Food security is relevant due to the uncertain availability of healthy food. Accordingly, it is necessary to know the biological potential of new crops as a food source to meet the basic nutritional needs of a growing population. This study aimed to analyze chemical extractions of the cultivated species Suaeda edulis and its wild relative S. esteroa to determine their biological and nutritional value. For analysis, we collected 25 plants of S. edulis in the chinampas-producing area of Xochimilco, Mexico City, and 25 plants of S. esteroa in Balandra beach, Baja California Sur, Mexico. We quantified total phenols, total flavonoids, and the total antioxidant capacity of free and conjugated fractions by Folin-Ciocalteu, aluminum trichloride, DPPH, and TEAC spectrophotometric methods. S. esteroa reflected a higher content of total phenols, total flavonoids, and total antioxidant capacity (free and conjugated) than the values of S. edulis. We determined 39.94 and 49.64% higher values of total phenol content in S. esteroa than S. edulis, 36 and 40.33% in total flavonoid content, 32.92 and 40.50% in total antioxidant capacity by DPPH, and 34.45 and 48.91% by TEAC for free and conjugated fractions, respectively. We identified 11 phenolic compounds in both halophytes; among them, the free form ferulic acid, gallic acid, and rutin showed high concentrations in S. edulis, whereas quercetin and ferulic acid were more abundant in S. esteroa. The conjugated fraction showed lower concentrations than the free fraction. In conclusion, we found a high biologically active potential of the halophytes studied; this could boost their consumption, which in turn would offer S. edulis and S. esteroa as new sustainable crops to help address food shortages in regions with water scarcity or soil salinity, as well as to counteract chronic degenerative diseases associated with obesity.
Citation: Francyelli Regina Costa-Becheleni, Enrique Troyo-Diéguez, Alan Amado Ruiz-Hernández, Fernando Ayala-Niño, Luis Alejandro Bustamante-Salazar, Alfonso Medel-Narváez, Raúl Octavio Martínez-Rincón, Rosario Maribel Robles-Sánchez. Determination of bioactive compounds and antioxidant capacity of the halophytes Suaeda edulis and Suaeda esteroa (Chenopodiaceae): An option as novel healthy agro-foods[J]. AIMS Agriculture and Food, 2024, 9(3): 716-742. doi: 10.3934/agrfood.2024039
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Food security is relevant due to the uncertain availability of healthy food. Accordingly, it is necessary to know the biological potential of new crops as a food source to meet the basic nutritional needs of a growing population. This study aimed to analyze chemical extractions of the cultivated species Suaeda edulis and its wild relative S. esteroa to determine their biological and nutritional value. For analysis, we collected 25 plants of S. edulis in the chinampas-producing area of Xochimilco, Mexico City, and 25 plants of S. esteroa in Balandra beach, Baja California Sur, Mexico. We quantified total phenols, total flavonoids, and the total antioxidant capacity of free and conjugated fractions by Folin-Ciocalteu, aluminum trichloride, DPPH, and TEAC spectrophotometric methods. S. esteroa reflected a higher content of total phenols, total flavonoids, and total antioxidant capacity (free and conjugated) than the values of S. edulis. We determined 39.94 and 49.64% higher values of total phenol content in S. esteroa than S. edulis, 36 and 40.33% in total flavonoid content, 32.92 and 40.50% in total antioxidant capacity by DPPH, and 34.45 and 48.91% by TEAC for free and conjugated fractions, respectively. We identified 11 phenolic compounds in both halophytes; among them, the free form ferulic acid, gallic acid, and rutin showed high concentrations in S. edulis, whereas quercetin and ferulic acid were more abundant in S. esteroa. The conjugated fraction showed lower concentrations than the free fraction. In conclusion, we found a high biologically active potential of the halophytes studied; this could boost their consumption, which in turn would offer S. edulis and S. esteroa as new sustainable crops to help address food shortages in regions with water scarcity or soil salinity, as well as to counteract chronic degenerative diseases associated with obesity.
A first episode of pneumonia with unknown cause was detected in Wuhan at the end of 2019 and reported to the World Health Organization (WHO) Country Office in China on 31 December 2019 [1]. This was the first case of the novel coronavirus (SARS-CoV-2) that quickly spread all over the world in the past few months. The WHO declared the outbreak a Public Health Emergency of International Concern on 30th January 2020; on 11th March 2020 WHO further determined that COVID-19 could be characterized as a pandemic [1].
Several studies [2]–[4] have concentrated on the biological and epidemiological factors governing COVID-19 transmission, while few others [5] have investigated the potential impact of socio-economic characteristics on governing the extent of COVID-19 diffusion in the population. Societal and economic factors can be of critical importance for accuracy of models of the outbreak because of the economic [6],[7] and health impacts of the drastic measures that have been put in place in an effort to slow the spread of the disease in those same countries (e.g social distancing, quarantine, lockdowns, testing, and reallocation of hospital resources) [8], [9] and worldwide.
In this paper, we take a reverse perspective and analyze how socio-economic determinants pre-dating the pandemic (data taken not later than 2019) relate to the number of reported cases, deaths, and the ratio detahs/cases of COVID-19 via machine learning methods. Our focus is on understanding the connection between epidemiological variables of the COVID-19 pandemic and the (i) level of health care infrastructure, (ii) general health of the population, (iii) economic factors, (iv) demographic structure, (v) environmental health, (vi) societal characteristics, and (vii) religious characteristics of a country. We hypothesize that different countries have different specific socio-economic features and incidence of the disease and therefore the implementation of government measures must be thoughtful and data evidence-driven and heterogeneous across countries and across resources in order to effectively combat COVID-19.
We analyze 32 interpretable models, including (i) regression models with both independent and proximity dependent outcomes; and (ii) variable selection through LASSO. After this step, we build two indices of variable importance for each of the determinants to estimate their overall association with the the number of cases, deaths, and deaths/cases rate. An Absolute Importance Index (AII) and a Signed Importance Index (SII) are constructed. The AII determines the presence of the variable in the top-10 absolute correlation ranking, while the SII takes in consideration the sign of the correlation. By focusing only on those findings that are common to a majority, the findings are less sensitive to the limitations of any single model considered.
Our analysis determines that the socio-economic status of a country follows some sort of Action-Reaction Principle, as it is not only heavily influenced by the pandemic (we did not address this in the study, but it is an established fact in the literature [10]–[12]), but it is also a distinct factor of the pandemic and must be taken into consideration by governments. The level of mobility, the quality of the health care system, the economic status of a country, and the features of a society are associated with the number of cases and deaths due to COVID-19.
Our work suggests that government resources (both in the form of equipment and staff) must not be allocated blindly, and highlights that different countries might benefit from different measures based on the specific country socio-economic status.
The remaining part of the manuscript is organized as follows. Section 2 is dedicated to a brief description of the datasets used, the description of the epidemiological variables that we are considering, and the description of the socio-economic determinants involved in our study. Moreover, it includes a summary of the methods used. More details on this are present in the Supplementary Material (Appendixes S1, S2, S3, S4). Section 3 is dedicated to the results of our analysis, while Section 4 to the discussion of the results, and Section 5 to the conclusions.
Remark. A complete literature review is not feasible, given that the global effort of researchers around the world has produced a massive amount of results on COVID-19. We apologize for all citations that are missing.
This section is dedicated to (i) the description of the datasets used for the input variables, the outcome variables, and the geographical weighting; and (ii) the methods used for the statistical analysis (linear regression, LASSO, and MICE). We refer to the Supporting Information for more details about the Data Sources (S1), technical results about our statistical methods (S2), and the tables of Descriptive Statistics (S3), and Importance indices (S4).
Multiple datasets have been combined for the analysis (See Appendix S1 for the specific information on the data sources, and relative websites). The datasets were then organized in three groups based on their role in the models: (i) the outcome variables Y, namely epidemiology variables, such as the number of cases, the number of deaths, and the ratio between them; (ii) the predictors X which include the socio-economic determinants; and (iii) the weighting matrix A, which includes geographic information.
The total number of reported cases and deaths attributed to COVID-19 as of 2nd May 2020 were obtained from Our World In Data [13] and used as outcome variables of our models.
As the only predictors of our models, 44 SE variables were chosen for our analyses based on their potential explanatory power and to facilitate comparisons with other published works [5]. Data were obtained from publicly available databases [14]–[20] for a total of 199 countries/regions, 32 of which only had data for all 44 variables of interest (see Appendix S1 for more details). Given the fact that the years for which data were available varied by country/region, we chose to use the most recent data available for each country, (oldest being 2010, most recent being 2019). SE factors were divided into 7 categories based upon the common theme to which each of our 44 variables most closely aligned. These categories are similar to some that have been used previously [5]: (i) Capacity of a country to deal with COVID-19 cases (Healthcare Infrastructure); (ii) Statistics indicative of the health of the population of a country (Health Statistics); (iii) Economic situation and tourism/mobility in a country (Economic Health); (iv) Demographic structure of a country, in particular the age structure and the spatial distribution of the population (Demographic Structure); (v) Societal characteristics such as the level of education, the possibility of access to technology, and features of government (Societal Characteristics); (vi) Pollution level and ecological footprint (Environmental Health); and (vii) Religious practices in the population (Religious Characteristics). The division of variables into each of these categories can be seen in Appendix S1.
