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Optimization of Eugenia punicifolia (Kunth) D. C. leaf extraction using a simplex centroid design focused on extracting phenolics with antioxidant and antiproliferative activities


Eugenia punicifolia (Kunth) D. C. (Myrtaceae) has been showing interesting biological activities in the literature which was correlated to its phenolic compounds. In the sense of a better recovering of phenolics with the best antioxidant and antiproliferative activities, an extraction, based on multivariate analytical approach, was developed from E. punicifolia leaves. The different extractor solvents (ethanol, methanol and water) and their binary and ternary combinations were evaluated using a simplex-centroid mixture design and surface response methodology. The optimized crude extracts were investigated for phenol and flavonoid content and compared to their antioxidant (EC50) and antiproliferative properties against HEp-2 (cell line derived from the oropharyngeal carcinoma) and mononuclear viability cells. Ethanolic extracts showed the best phenolic content with the highest antioxidant activity and moderated activity antiproliferative to HEp-2. ESI-QTOF–MS revealed the presence of quercetin and myricetin derivatives, which was correlated to activities tested. Then, simplex-centroid design allowed us to correlate the Eugenia punicifolia biological activities with the extracts obtained from solvent different polarity mixtures.


Many biological activities are related to the phenolics compounds. They are only not synthesized in plants for growth or reproduction, but to perform genetic, physiological, or biochemical roles [1, 2]. This class of compounds contributes to plant adaptation within its environment through activities like UV protection and defence against pathogens, animals and other plants. Which has led to their use for obtaining high value-added products like fine chemicals, phytotherapics, cosmetics and nutraceutical compounds [3]. Besides, these phytochemicals have been extensively described in the literature and traditional medicine.

Some of these phenolic compounds are antioxidants of natural origin receive increasing consumer preference over those of synthetic origin, due to factors such as tolerance, safety, a very low toxicity and the absence of side effects [4]. Antioxidants inhibit the initiation and propagation of oxidative chain reactions, which are responsible for generating reactive oxygen species (ROS), thus preventing the development of degenerative diseases and cancer [5]. Some recent evidence shows phenolics directly interact with proteins, making them ideal small molecules for modulating enzymes, transcription factors and receptors.

These phenolics are widely found in the Eugenia spp. (Myrtaceae) leaf extracts [6, 7] and fruits [8]. Myrtaceae are well known for their economic importance since they are cultivated worldwide. Guava (Psidium), jaboticaba (Myrciaria), pitanga (Eugenia) trees has commercial application and biological activity studies [9, 10]. From Eugenia punicifolia (Kunth) D. C. leaves it have reported some pharmacological activities like the recovery of inhibitory effects in nicotinic-cholinergic neurotransmission by its aqueous extract in rat diaphragm [11]; antioxidant activity and inhibited enzymes related to metabolic syndrome [12]; antinociceptive, anti-inflammatory and gastroprotective effects in rodents [6]; inflammation inhibition and skeletal muscle remodeling activation using a polymer implant containing pentacyclic triterpenes from E. punicifolia [13]; and ulcer-healing [14].

Previous studies indicated E. punicifolia hydroethanolic leaf extracts are a rich quercetin and myricetin source [6, 7]. Quercetin aglycones are found to play a critical role in the immunomodulatory action in inflammatory responses against H2O2, leading to a decrease in the generation of ROS (reactive oxygen species) [15, 16]. Myricetin exhibits a wide range of activities that include strong antioxidant, anticancer, antidiabetic and anti-inflammatory activities. For this reason, It also used as a preserving agent to extend the shelf life of foods containing oils and fats, since this phenolic compound is attributed with an ability to protect lipids against oxidation [17].

Just as there is a high demand for antioxidant compounds there is also an intense demand for anticancer compounds. Cancer cells are characterized by uncontrolled cellular division and their ability to migrate to other parts of the body. Tumour cells creates a microenvironment that provides a nurturing condition for malignant processes development. Oxidative stress is maintained in the tumour microenvironment to sustain cancer progression [18]. So, redox regulation seems to be the key factor in regulating carcinogenesis and may be modulated by phenolic antioxidants, as commonly found in Myrtaceae [18] [19],. Moreover, chemotherapeutic drugs can cause serious side effects and drug resistance, which pose limiting factors in the long-term use of these drugs. So, there is a great demand for alternative anticancer agents with low side effects [20].

In general, plant phenolics are more soluble in organic solvents like methanol, ethanol and aqueous acetone solutions, but the diversity of phenolics present in plant tissues challenge the standardization of extraction methods. One possible way is selecting a mixture of extracting solvents by the changes in the solvent proportions within the systems (binary, ternary or even multicomponent mixtures) via experimental mixture design, like simplex-centroid. Simplex-centroid provides an economic and time-saving method, unlike methods like trial-and-error, because it takes advantage of statistical criteria in order to minimize both the model error and the number of required experiments [21, 22]. Besides, in this system, it is possible to observe synergistic/antagonistic effects which were result of different compounds extracted [23].

So, in this work, a simplex centroid mixture design was used to find the most adequate solvent mixture for the extraction of phenolic compounds with best antioxidant and antiproliferative activities from E. punicifolia leaves. The total phenolic and flavonoid contents, antioxidant and antiproliferative activities were used as responses to select the best extractor. Additionally, the phenolics compounds were identified by elestrospray ionization, in negative mode, coupled to a hybrid mass spectrometer with quadrupole and time-of-flight analyses [ESI(−)Q/TOFMS].

Experimental section

Drugs and chemicals

Gallic acid, quercetin, and 2,2-diphenyl-1-picrylhydrazyl (DPPH) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Other analytical grade reagents were purchased locally.

Collection and preparation of plant material

Eugenia punicifolia leaves were collected on January (2010) by Dr Catarina dos Santos at the Instituto Florestal e Estações Experimentais—Floresta Estadual de Assis at 22°33′ to 22°37′ Lat. S–50°21′ to 50°24′ Long. W, Assis, State of São Paulo, Brazil. The specimen was identified by Dr Antônio C. G. Melo, and a voucher specimen (no.43.522) was deposited in the Herbarium D.Bento Pickel, São Paulo, SP, Brazil, for future reference, in compliance with legal norms and registered as Cotec 206108-005.298/2009.

