Background: Sedentariness is a substantial risk for many chronic diseases. We aimed to investigate the correlation of sedentary behavior and its indicators with low back pain (LBP) among adults and children. Methods: Original articles published up to April 28, 2020, using PubMed, Embase, Web of Science and Scopus were evaluated. Odds ratio  was considered the overall effect size for desired associations. Results: We reviewed 49 English articles with analytical observational study design, of which, 27 studies with cross sectional/survey design were retained in the meta-analysis. Among adults, sedentary lifestyle was a considerable risk factor for LBP ; prolonged sitting time  and driving time  were the significant risk factors. Sedentary behavior was associated with LBP in office workers (OR=1.23). Moreover, excess weight  and smoking  were associated with LBP. Among children, sedentary lifestyle was a remarkable risk factor for LBP ; prolonged TV watching  and computer/mobile using and console playing time  were significant risk factors for LBP. Consumption of coffee, however, has yield conflicting results to be considered as a risk factor. Moreover, the researches on the correlation between sedentariness and high-intensity LBP are scarce and inconclusive. Conclusion: Sedentary behavior, whether in work or leisure time, associates with a moderate increase in the risk of LBP in adults, children and adolescents. LBP prevalence increases with aging, and the LBP in childhood associates with the corresponding figure in adulthood.6 It is shown that musculoskeletal symptoms in the lower back are correlated with other body segments, including the neck, upper back, and shoulders.7Low back pain (LBP) is a paramount public health concern contributing to self-perceived disability and a high economic burden worldwide.8 The health concerns associated with sedentariness are not merely attributable to lack of movement, but also to other simulations such as leisure or work screen time activities, including computer and internet use, TV (television) watching, cell phone use, and playing videogames.9 Besides, sedentary behavior is related with all-cause mortality concomitant with overweight and obesity, diabetes, and cardiovascular diseases.11 The association of sedentary behavior with musculoskeletal conditions such as LBP has been widely investigated among the population of workers and non-workers.12Sedentary behaviors, on the other hand, are defined as activities with low energy expenditure, performed in rest positions. Sedentary behavior is a predictor of metabolic risk independent of physical inactivity.13 In addition, the previous reviews did not mention any association between sitting time itself and LBP in leisure time or at work.16 However, a mixture of whole-body vibration, awkward postures, and prolonged sitting increased the risk of LBP.14 On the other hand, a study among 136 teaching staff reported that physical inactivity was related to LBP; but tobacco use and level of alcohol intake did not have such association with LBP.17 In a research among 665 blue-collar workers, a longer duration of sitting periods at work was beneficial for LBP.18 The difference in study design, measurement methods, and participants  with different sedentary tasks may contribute to inconsistency for the correlation of sitting time and LBP in previous research.18With respect to sitting time, the findings regarding the association between sitting periods and LBP are inconsistent. One study among 704 participants demonstrated no independent association of sitting time in work time or the whole day with LBP. In this study, the body mass index moderated the mentioned association.19 Furthermore, in a cross-sectional study among 1221 school adolescents, playing video games (≥2 hours/day) and watching television (≥12 hours/week) were proposed as independent risk factors of LBP.20Excessive consumption of coffee and cigarette smoking were associated with an elevated likelihood of recurring LBP among 609 Polish residents. In this study hyperlipidemia, type 2 diabetes, and hypertension, were significantly associated with an increased likelihood of chronic LBP as well.Given the controversies in different articles, in this study, we investigated the relationship of different indicators of sedentary behavior and inactivity  with LBP, whether in leisure time or work time. We aimed to synthesize the available data to quantify the abovementioned associations to address inconsistencies in previous research. A brief systematic review has been presented in the case of a lack of required data for meta-analysis. In addition, we addressed the mentioned association among children and adolescents with a particular focus.We performed a comprehensive search through electronic databases, including PubMed, Embase, Web of Science and Scopus for records published up to April 28, 2020. Based on a PEO framework  for the eligibility of the research question, we combined the indicators or equivalents of sedentary behavior on the one hand and the equivalents of LBP, on the other hand, for building the search strings. To facilitate the process of screening, we refined the results via the following filters wherever those were available in the search engines: article, journals, English language, full text, human studies. All the analytical observational studies  in which the association between sedentary behavior and LBP was investigated, were favorable to be contained in our review. We did not include the experimental studies in which the effects of behavior intervention or experiment on LBP were studied.Studies with individuals with a specific medical condition such as scoliosis or renal failure who may spend most of their time for sedentary activities were excluded. Age range and type of occupation were not considered as limiting factors, i.e., children and adults with sedentary behavior and LBP were considered to be the subjects of our review.Factors contributing to sedentary behavior, including sitting time, screen time, smoking, consumption of coffee, and body mass index, were considered to be the individuals’ exposures.The onset or recurrence of nonspecific or mechanical LBP measured via different methods was the desired outcome in our study. We excluded the studies in which sciatica or any kind of radicular pain was investigated.Two independent reviewers (S.B.M and R.R) screened