Surgical Options**

204 Urinary Incontinence

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**12**

*Korea University Republic of Korea*

**Preoperative Factors as Predictors**

**with Mixed Urinary Incontinence**

Jin Wook Kim, Mi Mi Oh and Jeong Gu Lee

**of Outcome of Midurethral Sling in Women**

Mixed urinary incontinence (MUI) presents with characteristics of both stress urinary incontinence (SUI) and urge urinary incontinence (UUI). It has been generally assumed to respond less favourably to any type of interventional therapy, whether behavioural, pharmacologic, or surgical as compared with pure stress (effort) or urge urinary incontinence. These patients represent a therapeutic challenge: two pathologies coexist, and treatment of either condition may worsen the symptoms of the other. The result is likely to be a poor response to conservative or surgical interventions. (Chaliha & Khullar, 2004)

The development of midurethral sling (MUS) surgery has become the gold standard for surgical treatment of SUI. There is great variability in data regarding cure rate of MUI following mid urethral sling (MUS) surgery of both the stress and urge components. Moreover, the postoperative course of the urge component after surgery is unpredictable as it may resolve, persist or worsen. There are no consistent predictors for persistent worsening of urge components after sling surgery. While there have been various factors described in the literature to predict who will be more likely benefit, these have not been clearly defined. Further compounding the difficulty is the lack of appropriate tools in delineating the characteristics of a mixed presentation. The poor response to treatment in MUI patients have led investigators to attempt quantifying and comparing dominance of either spectrum to

Despite such limitations, advance of treatment has allowed more aggressive combined approach to MUI, necessitating the delineation of patient profiles appropriate for each treatment method. Here we will review the current investigations analysing the two distinct pathophysiologies of MUI, as well as the suggested factors determining the outcome

MUI is the coexistence of stress and urgency urinary incontinence and is defined as involuntary loss of urine associated with the sensation of urgency and also associated with exertion, effort, sneezing or coughing.(Haylen et al., 2010) Mixed incontinence can also be

dictate a priority of treatment and quantitatively assess outcome.

**1. Introduction**

following MUS treatment.

**2. Prevalence of MUI**


*2.8. Statistical Analysis*

Means and standard deviations of three to five independent replicates were used to present the data. Model analysis (ANOVA) and 3D plots resulting from the combination of variables were performed using XLSTAT 2019 and R analysis following the manufacturer's instructions (Addinsoft, Paris, France). A Student's *t*-test was performed for comparative statistical analysis of the impact of the di fferent cultivation sites (XLSTAT 2019, Addinsoft, Paris, France). Correlation analysis was performed with Past 3.0 (Øyvind Hammer, Natural History Museum, University of Oslo, Oslo, Norway) using the Pearson parametric correlation test and visualized using Heatmapper [35]. Principal Component Analysis (PCA) was performed with Past 3.0 (Øyvind Hammer, Natural History Museum, University of Oslo, Oslo, Norway). Significant thresholds at *p* < 0.05 or *p* < 0.05, <0.01 and <0.001 were used for all statistical tests and represented by di fferent letters or by \*, \*\* and \*\*\*, respectively.

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

#### *3.1. Development of the Ultrasound-Assisted Extraction Using Box–Behnken Design*

Multivariate techniques are used very e ffectively to optimize the extraction method from complex plant matrices such as food products and by-products [36]. Among the di fferent multivariate techniques, when three factors are considered, the Behnken–Box design is one of the most e ffective techniques [36,37]. The Behnken–Box matrix is a spherical and rotating design, which, viewed on a cube, consists of the central point and the middle of the edges [36,37]. Many parameters can influence the extraction of phenolic compounds from plant matrices [38], but three parameters are very widely distinguished when developing an USAE method: the type of solvent used, the frequency of ultrasound applied and the extraction time [11,21,30].

The choice of solvent is a crucial parameter to define when developing an extraction method. Various solvents, including methanol, ethanol, or acetone, are regularly used for the extraction of plant polyphenols [28,39]. Here, given our objective of developing an extraction method in accordance with green chemistry principles for future nutraceutical and/or cosmeceutical applications of the resulting extract, EtOH was considered as an extraction solvent. First, EtOH is one of the less toxic solvents for humans and more respectful of the environment than other organic solvents such as methanol for example [38,40]. In addition, its extraction capacity can easily be modulated by addition of water, making it an ideal solvent for the extraction of a wide range of variable polarity polyphenols. Finally, these two universal solvents (i.e., EtOH and water) are already widely used for various food and/or cosmetic applications [11,28,30,38,40].