Ethics statement

The experimental procedures using human blood were approved by the local Research Ethics Committee of the Faculty of Science and Letters at the University of São Paulo State Campus Assis (Approval Number 467783151.0000.5401). We obtained written consent, suggested and approved by the Committee, from each participant before initiating any research procedures.

Mixture design and extract preparation

Extraction solvents were mixtures of water, methanol and ethanol in various proportions according to a simplex centroid design, as shown in Fig. 1. The three points at the vertices of the triangle correspond to extractions carried out using pure solvents, water (w), methanol (m) or ethanol (e). The three midpoints of the sides of the triangle correspond to 1:1 binary mixtures of these solvents. Ternary mixtures using different proportions were also investigated. The solvent mixtures were obtained according to the augmented Simplex-Centroid design (Design Expert version 12 State-Ease Inc, Minneapolis, MN, USA). Each component of the mixture was studied in the proportion range from 0 to 100%, including 14 experiments, four replicates and three combinations for performing lack of fit tests for models associated with the response variables under consideration, according to the Design Expert software (Fig. 1). For each response, the most adequate model was adopted.

Fig. 1
figure 1

Simplex centroid design used to optimize the extraction. The points correspond to the mixtures in Table 1. The number 2 in four mixtures represents the number of experimental repetitions performed in each of them

For extract preparation, 5 g of crushed, dry leaves were moistened with the mixture of solvents v/v (1:10 plant/solvent ratio) submitted by dynamic maceration (3 ×2 h) at room temperature (25 ± 2 °C). After filtration, these combined solutions were concentrated under reduced pressure until complete solvent elimination to provide the extracts studied on this manuscript.

Polyphenol and flavonoid contents

The total polyphenol content was determined for all tested extracts using Folin-Ciocalteu’s assay with some modifications [24]. Thus, Folin–Ciocalteau (2.5 mL, 10% v/v) and sodium carbonate (2.0 mL, 4% w/v) solutions were added to an aliquot (0.5 ml) of ethanolic solution of each extract, followed by thorough mixing, incubation for 120 min in the dark at room temperature and absorbance measurement at 750 nm. A calibration curve (0.5–40 µg/mL) of gallic acid was used to express the results as mg gallic acid equivalent (GAE)/g dry extract. All measurements were performed in triplicate.

The total flavonoid content was determined by the aluminium chloride colorimetric assay with modifications [24]. Thus, absolute ethanol (1.5 mL), aluminium chloride (0.1 ml, 10% w/v), potassium acetate (0.1 mL, 1 M) and distilled water (2.8 mL) were added to an aliquot (0.5 mL) of ethanolic solution of each extract, followed by thorough mixing, incubation for 30 min in the dark at room temperature and measuring the absorbance at 425 nm. A calibration curve (5–60 µg/mL) of quercetin was used to express the results as mg quercetin equivalent (GAE)/g dry extract. All measurements were performed in triplicate.

ESI(-)-Q-TOF-MS analysis

Mass spectrometry analyses were performed on an Agilent 6500 series quadrupole time-of-flight (QTOF) mass spectrometer (Agilent, Santa Clara, CA, USA), operating in the negative ion mode. The VCap voltage was maintained at 4 kV, the gas temperature was set to 290 °C, the drying gas flow rate was 11 L min−1, and the nebulizer pressure was set at 45 psi. The fragmentor was maintained at 175 V and the skimmer voltage was 320 V. A mixture of ions with m/z 112.9856 and m/z 966.0007 was used as internal standards. Data were recorded in a mass range of m/z 100–1500, with a 1 spectrum s−1 scan rate and a 2 spectra s−1 MS/MS scan rate with an isolation width of 1.3 and collision energy of 35 eV. MS/MS precursor selection was set to four precursors per cycle, where these were chosen by the abundance only mode.

Determination of DPPH (2,2-Diphenyl-1-picrylhydrazyl) radical scavenging

The extracts for DPPH radical scavenging were estimated according to Enujiugha et al. with some modifications [25]. Thus, DPPH solution (0.5 mL, 0.03% in methanol) was added to an aliquot (2.0 mL) of methanolic solution of each extract (1.56–200 µg min−1), followed by vigorous mixing, incubation for 30 min in the dark at room temperature and measuring the absorbance at 517 nm. Absolute methanol (2 mL) was used as the blank (negative control) and quercetin and gallic acid solutions were used as positive controls. All tests were carried out in triplicate. The DPPH radical scavenging activity of each extract was calculated using Eq. 1:

$$ {\text{\% inhibition}} = \left( {1 - \frac{\text{Abs sample}}{\text{Abs control}}} \right)x 100 $$

where Abssample is the absorbance of each extract and Abscontrol is the absorbance of methanol in DPPH. Different sample concentrations were used to obtain antiradical curves for calculating the EC50 values. Antiradical curves were plotted referring to concentration on the x-axis and their relative scavenging capacity on the y-axis. The EC50 values were processed using Microsoft Excel 365 and Origin 8.6.

Culture conditions

The cell line HEp-2 (human laryngeal) was maintained in cell culture flasks with RPMI-1640 media (Roswell Park Memorial Institute medium, Sigma-Aldrich, St. Louis, MO, USA) supplemented with 5% fetal bovine serum and incubated at 37 °C in 5% CO2. The culture medium was exchanged every 2 days, with the application of antibiotics and antimycotics to avoid contamination. Mononuclear leukocytes were obtained from human peripheral blood (10 mL) from healthy volunteers. The leukocytes were isolated by Histopaque discontinuous gradient 1077 and 1119 (Sigma-Aldrich, St. Louis, MO, USA), following the manufacturer’s recommendations. For viability testing, 5 × 104 cells were used in a 96-well microplate. The gradient was centrifuged at 700×g and 25 °C for 30 min (380R, Hettich Zentrifugen, Tuttlingen, Alemanha) for cell collecting. The cells were newly centrifuged at 2000 rpm for 10 min (g-force 447,2), at 25 °C with saline solution (NaCl 0,9% w/v) and deposited on RPMI 5%. Cells count were carried out in a Neubauer chamber, plating 3 × 105 cells per well, in 2% F12 DMEM medium (Dulbecco’s Modified Eagle’s medium).