Latitude and longitude coordinates for capital cities were obtained for each country from the CEPII (Centre d'Études Prospectives et d'Informations Internationales) GeoDist database. Coordinates for 11 countries were not found in the GeoDist database and were obtained from Google.
Using these coordinates, we calculated the pairwise distances between cities using the Spherical Law of Cosines. Given two cities C1, C2 on the surface of the Earth with latitude and longitude coordinates (α1, β1) and (α2, β2), and assuming a constant radius of the Earth R = 6378 kms, we have that the distance between C1 and C2 is given by the following formula
The matrix A has then been computed such that the entries were given by
for i, j = 1,..., 199. Here d(·,·) a normalization factor computed as the average over the distances between every city Ci and every city Cj. We considered the ellipticity of the Earth to have a minor influence on our results and we we considered the spherical approximation appropriate. Our strategy relates to the theoretical work [21].
Multiple countries did not report values for some of the SE determinants, so we decided to do imputation, in order to be able to include all countries (both those with and without any missing information). To perform imputation, we used the R package mice which performs imputation via Multivariate Imputation by Chained Equations (MICE). The method assumes that the probability that a value is missing depends only on observed values and not on unobserved values [22], [23]. We assumed this throughout all our analyses.
The MICE algorithm [22], [23] produces a series of regression models, where each variable with missing data is modeled conditional upon the other variables in the data, and so according to its distribution. In the first step, MICE performs a simple imputation for every missing value in the dataset. The imputations for one of the variables are set back to missing, and this variable is considered as the dependent variable in a regression model with all the other variables used as independent variables, under the assumptions valid in generalized linear models [24]. The missing values for the variable playing the role of dependent variable at this step are then replaced with predictions (imputations) from the regression model. This procedure is then repeated for every variable and in an iterative fashion. At the end of the iteration process, MICE outputs one imputed dataset, and after the process stabilized, the distribution of the parameters governing the imputations is produced. The algorithm is independent on the order in which the variables are imputed. For a summary of the method, we refer to Appendix S2.
We considered 5 different outcome variables: (i) Y1 = # cases; (ii) Y2 = # deaths; (iii) Ỹ1 = # cases/total population; (iv) Ỹ2 = # deaths/total population; and (v) Y0 = # deaths/# cases. To determine how total population impacted each of the outcome variables Ỹ1, Ỹ2, and Y0 (those dependent variables scaled by population size), we ran each of these models with two different sets of explanatory variables: (i) All variables (|X| = 44); and (ii) All but total population (POP) count (|X| = 43).
Although the complete dataset contained 199 countries/regions, missing values resulted in preliminary linear regression models excluding 135 of those countries/regions [25]. As mentioned, we imputed the missing values using the mice package in R [22] and re-ran our models, so as to include all 199 countries. Models for automatic variable selection, such as LASSO [26], [27] were also considered. We used R Studio Version 1.2.5042 and libraries readxl, readr, gdata, mice, glment, caret for the computations.
To measure the importance of the variables across our models, we built two indices. An Absolute Importance Index (AII) and a Signed Importance Index (SII). The AII counts the number of time a variable appears in the top-10 correlated variables based on absolute correlation. On the other side, the SII counts the presence of a variable in the top-10 correlated variables with sign. For example if the variable X appears 12 times in the top-10 highest correlated variables with Y, 10 times positively correlated, then AII = 12, while SII = 8. We computed such indices globally and for each single type of outcome variable. Please refer to Appendix S4 for more details.
The results of the models produced with the imputed dataset are reported below in Table 1. Multiple and adjusted R2 values that describe the fit of each model can be found in Table 1 as well. We will describe the specific results about the importance of the variables grouping them by the 7 classes as in Appendix S1, but by joining Health Infrastructures and Health Statistics in one single subsection for convenience of explanation.
| Outcome Variable | With Pop. Tot. | No Geo. Weight. |
With Geo. Weight. |
||
| Mult. R2 | Adj. R2 | Mult. R2 | Adj. R2 | ||
| # cases (Y1) | Y | 0.8532 | 0.8113 | 0.5626 | 0.4376 |
| # deaths (Y2) | Y | 0.7918 | 0.7323 | 0.6206 | 0.5122 |
| 2*# cases/Pop. Tot. (Ỹ1) | Y | 0.5135 | 0.3745 | 0.5225 | 0.3860 |
| N | 0.5135 | 0.3785 | 0.5205 | 0.3874 | |
| 2*# deaths/Pop. Tot. (Ỹ2) | Y | 0.4238 | 0.2592 | 0.5249 | 0.3892 |
| N | 0.4235 | 0.2636 | 0.5229 | 0.3905 | |
| 2*# deaths/# cases (Y0) | Y | 0.2997 | 0.0996 | 0.4942 | 0.3597 |
| N | 0.2973 | 0.1023 | 0.4891 | 0.3474 | |
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In all the figures, the results are grouped based on the category. Half of the models included weighting with a geographical distance matrix (see Section 2 for more details). The number of times each variable appeared among the top-10 highest correlated variables in these models was tallied with its own sign (sign + if positively correlated, sign - if negatively correlated). The magnitude and direction of each bar represents the signed percentage of this tally. The letters in the figures represent the way the 44 socio-economic variables are divided into categories. A: Health Infrastructure, B: Health Statistics, C: Environmental Health, D: Economic Health, E: Demographic Structure, F: Societal Characteristics, and G: Religious Characteristics. Tables of detailed raw, signed, and weighted tallies can be found in Appendix S4.
The number of physicians, essential health coverage index, and death rate were among the top-10 variables in 25%, 25%, 15.63% of our models, respectively (Panels A,B in Figures 1–6; Appendix S4). The number of physicians correlated positively with Y1, Ỹ1, Y2, and Ỹ2 and negatively with Y0. Access to essential health services consistently correlated positively in our models, appearing in 50% of Y1, Y2, and Y0 models, though completely absent from the top-10 variables in Ỹ1 and Ỹ2 models. Similarly, crude death rate correlated positively in 25–50% of Y1, Y2, and Y0 models, but was never selected among the top-10 variables in Ỹ1 and Ỹ2 models.
Number of nurses and midwives, number of hospital beds, prevalence of diabetes, and crude birth rate consistently correlated negatively in 37.5%, 50%, 21.88%, and 43.75% of our models, respectively (Panels A,B in Figures 1–6; Appendix S4). Number of hospital beds appeared in all categories of models; it was least common in Y1 at 25%, most common in Y2 at 75%, and was in 50% of the models in the remaining categories. Number of nurses and midwives were identified as important in 100% of Ỹ1 models and 50% of Ỹ2 models but were notably absent from all other models. Birth rate appeared among our top-10 variables in 25% of Y1 and Ỹ1 models and 50% of Ỹ2 models. Prevalence of diabetes appeared in 25–75% of all model categories, with a notable absence from Y1 models.
Domestic government health expenditure was the only measure of economic health that was identified among the top-10 variables of all categories of models and consistently correlated positively with the outcome variables in all those models (Panel D in Figures 1–6; Appendix S4). In contrast, employment to population ratio and income distribution consistently correlated negatively with the outcome variables in all models and were identified as important determinants in 43.75% of all our models (Panel D in Figures 1–6; Appendix S4). GDP, trade, and government lending/borrowing correlated inconsistently (different for each model) across the models, with GDP and trade only identified as important variables in models lacking the geographical weighting matrix (Panel D in Figures 1–6; Appendix S4). Unemployment rate was never identified as important in any of the models.
Total population correlated negatively with Y1 and Y2, but became insignificant for Ỹ1, Ỹ2, and Y0 (Panel E in Figures 1–6; Appendix S4). Population density, the proportion of the population over the age of 65, and the proportion of immigrants (people of international stock) consistently correlated positively and were important factors in 28.13%, 25%, and 31.25% of our models, though never in Y0 (Panel E in Figures 1–6; Appendix S4). Although rural population was identified as an important variable in all categories of models, the pattern of correlation was inconsistent, correlating negatively in Y1, Y2, and Y0 models and positively in Ỹ1 and Ỹ2 models (Panel E in Figures 1–6; Appendix S4).