the relevant records using Endnote software (version 18) after removing duplicates. Thereafter, additional letters, books, review or conference papers, non-English language, and unavailable full texts that were not excluded in refining results in the search engines were excluded. Then, we reviewed the full texts of remained records entirely at the next step. We excluded the articles with topics, study design, or participants irrelevant to our review . Any dis21 One score to each item was given by each reviewer separately, if the criteria were fulfilled. For each paper, a mean STROBE scores ≥16.5, in the range 11 to 16.5 and lower than 11 were considered as high, moderate and low with respect to study quality, respectively.22 The agreement coefficient between researcher’s scores was more than 0.5.Two of the researchers (S.B.M and B.V) performed the study quality assessment independently. The STROBE scale, which consists of 22 items , was used for this purpose.2) were used to explore the heterogeneity of the included articles. The random-effects model with DerSimonian and Liard method23 was used when data accumulated from studies, differed in ways that would have impacted on the results , otherwise the fixed effect model with inverse variance method was conducted. Then, the effect of each study on the pooled OR was assessed using sensitivity analysis. We performed subgroup analyses to evaluate the source of heterogeneity based on the following possible variables; type of sedentary behaviors and occupation type. The Begg’s and Egger’s tests were performed to explore publication bias. P value < 0.05 from both tests indicated remarkable publication bias. All analyses were conducted in the Stata, version 11.2 .The desired effect size was considered as an odds ratio with 95% confidence interval . Cochran’s Q and inconsistency index . No evidence of publication bias was noted .The forest plot for the association between sedentary behaviors and LBP among children and adolescents is shown in P <0.001; I2 = 84.8%, P < 0.001). No evidence of publication bias was noted .The forest plot for the correlation of sedentary behaviors and LBP among the adult population is shown in P = 0.003; I2 = 6.6%, P = 0.37), computer/mobile using and console playing time  were significant risk factors for LBP   .P = 0.03; I2 = 85.5%, P < 0.001), and driving time  were the significant risk factors for LBP   .P = 0.02; I2 = 90.3%, P < 0.001) and smoking  were the significant risk factors for LBP among adult population. In children, excess wight  was associated with LBP as well.As shown in P value of Egger’s test = 0.41, and P value of Begg’s tests = 0.68).No evidence of publication bias for BMI was found . Therefore, we conducted the Trim and Fill method to explore the effect of publication bias on the meta-analysis results. However, no significant change in the pooled OR was noted.We found publication bias for smoking (2 = 81%).As shown in 45 study .We found no significant changes between the before-after sensitivity pooled OR for the association between sedentary lifestyle and LBP among children and adolescents. However, results showed a remarkable effect between before-after sensitivity pooled OR for the correlation between the sedentary lifestyle and LBP among adult population after excluding Zhang et al27 study and Sribastav et al42 study .Also, results showed significant changes between the before-after sensitivity pooled OR for the association between smoking and LBP among the adult population after excluding Mattila et alBesides, no remarkable changes between the before-after sensitivity pooled OR for the association between the sedentary lifestyle and LBP among healthcare workers and office workers were noted.50 Bending postures but not sedentary leisure time itself have been proposed to be associated with new LBP in nurses.54 However, three studies stated no association between sedentary habits and LBP.62Sedentary behavior at work with non-neutral posture correlates with LBP among workers.1 Among a population of truck drivers, the only factor correlated to LBP was the number of working hours.5 Also, the daily number of studying hours (>5 hours) precipitated the LBP in young adults.57 On the other hand, sitting time was not considered a risk factor for LBP in 3 studies.60Sitting behavior was associated with chronic LBP and functional disfunction among 70 call center employees.51 Similar to this finding, a cohort study by Croft et al reported that watching television > 3 hours/day did not enhanced the risk of recurrent LBP in the UK general population.52 Similarly, in a cohort study among Finnish children, the sedentary class boys  did not presented with increased risk for reporting LBP or consultation for LBP.66 More conflicting results have been reported in Shehab et al study in which the LBP correlated with female gender and TV watching time in children and adolescents.53LBP was reported more in school children playing videogames >2 hours/day and not for television watchers in the Gunzburg et al study.65 A retrospective study revealed that sedentary work was associated with LBP in both genders after a 24 year period.68 Also, in Shiri et al study, lifestyle including abdominal obesity and smoking increased the risk of LBP. Reduced risk of LBP was obtained via walking and cycling to work (OR = 0.75).67In Hussain et al. study, TV watching time in women was associated with greater LBP disability. The authors suggested that targeting the time spent TV watching would be effective in reducing LBP disability in adults at the level of community.63 The duration of sitting time both in work and leisure time was associated with LBP intensity in another study.56 Such a relationship was investigated in Ye et al study among 417 office workers. In contrast, in this study computer use ≥ 8 hours/day was not associated with high intensity LBP.64 In the Korshøj et al study, the sitting pattern was not correlated with the intensity of LBP.13 Moreover, in Hussain et al. cohort study on 5058 individuals, no significant associations between < 2.5 hours/week physical activity and ≥ 2 hours/day TV watching, with LBP intensity at follow-up were reported.65 Thus, further research is necessary to better elucidate the effect of sedentariness on the risk of high-intensity LBP.Regarding the intensity of LBP, Gupta et al showed a notable association between total sitting