MTT assay

Cytotoxicity was evaluated by the colourimetric method of MTT (3-(4,5-dimethylthiazol-2-yl) 2,5-Diphenyl Tetrazolium bromide) (Sigma), which consists of indirectly measuring cell viability using the mitochondrial enzyme activity of living cells. Cells (5 × 104 HEp-2 or 3 × 105 mononuclear cells/well) seeded into 96-well culture plates were incubated with different concentrations of Eugenia punicifolia, for 24 h at 37 °C. Subsequently, MTT (0.5 mg/mL) was added to each well and incubated at 37 °C for 4 h. After incubation, formazan crystals were diluted by adding dimethyl sulfoxide (DMSO, Sigma) and the optical density (O.D.) of samples was measured in a spectrophotometer at 570 nm. Cells incubated with either medium or 50 μM H2O2 were used as negative and positive controls (100% viable to cell death), respectively [26].

Optimization of E. punicifolia extraction using Simplex Centroid Design

Extraction solvents were mixtures of water (w), methanol (m) and ethanol (e) in according to a Simplex Centroid Design (Design Expert version 12 State-Ease Inc, Minneapolis, MN, USA), as shown on Fig. 1. Each component of the mixture was studied in the proportion range from 0 to 100%, including 14 experiments, four replicates and three combinations. The three points at the vertices of the triangle correspond to extractions carried out using pure solvents. The three midpoints of the sides of the triangle correspond to 1:1 binary mixtures of these solvents. Ternary mixtures using different proportions were also investigated. According to the Design Expert software (Fig. 1). A Scheffé special cubic model was expressed for each response function from the simplex-centroid design (Eq. 2):

$$ y = \mathop \sum \limits_{i = 1}^{q = 3} b_{i }^{*} x_{i} + \mathop \sum \limits_{i < j}^{q = 3} \mathop \sum \limits_{j}^{q = 3} b_{ij }^{*} x_{i } x_{j } + b_{123 }^{*} x_{1 } x_{2 } x_{3 } $$

where y is the estimated response, b* is the coefficient estimated by the least squares method and xi is the independent variable, with 0 ≤ xi ≤ 1 and ∑ xi = 1 (i.e., 100 wt%). The \( b_{i }^{*} \) parameter is the linear coefficient related to pure component i, \( b_{ij }^{*} \) is the quadratic coefficient of binary interaction for components i and j and \( b_{123 }^{*} \) is the special cubic coefficient of ternary interaction for components 1, 2 and 3 [27].

For each response, the most adequate model was adopted. The mathematical models were subjected to analysis of variance (ANOVA) and best fitted response models were achieved by displaying statistical parameters such as: R2 (correlation coefficient), adjusted R2 (adjusted correlation coefficient), lack of fit, regression F-value and regression p value by using analysis of variance (ANOVA). From ANOVA analysis, the model was considered significant when (p > 0.0001), indicating the good fit of model. The F test was made sure no more than 0.05 (significant) and to the lack of fit was assured to be more 0.05 (insignificant). Contour plots of the responses were generated from adjusted models. The tests of statistical significance for the model and the parameters, as well as the lack of fit test, were performed at a 95% significance level. From Table 1, ANOVA followed a Bonferroni’s test was applied.

Table 1 Results of Simplex-Centroid experimental design for total phenols and flavonoids, EC50, HEp-2 cell line and mononuclear cells

Results and discussion

In the process of phenolics extraction from E. punicifolia, it was expected that interaction effects could occur when one mixture of the extractive system interacted with another, influencing the response variable. Moreover, the phenolics extraction was already reported to be affected by the chemical complexity of the studied plant and by the polarity of the solvent used for extraction. In general, good selectivity could be achieved when using solvents and their mixtures by applying design modelling and multivariate analysis [28]. The simplex-centroid design permitted quantification of the interaction effects of solvent mixtures by adjusting their proportions [29]. The presence of interactions among extractive solvents had important implications for the understanding the extractive system in our work.

Optimization of sample preparation

Eugenia punicifolia dried and powdered leaves were extracted by using three pure solvents (water, methanol, and ethanol), or mixtures of these solvents provided by Simplex Centroid Design (Table 1). Table 1 also shows the effects of different solvents on response variables: TPC, TFC, antioxidant activity (EC50, measured by DPPH radical scavenging determination), HEp-2 antiproliferative activity (HEp-2), and mononuclear cell viability (MCV).

Model fitting: initial considerations

Analysis of variance (ANOVA) was used for the evaluation of the fitted mathematical model, with a confidence interval of 95% (Table 1). ANOVA analyses were done to choose the best model for TPC, TFC, antioxidant activity (measured EC50), HEp-2, and MCV. The model performance was determined by calculating the R2 coefficient. From the literature, it is usual to consider a mathematical equation reasonable when R2 > 70% because this determination coefficient indicates the suitability of the regression model. However, the closer the R2 value to unity, the better and more significantly the model fits to the real data [23, 30]. In our work, the R2 values for TPC (0.7514, linear model), TFC (0.8272, linear model), antioxidant activity (0.8067, reduced quadratic model), HEp-2 (0.8839, linear model), and MVC (0.8931, reduced special cubic model) indicated that the models were able to explain 75, 83, 81, 88, and 93% of experimental data variability, respectively. Adjusted R2 was used to analyze how the experimental results fitted with theoretical results. In our work, adjusted R2 ranged from 0.6962–0.9320, which showed that the experimental results fitted with the theoretical results (Table 2).

Table 2 ANOVA results for response surface models used subsequently in optimizing TPC, TFC, EC50, HEp-2 and MVC on the extraction parameters from E. punicifolia leaves

The coefficient of variation (CV) is a measure of deviation from the mean value, indicating the precision and repeatability of an assay. It is usual to consider CV < 10% to indicate a reliable and precise model. CV values found in this manuscript (Table 2) showed the reliability and precision of the model. These results demonstrated that the variability of the model was compatible with the variability that occurred experimentally.

A regression model obtained from ANOVA analysis was evaluated by using F statistics and a lack of fit test. It was considered a highly significant model if, besides the low value of p (< 0.05), the computed F-value was greater than the tabulated F-value.