The number of airline passengers per year was nearly always important for Y1 and Y2 models, correlating positively in 100% and 75% of those models, respectively, though it never appeared in Ỹ1, Ỹ2, or Y0 models (Panel D in Figures 1–6; Appendix S4). Although the number of tourist arrivals was consistently positively correlated with 50% of Y1, Y2, and Y0 models, the direction of correlation was inconsistent in Ỹ1 and Ỹ2, despite being identified as a key determinant in each of those model categories (appearing in 75% and 100% of those models, respectively; Panel D in Figures 1–6; Appendix S4). The geographical weighting matrix was always present in models in which tourist arrivals correlated negatively, while the absence of this matrix in the models always coincided with tourist arrivals correlating positively.
Ecological footprint was identified as an important variable in 25% of Y1, Y2, and Y0 models, in which it consistently correlated positively (Panel C in Figures 1–6; Appendix S4). Air pollution never appeared as an important determinant.
The proportion of individuals with access to internet was identified as an important determinant in 28.13% of all our models; in Ỹ1, Ỹ2, and Y0 models, it correlated negatively, whereas in Y2 models it correlated positively (Panel F in Figures 1–6; Appendix S4). Government effectiveness and economic freedom score correlated negatively with Y0 and were identified as important in 75% of those models, but no other models (Panel F in Figures 1–6; Appendix S4). Personal freedom also correlated negatively with Y0 in 50% of those models, though it correlated positively in one Y1 model (Panel F in Figures 1–6; Appendix S4). Human freedom correlated positively in 50% of Y0 models, but negatively in 25% of Ỹ1 and Ỹ2 models (Panel F in Figures 1–6; Appendix S4). The average number of people per household appeared in 25% of Ỹ1 models, in which it correlated positively, but was not important in any of the other models (Panel F in Figures 1–6; Appendix S4). Education level, rule of law, and control of corruption never appeared among the top-10 variables in any of our models.
The percentage of the population identifying as Christian appeared in 40.63% of models and was a consistently positive correlate in all model categories (Panel G in Figures 1–6; Appendix S4). In contrast, the percentage of the population identifying as Buddhist, appeared in 50% of Y1, Y2, and Y0 models and was a consistently negative correlate in those models (Panel G in Figures 1–6; Appendix S4). The remaining religious categories appeared rarely among the most important variables in our models, but when present, most correlated negatively (Panel G in Figures 1–6; Appendix S4).
We divide our discussion into sections based on the category of socio-economic data. The relationships discussed here are all multivariate correlations, namely correlations occurring in models that contain all socio-economic data.
In countries in which the population has greater access to essential healthcare services and in which the government invests more capital into healthcare, the results surprisingly showed an increase in the number of cases, number of deaths, and number of deaths/cases of COVID-19. In contrast, countries that have greater numbers of nurses and midwives and hospital beds per capita and in which diabetes is more prevalent have smaller numbers of cases, deaths, and deaths/cases. Interestingly, the number of physicians correlated positively with the number of cases and number of deaths, but negatively with the number of deaths/cases. Although seemingly contradictory, taken together, these data may provide indications that government healthcare spending needs to be allocated appropriately in order to effectively combat diseases like COVID-19. This explanation is still not satisfactory, as the possible inefficiencies in the healthcare system do not explain why countries with less capital investment in healthcare would not be struggling with the same inefficiencies. A further reason for this surprising result could be that the population of developed countries is more mobile, and some government epidemic prevention measures are not effective, resulting in higher number of cases and deaths. To narrow down this range of possible interpretations, we would need (among the others) more detailed data about resource allocation to hospitals (e.g. whether or not they experienced shortages of critical equipment). In addition to meeting basic space requirements in the form of hospital beds, access to medical personnel, like nurses and midwives, who interact for greater periods of time directly with patients, may facilitate the treatment of and recovery from both chronic conditions like diabetes and acute conditions like COVID-19. This may be a particularly important consideration for developing countries [6], which may have less effective medical infrastructure in place. The analysis seems also to caution against clustering doctors into large, centralized healthcare facilities when suitable care can be provided at home or in less dense facilities.
Employment to population ratio and income distribution, as measured by the GINI index, were identified as important variables in 43.75% of models, and were consistently negatively correlated with number of cases, number of deaths, and number of deaths/cases of COVID-19, both with and without standardization by population. These data suggest that countries in which greater proportions of the population are employed, and where there is less economic disparity within the population, can be expected to feel the effects of COVID-19 less strongly. Indeed, these results appear to be corroborated by the current condition in the United States, a country in which concern about widening economic stratification and discrepancy is frequently discussed and the number of cases and deaths of COVID-19 are continuing to rise rapidly at the time of writing this article (First Trimester of 2021).
Although the roles of GDP and trade were less prominent (appearing in only 12.5% and 6.25% of models, respectively) and were inconsistently correlated with COVID-19 variables, it is important to note that these variables only appeared in models in which there was no weighting by geographical distance. This is interesting given that [5] found that GDP was a strong positive predictor of both COVID-19 cases and deaths. Our results may indicate an influence of geographical clustering of countries with similar economic strength/health, and could potentially be used as an indication of regions in which countries can be expected to exhibit similarities in vulnerability to diseases like COVID-19.
Variables relating to demographic structure consistently played a bigger role in models of number of cases and number of deaths than in number of deaths/cases. Indeed, only a single demographic variable (rural population) appeared in only one out of eight of our models in which the number of deaths/cases was the outcome variable. Thus, although the percentage of the population aged 65+, population density, and the percentage of the population of international origin (immigrants) all positively correlated with the number of cases and number of deaths, they do not appear to have an influence in models of the death rate when standardized by the number of cases of COVID-19. Total population correlated negatively with number of COVID-19 cases and deaths (Y1 and Y2), but it was no longer identified as an important variable when these outcome variables were standardized by population (Ỹ1 and Ỹ2) or in models using deaths/cases (Y0). This may suggest a sub-exponential growth of the number of infections [30].
The degree of mobility, as indicated by the number of airline passengers, correlated positively with number of cases, number of deaths, and number of deaths/cases, though this effect seemed to disappear in models that were standardized by total population size. Although short-term travel restrictions play an important role in reducing the impact of COVID-19, the fact that the number of airline passengers does not appear in models with outcome variables scaled by population size (Ỹ1 and Ỹ2) suggests that increased mobility does not increase COVID-19 cases or deaths to a level disproportionate with the total population of the country. Further, our other variable that directly measures mobility (number of tourist arrivals per year) showed an interesting reversal in correlation structure when weighting by geography was added to our models: tourist arrivals correlated positively in models without the geographical weighting structure, but switched to correlating negatively in all models weighted with geographical data. Interestingly, ecological footprint, which in part could be heavily influenced by domestic mobility (e.g. car travel), appeared in a few models and was consistently positively correlated with cases, deaths, and deaths/cases. Thus, it is clear that the relationship between international travel and COVID-19 is a complicated one, and blanket policies restricting travel may not accurately reflect the impact that such mobility may have on the impact of COVID-19 in national populations.
In general, religion, or lack thereof, plays a very minor role in our models, the exceptions being the percentage of the population identifying as Christian, which consistently correlates positively with all our outcome variables, and the percentage of the population identifying as Buddhist, which consistently correlates negatively with all our outcome variables. This is interesting given the speculation early in the pandemic that religious gatherings could be sources of superspreading events [31].
Societal characteristics consistently increase in importance in models in which the number of deaths are standardized by the number of cases compared to models with number of cases or number of deaths as the outcome variables. Further, the majority of the societal variables that correlated strongly in our models had a net negative correlation. In particular, countries in which a greater proportion of their population have greater economic freedom (freedom to voluntarily acquire and dispose of their property [19]), and to a lesser extent personal freedom (freedom of movement, assembly, religion etc. in addition to safety and security [19]), and in which the government is more effective, tend to have lower numbers of deaths/cases. Further, countries in which internet usage is more widespread tend to see a drop in the number of cases, number of deaths, and number of deaths/cases. In contrast, education level, control of corruption, and rule of law never appeared among the top-10 variables in any of our models. Together, these results may suggest that countries with governments capable and effective at enacting policies for the protection of their citizens, who in their turn have the resources to keep themselves informed and freedom to act in their own best interests, may fare better against COVID-19. Note that the implied association between internet access and a more informed public is somehow speculative, possibly non-obvious in the days of misinformation on social media, and certainly requires further attention on its own. It might be more plausible that countries with high levels of internet access are simply in a better position to have a larger number of people work from home, which may not be an option otherwise.
Our study is static and photographs the situation at 2nd May 2020. Furthermore, looking at data collected at a single point in time does not take in consideration the fact that the pandemic did not begin to spread in every location simultaneously. For example, European countries began to be affected much earlier than the US. Thus, the strong association between investment into healthcare and number of cases may simply be a consequence of the fact that Europe, a region where healthcare investment is consistently high, was one of the earliest regions to be affected. Also, regions that are affected in the early stages of a pandemic may be hardest-hit even with high capital investment simply because, at that point in time, the medical community still lacks the data and expertise necessary to effectively treat patients. Finally, it may also be that countries with more developed health infrastructure are able to test and diagnose more patients and that COVID-19 deaths will then be more likely to be correctly attributed to the disease.