time and high LBP intensity among 201 participants (OR = 1.43).19 Also, a survey of 134 postmenopausal women reported a significant association of drinking coffee (yes/no) with LBP (OR = 3.1).32 However, in the Aggarwal study, regular or occasional coffee intake was not associated with LBP among undergraduate students.62 The absence of association was found in the Ganesan et al study as well.57 Abovementioned studies were all cross-sectional in their design. The effect of coffee on back pain may be dose-dependent or through indirect mechanisms such as via affecting bone health.32 To better clarify this issue, further exploration is necessary, with a standard measurement of coffee/caffeine intake, especially in longitudinal research.In a survey, Citko et al showed that coffee drinking, 6 cups per day or more, increased the risk of non-specific LBP recurrence by 16 times compared to smaller amounts in medical personnel.Our study explored the association of sedentary behavior and LBP. The results demonstrate the role of sedentary behavior as a risk factor for the increased incidence of LBP, both in adults and children .12 However, given that more articles have been published in recent years, we were able to obtain the pooled OR for the abovementioned association for adults and children separately. In addition, in the previous review, only prolonged sitting was considered as sedentary behavior, whereas we conducted subgroup meta-analyses according to the type of sedentary behaviors and occupation type.A similar systematic review to ours, published in 2009, was performed on 15 observational studies up to 2006 and revealed that there was no correlation between sedentary behavior both in work or leisure time with LBP.69 One study showed that children spend 51.4% of their working time in sedentary lifestyle. These common behaviors may be established in childhood and track through later life.70 Sedentary behavior is linked to various musculoskeletal pain conditions.71The time spent in sedentary lifestyle has become a significant health concern. The sedentary behavior prevalence is high, even in developed countries.72 Another study revealed that smokers have a higher incidence of LBP compared to nonsmokers; these associations were fairly modest (OR = 1.32 for former and OR = 1.31 for the current smokers). Of note, the association between current smoking and LBP was more remarkable in adolescents than in adults (OR = 1.82 vs. 1.16).73 Similarly, we found that increased body mass index in adults and children and smoking in adults, are risk factors for LBP , in whom the sedentary behavior has been investigated. These finding suggest that smoking and body mass index interact with sitting and LBP.13 In addition, to avoid heterogeneity, we identified two main occupation categories as healthcare workers and office workers among the included studies. We found that sedentary behavior is a risk for LBP in office workers (OR = 1.23). In previous research, prolonged sitting and computer use were contributed to LBP in office workers.48Despite the controversies observed in included articles in our systematic review, the pooled effects sizes obtained from meta-analysis of other studies revealed that prolonged sitting time and prolonged driving time are significant risk factors of LBP among adults . However, prolonged screen time and standing time were not associated with LBP in adults. Also, among children, prolonged TV watching (OR = 1.23), and computer/mobile using or console playing time (OR = 1.63) were associated with LBP. A meta-analysis study indicated that excess weight is a risk factor for LBP in both genders.19 Specifically, prolonged sitting is contributed to decreased postural change, as well as muscle strength and disk degenerations.13 Obesity or overweight causes overload on the spinal tissues and contributes to disk herniation and LBP. On the other hand, obesity is associated with other disorders such as diabetes and hyperlipidemia that are also correlated to LBP by different mechanisms.74 Smoking can alter the blood supply of vertebral disks via the processes of vasoconstriction and atherosclerosis. Impaired perfusion of vertebral structures leads to degenerative changes and LBP. Besides, smoking is a risk factor of osteoporosis or is a behavior seen only the people with massive physical works; thus, it has direct and indirect effects on the LBP.73 Moreover, coffee consumption is proposed to be associated with flushing magnesium from the body and increased painful contractions of paraspinal muscles.75As for underlying pathways, decreased level of water supply to the vertebral disc, which in turn leads to degenerative changes and disk herniations, reduced strength and muscular power, and developing hyperlordosis are some proposed pathophysiological mechanisms for sedentary behavior contributing to LBP.The data heterogeneity of included studies in our review can be explained in part by variations in study designs, study population, sample sizes, occupation type, gender, race, and age range. However, beyond those, some factors seem to be more important, as follows.36 In the Ben Ayed et al study, however, participants were asked about discomfort and pain in the low back area during the prior month.20 Some authors, though, defined recurrent LBP as pain episodes of at least three times in the last 12 months and chronic LBP as the pain persisted for at least 12 weeks based on the Nordic Musculoskeletal Questionnaire.19 Many studies, however, did not differentiate chronic LBP from acute LBP.First, the definition of LBP and its measurement scales were considerably different in the studies. For instance, experiencing LBP during the current week for at least 48 hours via the Roland-Morris Disability Questionnaire was measured in the Inoue et al. study.28 some others have turned their attention to the screen time or a combination of both.49 In some other studies, sedentary habits were not categorized into any different types.68 In addition, the common measurement tool for sedentary behavior is subjective self-reported questionnaires, which, in turn, are prone to information bias from participants. However, a few studies used objective-based tools such as an accelerometer or textile pressure mat to estimate the sitting time.63 Thus, to make accurate estimations, we performed subgroup analyses for specified sedentary behavior in the papers, both