From Table 2, ANOVA indicated a significant regression for the linear models for TPC and TFC at the 95% confidence level (TPC: F = 13.60, p = 0.0019; TFC: F = 23.94, p = 0.0002). Additionally, the lack of fit test was not significant for these models (TPC: F = 1.36, p = 0.4179; TFC: F = 1.60, p 0.3794). For EC50, the reduced quadratic model was significant (F = 11.13, p 0.0032) and the lack of fit test was not significant (F = 1.84, p 0.3934). A linear model for HEp-2 (F = 30.46, p = 0.0002) and reduced special cubic for MVC (F = 23.99, = 0.0003) were significant, with the lack of fit not significant for either (F = 1.83, p 0.3941; F = 1.40, p 0.4074, respectively).

Effects of the solvent system on the response variable

Total phenol content (TPC) and total flavonoid content (TFC)

In the literature, there is not a general consensus regarding the use of a single solvent or mixture of solvents ideal for obtaining bioactive compounds from plant materials, however it is frequently stated that hydroethanolic extraction is the most appropriate alternative to recover phenolic compounds from plants [31]. Compared to the alternatives, water alone was not a good cold solvent extractor (at room temperature). Nevertheless, the negative effect of its presence in a mixture was not too expressive, since aqueous mixtures had interesting results too. This situation was also described by Kalia et al. [24], where it was defined that a 50% ethanol-aqueous mixture was the most efficient for extracting polyphenols.

Phenolic compounds, such as acids, flavonoids, and tannins, could be differentially influenced by the polarity of the solvents and the solubility of each compound in the solvent used for the extraction process. Methanol, ethanol, water, or mixtures of these solvents are used to extract phenolic compounds from plant parts like leaves, roots, and fruits. Ethanol or ethanol–water mixtures are the most used solvents for extraction because of their low toxicity. Moreover, hydroethanolic solutions have some similarities with popular medicinal Brazilian preparations, usually known as “garrafada”—a preparation made by plant part maceration in brandies like “cachaça” [32]. However, there are some reports that correlate methanol and methanolic solutions to flavonoid enriched extracts [33]. In summary, the solvent choice is always made to get the best phenolic extraction performance with the maximum biological activity possible and with minimum toxicity.

Considering only the significant effects, the linear model is represented by polynomial Eq. 3:

$$ \varvec{TPC} = 282.62 w + 339.68e + 327.39m $$

All linear terms were positive and the highest coefficient value from Eq. 3 (+339.68) showed ethanol as the solvent that enhanced phenolic extraction the most, followed by methanol (+ 327.39) and water (+ 282.62). These solvents therefore had a positive effect on the recovery of phenolic compounds. From Eq. 3, a three-dimensional plot (Fig. 2a) was proposed to get a better picture and interpretation of the adjusted mathematical model, and to further investigate the effects of interactions between the variables studied. Through Fig. 2a, it could also be seen that the ethanolic extraction enabled a higher quantity of phenolic compounds, but when ethanol was at lower concentrations in the mixture, lower quantities of phenolics were obtained. On the other hand, water also demonstrated a positive effect when mixed with ethanol. Confirming this trend, TPC contents oscillated between 164.15 ± 0.02 mg GAE/g extract (MeOH:H2O 50%, EE13) and 412.59 ± 0.01 mg GAc/g extract (EtOH 100%, EE5).

Fig. 2
figure 2

Contour and three-dimensional graph for the analysis of the linear model’s response surface predicted for the extraction of total phenols and flavonoids content according to the percentage of the solvents: a water, b ethanol, c methanol

Comparing different methods of polyphenol solvent extractions from Eugenia uniflora leaves, great variation was observed in the polyphenol content (51–504 mg GAc/g dry extract) and ethanolic extracts also had more expressive results [34]. For EtOH: H2O solvent mixtures, E. punicifolia produced 74.86 ± 0.02 mg GAc/g from hydroethanolic leaf extracts (EtOH:H2O 70%) [7]. The current results proved consistent with the literature since, in the hydroethanolic extracts cited above, phenolic compounds such as gallic, quinic, and syringic acids, and derivatives such as glycosyringic and ferruloylquinic acid, were mainly found [7].

For flavonoids, the best solvent extractor was not one of the extremes of the dielectric constant, because these phenolics present different polarities. This is because phenolic compounds include a broad range of compounds, from simple phenols with low molecular weight and a single aromatic ring to large, complex tannins and derived polyphenols [1].

The TFC linear model is represented by polynomial Eq. 4:

$$ \varvec{TFC} = 97.80w + 127.18e + 132.14m $$

The Eq. 4 terms suggested that each component of the mixture generates a positive (synergistic) effect on total flavonoid recovery. Methanol (+ 132.14) was a little more effective than ethanol (+ 127.18), but there was a decrease in the flavonoid content after the introduction of water into the solvent mixture. Therefore, ethanol-enriched solvents would be much more interesting in extraction, due to their low toxicity, but extracts made from methanol were richer in flavonoid content (Fig. 2b).

Analyzing the data from Table 1 and comparing with Fig. 3, it can be seen (in light red) that 100% MeOH is the best solvent for flavonoid extraction (137.43 ± 0.02 mg Q/g, EE6), followed by EtOH:MeOH 50% v/v (131.32 ± 0.04, EE10). This tendency was observed in Fig. 2b from pure H2O (101.86 ± 0.01 mg Q/g, EE4). From our own results, a lower content of these phenolics was produced by E. punicifolia leaf extracts using EtOH:H2O 70% v/v (32.00 ± 0.02 mg Q/g extract) when compared to the other Eugenia spp. [7].

Fig. 3
figure 3

Contour and three-dimensional for the analysis of the model’s response surface predicted for EC50 (a), HEp-2 (b) and mononuclear cell viability (c)

Antioxidant activities

An antioxidant is defined as a molecule capable of slowing or preventing the oxidation of other molecules [35]. These molecules prevent mutations to macromolecules by quenching ROS and decreasing the ROS induced oxidative damage. An epigallocatechin gallate from green tea and genistein from soybeans can also act as inhibitors of angiogenesis [36, 37] and DNA methyltransferases (DNMT) in vitro [38]. Accordingly, there are many reviews on the relationships between oxidative damage and various diseases including Alzheimer’s disease, ageing, arthritis, inflammation, diabetes, Parkinson’s disease, and atherosclerosis, among others. As a result, many diseases have been treated with antioxidants to prevent oxidative damage [35].