All of these limitations suggest to follow up this analysis with the analysis of the time evolution of the disease, its relationship with socio-economic covariates, and to account for the possibility of time-lag between different countries.
On another note, it has been shown that underreporting can influence the severity of the pandemic [2], [28], [29], [32]. A future work will incorporate estimates of underreporting in our models.
In this paper, we have studied the relationship between socio-economic determinants and the reported number of cases, deaths, and the ratio of deaths/cases in each country during the first months of the COVID-19 pandemic, by means of machine learning methods. We analyzed a total of 32 interpretable models and built two importance indices (AII and SII) for the covariates. Our statistical models included linear regression with independent outcomes and geographically weighted outcomes, and variable selection methods such as LASSO. We analyzed the raw data and MICE-imputed datasets.
Our analysis suggests that governments might need to allocate healthcare resources heterogeneously, with a possible benefit in decentralizing healthcare. This could be a problem for developing countries, where the means are limited. As of May 2nd, 2020, countries with more economic equity among their citizens seemed less hit by COVID-19, possibly indicating the importance of having a minimal baseline assistance across the whole population of a country. The analysis of the demographic structure mildly indicated that the disease grows sub-exponentially in the first months of the diffusion. The reduced degree of mobility across countries, for example the degree to which tourism is constrained, had a positive effect in reducing the number of cases, deaths, and death rate per cases. However, there is an indication that a smart and alternating policy could lead to further containment of the disease. Furthermore, our analysis highlighted the benefit of informing the population for government measures to be more effective.
Together, our results seem to indicate that blanket policies are sub-optimal and government measures related to healthcare and immigration have the potential to both help and damage the population, as, if not appropriately taken, they can lead to an increase or reduced decrease of COVID-19 cases, deaths, and deaths/cases rate.
| [1] | WHO (World Health Organization) (2024) Available from: https://www.who.int/health-topics/obesity#tab = tab_2. |
| [2] |
Čolak E, Pap D (2021) The role of oxidative stress in the development of obesity and obesity-related metabolic disorders. J Med Biochem 40: 1–9. https://doi.org/10.5937/jomb0-24652 doi: 10.5937/jomb0-24652
|
| [3] |
Mahmoud AM (2022) An overview of epigenetics in obesity: The role of lifestyle and therapeutic interventions. J Mol Sci 23: 1341. https://doi.org/10.3390/ijms23031341 doi: 10.3390/ijms23031341
|
| [4] | Ylli D, Sidhu S, Parikh T, et al. (2022) Endocrine changes in obesity. Endotext[Internet]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK279053/. |
| [5] |
Lee CJ, Sears CL, Maruthur N (2020) Gut microbiome and its role in obesity and insulin resistance. Ann N Y Acad Sci 1461: 37–52. https://doi.org/10.1111/nyas.14107 doi: 10.1111/nyas.14107
|
| [6] |
Aziz T, Hussain N, Hameed Z, et al. (2024) Elucidating the role of diet in maintaining gut health to reduce the risk of obesity, cardiovascular and other age-related inflammatory diseases: recent challenges and future recommendations. Gut Microbes 16: 2297864. https://doi.org/10.1080/19490976.2023.2297864 doi: 10.1080/19490976.2023.2297864
|
| [7] |
Alruwaili H, Dehestani B, le Roux CW (2021) Clinical impact of liraglutide as a treatment of obesity. Clin Pharmacol: Adv and Appl 13: 53–60. https://doi.org/10.2147/CPAA.S276085 doi: 10.2147/CPAA.S276085
|
| [8] |
Huang PF, Wang QY, Chen RB, et al. (2024) A new strategy for obesity treatment: Revealing the frontiers of anti-obesity medications. Curr Mol Med 2024: 1–14. https://doi.org/10.2174/0115665240270426231123155924 doi: 10.2174/0115665240270426231123155924
|
| [9] |
Ray A, Stelloh C, Liu Y, et al. (2024) Histone modifications and their contributions to hypertension. Hypertens 81: 229–239. https://doi.org/10.1161/HYPERTENSIONAHA.123.21755 doi: 10.1161/HYPERTENSIONAHA.123.21755
|
| [10] |
Rathod P, Yadav RP (2024) Gut microbiome as therapeutic target for diabesity management: Opportunity for nanonutraceuticals and associated challenges. Drug Delv Transl Res 14: 17–29. https://doi.org/10.1007/s13346-023-01404-w doi: 10.1007/s13346-023-01404-w
|
| [11] |
Ayed K, Nabi L, Akrout R, et al. (2023) Obesity and cancer: focus on leptin. Mol Biol Rep 50: 6177–6189. https://doi.org/10.1007/s11033-023-08525-y11 doi: 10.1007/s11033-023-08525-y11
|
| [12] |
Kumar N, Goel N (2019) Phenolic acids: Natural versatile molecules with promising therapeutic applications. Biotechnol Rep 24: e00370. https://doi.org/10.1016/j.btre.2019.e00370 doi: 10.1016/j.btre.2019.e00370
|
| [13] |
Rudrapal M, Khairnar SJ, Khan J, et al. (2022) Dietary polyphenols and their role in oxidative stress-induced human diseases: Insights into protective effects, antioxidant potentials and mechanism (s) of action. Front Pharmacol 13: 283. https://doi.org/10.3389/fphar.2022.806470 doi: 10.3389/fphar.2022.806470
|
| [14] |
Rodríguez-Pérez C, Segura-Carretero A, del Mar Contreras M (2019) Phenolic compounds as natural and multifunctional anti-obesity agents: A review. Crit Rev Food Sci Nutr 59: 1212–1229. https://doi.org/10.1080/10408398.2017.1399859 doi: 10.1080/10408398.2017.1399859
|
| [15] | Bento C, Gonç alves AC, Jesus F, et al. (2017) Chapter 6—Phenolic compounds: Sources, properties, and applications. In: Porter R, Parker N (Eds.), Bioactive Compounds, 271–299. |
| [16] |
Gaur G, Gä nzle MG (2023) Conversion of (poly) phenolic compounds in food fermentations by lactic acid bacteria: Novel insights into metabolic pathways and functional metabolites. Curr Res Food Sci 6: 100448. https://doi.org/10.1016/j.crfs.2023.100448 doi: 10.1016/j.crfs.2023.100448
|
| [17] |
Dantas SBS, Moraes GKA, Araujo AR, et al. (2023) Phenolic compounds and bioactive extract produced by endophytic fungus Coriolopsis rigida. Nat Prod Res 37: 2037–2042. https://doi.org/10.1080/14786419.2022.2115492 doi: 10.1080/14786419.2022.2115492
|
| [18] |
Cecchi L, Ghizzani G, Bellumori M, et al. (2023) Virgin olive oil by-product valorization: An insight into the phenolic composition of olive seed extracts from three cultivars as sources of bioactive. Molecules 28: 2776. https://doi.org/10.3390/molecules28062776 doi: 10.3390/molecules28062776
|
| [19] |
Baccouri B, Mechi D, Rajhi I, et al. (2023) Tunisian wild olive leaves: Phenolic compounds and antioxidant activity as an important step toward their valorization. Food Anal Meth 16: 436–444. https://doi.org/10.1007/s12161-022-02430-z doi: 10.1007/s12161-022-02430-z
|
| [20] | Lozoya-Pérez NE, Orona-Tamayo D, Paredes-Molina DM, et al. (2024) Chapter 28—Microalgae: A potential opportunity for proteins and bioactive compounds destined for food and health industry. In: Nadathur S, Wanasundara JPD, Scanlin L (Eds.), Sustainable Protein Sources (Second Edition), Advances for a Healthier Tomorrow, Academic Press, 581–597. https://doi.org/10.1016/B978-0-323-91652-3.00018-6 |