for adults and children separately. For future research, focusing on objective-based measurement of sedentary behaviors is highly suggested.Second, sedentary behavior has diverse definitions and types in various studies. While some authors explored the association of sitting time merely with LBP,48 Thus, the variety in the combination of these factors in different participants seems to be accounted for the data heterogeneity.The third is that LBP, as a complex multifactorial disease, is affected by psychological conditions and the tasks performed in non-sitting positions at work or leisure time. Therefore, just a part of the variation in LBP is because of sedentary-related risk factors.We applied different statistical methods to obtain the desired associations for adults and children separately, as the risk factors of LBP may be different in these age groups. We found new data and demonstrated significant but moderate associations between different sedentary behaviors and LBP. Regarding the large number of studies conducted in this field , the results can be well generalized to different communities.76 As LBP is a complex disorder, health education to reduce the prevalence or occurrence of LBP should be address the risk factors as much as possible including sedentary behavior.A recent meta-analysis evaluating the lifestyle interventions to reduce sedentary behavior among five categories of population with a clinical condition  demonstrated that after multicomponent interventions, individuals with different medical conditions successfully reduced their sedentary behavior (by 64 minutes/day). The interventions consisted of the use of technologies, social facilitation, motivational counselling and self-monitoring.In brief, according to our meta-analysis, sufficient evidence exists from recent studies that indicate the association of different types of sedentary behavior with the occurrence or recurrence of LBP both in adults and children. Given the increasing trend of sedentary behavior worldwide, especially in the era of the COVID-19 pandemic, meticulous and robust preventive strategies are suggested to be applied to avoid the establishment of sedentariness early in childhood and to prevent its’ musculoskeletal consequences such as LBP.We want to thank our colleagues in Isfahan University of medical Sciences who helped us working on this project.This study was funded and supported by Isfahan University of Medical Sciences, Isfahan, Iran.None to declare.The protocol of the current review has been qualified in the Isfahan University of Medical Sciences, Isfahan, Iran (code: 199298) and has been approved in the National regulatory ethics committee (IR.MUI.MED.REC.1399.507). The study protocol and its details have been registered in the international prospective register of systematic reviews, PROSPERO with identification code: CRD42020187175.SBM contributed to the conception of the work, data search, screening of records, study quality assessment, data extraction, manuscript preparation, manuscript revision, final approval of the manuscript, and agreed to be accountable for all aspects of the work. RR contributed to the screening of records, study quality assessment, data extraction, statistical analysis, interpretation of data, manuscript preparation, manuscript revision, final approval of the manuscript, and agreed to be accountable for all aspects of the work. BV contributed to study quality assessment, manuscript preparation, manuscript revision, final approval of the manuscript, and agreed to be accountable for all aspects of the work. RK contributed to the conception of the work, manuscript preparation, manuscript revision, final approval of the manuscript, and agreed to be accountable for all aspects of the work. All authors approved the final version of manuscript and took the responsibility for all aspects of the work.


PI of the predicted stN by GEP, ANN and ANFIS for training are 0.0416, 0.1423, and 0.1016, respectively, and for lgN these values are 0.1169, 0.2990 and 0.1542, respectively. Corresponding OF values are 0.2300, 0.1200, and 0.090 for stN, and 0.1000, 0.2700, and 0.1500 for lgN. The superiority of the GEP method to the other techniques can be seen from the fact that the GEP technique provides suitable connections based on practical experimental work and does not rely on prior solutions. It is concluded that the GEP model can be used to predict the bearing capacity of circular CFST columns to avoid any laborious and time-consuming experimental work. It is also recommended that further research should be performed on the data to develop a prediction equation using other techniques such as Random Forest Regression and Multi Expression Program.The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop a prediction Multiphysics model for the circular CFST column by using the Artificial Neural Network (ANN), the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Gene Expression Program (GEP). The database for this study contains 1667 datapoints in which 702 are short CFST columns and 965 are long CFST columns. The input parameters are the geometric dimensions of the structural elements of the column and the mechanical properties of materials. The target parameters are the bearing capacity of columns, which determines their life cycle. A Multiphysics model was developed, and various statistical checks were applied using the three artificial intelligence techniques mentioned above. Parametric and sensitivity analyses were also performed on both short and long GEP models. The overall performance of the GEP model was better than the ANN and ANFIS models, and the prediction values of the GEP model were near actual values. The  Concrete filled steel tube (CFST) is a composite construction element. CFST columns are advantageous due to greater seismic resistance and load bearing capacity, lesser size utilization, good aesthetics, and high fire resistance . The comL/D ≤ 4 (for circular columns) or L/B ≤ 4 (for rectangular columns), where L is the length of the specimens, and D and B represent the outer diameter of the circular section the width of the rectangular sections respectively, slender columns having L/B > 4 or L/D > 4 [Numerous researchers have conducted experimental studies to check the effect of different parameters on the axial capacity of circular short and long CFST columns . These p L/D > 4 .c′f) of 67 MPa and 90 MPa and steel tube yield stress (yf) of 420 MPa and 440 MPa, respectively. The recently released AISC 360-16 [c′f of 69 MPa and 100 MPa and yf is limited to 525 MPa and 690 MPa, respectively. The Eurocode [c′f and yf up to 50 MPa and 460 MPa, respectively. In comparison, Liew and Xiong [c′f and yf up to 113 MPa and 762 MPa, respectively, in CFST columns. Mursi and Uy [yf up to 761 MPa and 853 MPa, respectively.CFST columns have been used for operation in extreme conditions under high axial loads. High loads require greater dimensions of the CFST cross-section, high strength steel tube and concrete. The dimensions and strength of the material are limited in practice, and these empirically determined limitations are included in building codes. Each code differently interprets the effect of confinement on the overall bearing capacity of CFST columns. Chinese code  and JapaC 360-16  is appliEurocode  permits nd Xiong  extendednd Xiong  use fc′ i and Uy  and SakiDifferent studies recommended various methods for the estimation of the bearing capacity of CFST columns . ResearcOther studies  proposedFurthermore, the soft computing algorithms solve problems by training the available data set to obtain results, which are then validated via validation set data . HoweverD: diameter of the tube, t: thickness of the tube, L: length of the tube, L/D: length to diameter ratio, te: eccentricity at the top face or loading face, be: eccentricity at the bottom face, yf: yield strength of the tube and cf: compressive strength of the infilled concrete. The schematic layout showing the input variables used to predict the capacity of CFST columns has been provided in In addition, experimental research is costly and requires abundant resources and time to carry out an accurate strength analysis. A slight mistake in computing the quantities and casting process may cause a malicious impact on the strength. Besides, the machine learning algorithm only requires an initial data set to efficiently predict the desired property. In this research, an effort has been made to address the limitation of the provision of the standard code by developing a GEP based empirical equation for short and slender CFST beam columns considering several input variables, i.e., st/NlgN), eight inputs were selected, while in the hidden layer the input parameters  were multiplied by a suitable weight factor for connection. A threshold value at every node (jθ) is added to the weighted input values after their summation. The resultant input (Ij) is passed through the linear transfer function, which is called the transfer phase. The various activation transfer function (AFs) usually used in ANNs are the linear sigmoid, the stepped hyperbolic tangent and the logistic, among others [Artificial Neural Networks (ANNs) are computer algorithms that anticipate and classify the issues concerned with the effective processing of data . As its g others . An actig others . Activatg others , the trag others , swish ag others , and Mulg others  etc.st jN or lg jN) is obtained as the resulting output, and the input of a PE is basically the output of the previous PE. For the hidden and output layer, every neuron utilizes the Logistic function (Equation (1)) as an activated function [In this research, the transfer functions used for the modelling of ANN models are TRANSIG and PURELIN. On the one hand, these transfer functions are capable of effectively increasing neurons in each layer and in each transfer function to improve the statistical indices of the training dataset. However, on the other hand, they also decrease the accuracy of the testing and validation datasets ,52. Dorofunction . MoreoveTo achieve an output with the least minimum error, the best combination of weights is achieved by adjusting the weight to the set rules at the time when the information from the input layer is passed by the ANN in the training stage. Another training set is used to validate the trained model. The method and implementation of ANN modeling is discussed in greater depth elsewhere and is beyond the scope of this review ,56,57.An attractive computation intelligence modelling tool, adaptive neuro-fuzzy inference scheme (ANFIS), blends the learning capabilities of ANNs with the reasoning capability of fuzzy logic. ANFIS has a better prediction potential and is a better alternative for computing nonlinear complex problems with greater precision . With si(a)data collection,(b)ANFIS growth,(c)variables selection,(d)training and testing,(e)resultsto express values in another way. In addition, the schematic layout of the ANFIS model for eight input variables  is shown in The ANFIS technique is divided into five steps:D is A1) and (t is B1) THEN,Statement 1: IF (D is A2) and (t is B2) THEN,nf denotes the fuzzy outputs  for the fuzzy inputs , according to the fuzzy statement, iA and iB denote the fuzzy sets, and ip, iq, and ir denote the arrangement elements determined in the training cycle.Statement 2: IF  and µBk-2(t) differentiate the method of applying any fuzzy membership function. Equation (8) gives the µAk(D) for a bell-shaped membership functionka, kb and kc are the factors affecting this membership function.This layer is also called the fuzzification layer. The adaptive Pes provide outputs in Equations (6) and (7), which describe the fuzzy membership functions of the input model parameters and the original fuzzy rule foundation.This layer’s output is the preset rules’ firing power for a given input pattern. The nodes in the second layer are constant and perform simple multiplication, with the output’s parameters mentioned below (Equation (9)),Following the pattern of the second layer, the third layer also has fixed nodes for normalizing the firing strength of the previous layer. Equation (10) represents the output:In this layer, considering the first order Sugeno model, nodes are adaptive. Their outputs are represented as products of normalized firing intensity and first-order polynomial, with the first order Sugeno model taken into consideration. As a result, the output is given by (Equation (11)):In this layer, a fixed node summits the weighted magnitude of rules achieved from the prior layer, yielding Equation (12) as the model’s output.ka, kb, and kc, also known as premise parameters, are linked to input membership functions. Similarly, the three adaptable parameters kp, kq, and kr, also known as consequent parameters, are analogous to first-order polynomials and are found in the fourth sheet [It is worth noting that only the first and fourth layers of the ANFIS architecture are adaptive. In the first layer, the three adaptable parameters th sheet .