The DPPH assay is commonly applied to the investigation of antioxidant molecules because it is a simple and sensible method. The DPPH radical acts receiving a hydrogen from an antioxidant molecule, and this change is noticed by a spectrophotometer as the colour is altered from purple to yellow, with the absorbance at 517 nm decreasing proportionally [35]. Therefore, the lower the EC50, the more active the compound is. The result is independent of the concentration of the sample and comparison with other extracts is more simple and trustworthy [39].

The positive terms reduced quadratic model for antioxidant activity, measured by EC50, suggested that each component of the mixture generated a positive impact on the antiradical effect (Eq. 5). The fluctuation in EC50, depending on the different proportion of solvents tested here (water/ethanol/methanol), can also be seen in the Additional file 1: Figure S1.

$$ \varvec{EC}_{50} = 4.76w + 4.01e + 4.33m + 16.37 we $$

Since lower values of EC50 present better antioxidant activity, Ethanol (+4.01) is a little more effective than methanol (+ 4.33) and water (+ 4.76). Moreover, if water was introduced in the solvent mixture, there was an increase in the EC50 value (synergistic effect) and a loss of activity (Fig. 3a). From Table 1, the best antioxidant capacity was found with 100% EtOH (3.39 ± 0.00 μg/mL, EE1), but in general there was an EC50 range of 3.39–8.38 µg/mL. This result indicated that leaf extraction generated very active extracts, i.e., all of them had a good capacity for free radical scavenging.

For flavonoids, the antioxidant activity depends of their proton donor ability which always correlated with of the number of hydroxyl groups in these phenolics [10]. For example, quercetin has been greater antioxidant activity if compared a rutin, a quercetin glycosylated molecule, because of the hydroxyl groups available [8]. Thus, quercetin and myricetin derivatives found in Eugenia extracts seem to be determinants for antioxidant activity [17]. For hydrolyzable tannins, a correlation between antiradical activity measurements and molecular weight of ellagitannins and ellagic acid was described. Tannins’ antioxidant capacity is also attributed to the presence of hydroxy functions, which exhibit a greater ability to donate a hydrogen atom and support the unpaired electron compared to those of low molecular weight tannins, but the presence of a sugar moiety would decrease this effect [40].

As described here, the ethanolic leaf extracts of E. pyryformis presented a yield EC50 of 1.10 µg/mL. However, using the same extract conditions, E. chlorophylla showed an EC50 of 33.72 µg/mL [41] and Myrcia spp. (Myrtaceae) reached similar results to DPPH (EC50 = 8.61–16.2 µg/mL) from EtOH:H2O 70:30% extracts [19]. For this reason, it is always good to keep in mind that it is necessary to do a new test for each plant, since the chemical composition could be different.

Antiproliferative activities: HEp-2 and MCV

Antiproliferative activity is the first step in anti-cancer chemotherapeutic agent analysis. Many studies have demonstrated that polyphenols, including flavonoids, can act through several mechanisms of action at different points during the carcinogenesis process, either alone or in a synergistic way [42]. The positive terms for the linear model tested for antiproliferative against Hep-2 cells are represented by polynomial Eq. 6:

$$ \varvec{HEp}2 = 66.39w + 55.76e + 127.11m $$

From the above equation, the antiproliferative activity against HEp-2 from ethanol (+ 56.76) and water (+ 66.39) could be established, whilst extracts prepared from methanol (+ 127.11) seemed to have discrete proliferative activity, as this term exceeded 100%. This trend could be better observed from Fig. 3b, where methanol extracts showed the higher response. Since methanol rich extracts were flavonoid rich, it was expected that those phenolics were responsible for the observed effect.

From Table 1, moderate antiproliferative activity against HEp-2 could be observed at first glance from EE6 (43.37 ± 0.19%, 100% MeOH) and EE5 (54.60 ± 0.17%, 100% EtOH). However, for the replicate extracts EE14 (121.87 ± 0.04%, 100% MeOH) and EE2 (189.90 ± 0.33%, 100% EtOH), the opposite effect was observed, i.e., proliferative activity. Thus, the simplex centroid design was crucial in determining the effect of the extracts on the analyzed response.

The next step was describing the effects of solvent extraction on mononuclear cell viability. The fluctuation in cell viability results, depending on the different proportion of solvents tested here (water/ethanol/methanol), can also be seen in the Additional file 1: Figure S1. In Eq. 7, a synergistic trend of MeOH:H2O (+ 395.29) and MeOH (+ 223.57) extracts increasing the cell viability was noted. The most interesting result was the antagonistic effect exerted by the ternary mixture (− 3976.89). This trend, confirmed by data from Table 1 and Fig. 3c, showed a high value for EE13 (290.06 ± 13.60, MeOH:H2O 50:50 v/v) followed by EE14 (236.67 ± 20.37, 100% MeOH).

$$ \varvec{MCV} = 159.15w + 196.29e + 223.57m + 110.05 we + 395.29wm + 40.04 em - 3976.89 wem $$

There is a coherence between the results above, since the same extracts show proliferative activity and growth of normal cells. These results were interpreted as an effect of flavonoid enriched extracts. Myrtaceae plants are described as rich in flavanol derivatives such as quercetin and myricetin [19]. Quercetin is usually described as being able to react with ROS, both in vitro and in vivo. This molecule acts as an anti-tumour drug in vitro by exerting pro-apoptotic activity, especially on leukemic cells, but not on immune cells, as several chemotherapeutic drugs do [43].

Myricetin acts as an antioxidant at lower concentrations and it has pro-oxidant effects at higher concentrations. The presence of the catechol moiety in the B-ring was linked to a strong DPPH scavenging activity. Myricetin is also described as cytotoxic towards several human cancer cell lines, including hepatic, skin, pancreatic, and colon cancer cells. When there is an increase in the concentration, this molecule showed an enhanced anti-proliferative activity against HL-60 cells. This compound displayed cytotoxicity towards chronic myeloid human leukaemia K562 cells and normal peripheral blood mononuclear cells isolated from the blood of a healthy humans, and it also exhibited moderate cytotoxicity towards human laryngeal carcinoma HEp-2 cells [17].