| [21] |
Ruiz Hernández AA, Rouzaud Sández O, Frías J, et al. (2022) Optimization of the duration and intensity of UV-A radiation to obtain the highest free phenol content and antioxidant activity in sprouted sorghum (Sorghum bicolor L. Moench). Plants Food Hum Nutr 77: 317–318. https://doi.org/10.1007/s11130-021-00938-z doi: 10.1007/s11130-021-00938-z
|
| [22] |
Ma D, Wang C, Feng J, et al. (2021) Wheat grain phenolics: A review on composition, bioactivity, and influencing factors. J Sci Food Agric 101: 6167–6185. https://doi.org/10.1002/jsfa.11428 doi: 10.1002/jsfa.11428
|
| [23] |
Klimek-Szczykutowicz M, Prokopiuk B, Dziurka K, et al. (2022) The influence of different wavelengths of LED light on the production of glucosinolates and phenolic compounds and the antioxidant potential in in vitro cultures of Nasturtium officinale (watercress). PCTOC 149: 113–122. https://doi.org/10.1007/s11240-021-02148-6 doi: 10.1007/s11240-021-02148-6
|
| [24] |
Hassan MA, Xu T, Tian Y, et al. (2021) Health benefits and phenolic compounds of Moringa oleifera leaves: A comprehensive review. Phytomedicine 93: 153771. https://doi.org/10.1016/j.phymed.2021.153771 doi: 10.1016/j.phymed.2021.153771
|
| [25] | Flowers TJ, Hajibagheri MA, Clipson NJW (1986) Halophytes. Q Rev Biol 61: 313–337. Available from: https://www.journals.uchicago.edu/doi/abs/10.1086/415032. |
| [26] | Flowers TJ, Colmer TD (2008) Salinity tolerance in halophytes. New Phytol 179: 945–963. Available from: https://www.jstor.org/stable/25150520. |
| [27] |
Munns R, Tester M (2008) Mechanisms of salinity tolerance. Annu Rev Plant Biol 59: 651–681. https://doi.org/10.1146/annurev.arplant.59.032607.092911 doi: 10.1146/annurev.arplant.59.032607.092911
|
| [28] |
Lopes M, Sanches-Silva A, Castilho M (2023) Halophytes as source of bioactive phenolic compounds and their potential applications. Crit Rev Food Sci Nutr 63: 1078–1101. https://doi.org/10.1080/10408398.2021.1959295 doi: 10.1080/10408398.2021.1959295
|
| [29] |
Bose J, Rodrigo-Moreno A, Shabala S (2014) ROS homeostasis in halophytes in the context of salinity stress tolerance. J. Exp. Bot 65: 1241–1257. https://doi.org/10.1093/jxb/ert430 doi: 10.1093/jxb/ert430
|
| [30] | Tanveer M, Ahmed HAI (2020) ROS signaling in modulating salinity stress tolerance in plants. In: Hasanuzzaman M, Tanveer M (Eds.), Salt and Drought Stress Tolerance in Plants. Signaling and communication in plants, Springer, Cham, 229–314. https://doi.org/10.1007/978-3-030-40277-8_11 |
| [31] |
Hameed A, Ahmed MZ, Hussain T, et al. (2021) Effects of salinity stress on chloroplast structure and function. Cells 10: 2023. https://doi.org/10.3390/cells10082023 doi: 10.3390/cells10082023
|
| [32] |
Hamed KB, Ellouzi H, Talbi OZ, et al. (2013) Physiological response of halophytes to multiple stresses. Funct Plant Biol 40: 883–896. https://doi.org/10.1071/FP13074 doi: 10.1071/FP13074
|
| [33] |
Hasanuzzaman M, Raihan MRH, Masud AAC, et al. (2021) Regulation of reactive oxygen species and antioxidant defense in plants under salinity. Int J Mol Sci 22: 9326. https://doi.org/10.3390/ijms22179326 doi: 10.3390/ijms22179326
|
| [34] | Kumar S, Abedin MM, Singh AK, et al. (2020) Role of phenolic compounds in plant-defensive mechanisms. In: Lone R, Shuab R, Kamili A (Eds.), Plant Phenolics Sustainable Agriculture, Springer, Singapore, 1: 517–532. https://doi.org/10.1007/978-981-15-4890-1_22 |
| [35] |
Baysal I, Ekizoglu M, Ertas A, et al. (2021) Identification of phenolic compounds by LC-MS/MS and evaluation of bioactive properties of two edible halophytes: Limonium effusum and L. sinuatum. Molecules 26: 4040. https://doi.org/10.3390/molecules26134040 doi: 10.3390/molecules26134040
|
| [36] |
Pungin A, Lartseva L, Loskutnikova V, et al. (2023) Effect of salinity stress on phenolic compounds and antioxidant activity in halophytes Spergularia marina (L.) Griseb. and Glaux maritima L. cultured in vitro. Plants 12: 1905. https://doi.org/10.3390/plants12091905 doi: 10.3390/plants12091905
|
| [37] |
Custodio L, Garcia-Caparros P, Pereira CG, et al. (2022) Halophyte plants as potential sources of anticancer agents: A comprehensive review. Pharmaceutics 14: 2406. https://doi.org/10.3390/pharmaceutics14112406 doi: 10.3390/pharmaceutics14112406
|
| [38] |
Pereira CG, Locatelli M, Innosa D, et al. (2019) Unravelling the potential of the medicinal halophyte Eryngium maritimum L.: In vitro inhibition of diabetes-related enzymes, antioxidant potential, polyphenolic profile and mineral composition. S African J Bot 120: 204–212. https://doi.org/10.1016/j.sajb.2018.06.013 doi: 10.1016/j.sajb.2018.06.013
|
| [39] |
Cho JY, Park KH, Hwang DY, et al. (2015) Antihypertensive effects of Artemisia scoparia Waldst in spontaneously hypertensive rats and identification of angiotensin I converting enzyme inhibitors. Molecules 20: 19789–19804. https://doi.org/10.3390/molecules201119657 doi: 10.3390/molecules201119657
|
| [40] | García E (1988) Modificaciones al sistema de clasificación climática de Kö ppen. Instituto de Geografía, UNAM, México, D.F. |
| [41] |
González Carmona E, Torres Valladares CI (2014) La sustentabilidad agrícola de las chinampas en el valle de México: caso Xochimilco. Rev Mex de Agron 34: 698–709. https://doi.org/10.22004/ag.econ.173283 doi: 10.22004/ag.econ.173283
|
| [42] | Troyo Diéguez E, Mercado Mancera G, Cruz Falcón A, et al. (2014) Análisis de la sequía y desertificación mediante índices de aridez y estimación de la brecha hídrica en Baja California Sur, noroeste de México. Invest Geogr 85: 66–81. https://doi.org/10.14350/rig.32404 |
| [43] | Guevara Olivar BK, Ortega Escobar HM, Ríos Gómez R, et al. (2015) Morfología y geoquímica de suelos de Xochimilco. Terra Latinoamericana 33: 263–273. Available from: http://www.scielo.org.mx/scielo.php?script = sci_arttext & pid = S0187-57792015000400263. |
| [44] | Ferren Jr WR, Whitmore SA (1983) Suaeda esteroa (Chenopodiaceae), a new species from estuaries of Southern California and Baja California. J Calif Bot Soc Madroño 30: 181–190. Available from: https://www.jstor.org/stable/41424425. |
| [45] |
Salazar-Lopez NJ, Loarca-Piña G, Campos-Vega R, et al. (2016) The extrusion process as an alternative for improving the biological potential of sorghum bran: phenolic compounds and antiradical and anti-inflammatory capacity. Evidence-Based Complementary Altern Med 2016: 8387975. https://doi.org/10.1155/2016/8387975 doi: 10.1155/2016/8387975
|
| [46] |
Quan TH, Benjakul S, Sae-leaw T, et al. (2019) Protein–polyphenol conjugates: Antioxidant property, functionalities, and their applications. Trends Food Sci Technol 91: 507–517. https://doi.org/10.1016/j.tifs.2019.07.049 doi: 10.1016/j.tifs.2019.07.049
|
| [47] |
Adom KK, Liu RH (2002) Antioxidant activity of grains. J Agric Food Chem 50: 6182–6187. https://doi.org/10.1021/jf0205099 doi: 10.1021/jf0205099
|
| [48] |
Valenzuela-González M, Rouzaud-Sández O, Ledesma-Osuna AI, et al. (2022) Bioaccessibility of phenolic compounds, antioxidant activity, and consumer acceptability of heat-treated quinoa cookies. Food Sci Technol 42: e43421. https://doi.org/10.1590/fst.43421 doi: 10.1590/fst.43421