(a)Head consisting of function or terminal symbols(b)Tail containing only the terminal symbols.The gene expression programming approach, which is founded on Darwin’s evolution theory and Mendel’s genetic theory, is the most intellectually appealing computational knowledge algorithm ,61 ThereThe complexity of each parameter is represented by head size and the number of genes that control the number of sub-Ets.The chromosomes are then expressed by Ets that come in a range of shapes and sizes. Then, in line with their percentages, the principal genetic operators of crossover, mutation, transposition, and recombination  are performed on the chromosomes . Figure When the stopping condition (the maximum number of generations or a satisfactory solution) is reached, the whole process is finished . If the It follows from the above review that the variety of existing models does not allow rational predicting of the ultimate axial capacity of uniaxially loaded CFST columns. The article aimed to develop a prediction Multiphysics model for the circular CFST column by using Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Gene Expression Program (GEP).L), tube thickness (t) and tube diameter (D), and eccentricities at end supports . The material properties of steel and concrete include the yield stress (yf) and compressive strength (cf) of concrete. The concrete compressive strength obtained from the experimental tests collected from the literature was based on both available cylinder and cube specimens. Cube strength was converted to cylinder strength through related conversion factors. Furthermore, cylinder strength was used in the design equations [A robust database is needed to successfully apply the machine learning algorithms . Thai et al.  collectequations  to avoidL/D ratio is witness to the difference between short and long CFST columns. The reader is advised to note that these ranges surpass the design codes currently in use. As a result, this database can be used to build an ANN, ANFIS and GEP model with enhanced prediction capabilities that are more inclusive than codal provisions. It should be remembered that the magnitude eccentricity is a part of the affecting parameters in both classes. Therefore, the developed models can be confidently used for both axial and moment capacity of circular CFST columns. Skewness and kurtosis are related to the distribution of data. If the larger portion of the data for a particular variable is to the left of the mean, then this shows positive skewness (right tailed). Furthermore, if most of the data is to the right of mean value, then this shows negative skew (left tailed), and skewness is zero for perfectly symmetrical distribution . At the same time, the kurtosis indicates the heaviness of the tail related to the normal distribution. The leptokurtic or positive kurtosis dictates that the data is higher than the normal distribution, whereas the platykurtic or negative kurtosis reveals that the data is flatter than the normal distribution. The values of kurtosis and skewness for each input and output is also provided in For each class, descriptive statistics like distribution shape (kurtosis and skewness), central tendency (mean and median), dispersion of data (Standard deviations) and data extremities (maximum and minimum) for different geometric and material characteristics are provided in stN and lgN were found to be dependent on the following factors (Equation (14)):The specification of the significantly affecting input parameters is the first step in designing the appropriate model. The D is the diameter of the tube, t is the thickness of the tube, L is the length of the tube, L/D is the ratio between length and diameter of the tube, te and be is eccentricity at top and bottom face, yf is the yield strength of tube, and cf is the compressive strength of the tube.Here, D, t, L, L/D, te, be, yf, cf), and the output layer had stN and lgN for ANN. After using the Levenberg-Marquardt algorithm and choosing random data division, the number of hidden neurons was set to ten. In addition, the network form was chosen as feed-forward back-propagation. To achieve a better output at the required number of hidden layers, trial and error methods should be used [Both ANN and ANFIS simulations were performed in the MATLAB R2020b environment using the neural network and fuzzy logic toolbox, respectively. The 702 experimental records of short CFST columns were randomly divided into 70% training (495 datapoints) and 30% testing (207 datapoints), and similarly for second class, i.e., long CFST columns, out of a total of 965 experimental records, 70% (676 datapoints) and 30% (289 datapoints) were accumulated in the training and testing set, respectively . The tra be used . Table 2.ANFIS provides only a single output; unlike ANNs, the outputs were handled separately using the same set of input parameters in both ANN and ANFIS models. The sub-cluster FIS was first generated because the database contained a huge number of data points, in which subtractive clustering with a hybrid optimization technique (least square and back-propagation technique) was used to train the FIS by building a triangular membership function (trimf) . FurtherTable 2GeneXproTools version 5.0 was used to implement the GEP algorithm ,75. FromFurthermore, multiple evolved models are strongly influenced by head size and genes. The error in the formulated model is reduced by the higher values of these setting parameters, which results in a higher value of the coefficient of regression. stN and lgN prediction  were measured using five standard statistical metrics, including correlation coefficient (R), determination coefficient (R2), root mean square error (RMSE), mean absolute error (MAE), relative squared error (RSE), mean absolute percent error (MAPE) and relative root mean square error (RRMSE) in the training testing sets [PI) has been calculated as another metric, ruled primarily by RRMSE and R [Statistical performance for the ing sets ,78,79. ISE and R . Equatioith predicted and expected outcomes, respectively, n is the total number of experiments. To determine