Unfortunately, less information is available regarding possible synergistic or antagonistic biochemical interactions among polyphenols that may affected their antiproliferative activity. Identifying potential interactions among these compounds may help define the efficiency of polyphenol-containing extracts in cancer prevention and relate this to the structure function activity of the compounds. For example, quercetin and ellagic acid were tested on cell death and proliferation-related variables. In this case, ellagic acid had potentiated the effects of quercetin on the reduction of proliferation and viability and the induction of apoptosis in the MOLT-4 human leukaemia cell line [44].

Mononuclear cell proliferation may indicate lymphocyte proliferation, which is responsible for the memory of our immunity [45]. It would be ideal if one of the tested extracts had both functions, i.e., increasing mononuclear cell proliferation and reducing tumour cell proliferation, but any reduction in tumour cell proliferation should be considered a good result because it helps in reducing tumour mass.

ESI–MS analysis

The chemical profile of the E. punicifolia extracts, determined by ESI(−)-Q/TOFMS, allowed for identification of phenolic acids, quercetin and myricitrin derivatives, and other phenolic compounds. The most characteristic corresponding molecular formulas and their fractions and MS/MS fragments are shown in Table 3.

Table 3 Phenolic identified by ESI(−)-MS found in ethanolic Eugenia punicifolia extracts

All peaks were tentatively assigned based on their accurate masses and MS/MS patterns. Monogalloylglucose, with its m/z 331 [M–H] ion, dissociated to yield an m/z 169 ion after a glucosyl group loss [M−H-162]. Digalloylglucose, with its m/z 483 [M−H]-ion, dissociated to yield an m/z 169 ion after sequential removal of a galloyl group [M−H-152] and a glucosyl group [M−H-162]. The quercetin arabinopyranoside isomer at m/z 433 [M−H] produced the MS/MS fragmentation of m/z 300 [M−H-132] due to the loss of pentoside. The ion at m/z 447, assigned to quercetin-3-O-β-rhamnose, after a loss of sugar, produced a deprotonated aglycone ion at m/z 301 [M−146-H,]. Myricitrin, was assigned at m/z 463 [M–H], after a deprotonated a myricetin ion at m/z 317 [M−146-H]. At m/z 615, the MS/MS peak fragmentation produced an ion at m/z 463 [M−152-H] and a deprotonated quercetin at m/z 301 [M−162-H], indicative of quercetin-3-O-β-(6″galloyl) hexose. These flavanol derivatives were previously reported in other Eugenia species and are usually associated with antioxidant and antiproliferative activities. Ellagic acid was characterized by an ion of m/z 301[M-162-H]. HHDP-glucose isomers were assigned as a signal at m/z 481[M-162–18-H] (loss of glucose plus H2O, 180 units). At m/z 783, the MS/MS peak fragmentation produced an ion at m/z 481 [M−H-302] and after losing an HHDP-glucose [M−H-481], an ion at m/z 301 which assigned to ellagic acid. This fragmentation pattern was assigned to a bis-HHDP-glucose isomer [19]. All these results are consistent with data reported for other Eugenia spp. [7, 19] (Fig. 4).

Fig. 4
figure 4

ESI(−)-MS of the selected E. punifolia leaves extract. a Ethanolic extract (EE5). b Methanolic extract


Since the purpose of this work is to report the optimized studies of solvent extraction that result in a better biological activity, it is possible to point out some inferences. From simplex centroid design analysis, the extraction of antioxidant compounds from E. punicifolia leaves showed that solvents richer in ethanol were the most adequate solution. Since this solvent is the best extractive for all kind of phenolic compounds, this activity was linked to compounds such as phenolic acid and tannins. This solvent, less toxic than methanol, still has a small antiproliferative activity against HEp-2 and some mononuclear cell viability. These results encourage us in the search for more industrial applications of this extract since they are very promising.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.




EC50 :

Half maximal effective concentration


Electrospray Ionization Quadrupole time-of-flight Mass Spectrometry


Human epithelial type 2




Reactive oxygen species


  1. Dai J, Mumper RJ (2010) Plant phenolics: extraction, analysis and their antioxidant and anticancer properties. Molecules 15:7313–7352.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  2. Starmans DAJ, Nijhuis HH (1996) Trends in food science & technology. Extr Second Metab Plant Mater Rev 7:191–197

    CAS  Google Scholar 

  3. Bourgaud F, Gravot A, Milesi S, Gontier E (2001) Production of plant secondary metabolites: a historical perspective. Plant Sci 161:839–851.

    Article  CAS  Google Scholar 

  4. Lee MT, Lin WC, Yu B, Lee TT (2016) Antioxidant capacity of phytochemicals and their potential effects on oxidative status in animals—A review. Asian-Australas J Anim Sci 30:299–308.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  5. Bouriche H, Kada S, Senator A, Demirtas I, Ozen T, Toptanci BÇ, Kizil G, Kizil M (2017) Phenolic content and biomolecule oxidation protective activity of Globularia alypum extracts. Braz Arch Biol Technol.

    Article  Google Scholar 

  6. Basting RT, Nishijima CM, Lopes JA, Santos RC, Lucena Périco L, Laufer S, Bauer S, Costa MF, Santos LC, Rocha LRM, Vilegas W, Santos ARS, dos Santos C, Hiruma-Lima CA (2014) Antinociceptive, anti-inflammatory and gastroprotective effects of a hydroalcoholic extract from the leaves of Eugenia punicifolia (Kunth) DC. in rodents. J Ethnopharmacol 157:257–267.

    Article  PubMed  Google Scholar 

  7. Costa MF, Jesus TI, Lopes BRP, Angolini CFF, Montagnolli A, de Gomes LP, Pereira GS, Ruiz ALTG, Carvalho JE, Eberlin MN, dos Santos C, Toledo KA (2016) Eugenia aurata and Eugenia punicifolia HBK inhibit inflammatory response by reducing neutrophil adhesion, degranulation and NET release. BMC Complement Altern Med.

    Article  PubMed Central  PubMed  Google Scholar 

  8. Denardin CC, Hirsch GE, da Rocha RF, Vizzotto M, Henriques AT, Moreira JCF, Guma FTCR, Emanuelli T (2015) Antioxidant capacity and bioactive compounds of four Brazilian native fruits. J Food Drug Anal 23:387–398.