|
| [49] |
Ruiz-Hernández AA, Cárdenas-López JL, Cortez-Rocha MO, et al. (2021) Optimization of germination of white sorghum by response surface methodology for preparing porridges with biological potential. CyTA J Food 19: 49–55. https://doi.org/10.1080/19476337.2020.1853814 doi: 10.1080/19476337.2020.1853814
|
| [50] |
Salazar-López NJ, Astiacarán-García H, González-Aguilar GA, et al. (2017) Ferulic acid on glusose dysregulation, dyslipdemia, and inflammation in diet-induced obese rats: An integrated study. Nutrients 9: 675. https://doi.org/10.3390/nu9070675 doi: 10.3390/nu9070675
|
| [51] |
Lee KM, Kalyani D, Tiwari MK, et al. (2012) Enhanced enzymatic hydrolysis of rice straw by removal of phenolic compounds using a novel laccase from yeast Yarrowia lipolytica. Bioresour. Technol 123: 636–645. https://doi.org/10.1016/j.biortech.2012.07.066 doi: 10.1016/j.biortech.2012.07.066
|
| [52] | Social Science Statistics (2024) The Kolmogorov-Smirnov Test of Normality. Available from: https://www.socscistatistics.com/tests/kolmogorov/default.aspx. |
| [53] | Statistics Kingdom (2024) Levenexs Homocedasticity Test of Similarity of Variances. Available from: https://www.statskingdom.com/230var_levenes.html. |
| [54] | Hammer Ø, Harper, DAT, Ryan, PD (2001) PAST: Paleontological statistics software package for education and data analysis. Electron Paleontol 4: 9. Available from: https://www.nhm.uio.no/english/research/resources/past/. |
| [55] |
Agati G, Azzarello E, Pollastri S, et al. (2012) Flavonoids as antioxidants in plants: location and functional significance. Plant Sci 196: 67–76. https://doi.org/10.1016/j.plantsci.2012.07.014 doi: 10.1016/j.plantsci.2012.07.014
|
| [56] | Singh A, Roychoudhury A (2024) Role of phenolic acids and flavonoids in the mitigation of environmental stress in plants. In: Singh A, Roychoudhury A (Eds.), Biology and Biotechnology of Environmental Stress Tolerance in Plants, Apple Academica Press, 1st Edition, 22. Available from: https://www.taylorfrancis.com/chapters/edit/10.1201/9781003346173-11/role-phenolic-acids-flavonoids-mitigation-environmental-stress-plants-ankur-singh-aryadeep-roychoudhury. |
| [57] |
Zhang Y, Li Y, Ren X, et al. (2023) The positive correlation of antioxidant activity and prebiotic effect about oat phenolic compounds. Food Chem 402: 134231. https://doi.org/10.1016/j.foodchem.2022.134231 doi: 10.1016/j.foodchem.2022.134231
|
| [58] |
Kumar K, Debnath P, Singh S, et al. (2023) An overview of plant phenolics and their involvement in abiotic stress tolerance. Stresses 3: 570–585. https://doi.org/10.3390/stresses3030040 doi: 10.3390/stresses3030040
|
| [59] |
Adhikary S, Dasgupta N (2023) Role of secondary metabolites in plant homeostasis during biotic stress. Biocatal Agric Biotechnol 50: 102712. https://doi.org/10.1016/j.bcab.2023.102712 doi: 10.1016/j.bcab.2023.102712
|
| [60] |
Parada J, Aguilera JM (2007) Food microstructure affects the bioavailability of several nutrients. J Food Sci 72: R21–R32. https://doi.org/10.1111/j.1750-3841.2007.00274.x doi: 10.1111/j.1750-3841.2007.00274.x
|
| [61] |
Gao Y, Ma S, Wang M, et al. (2017) Characterization of free, conjugated, and bound phenolic acids in seven commonly consumed vegetables. Molecules 22: 1878. https://doi.org/10.3390/molecules22111878 doi: 10.3390/molecules22111878
|
| [62] |
Bautista I, Boscaiu M, Lidón A, et al. (2016) Environmentally induced changes in antioxidant phenolic compounds levels in wild plants. Acta Physiol Plant 38: 9. https://doi.org/10.1007/s11738-015-2025-2 doi: 10.1007/s11738-015-2025-2
|
| [63] |
Aloisi I, Parrotta L, Ruiz KB, et al. (2016) New insight into quinoa seed quality under salinity: Changes in proteomic and amino acid profiles, phenolic content, and antioxidant activity of protein extracts. Front Plant Sci 7: 656. https://doi.org/10.3389/fpls.2016.00656 doi: 10.3389/fpls.2016.00656
|
| [64] |
Muñoz-Bernal Ó A, Torres-Aguirre GA, Núñez-Gastélum JA, et al. (2017) Nuevo acercamiento a la interacción del reactivo de Folin-Ciocalteu con azúcares durante la cuantificación de polifenoles totals. TIP 20: 23–28. https://doi.org/10.1016/j.recqb.2017.04.003 doi: 10.1016/j.recqb.2017.04.003
|
| [65] |
Everette JD, Bryant QM, Green AM, et al. (2010) Thorough study of reactivity of various compound classes toward the Folin-Ciocalteu reagent. J Agric Food Chem 58: 8139–8144. https://doi.org/10.1021/jf1005935 doi: 10.1021/jf1005935
|
| [66] |
Liu W, Feng Y, Yu S, et al. (2021) The flavonoid biosynthesis network in plants. Int J Mol Sci 22: 12824. https://doi.org/10.3390/ijms222312824 doi: 10.3390/ijms222312824
|
| [67] |
Agati G, Brunetti C, Fini A, et al. (2020) Are flavonoids effective antioxidants in plants? Twenty years of our investigation. Antioxidants 9: 1098. https://doi.org/10.3390/antiox9111098 doi: 10.3390/antiox9111098
|
| [68] |
Dias MC, Pinto DC, Silva AM (2021) Plant flavonoids: Chemical characteristics and biological activity. Molecules 26: 5377. https://doi.org/10.3390/molecules26175377 doi: 10.3390/molecules26175377
|
| [69] | Ahmad R, Hussain S, Anjum MA, et al. (2019) Oxidative stress and antioxidant defense mechanisms in plants under salt stress. In: Hasanuzzaman M, Hakeem K, Nahar K, et al. (Eds.), Plant Abiotic Stress Tolerance: Agronomic, molecular, and biotechnological approaches, Springer, Cham, 191–205. https://doi.org/10.1007/978-3-030-06118-0_8 |
| [70] |
Marchiosi R, dos Santos WD, et al. (2020) Biosynthesis and metabolic actions of simple phenolic acids in plants. Phytochem Rev 19: 865–906. https://doi.org/10.1007/s11101-020-09689-2 doi: 10.1007/s11101-020-09689-2
|
| [71] |
Giuffrè AM (2019) Bergamot (Citrus bergamia, Risso): The effects of cultivar and harvest date on functional properties of juice and cloudy juice. Antioxidants 8: 221. https://doi.org/10.3390/antiox8070221 doi: 10.3390/antiox8070221
|
| [72] |
Deng GF, Shen C, Xu XR, et al. (2012) Potential of fruit wastes as natural resources of bioactive compounds. Int J Mol Sci 13: 8308–8323. https://doi.org/10.3390/ijms13078308 doi: 10.3390/ijms13078308
|
| [73] |
Christodoulou MC, Orellana Palacios JC, Hesami G, et al. (2022) Spectrophotometric methods for measurement of antioxidant activity in food and pharmaceuticals. Antioxidants 11: 2213. https://doi.org/10.3390/antiox11112213 doi: 10.3390/antiox11112213
|
| [74] | Bakhshi S, Abbaspour H, Saeidisar S (2018) Study of phytochemical changes, enzymatic and antioxidant activity of two halophyte plants: Salsola dendroides Pall and Limonium reniforme (Girard) Lincz in different seasons. J Plant Environ Physiol 46: 79–92. |
| [75] |
Castañeda-Loaiza V, Oliveira M, Santos T, et al. (2020) Wild vs cultivated halophytes: Nutritional and functional differences. Food Chem 333: 127536. https://doi.org/10.1016/j.foodchem.2020.127536 doi: 10.1016/j.foodchem.2020.127536
|
| [76] |
Skroza D, Šimat V, Vrdoljak L, et al. (2022) Investigation of antioxidant synergisms and antagonisms among phenolic acids in the model matrices using FRAP and ORAC methods. Antioxidants 11: 1784. https://doi.org/10.3390/antiox11091784 doi: 10.3390/antiox11091784