US frequency is a crucial parameter to consider because of its significant impact on the extraction e fficiency. Indeed, this parameter modulates the cavitation e ffect as well as the di ffusion coe fficient of the target compound in the extraction solvent. Consequently, it improves the solubilization of the compound in the extraction solvent, thus increasing the extraction e fficiency [28]. In addition, increasing US frequency can also lead to a drastic reduction in extraction time, thereby reducing energy consumption, which is in accordance with the green chemistry principles [41]. However, depending on the compound and the plant matrix subjected to the extraction, application of high US frequency can alter the native structure of the compound, which not only decreases the extraction yield, but also considerably reduces its biological activity, thus negating any valuation interest [30]. Therefore, during the development of an USAE method, US frequency must be optimized very carefully depending on the compound, and the plant matrix subjected to the extraction.

Finally, regarding the extraction time, it is important to consider that its increase does not necessarily lead to any improvement in extraction yield, since, contrarily, a prolonged exposure to US can lead to the increased degradation of the compound [30]. In addition, in order to reduce the impact of energy consumption in the green chemistry context, optimizing the extraction time also appears to be essential [41].

Having these considerations in mind, in order to develop a rapid, green and e fficient USAE of TPC for the valorization of AOR, we therefore considered a Behnken–Box matrix with the following three parameters: aqEtOH concentration (X1), ultrasound (US) frequency (X2), and extraction duration (X3) as described in Table 1.

Table 2 presents the experimental and predicted TPC obtained from almond oil residues for the 18 di fferent observations (run ID) corresponding the di fferent USAE conditions of the Behnken–Box matrix having been determined randomly (run order) after an *in silico*-assisted procedure generated by the XL-Stat2019.4.1 software.

Here, the TPC extracted from AOR ranged from: 5.03 mg/g DW (Obs3; obtained after 30 min extraction in water bath (no US application) using pure water as extraction solvent) to 11.44 mg/g DW (Obs16; obtained after 30 min at an ultrasonic bath running at a US frequency of 22.5 kHz using 50% (*v*/*v*) aqEtOH as extraction solvent) (Table 2). These results provide a first indication on the interest of using an ultrasound and on the choice of an extraction solvent. We noted a good repeatability of the

central point (i.e., Obs4, 5, 9, 11, 16, and 18), with a mean TPC of 11.37 ± 0.05 mg/g DW corresponding to a relative standard deviation (RSD) of 0.47%, thus highlighting the high reliability of these results. Given the nature of the starting material used in the present study, this range of TPC is in fairly good agreement with the data in the literature obtained with almond and/or almond by-products from California, Portugal, Italia, and Tunisia [3,5,18,42].

A multiple regression analysis was applied to the model of the TPC as a function of the three different extraction variables. Under the described conditions, the TPC (YTPC, in mg/g DW) as a function of the three different extraction variables (Table 1) in the form of a polynomial equation was (Table 3):

> YTPC = 11.370 − 0.354X1 + 0.690X2 + 0.166X3 − 3.554X12 − 1.756X22 − 1.724X32 − 0.540X1X2 − 0.743X1X3 + 0.485X2X3

**Table 3.** Statistical analysis of the regression coefficients of USAE of TPC from AOR.

SD standard deviation; \*\*\* significant *p* < 0.001; \*\* significant *p* < 0.01.

The statistical analysis of the regression coefficients confirmed the relevance of our choice in the extraction variables and their respective levels for the development of the present USAE method if we refer to the level of significance with which these variables influenced the extraction (Table 3). The linear coefficients X1 (aqEtOH concentration) and X2 (extraction time) were statistically highly significant at *p* < 0.001, with an X1 coefficient being negative (high EtOH concentration reduced TPC) and X2 being positive (application of US treatment had a positive effect on TPC). An extraction duration (X3) coefficient was also significant at *p* < 0.01, but with a coefficient value close to zero indicating that a prolonged extraction period can lead to poorer extraction yield as a consequence of degradation as described in the literature [30,41,43]. All the quadratic and interaction coefficients were statistically highly significant at *p* < 0.001, but their values negative or close to zero indicated a negative or a lower impact to the extraction efficient.

The results of the analysis of variance (ANOVA) and model fitting are presented in Table 4. An elevated F-value (567.558) and low *p*-value (*p* < 0.0001) indicated the statistically highly significance of the model that could predict TPC as a function of the variable values with a great precision. The low non-significant value obtained for the lack of fit confirmed this trend. The value for the determination coefficient (*R*2 = 0.997 (with adjusted value of 0.998) for the model as well as the coefficient value (CV = 0.976) indicated the precision of the model as well as the adequacy between the model and experimental values, respectively. The model precision in the prediction of the TPC is further depicted by the predicted vs. experimental TPC plot presented in Figure S1.