    Article  CAS  PubMed  Google Scholar 

  9. Grattapaglia D, Vaillancourt RE, Shepherd M, Thumma BR, Foley W, Külheim C, Potts BM, Myburg AA (2012) Progress in Myrtaceae genetics and genomics: eucalyptus as the pivotal genus. Tree Genet Genomes 8:463–508.

    Article  Google Scholar 

  10. Cascaes M, Guilhon G, Andrade E, Zoghbi M, Santos L (2015) Constituents and pharmacological activities of Myrcia (Myrtaceae): a review of an aromatic and medicinal group of plants. Int J Mol Sci 16:23881–23904.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  11. Grangeiro MS, Calheiros-Lima AP, Martins MF, Arruda LF, Garcez-do-Carmo L, Santos WC (2006) Pharmacological effects of Eugenia punicifolia (Myrtaceae) in cholinergic nicotinic neurotransmission. J Ethnopharmacol 108:26–30.

    Article  PubMed  Google Scholar 

  12. Galeno DML, Carvalho RP, de Araújo Boleti AP, Lima AS, de Almeida PDO, Pacheco CC, de Souza TP, Lima ES (2014) Extract from Eugenia punicifolia is an antioxidant and inhibits enzymes related to metabolic syndrome. Appl Biochem Biotechnol 172:311–324.

    Article  CAS  Google Scholar 

  13. Leite PEC, Lima-Araújo KG, França GR, Lagrota-Candido J, Santos WC, Quirico-Santos T (2014) Implant of polymer containing pentacyclic triterpenes from Eugenia punicifolia inhibits inflammation and activates skeletal muscle remodeling. Arch Immunol Ther Exp (Warsz) 62:483–491.

    Article  CAS  Google Scholar 

  14. Périco LL, Rodrigues VP, Ohara R, Nunes VVA, da Rocha LRM, Vilegas W, dos Santos C, Hiruma-Lima CA (2019) Can the gastric healing effect of Eugenia punicifolia be the same in male and female rats? J Ethnopharmacol 235:268–278.

    Article  CAS  PubMed  Google Scholar 

  15. Shokoohinia Y, Rashidi M, Hosseinzadeh L, Jelodarian Z (2015) Quercetin-3-O-β-d-glucopyranoside, a dietary flavonoid, protects PC12 cells from H2O2-induced cytotoxicity through inhibition of reactive oxygen species. Food Chem 167:162–167.

    Article  CAS  PubMed  Google Scholar 

  16. Vieira V, Prieto MA, Barros L, Coutinho JAP, Ferreira O, Ferreira ICFR (2017) Optimization and comparison of maceration and microwave extraction systems for the production of phenolic compounds from Juglans regia L. for the valorization of walnut leaves. Ind Crops Prod 107:341–352.

    Article  CAS  Google Scholar 

  17. Semwal D, Semwal R, Combrinck S, Viljoen A (2016) Myricetin: a dietary molecule with diverse biological activities. Nutrients. 8:90.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  18. Vinayak M (2018) Molecular action of herbal antioxidants in regulation of cancer growth: scope for novel anticancer drugs. Nutr Cancer 70:1199–1209.

    Article  CAS  PubMed  Google Scholar 

  19. Santos C, Galaverna R, Angolini C, Nunes V, de Almeida L, Ruiz A, de Carvalho J, Duarte R, Duarte M, Eberlin M (2018) Antioxidative, antiproliferative and antimicrobial activities of phenolic compounds from three Myrcia species. Molecules 23:986.

    Article  CAS  PubMed Central  Google Scholar 

  20. Poljsak B, Milisav I (2019) the role of antioxidants in cancer, friends or foes? Curr Pharm Des 24:5234–5244.

    Article  CAS  Google Scholar 

  21. Abdullah N, Chin NL (2010) Simplex-centroid mixture formulation for optimised composting of kitchen waste. Bioresour Technol 101:8205–8210.

    Article  CAS  PubMed  Google Scholar 

  22. Soares PK, Bruns RE, Scarminio IS (2009) Statistical mixture design investigation of fractionated and total extracts from Erythrina speciosa Andrews leaves. J Sep Sci 32:644–652.

    Article  CAS  PubMed  Google Scholar 

  23. Rasera GB, Hilkner MH, de Alencar SM, de Castro RJS (2019) Biologically active compounds from white and black mustard grains: an optimization study for recovery and identification of phenolic antioxidants. Ind Crops Prod 135:294–300.

    Article  CAS  Google Scholar 

  24. Kalia K, Sharma K, Singh HP, Singh B (2008) Effects of extraction methods on phenolic contents and antioxidant activity in aerial parts of Potentilla atrosanguinea Lodd. and quantification of its phenolic constituents by RP-HPLC. J Agric Food Chem 56:10129–10134.

    Article  CAS  PubMed  Google Scholar 

  25. Enujiugha VN, Talabi JY, Malomo SA, Olagunju AI (2012) DPPH radical scavenging capacity of phenolic extracts from African Yam Bean (Sphenostylis stenocarpa). Food Nutr Sci 03:7–13.

    Article  Google Scholar 

  26. Stocco B, Toledo K, Salvador M, Paulo M, Koyama N, Torqueti Toloi MR (2012) Dose-dependent effect of Resveratrol on bladder cancer cells: chemoprevention and oxidative stress. Maturitas. 72:72–78.

    Article  CAS  PubMed  Google Scholar 

  27. Cornell JA (2002) Experiments with mixtures: designs, models, and the analysis of mixture data, 1st edn. Wiley, New York.

    Book  Google Scholar 

  28. de Mendes MKA, dos Oliveira CBS, Veras MDA, Araújo BQ, Dantas C, Chaves MH, Lopes Júnior CA, Vieira EC (2019) Application of multivariate optimization for the selective extraction of phenolic compounds in cashew nuts (Anacardium occidentale L.). Talanta 205:120100.

    Article  CAS  PubMed  Google Scholar 

  29. Marcheafave GG, Tormena CD, Pauli ED, Rakocevic M, Bruns RE, Scarminio IS (2019) Experimental mixture design solvent effects on pigment extraction and antioxidant activity from Coffea arabica L. leaves. Microchem J 146:713–721.