|
| [77] | Kutchan TM, Gershenzon J, Moller BL, et al. (2015) Natural products. In: Buchanan BB, Gruissem W, Jones RL (Eds.), Biochemistry and Molecular Biology of Plants, Wiley, New York, 1132–1205. |
| [78] |
Boerjan W, Ralph J, Baucher M (2003) Lignin biosynthesis. Annu Rev Plant Biol 54: 519–546. https://doi.org/10.1146/annurev.arplants.54.031902.134938 doi: 10.1146/annurev.arplants.54.031902.134938
|
| [79] |
Vanholme R, Demedts B, Morreel K, et al. (2010) Lignin biosynthesis and structure. Plant Physiol 153: 895–905. https://doi.org/10.1104/pp.110.155119 doi: 10.1104/pp.110.155119
|
| [80] |
Wildermuth MC (2006) Variations on a theme: synthesis and modification of plant benzoic acids. Curr Opin Plant Biol 9: 288–296. https://doi.org/10.1016/j.pbi.2006.03.006 doi: 10.1016/j.pbi.2006.03.006
|
| [81] |
Widhalm JR, Dudareva N (2015) A familiar ring to it: biosynthesis of plant benzoic acids. Mol Plant 8: 83–97. https://doi.org/10.1016/j.molp.2014.12.001 doi: 10.1016/j.molp.2014.12.001
|
| [82] | Heller W, Forkmann G (2017) Biosynthesis of flavonoids. In: Harborne JB (Ed.), The Flavonoids Advances in Research Since 1986, Routledge, 1st Edition, 37. Available from: https://www.taylorfrancis.com/chapters/edit/10.1201/9780203736692-11/biosynthesis-flavonoids-werner-heller-gert-forkmann. |
| [83] |
Mandal SM, Chakraborty D, Dey S (2010) Phenolic acids act as signaling molecules in plant-microbe symbioses. Plant Signaling Behav 5: 359–368. https://doi.org/10.4161/psb.5.4.10871 doi: 10.4161/psb.5.4.10871
|
| [84] |
Padayachee A, Netzel G, Netzel M, et al. (2012) Binding of polyphenols to plant cell wall analogues-Part 2: Phenolic acids. Food Chem 135: 2287–2292. https://doi.org/10.1016/j.foodchem.2012.07.004 doi: 10.1016/j.foodchem.2012.07.004
|
| [85] |
Hajlaoui H, Arraouadi S, Mighri H, et al. (2022) HPLC-MS profiling, antioxidant, antimicrobial, antidiabetic, and cytotoxicity activities of Arthrocnemum indicum (Willd.) Moq. extracts. Plants 11: 232. https://doi.org/10.3390/plants11020232 doi: 10.3390/plants11020232
|
| [86] |
Jdey A, Falleh H, Jannet SB, et al. (2017) Phytochemical investigation and antioxidant, antibacterial and anti-tyrosinase performances of six medicinal halophytes. S African J Bot 112: 508–514. https://doi.org/10.1016/j.sajb.2017.05.016 doi: 10.1016/j.sajb.2017.05.016
|
| [87] |
Sánchez-Gavilán I, Ramírez Chueca E, de la Fuente García V (2021) Bioactive compounds in Sarcocornia and Arthrocnemum, two wild halophilic genera from the Iberian Peninsula. Plants 10: 2218. https://doi.org/10.3390/plants10102218 doi: 10.3390/plants10102218
|
| [88] |
De Oliveira DM, Finger‐Teixeira A, Rodrigues Mota T, et al. (2014) Ferulic acid: A key component in grass lignocellulose recalcitrance to hydrolysis. Plant Biotechnol J 13: 1224–1232. https://doi.org/10.1111/pbi.12292 doi: 10.1111/pbi.12292
|
| [89] |
Buanafina MMDO, Morris P (2022) The impact of cell wall feruloylation on plant growth, responses to environmental stress, plant pathogens and cell wall degradability. Agronomy 12: 1847. https://doi.org/10.3390/agronomy12081847 doi: 10.3390/agronomy12081847
|
| [90] |
Deng Y, Feng Z, Yuan F, et al. (2015) Identification and functional analysis of the autofluorescent substance in Limonium bicolor salt glands. Plant Physiol Biochem 97: 20–27. https://doi.org/10.1016/j.plaphy.2015.09.007 doi: 10.1016/j.plaphy.2015.09.007
|
| [91] | Fitzpatrick LR, Woldemariam T (2017) Small-molecule drugs for the treatment of inflammatory bowel disease. In: Comprehensive Medicinal Chemistry III, Elsevier Inc., 495–510. |
| [92] |
Zhang X, Ran W, Li X, et al. (2022) Exogenous application of gallic acid induces the direct defense of tea plant against Ectropis obliqua caterpillars. Front Plant Sci 13: 833489. https://doi.org/10.3389/fpls.2022.833489 doi: 10.3389/fpls.2022.833489
|
| [93] | Waśkiewicz A, Muzolf-Panek M, Goliński P (2013) Phenolic content changes in plants under salt stress. In: Ahmad P, Azooz M, Prasad M (Eds.), Ecophysiology and Responses of Plants under Salt Stress, Springer, New York, 283–314. https://doi.org/10.1007/978-1-4614-4747-4_11 |
| [94] |
Singh P, Arif Y, Bajguz A, et al. (2021) The role of quercetin in plants. Plant Physiol Biochem 166: 10–19. https://doi.org/10.1016/j.plaphy.2021.05.023 doi: 10.1016/j.plaphy.2021.05.023
|
| [95] |
Agati G, Brunetti C, Di Ferdinando M, et al. (2013) Functional roles of flavonoids in photoprotection: new evidence, lessons from the past. Plant Physiol Biochem 72: 35–45. https://doi.org/10.1016/j.plaphy.2013.03.014 doi: 10.1016/j.plaphy.2013.03.014
|
| [96] |
Cesco S, Mimmo T, Tonon G, et al. (2012) Plant-borne flavonoids released into the rhizosphere: impact on soil bioactivities related to plant nutrition. A review. Biol Fertil Soils 48: 123–149. https://doi.org/10.1007/s00374-011-0653-2 doi: 10.1007/s00374-011-0653-2
|
| [97] |
Ng JLP, Hassan S, Truong TT (2015) Flavonoids and auxin transport inhibitors rescue symbiotic nodulation in the Medicago truncatula cytokinin perception mutant cre1. Plant Cell 27: 2210–2226. https://doi.org/10.1105/tpc.15.00231 doi: 10.1105/tpc.15.00231
|
| [98] |
Peer W, Blakeslee J, Yang H, et al. (2011) Seven things we think we know about auxin transport. Mol Plant 4: 487–504. https://doi.org/10.1093/mp/ssr034 doi: 10.1093/mp/ssr034
|
| [99] |
Agati G, Matteini P, Goti A, et al. (2007) Chloroplast‐located flavonoids can scavenge singlet oxygen. New Phytol 174: 77–89. https://doi.org/10.1111/j.1469-8137.2007.01986.x doi: 10.1111/j.1469-8137.2007.01986.x
|
| [100] |
Chagas MDSS, Behrens MD, Moragas-Tellis CJ, et al. (2022) Flavonols and flavones as potential anti-inflammatory, antioxidant, and antibaterial compounds. Oxid Med Cell Longevity 2022: 9966750. https://doi.org/10.1155/2022/9966750 doi: 10.1155/2022/9966750
|
| [101] | de Souza M M, da Silva B, Badiale-Furlong E, et al. (2021) Phenolic acid profile, quercetin content, and antioxidant activity of six Brazilian halophytes. In: Grigore MN (Ed.), Handbook of Halophytes: From Molecules to Ecosystems towards Biosaline Agriculture, Springer, Cham, 1395–1419. https://doi.org/10.1007/978-3-030-57635-6_44 |
| [102] |
Shomali A, Das S, Arif N, et al. (2022) Diverse physiological roles of flavonoids in plant environmental stress responses and tolerance. Plants 11: 3158. https://doi.org/10.3390/plants11223158 doi: 10.3390/plants11223158
|
| [103] | Frutos MJ, Rincón-Frutos L, Valero-Cases E (2019) Chapter 2.14—Rutin. In: Nonvitamin and Nonmineral Nutritional Supplements, Academic Press, 111–117. https://doi.org/10.1016/B978-0-12-812491-8.00015-1 |
| [104] |
Hodaei M, Rahimmalek M, Arzani A, et al. (2018) The effect of water stress on phytochemical accumulation, bioactive compounds and expression of key genes involved in flavonoid biosynthesis in Chrysanthemum morifolium L. Ind Crops Prod 120: 295–304. https://doi.org/10.1016/j.indcrop.2018.04.073 doi: 10.1016/j.indcrop.2018.04.073
|
| [105] |
Sánchez-Gavilán I, Ramírez E, de la Fuente V, et al. (2021) Bioactive compounds in Salicornia patula Duval-Jouve: A mediterranean edible euhalophyte. Foods 10: 410. https://doi.org/10.3390/foods10020410 doi: 10.3390/foods10020410
|
| [106] |