To better understand the complexity of the model, 3D plots representing TPC as a function of the extraction parameters were drawn (Figure 1).

The calculated, but small, values of the linear coefficients of the second-order polynomial equation for X2 (US frequency) and X3 (extraction duration), as well as their interaction coefficient X2X3 (US frequency x duration) indicate that a controlled increase of these parameters will have a global favorable consequences for the TPC extracted from AOR. However, their small values, in association with the negative values calculated for their quadratic coefficients (X22 and X32, respectively), but also of all the coefficient involving aqEtOH concentration (i.e., linear coefficient X1, quadratic coefficient X12, and the interaction coefficients X1X2 and X1X3), indicate that the TPC extracted from AOR according to these extraction parameters will reach a maximum value before decreasing for high values of these parameters. These considerations were clearly observed on the 3D plots (Figure 2). For each 3D plot, a first tendency was observed with a higher TPC extracted from AOR with increased aqEtOH concentration, application of US as well as prolonged extraction time. However, after reaching a maximal value for TPC extracted from AOR, a further increase in the aqEtOH concentrations as well as application of higher US frequency and/or prolonged extraction duration resulted in a pronounced drop of the TPC (Figure 2).

**Table 4.** ANOVA of the predicted model used for USAE of TPC from almond residues.

df: degree of freedom; Cor. Total: corrected total; *R*2: determination coefficient; *R*2 adj: adjusted *R*2; CV variation coefficient value; \*\*\* significant *p* < 0.001.

**Figure 2.** Predicted surface response plots of the TPC extraction yield (in mg/g DW) as a function of aqEtOH concentration and US frequency, aqEtOH concentration and extraction duration, as well as US frequency and extraction duration.

In various concentrations in mixture with water, aqEtOH solutions have been widely used as eco-friendly solvents to extract a wide range of polyphenols from plant matrices [11,21,30,38,40] including various almond products [13,18,44]. However, to obtain optimal results, the concentration of aqEtOH must be adapted because it is very dependent on the polyphenolic compound(s) as well as on the plant matrix considered [28,38,40]. Alongside, it is clearly established that, during USAE, high US frequency associated with extended extraction duration could reveal destructive through the induction of polyphenols oxidation, in particular in the presence of water [21,28,30]. Consequently, if these parameters are not finely controlled (optimized), this can lead to a sharp reduction in the extraction yield, quantitatively but also qualitatively with a drastic decay observed in the biological interest of the sample extract [11,29,30]. Using the Box–Behnken matrix for optimizing these parameters and using the resulting adjusted second order polynomial equation, optimal conditions for the extraction of phenolics from our Moroccan AOR were: 53.0% (*v*/*v*) aqEtOH as solvent, 27.0 kHz for the US frequency and an extraction duration of 29.4 min. Using these optimal conditions resulted in a TPC

of 11.63 ± 0.15 mg/g DW (Figure 2). The optimal aqEtOH concentration obtained here is in line with results obtained for almond phenolics extraction very recently described [13,18]), although the starting byproduct material or the extraction method used were different from our study.

The present method was then validated in respect with the AOAC standards. According to these standards, the parameter values of this validation procedure were adequate in terms of interday and intraday precision, but also repeatability and stability (Table 5). Indeed, the RSDs of both intraday and interday precisions were of 0.05 and 0.28%, respectively. The RSDs of the repeatability corresponding to five different extraction repeats of five samples from the same batch was of 1.30%. The recovery rates at three different addition levels of chlorogenic acid in the sample before extraction were between 100.26 and 101.13% reflect the accuracy of the present method.

**Table 5.** Validation parameters of the developed method for quantifying TPC from almond residues.

1 performed at 3 concentration level additions of gallic acid prior to extraction using optimal conditions with US (i.e., 0.5, 1.0, and 2.0 mg/g DW additions). Means ± SD standard deviations or % RSD of three independent extractions.