    Article  CAS  Google Scholar 

  30. Mohamed-Mahzir K, Abd-Gani S, Hasanah-Zaidan U, Halmi M (2018) Development of Phaleria macrocarpa (Scheff) boerl fruits using response surface methodology focused on phenolics, flavonoids and antioxidant properties. Molecules. 23:724.

    Article  CAS  PubMed Central  Google Scholar 

  31. Pires FB, Dolwitsch CB, Dal Prá V, Faccin H, Monego DL, de Carvalho LM, Viana C, Lameira O, Lima FO, Bressan L, da Rosa MB (2017) Qualitative and quantitative analysis of the phenolic content of Connarus var angustifolius, Cecropia obtusa, Cecropia palmata and Mansoa alliacea based on HPLC-DAD and UHPLC-ESI-MS/MS. Rev Bras Farmacogn 27:426–433.

    Article  CAS  Google Scholar 

  32. Mendes JCC (2010) Brazilian pharmacopoeia. Fundação Oswaldo Cruz, Brasília, Agência Nacional de Vigilância Sanitária

    Google Scholar 

  33. Félix-Silva J, Giordani RB, da Silva Jr AA, Zucolotto SM, de Fernandes-Pedrosa MF (2014) Jatropha gossypiifolia L. (Euphorbiaceae): a review of traditional uses, phytochemistry, pharmacology, and toxicology of this medicinal plant. Evid Based Complement Alternat Med 2014:1–32.

    Article  Google Scholar 

  34. Martinez-Correa HA, Magalhães PM, Queiroga CL, Peixoto CA, Oliveira AL, Cabral FA (2011) Extracts from pitanga (Eugenia uniflora L.) leaves: Influence of extraction process on antioxidant properties and yield of phenolic compounds. J Supercrit Fluids 55:998–1006.

    Article  CAS  Google Scholar 

  35. Moon J-K, Shibamoto T (2009) Antioxidant assays for plant and food components. J Agric Food Chem 57:1655–1666.

    Article  CAS  PubMed  Google Scholar 

  36. Bagul M, Kakumanu S, Wilson TA (2015) Crude garlic extract inhibits cell proliferation and induces cell cycle arrest and apoptosis of cancer cells in vitro. J Med Food 18:731–737.

    Article  CAS  PubMed  Google Scholar 

  37. Brglez Mojzer E, Knez Hrnčič M, Škerget M, Knez Ž, Bren U (2016) Polyphenols: extraction methods, antioxidative action, bioavailability and anticarcinogenic effects. Molecules 21:901.

    Article  CAS  PubMed Central  Google Scholar 

  38. Fang M, Chen D, Yang CS (2007) Dietary polyphenols may affect DNA methylation. J Nutr 137:223S–228S.

    Article  CAS  PubMed  Google Scholar 

  39. Locatelli M, Gindro R, Travaglia F, Coïsson J-D, Rinaldi M, Arlorio M (2009) Study of the DPPH-scavenging activity: development of a free software for the correct interpretation of data. Food Chem 114:889–897.

    Article  CAS  Google Scholar 

  40. Landete JM (2011) Ellagitannins, ellagic acid and their derived metabolites: a review about source, metabolism, functions and health. Food Res Int 44:1150–1160.

    Article  CAS  Google Scholar 

  41. Salvador MJ, de Lourenço CC, Andreazza NL, Pascoal ACRF, Stefanello MÉA (2011) Antioxidant capacity and phenolic content of four Myrtaceae plants of the south of Brazil. Nat. Prod. Commun. 6:1934578X1100600.

    Article  Google Scholar 

  42. Ramos A, Peixoto J, Cabral M, Amrein AM, Tiuman T, Cottica S, Souza I, Ruiz AL, Foglio MA, Carmo M, Sarragiotto MH, Baldoqui D (2019) Chemical constituents, antiproliferative and antioxidant activities of Vernonanthura nudiflora (Less.) H. Rob. J Braz Chem Soc.

    Article  Google Scholar 

  43. Lugli E, Ferraresi R, Roat E, Troiano L, Pinti M, Nasi M, Nemes E, Bertoncelli L, Gibellini L, Salomoni P, Cooper EL, Cossarizza A (2009) Quercetin inhibits lymphocyte activation and proliferation without inducing apoptosis in peripheral mononuclear cells. Leuk Res 33:140–150.

    Article  CAS  PubMed  Google Scholar 

  44. Mertens-Talcott SU, Talcott ST, Percival SS (2003) Low concentrations of quercetin and ellagic acid synergistically influence proliferation, cytotoxicity and apoptosis in MOLT-4 human leukemia cells. J Nutr 133:2669–2674.

    Article  CAS  PubMed  Google Scholar 

  45. MacIver NJ, Michalek RD, Rathmell JC (2013) Metabolic regulation of T lymphocytes. Annu Rev Immunol 31:259–283.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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This work was supported by Grants from the two following Fapesp projects (Fundação Amparo a Pesquisa do Estado de São Paulo-São Paulo Research Foundation): Project number 2017/15610-0 and 2014/19504-1, for collecting plant, extraction, analysis and writing of this manuscript.

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Authors and Affiliations



ALM performed all lab work related to obtaining of extracts and chemical assay. BE contributed with the statistical analysis and the manuscript preparation. BRPL contributed with the in vitro tests using cell culture techniques. MNE and CFFA planned and performed the Mass Spectrometry assays. KAT performed in vitro tests using cell culture and help to write the manuscript. EGFN planned the experimental work for obtaining the extracts and contributed with statistical analysis of the data as well as helped to write the manuscript. CS was the leader of the project, holds the original idea, designed the statistical experiments, coordinated the experimental activities among all engaged labs and wrote the highest percentage of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Catarina dos Santos.

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Supplementary information

Additional file 1: Figure S1.

DPPH antiradicalar effects. Cell viability from Hep-2 and mononuclear cells incubated with different extracts. The numbers below each column is correspondent those Table 1. The difference statistical (p < 0.05) between the tested extracts is appointed by numbers above each column.

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dos Santos, C., Mizobucchi, A.L., Escaramboni, B. et al. Optimization of Eugenia punicifolia (Kunth) D. C. leaf extraction using a simplex centroid design focused on extracting phenolics with antioxidant and antiproliferative activities. BMC Chemistry 14, 34 (2020).

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