Chekroun-Bechlaghem N, Belyagoubi-Benhammou N, Belyagoubi L, et al. (2019) Phytochemical analysis and antioxidant activity of Tamarix africana, Arthrocnemum macrostachyum and Suaeda fruticosa, three halophyte species from Algeria. Plant Biosyst-Int J Deal Asp Plant Biol 153: 843–852. https://doi.org/10.1080/11263504.2018.1555191 doi: 10.1080/11263504.2018.1555191
|
| [107] |
Zeb A (2020) Concept, mechanism, and applications of phenolic antioxidants in foods. J Food Biochem 44: e13394. https://doi.org/10.1111/jfbc.13394 doi: 10.1111/jfbc.13394
|
| [108] |
Zengin G, Aumeeruddy-Elalfi Z, Mollica A, et al. (2018) In vitro and in silico perspectives on biological and phytochemical profile of three halophyte species—A source of innovative phytopharmaceuticals from nature. Phytomedicine 38: 35–44. https://doi.org/10.1016/j.phymed.2017.10.017 doi: 10.1016/j.phymed.2017.10.017
|
| [109] |
Vilela C, Santos SA, Coelho D, et al. (2014) Screening of lipophilic and phenolic extractives from different morphological parts of Halimione portulacoides. Ind Crops Prod 52: 373–379. https://doi.org/10.1016/j.indcrop.2013.11.002 doi: 10.1016/j.indcrop.2013.11.002
|
| [110] | Stanković M, Jakovljević D (2021) Phytochemical diversity of halophytes. In: Grigore MN (Ed.), Handbook of Halophytes, Springer, Cham, 2089–2114. https://doi.org/10.1007/978-3-030-57635-6_125 |
| [111] |
Chiavaroli A, Sinan KI, Zengin G, et al. (2020) Identification of chemical profiles and biological properties of Rhizophora racemosa G. Mey. extracts obtained by different methods and solvents. Antioxidants 9: 533. https://doi.org/10.3390/antiox9060533 doi: 10.3390/antiox9060533
|
| [112] |
Cho JY, Yang X, Park KH, et al. (2013) Isolation and identification of antioxidative compounds and their activities from Suaeda japonica. Food Sci Biotechnol 22: 1547–1557. https://doi.org/10.1007/s10068-013-0250-2 doi: 10.1007/s10068-013-0250-2
|
| [113] | Pratyusha S (2022) Phenolic compounds in the plant development and defense: An overview. In: Plant stress physiology-perspectives in agriculture, 125–140. |
| [114] |
Sun R, Sun XF, Wang SQ, et al. (2002) Ester and ether linkages between hydroxycinnamic acids and lignins from wheat, rice, rye, and barley straws, maize stems, and fast-growing poplar wood. Ind Crops Prod 15: 179–188. https://doi.org/10.1016/S0926-6690(01)00112-1 doi: 10.1016/S0926-6690(01)00112-1
|
| [115] |
Ueda M, Sawai Y, Shibazaki Y, et al. (1998) Leaf-opening substance in the nyctinastic plant, Albizzia julibrissin Durazz. Biosci Biotechnol Biochem 62: 2133–2137. https://doi.org/10.1271/bbb.62.2133 doi: 10.1271/bbb.62.2133
|
| [116] |
Li ZH, Wang Q, Ruan X, et al. (2010) Phenolics and plant allelopathy. Molecules 15: 8933–8952. https://doi.org/10.3390/molecules15128933 doi: 10.3390/molecules15128933
|
| [117] |
Begum AA, Leibovitch S, Migner P, et al. (2001) Specific flavonoids induced nod gene expression and pre‐activated nod genes of Rhizobium leguminosarum increased pea (Pisum sativum L.) and lentil (Lens culinaris L.) nodulation in controlled growth chamber environments. J Exp Bot 52: 1537–1543. https://doi.org/10.1093/jexbot/52.360.1537 doi: 10.1093/jexbot/52.360.1537
|
| [118] |
Chaudhry UK, Ö ztürk ZN, Gö kç e AF (2024) Assessment of salt and drought stress on the biochemical and molecular functioning of onion cultivars. Mol Biol Rep 51: 37. https://doi.org/10.1007/s11033-023-08923-2 doi: 10.1007/s11033-023-08923-2
|
| [119] |
Ri I, Pak S, Pak U, et al. (2024) How does UV-B radiation influence the photosynthesis and secondary metabolism of Schisandra chinensis leaves?. Ind Crops Prod 208: 117832. https://doi.org/10.1016/j.indcrop.2023.117832 doi: 10.1016/j.indcrop.2023.117832
|
| [120] |
Scalzini G, Vernhet A, Carillo S, et al. (2024) Cell wall polysaccharides, phenolic extractability, and mechanical properties of Aleatico winegrapes dehydrated under sun or in controlled conditions. Food Hydrocoll 149: 109605. https://doi.org/10.1016/j.foodhyd.2023.109605 doi: 10.1016/j.foodhyd.2023.109605
|
| [121] |
Panth N, Park SH, Kim HJ, et al. (2016) Protective effect of Salicornia europaea extracts on high salt intake-induced vascular dysfunction and hypertension. Int J Mol Sci 17: 1176. https://doi.org/10.3390/ijms17071176 doi: 10.3390/ijms17071176
|
| [122] |
Nikalje GC, Srivastava AK, Pandey GK, et al. (2018) Halophytes in biosaline agriculture: Mechanism, utilization, and value addition. Land Degrad Dev 29: 1081–1095. https://doi.org/10.1002/ldr.2819 doi: 10.1002/ldr.2819
|
| [123] |
Ventura Y, Sagi M (2013) Halophyte crop cultivation: The case for Salicornia and Sarcocornia. Environ Exp Bot 92: 144–153. https://doi.org/10.1016/j.envexpbot.2012.07.010 doi: 10.1016/j.envexpbot.2012.07.010
|
| [124] |
Ventura Y, Wuddineh WA, Myrzabayeva M, et al. (2011) Effect of seawater concentration on the productivity and nutritional value of annual Salicornia and perennial Sarcocornia halophytes as leafy vegetable crops. Sci Hortic 128: 189–196. https://doi.org/10.1016/j.scienta.2011.02.001 doi: 10.1016/j.scienta.2011.02.001
|
| [125] |
Ghasemzadeh A, Ghasemzadeh N (2011) Flavonoids and phenolic acids: Role and biochemical activity in plants and human. J Med Plants Res 5: 6697–6703. https://doi.org/10.5897/JMPR11.1404 doi: 10.5897/JMPR11.1404
|
| [126] |
Ozcan T, Akpinar-Bayizit A, Yilmaz-Ersan L, et al. (2014) Phenolics in human health. Int J Chem Eng Appl 5: 5. https://doi.org/10.7763/IJCEA.2014.V5.416 doi: 10.7763/IJCEA.2014.V5.416
|
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Figures(7) / Tables(4)
Francyelli Regina Costa-Becheleni, Enrique Troyo-Diéguez, Alan Amado Ruiz-Hernández, Fernando Ayala-Niño, Luis Alejandro Bustamante-Salazar, Alfonso Medel-Narváez, Raúl Octavio Martínez-Rincón, Rosario Maribel Robles-Sánchez. Determination of bioactive compounds and antioxidant capacity of the halophytes Suaeda edulis and Suaeda esteroa (Chenopodiaceae): An option as novel healthy agro-foods[J]. AIMS Agriculture and Food, 2024, 9(3): 716-742. doi: 10.3934/agrfood.2024039
| Outcome Variable | With Pop. Tot. | No Geo. Weight. |
With Geo. Weight. |
||
| Mult. R2 | Adj. R2 | Mult. R2 | Adj. R2 | ||
| # cases (Y1) | Y | 0.8532 | 0.8113 | 0.5626 | 0.4376 |
| # deaths (Y2) | Y | 0.7918 | 0.7323 | 0.6206 | 0.5122 |
| 2*# cases/Pop. Tot. (Ỹ1) | Y | 0.5135 | 0.3745 | 0.5225 | 0.3860 |
| N | 0.5135 | 0.3785 | 0.5205 | 0.3874 | |
| 2*# deaths/Pop. Tot. (Ỹ2) | Y | 0.4238 | 0.2592 | 0.5249 | 0.3892 |
| N | 0.4235 | 0.2636 | 0.5229 | 0.3905 | |
| 2*# deaths/# cases (Y0) | Y | 0.2997 | 0.0996 | 0.4942 | 0.3597 |
| N | 0.2973 | 0.1023 | 0.4891 | 0.3474 | |
DownLoad:
CSV
| Outcome Variable | With Pop. Tot. | No Geo. Weight. |
With Geo. Weight. |
||
| Mult. R2 | Adj. R2 | Mult. R2 | Adj. R2 | ||
| # cases (Y1) | Y | 0.8532 | 0.8113 | 0.5626 | 0.4376 |
| # deaths (Y2) | Y | 0.7918 | 0.7323 | 0.6206 | 0.5122 |
| 2*# cases/Pop. Tot. (Ỹ1) | Y | 0.5135 | 0.3745 | 0.5225 | 0.3860 |
| N | 0.5135 | 0.3785 | 0.5205 | 0.3874 | |
| 2*# deaths/Pop. Tot. (Ỹ2) | Y | 0.4238 | 0.2592 | 0.5249 | 0.3892 |
| N | 0.4235 | 0.2636 | 0.5229 | 0.3905 | |
| 2*# deaths/# cases (Y0) | Y | 0.2997 | 0.0996 | 0.4942 | 0.3597 |
| N | 0.2973 | 0.1023 | 0.4891 | 0.3474 | |