the relative correlation between models and experimental outputs  is used, as shown in Equation (22) [In a range of machine learning techniques, due to unnecessary data training, the models appear to overfit  and leadion (22) ,85:(22)OT and V (subscripts) correspond to training and testing set points, and n represents the total number of records. The better-predicted formula must have a lower OBF value , as the consequence of R, RRMSE and proportional percentage of dataset records are considered. Eight different suitable parameter combinations were executed, and the least OF was chosen in this study.Here The regression plots between actual and predicted bearing capacities for short and long CFST columns of ANN, ANFIS and GEP models are clearly presented in R2) for all the proposed models is greater than 90% in both training and testing stages following the trend: R2(ANN) > R2(GEP) > R2(ANFIS), reflecting the shortcoming of neural and fuzzy arrangement in the projected ANFIS model. The mean correlation coefficient (R) in the projected models for stN is also maximum for ANN (0.9986) tracked by GEP (0.9922) and ANFIS (0.9874). For a stronger correlation, the R-value will be higher  for an acceptable model [stN, the R2 for ANN model is highest, i.e., 99.73% and 99.72% for training and testing data, respectively, and 98.20% and 98.74% for the GEP model, respectively.Moreover, the coefficient of determination  and (24). Based on the total number of records, the projected formulae are in close agreement with standard limits for an ideal model and can be confidently and reliably used for the prediction of bearing capacity of short and long circular CFST columns [The two GEP based empirical formulae for future prediction of bearing capacity of short and long circular CFST columns are derived using the GEP algorithm. The sub-ETs’ links to these formulae for  columns ,89,90. is lower than testingRMSE (193.20), satisfying the stated condition. Similarly, these values for lgN are (196.22) and (325.12), respectively. The testingRSE of ANN models for stN and lgN is also minimal and nearly equals zero, i.e., (0.00274) and (0.0086), respectively. The details of the statistical analysis of all the proposed models for stN and lgN in both stages (training and testing), are provided in It can be seen from R exceeding 0.90, lower error statistics were recorded. Unlike ANN and GEP, the magnitude of errors  is higher. The percent error histogram and distribution of MAPE is shown in stN and lgN, respectively, have percent error less than 10%.The ANFIS model also delivers a good result based on performance criteria. Along with high values of testingMAE and testingRMSE of ANFIS models is (55.6)% and (71.7)% greater than that of ANN models in the case of stN and (60.8)% and (63.2)% greater in the case of lgN, respectively. Like the ANN model, the testingRSE for ANFIS models is also near to 0. Thus, based on the above facts, the prediction of bearing capacity of short and long CFST columns can also be obtained through these reliable and accurate models.In comparison with ANN models, it gives more high-error values. However, as presented in R or R2 and the magnitude of error statistics. However, as shown in stN and lgN have percent error below 10%, which are less than the ANFIS model. In the GEP model, the RMSE and RSE values for the testing set are lower than the ANFIS model by 22.6% and 36% for stN, respectively, and 44.6% and 69.3% lower for lgN, respectively.The GEP model provides better results than the ANFIS model but is worse than the ANN model based on PI and OF are less than 0.2 . The PI and OF values of GEP models for the stN and lgN fall within the prescribed limit and show an outstanding performance. However, the PI in the training and testing stage of the ANFIS model for lgN are equal to 0.2990 and 0.2652. Its OF value is also equal to 0.2700. In the ANN model for stN, the PI in the testing stage is 0.361, which is considerably higher than the prescribed limit. Consequently, the OF for the model is 0.2300. Thus, the performance of the ANN and ANFIS model based on PI and OF is ambiguous and marked as satisfactory, although the ANFIS model for short and ANN model for long CFST columns perform well. Lewis [stN, while all other models including lgN-ANN falls in the “acceptable prediction” category.The literature recommended an RRMSE of between 0 and 0.1 for an excellent model, and for a good model between 0.1 and 0.2. As shown in l. Lewis  suggeste0%<MAPE) ,95. In astN and lgN tests utilizing the corresponding GEP models is significantly quicker considering the time needed by the traditional test technique [stN and lgN.The GEP suggested models for forecasting bearing capacity surpasses the other two models since this method provides simplified expressions, presented as Equations (23) and (24). With these formulas, the overall time needed for both the echnique . Thus, tk’/k) moving through the origin must reach one. The results of performance indicators (m and n) must not be above 0.1. The criterion of third external testing presented in this research was given by [Rm > 0.5 and in this research, this is fulfilled by both GEP models. Likewise, the square correlation coefficient amongst predicted and actual data (R02), and amongst experimental and actual data (R0′2) must be nearer to 1 [Furthermore, several other external testing criteria for the GEP model were suggested in the literature and are presented in given by , which irer to 1 . It can SA) on the GEP models utilizing Equations (24) and (25):Sensitivity analysis shows how an output of a proposed model is sensitive to a certain change in its input parameters. It ranks parameters by capturing the behavior of the model in response to the changes in a particular parameter and indicates the effectiveness of each parameter ,72,98. Tyf), and eccentricity at bottom (be) parameters is negligible for both models with relative contribution of 0.09%, 0.03%, 0.3% for short and 0.03%, 0.3%, 0.5% for long CFST columns, respectively. The impact of length (L) in long columns and eccentricity at the top (te) in short columns is also less, with a relative contribution of 0.44% and 0.41%, respectively. According to [Here, rding to , the beastN and lgN with varying input variables, i.e., D, t, L, L/D, te, be, yf, and cf. It is well understood that D and t are important factors controlling the bearing capacity of CFST columns. The capacity of both short and long columns follows a degree polynomial curve with variation diameter and thickness of the tube. Similar trends were also noticed for the compressive strength and yield strength of the steel tube. Increasing the compressive strength of concrete will divert the failure control mode to the yield strength of the steel tube and vice versa. The strength of the concrete inside the CFST is responsible for the stiffness of the CFST columns [lgN and stN, as observed and stated by many researchers in the past [Secondly, parametric analysis is performed to verify the efficiency of input variables and the strength of the GEP model. This is accomplished by changing one parameter with a particular increment while keeping all other parameters constant at their mean values. The main characteristics of the input parameters of CFST columns are material and geometric properties.  