The efficiency of the present USAE method was compared with conventional heat reflux extraction (HRE) using the same conditions, in particular an aqEtOH concentration (53.0% (*v*/*v*) and an extraction time of 29.4 min. The difference between USAE and HRE being the application of an US frequency of 27 kHz for the present optimized UASE extraction procedure, while no US was applied for the HRE protocol operating in a classical water bath. The comparison of these extractions is depicted in Figure 2. A significant gain of 30% in TPC extracted from AOR was observed with the optimized USAE (11.63 ± 0.15 mg/g DW) as compared to conventional HRE (8.96 ± 0.21 mg/g DW) (Figure 3). Increasing the extraction time for the HRE to one hour did not achieve performance levels similar to those obtained with USAE (9.24 ± 0.37 mg/g DW). Consequently, it appears that the USAE method developed in the present study is of real interest according to the principles of green chemistry [45], not only in terms of the use of a renewable green solvent, but also in terms of reducing the energy consumption. We hypothesize that this efficiency could be partly explained by the hot spot hypothesis indicating that the cavitation bubbles, after their collapse, act as a microreactor locally generating, in the surrounding solvent, a high temperature environment and pressure leading to more efficient rupture of the plant matrix subjected to extraction and increased release as well as solubilization of phenolic compounds [28].

**Figure 3.** TPC extracted from AOR using the optimal USAE (with US) conditions and comparison with conventional heat reflux method (HRE; without US). Means ± SD standard deviations of three independent extractions; \*\*\* significant at *p* < 0.001.

#### *3.2. Application to the Analysis of Samples from Di*ff*erent Cultivation Sites*

The present USAE was then applied to the quantification of phenolics in samples from three different local Beldi genotypes cultivated at three different locations in Eastern Morocco. In addition to the TPC, the concentration in protocatechuic acid, *p*-hydroxybenzoic acid, chlorogenic acid, and *p*-coumaric acid, reported as the main phenolic acids possibly accumulated in almond by-products [3,5,13,18,42,44,46], were also determined by HPLC after comparison with authentic commercial standards. Figure 4a shows a typical HPLC chromatogram, recorded at 325 nm, of the AOR extract obtained after USAE and showing the sepration of these four important phenolic acids: protocatechuic acid (1), *p*-hydroxybenzoic acid (2), chlorogenic acid (3), and *p*-coumaric acid (4) (Figure 4b).

**Figure 4.** (**a**) Representative HPLC chromatogram (here with detection set at 325 nm) of an extract prepared by USAE of AOR (*Beldi* cultivar) grown in the Ain Sfa (34◦4642.4" N, 002◦0928.9" W) pilot location in the eastern Morocco; (**b**) structures and their corresponding numbers on the HPLC chromatogram of the main phenolic compounds considered in this study: protocatechuic acid (1), *p*-hydroxybenzoic acid (2), chlorogenic acid (3), and *p*-coumaric acid (4).

In order to quantify these four phenolic compounds in different samples, 6-points calibration curves of the peak areas (y) against the injected amounts (x) of protocatechuic acid and *p*-hydroxybenzoic acid at 295 nm and chlorogenic acid and *p*-coumaric acid at 325 nm were obtained with a linearity over wide ranges from 0.5 to 200 mg/L of injected solutions and *R*2 greater than 0.999 (Table 6). The LODs ranged from 0.12 to 0.22 mg/mL, and LOQ from 0.38 to 0.73 mg/mL, for protocatechuic acid and chlorogenic acid, respectively (Table 6).

**Table 6.** Quantification parameters of the HPLC method used to quantity protocatechuic acid, *p*-hydroxybenzoic acid, chlorogenic acid, and *p*-coumaric acid after their USAE from AOR.

Applied to the quantification of TPC, protocatechuic acid, *p*-hydroxybenzoic acid, chlorogenic acid and *p*-coumaric acid in AOR resulting from samples of three different native *Beldi* genotypes (#1 to #3) cultivated at three different pilot locations in the Eastern Morocco (Sidi Bouhria (SID); Ain Sfa (AIN); Rislane (RIS)), the results are presented in Table 7.

**Table 7.** Variations in TPC, and protocatechuic acid, *p*-hydroxybenzoic acid, chlorogenic acid and *p*-coumaric acid contents in AOR from samples of three different native *Beldi* genotypes produced at three different pilot locations in the Eastern Morocco.

Samples were AOR from three different native *Beldi* genotypes (#1 to #3) cultivated at three different pilot locations in the Eastern Morocco: Sidi Bouhria (SID; 34◦4413.6" N, 002◦2015.0" W); Ain Sfa (AIN; 34◦4642.4" N, 002◦0928.9" W); Rislane (RIS; 34◦4459.8" N, 002◦2644.7" W). Values are means ± SD of three independent replicates. Different letters represent significant differences between the various extraction conditions (*p* < 0.05).

TPC ranged from 8.87 to 13.86 mg/g DW for extracts from samples AIN#1 and AIN#3, respectively; sample from genotype #3 cultivated at Ain Sfa being 56.25% richer in TPC than genotype #1 cultivated at the same location. The four quantified phenolic acids occurred for approximately 80% of the TPC. In decreasing contents: (1) chlorogenic acid was the main phenolic accumulated in the sample extracts with contents ranging from 5.29 ± 0.12 to 8.14 ± 0.10 mg/g DW for extracts from samples AIN#1 and AIN#3, respectively (sample from genotype #3 cultivated at Ain Sfa being 53.87% richer in chlorogenic acid than genotype #1 cultivated at the same location); (2) protocatechuic acid content ranged from 1.29 ± 0.06 to 2.03 ± 0.07 mg/g DW for extracts from samples AIN#1 and AIN#3, respectively (sample from genotype #3 cultivated at Ain Sfa being 57.36% richer in protocatechuic acid than genotype #1 cultivated at the same location, corresponding to the highest observed variation range); (3) *p*-hydroxybenzoic acid content ranged from 0.75 ± 0.03 to 1.13 ± 0.02 mg/g DW for extracts from samples AIN#1 and RIS#1 for the lowest content vs. sample AIN#3 for the highest content (sample from genotype #3 cultivated at Ain Sfa being 50.60% richer in *p*-hydroxybenzoic acid than genotype #1 cultivated both at Ain Sfa and Rislane); (4) *p*-coumaric acid content ranged from 0.21 ± 0.04 to 0.30 ± 0.02 mg/g DW for extracts from samples AIN#1 and AIN#3, respectively (sample from genotype #3 cultivated at Rislane being 42.85% richer in *p*-coumaric acid than genotype #1 cultivated at Ain Sfa, corresponding to the lowest observed variation range). The concentrations determined here for each phenolic compound were in the range of variations observed by Kahlaoui et al. [18] for different varieties of almond byproducts from Italia and Tunisia. Extraction of phenolic compounds from a variety of oilcakes such as hemp, canola, linseed, black cumin, sesame, fennel, sunflower, rapeseed, camelina, or milk thistle has been reported [9,11,16,17,20–22,24–27]. The TPC obtained from AOR using the present USAE method is at the top of the range compared to these other sources. Chlorogenic

acid content has been reported to be high in sunflower oilcakes where its presence is problematic for the valorization of its derived protein meal by-product [26]. The other phenolic acids from AOR have been extracted from various oilcakes, such as flax, canola, and black cumin seedcakes for *p*-coumaric acid [9,20,21,24], black cumin, and camelina for *p*-hydroxybenzoic acid [9], while protocatechuic acid and *p*-hydroxybenzoic acid have been reported in camelina by-products [25]. Note that other types of phenolics such as lignans and or flavonoids in flax, sesame, and milk thistle seedcakes have been reported [16,21–24]. Interestingly, synergistic interactions between phenolic compounds could occur at concentrations found in nature for antioxidant activity [47]. Antagonism have been also described [47]. Therefore, the di fferent compositions, but also the concentrations observed in di fferent oilseed cakes, could result in di fferent synergistic and/or antagonistic interactions towards their antioxidant capacity. This hypothesis is going to deserve future studies. However, it is also important to consider that these concentrations are subject to change as a result of both genetic and environmental influences as observed in milk thistle, flax, sesame, but also in some almond cultivars [34,48,49].

Indeed, it is generally accepted that the genetic background, but also the environmental conditions, such as the location (i.e., soil conditions) or the climate, could have a great influence on the accumulation of phenolic compounds [3,18,34,42,46,50]. The present preliminary results obtained from three native genotypes cultivated on the same year at three di fferent location sites from Eastern Morocco suggested a prominent influence of genetic over environment, since the impact of the genotype was more important than the influence of the cultivation site. Indeed, for each considered cultivation sites, the genotype #1 accumulated more phenolic compounds than the genotype #3, whereas both the highest and the lowest accumulation were observed on the same location (i.e., Ain Sfa experimental site). Analyses of the variance (ANOVA) confirmed this absence of any significant influence of the cultivation site. Future works will be conducted with more genotypes as well as more experimental sites over several cultivation years to confirm or infirm this trend. However, the prominent influence of genetic background on the accumulation of phenolic compounds in almonds was reported by several authors [3,18,42,46], whereas the influence of environmental conditions on the same genotype was less studied. Bolling et al. [42] reported that the cultivation season influenced less polyphenolic accumulation than the genotype. The influence of cultivation site of the same genotype will deserve further works.

An improvement in the quantity of phenolic compounds produced in the future may also be envisaged, in the future, by combining this USAE with base or acid hydrolysis to release the wall-bound phenolics or extract further antioxidant compounds from lignin. A gain of 30% in chlorogenic acid content was reported in sunflower seed cakes after the release of wall-bound phenolics. Nonetheless, it has been stated that coupling US to base and/or acid extraction is highly destructive for some forms of phenolic compounds [22]. The use of cell wall degrading enzymes such as cellulase could be an alternative to destructive chemical hydrolysis [24]. USAE coupled with cellulase hydrolysis of phenolic compounds have been already reported [51]. Future works will be dedicated to exploring this possibility.

#### *3.3. Determination of the Antioxidant Potential of the Extracts and Correlation Analysis*

Our next goal was to ensure that the potential biological activities is retained during the USAE procedure. For this, we then determined the antioxidant potential of these nine characterized sample extracts from AOR by using both (1) in vitro cell free assays based on the chemistry of the antioxidant reaction with di fferent mechanisms—either proton transfer or electron transfer based assays, as well as (2) *in cellulo* using eukaryotic yeast cells subjected to oxidative stress induced by UV either in the presence and absence of the extracts to have an idea of their cellular antioxidant potential. Indeed, if they were preserved, this antioxidant biological activity would be of such a nature as to be of interest for both future nutraceutical and/or cosmetic applications of these AOR extracts.

The protective antioxidant action developed by plant extracts can be influenced by many internal and external factors impacting their phytochemical compositions such as genetics (the use

of di fferent genotypes in our case) but also the environment (the use of di fferent culture sites in our case) [3,18,34,42,46,50]. Furthermore, their antioxidant activity is generally based on complex mechanisms, which, in order to shorten, depending on the nature of the compounds present in the extract, can be based in particular on radical scavenging mechanisms. Here, to get an idea relating both to the antioxidant capacity but also to explore the possible mechanisms involved depending on the composition of the extract, we used three di fferent in vitro cell-free assays: the DPPH, ABTS, and CUPRAC assays. These tests are based on di fferent reaction mechanisms and could provide us a raw idea of the chemistry involved in the radical scavenging activity of the extract. Based on the chemical reaction involved, these in vitro cell free antioxidant assays can be roughly divided into di fferent categories, with an ABTS assay based on a hydrogen atom transfer reaction (HAT), a CUPRAC assay based on an electron transfer reaction (ET), and the DPPH assay being considered as a mixed assay [52,53]. The results of these antioxidant assays expressed in μM of Trolox equivalent antioxidant capacity (TEAC) per gram DW for the nine extracts obtained after USAE of AOR are presented in Table 8.

**Table 8.** Variations in in vitro cell free (ABTS, DPPH and CUPRAC) and cellular (TBARS) antioxidant potential of extracts obtained from USAE of AOR from three di fferent native *Beldi* genotypes produced at three di fferent pilot locations in Eastern Morocco.

1 TEAC: Trolox equivalent antioxidant capacity (TEAC); Samples were AOR from three different native *Beldi* genotypes (#1 to #3) cultivated at three different pilot locations in the Eastern Morocco: Sidi Bouhria (SID); Ain Sfa (AIN); Rislane (RIS). Values are means ± SD of 3 independent replicates. Different letters represent significant differences between the various extraction conditions (*p* < 0.05).

Antioxidant activity ranged from 216.94 ± 12.32 to 401.52 ± 11.44 μM TEAC/g DW for ABTS assay, and from 275.84 ± 34.88 to 357.33 ± 24.24 μM TEAC/g DW using a DPPH assay. For these two in vitro cell free antioxidant assays, the AOR extract from the genotype #3 produced at Ain Sfa showed the highest antioxidant capacity, whereas the extract obtained from the genotype #1 produced at the same location displayed the lowest antioxidant values. On the contrary, results for CUPRAC assay, ranging from 129.69 ± 0.32 to 205.92 ± 17.11 μM TEAC/g DW, showed that this genotype #1 produced at Ain Sfa possessed the highest antioxidant capacity as compared to the genotype #3 from Sidi Bouhria.

Although interesting from a strictly predictive point of view based on chemical reactions, these in vitro tests do not necessarily have a great similarity with in vivo systems. The validity of these antioxidant data must therefore be considered as limited to an interpretation within the meaning of the chemical reactivity with respect to the considered radicals generated in vitro, and have to be confirmed in vivo. In order to have an improved understanding and better reflect the in vivo situation, the antioxidant activity of these nine extracts has also been studied further for their capacity to inhibit the lipid peroxidation membrane generated by oxidative stress induced by UV-C in yeast cells. Yeast cells represent an excellent model for assessing antioxidant capacity in vivo in the context of cellular oxidative stress [54]. It is indeed an attractive and reliable eukaryotic model, whose defense and adaptation mechanisms to oxidative stress are well known and can be extrapolated to human cells presenting mechanisms certainly more complex but well conserved with this model [55,56]. Here, measured in vivo anti-lipoperoxidation activity (inhibition of malondialdehyde (MDA) formation), determined using the TBARS assay, ranged from 50.62 ± 2.46 to 69.12 ± 0.34%. Therefore, this in vivo antioxidant evaluation assay confirmed the trend observed with the HAT-based in vitro assay, and confirmed that the AOR extract from the genotype #3 produced at Ain Sfa showed the highest antioxidant capacity, particularly as compared to extracts obtained from the genotype #1 produced at the same location.

As shown in Figure 5, higher antioxidant capacity measured with HAT-based antioxidant assay appeared systematically associated with a higher accumulation of phenolics, whereas association with the ET-based antioxidant assay (i.e., CUPRAC) appeared more complex and not directly linked to the accumulation of theses phenolics (Figure 5a).

**Figure 5.** (**a**) hierarchical clustering analysis (HCA) showing the relation between the phytochemical composition and antioxidant activity of each extracts from AOR of Eastern Morocco obtained by USAE; (**b**) principal component analysis (PCA) showing the discrimination of the different extracts from AOR of Eastern Morocco obtained by USAE.

Principal component analysis was performed to further discriminate these nine samples (Figure 5b). The resulting biplot representation accounts for 92.64% (F1 + F2) of the initial variability of the data as shown in Figure 4b. The discrimination occurs mainly in the first dimension (PC1) which explains 85.76% of the initial variability. The loading plots (F) confirmed the strong link between phytochemical composition, in particular the presence of the phenolics, and the HAT-based as well as cellular antioxidant capacity.

In order to link the antioxidant capacity to the presence of a particular phytochemical, a Pearson correlation analysis was applied (Figure 6).

This analysis provided evidence of the strong and highly significant correlation between both HAT-based in vitro assays as well as cellular antioxidant assay and TPC of the extract, in particular with the presence of chlorogenic acid, protocatechuic acid, and *p*-hydroxybenzoic acid (Figure 6, Table S1). The presence of *p*-coumaric acid was significantly correlated with a DPPH assay only. On the contrary, none of these phytochemicals, here analyzed, were significantly correlated with the in vitro ET-based antioxidant CUPRAC assay.

**Figure 6.** Pearson correlation analysis (PCC) of the relation between the main phytochemicals (protocatechuic acid, *p*-hydroxybenzoic acid, chlorogenic acid and *p*-coumaric acid) from AOR extracts obtained after USAE and the different antioxidant assays (ABTS, DPPH, CUPRAC and TBARS). \*\*\* significant *p* < 0.001; \*\* significant *p* < 0.01; \* significant *p* < 0.05; actual PCC values are indicated in Supplementary Materials Table S1.

Altogether, these results showed a higher antioxidant activity, expressed in μM TEAC/g DW, determined with the ABTS and DPPH assays as compared to the CUPRAC assay. Therefore, these results suggested the prominence of the HAT- over the ET-based mechanism for the antioxidant action of these extracts. In good agreement, several authors have reported an antioxidant activity of extracts from various almond products based on HAT mechanism [18,42,44,46]. Similarly, a higher relation between HAT assay and phenolic acids as compared to flavonoids have been previously reported [52,57]. This observation is also in line with the results of Liang and Kitts [58] that reported a relatively stronger scavenging capacity of radicals generated by the ABTS and DPPH assays for chlorogenic acid, the main phenolic acid of our AOR extracts, and its derivatives. The authors attributed this observation to the available hydroxyl groups of these compounds. The presence of flavonoids has been also reported in almond products [13,18,42,44,46]. Here, we cannot exclude the presence of flavonoids potentially linked to the ET-based antioxidant activity evidenced by the CUPRAC assay. Prgomet et al. [13]) have reported in the presence of flavonoids in almond skin (i.e., isorhamnetin derivatives). Future works will be conducted to study in detail the flavonoid fraction of our AOR extracts. The cellular antioxidant assay using yeast further confirmed the interest of this system to study natural antioxidant from plant extracts [34,52] as also previously reported for other natural antioxidants such as thiamine and/or melatonin [55,56]. Natural antioxidants have aroused increasing interest over the past decade due to their possible use as an alternative to potentially dangerous synthetic antioxidants such as butylated hydroxyanisole (BHA) or butylated hydroxytoluene (BHT) in various food or cosmetic formulations [6–8]. Some natural antioxidant phenolics have already been shown to be as efficient in stabilizing nonpolar systems such as bulk oil or various types of emulsions as these synthetic antioxidants [7,9–11]. These preliminary results indicate a potential use as natural antioxidants of our AOR extracts generated by the present validated USAE.


*2.6. Yellow Light Effects on Shoot Proliferation and Plantlet Morphology*

The reduction of apical dominance seems to be the main effect determined by YL and by the GL [128,135]. YL applied to cultures of *Prunus domestica* rootstocks Mr.S.2/5 and GF677 reduced apical dominance [199]; in *Malus domestica* rootstock M9, this light induced a production of axillary shoots greater than that detected under BL and FRL but still lower than that detected under RL [135]. Similar to the RL, the YL and GL induced a greater elongation of the internodes and outgrowth axillary shoots than the BL; in particular, the YL stimulated longer internodes in *Prunus domestica* rootstocks Mr.S.2/5 [142]. YL illumination induced higher proliferation in *Populus alba* × *P. berolinensis* [129].

YL irradiation followed by the RL one induced higher shoot proliferation (98%), a higher number of shoots per explants and early PLB formation, differentiation and shoot initiation in *Dendrobium sonia* [116]. YL elicited response of callus multiplication in *Vitis vinifera* [200]. YL also determined a higher leaf area and fresh weight and a lower shoot length in *Dendrobium sonia* [116]. YL showed a smaller increase in mean fresh weight as compared to BL but less than RL [135].

The YL positively affected growth in *Lactuca sativa* [201]. Based on current knowledge, the behavior of in vitro cultures subjected to YL would not be attributable to the actions of PHYs and BL photoreceptors.


*Edited by Maryam Takht Ravanchi*

Natural gas as a non-renewable hydrocarbon is used as an energy source for cooking, heating, vehicle fuel, and electricity generation. It is also used as a chemical feedstock in the manufacturing of plastics and organic chemicals. This book brings together new perspectives and future developments in natural gas. Chapters address such topics as adsorbed natural gas, fermentation processes for producing value-added products from natural gas, processes for separating C3+ hydrocarbon from natural gas, natural gas dehydration, and much more.

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

Natural Gas - New Perspectives and Future Developments

Natural Gas

New Perspectives and Future Developments

*Edited by Maryam Takht Ravanchi*


**4. Conclusion**

Maritime is lagging behind other areas of business and industry in terms of digitization. This is especially true in developing countries. Therefore, in this chapter, the results of two case studies on digitalization in maritime, conducted in developing countries, are presented, along with some comparative analyzes with developed countries in certain areas. These studies were conducted on a relatively small sample and in the future, longitudinal studies should be conducted on a larger sample and in a larger number of countries in transition. The results should be presented to ICT&S designers in developed countries in order to find solutions for intelligent design of ICT&S in maritime in perspective. These systems should provide benefits for all. If we strive for sustainable development, then it must ensure development for all. Entire continents cannot be excluded from the plans for further development of ICT&S, artificial intelligence, and virtual reality. This, of course, requires a broader platform, which includes standardization of ports and ships as basic maritime structures onto which ICT&S are built.

General knowledge about new technologies in developing countries exists, but there is a lack of expertise when it comes to hardware, software, network architecture, cyber safety, security, etc. Furthermore, developing countries suffer lack of funds for investments in advanced ICT&S. These countries are excluded from the strategic plans on the development and implementation of new ICT&S solutions. The governments should consequently invest more into attaining sound knowledge in contemporary ICT&S, including disruptive technologies like blockchain and its implementation in global supply chains. Standardization in maritime is the key enabler of faster modernization. In addition, profound research of internet connectivity at sea is of crucial importance for further development of IoT applications in maritime as the key condition for achieving equal presence of ICT&S applications on land and at sea. Maritime industry and high(er) maritime education institutions should cooperate much more closely to alleviate the gap between developing and developed countries in terms of achieving a higher level of graduates' employability, while the sphere of digitalization can provide many opportunities in this respect. Rising awareness about digital transition in maritime and opening discussions among professionals and academics should become common practice. The involvement of legislatures and tight collaboration of key stakeholders in maritime emerging economies are necessary regarding harmonization of ICT&S deployment across global maritime ecosystem and overcoming the existing gap in digitalization.

*On Maritime Digitalization in Emerging Environments DOI: http://dx.doi.org/10.5772/intechopen.104185*