columns . Stiffne columns . Moreovethe past .lgN and stN.Hence the results of the current study are similar to those found in past research studies accumulated in the database . The parstN) and long columns (lgN) were developed through ANN and ANFIS and GEP. Two databases were extracted from the literature by collecting 702 datasets of short and 965 datapoints of long circular CFST columns. The conclusion is drawn below.In this study, prediction models for the bearing capacity of circular CFST short , relative root mean square error (RRMSE), performance index (PI) and objective function (OF). The PI of the predicted stN by GEP, ANN and ANFIS for training are 0.0416, 0.1423, and 0.1016, respectively, and for Nlg these values are 0.1169, 0.2990 and 0.1542, respectively. Corresponding OF values are 0.2300, 0.1200, and 0.090 for stN, and 0.1000, 0.2700, and 0.1500 for lgN. The superiority of the GEP method to the other techniques can be seen from the fact that the GEP technique provides suitable connections based on the practical experimental work and does not undertake prior solutions. In reference to MAPE indicator, the ANN provides excellent forecasting results for stN, while all other models including lgN-ANN fall in the “acceptable prediction” category.The statistical indicators used to evaluate the performance of the model were mean absolute error (MAE), root square error (RSE), root means square error (RMSE), correlation coefficient (stN: D (55.45) > T (20.45) > L (17.109) > cf (5.526) > te (0.41) > L/D (0.34) > be (0.32) > yf (0.096); whereas, in the case of lgN, it followed the order: D (59.83) > T (33.73) > te (3.844) > cf (1.302) > L/D (0.541) > L (0.443) > be (0.282) > yf (0.033). Parametric analysis showed a trend similar to the findings in previous literature. The effect of input parameters on the bearing capacity of circular short (stN) and long (lgN) CFST columns was studied. Thus, it can be concluded from this research that artificial intelligence techniques can be effectively employed to solve various complex engineering problems, especially in structural and material engineering. A simple, reliable, and accurate model can be developed which can perform better on unseen data.Sensitivity analysis was performed and the following input importance with increasing pattern was observed for stN) and long (lgN) CFST columns with eccentric loading using simple geometric and material properties. These models can replace tedious, time consuming and costly experimental work for finding the bearing capacity of CFST columns.The overall comparison shows that the most reliable and accurate technique for developing prediction models is GEP. The prediction models developed through the GEP technique are simpler than ANN and ANFIS models. It is, therefore, suggested that the developed GEP equations (Equations (22) and (23)) are used in routine design for circular short (It is recommended to study tother AI techniques such as Random Forest Regression (RFR) and Multi-Expression Programming (MEP), etc., on the same data, and the models should be compared for accuracy, reliability, and ability to correlate the predicted data with the experimental data.


Arthrospira platensis (Cyanobacteria) and Chlorella vulgaris (Chlorophyta) become widely attractive. Microalgae are rich in carbohydrates, proteins, and polyunsaturated fatty acids that have high bioactivity. Recently, scientists are studying the microalgae polysaccharides (PS) or their derivatives (as dietary fibers) for their potential action as a novel prebiotic source for functional foods. Besides, the microalgae prebiotic polysaccharides are used for medication due to their antioxidant, anticancer, and antihypertensive bioactivities. This review provides an overview of microalgae prebiotics and other macromolecules’ health benefits. The phytochemistry of various species as alternative future sources of novel polysaccharides were mentioned. The application as well as the production constraints and multidisciplinary approaches for evaluating microalgae phytochemistry were discussed. Additionally, the association between this potential of combining techniques like spectroscopic, chromatographic, and electrochemical analyses for microalgae sensation and analysis novelty compared to the chemical methods was emphasized.There is a global interest in the novel consumption, nutritional trends, and the market of new prebiotic sources and their potential functional impacts. Commercially available nutritional supplements based on microalgae that are approved to be edible by FDA, like  The international community’s attention has lately risen in a major way to the emerging market of dietary fibers and other phytochemical supplements, which are considered to be safe and have significant functional impacts. As an important example, microalgae are considered as future superfoods, as mentioned by Torres-Tiji et al. . They haIn addition, many studies have reported that microalgae have high antiradical activity coming from their unique chemical structure . FurtherPhaeodactylum tricornutum produces up to 40% eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) of its fatty acids, as a functional bioactive molecule  , lipids, and proteins are precursors of essential pathways and are novel sources of bioactive molecules. As an example, microalgae polysaccharides, provide many intriguing aspects in the food science and nutrition area as a viable alternative in food technology. They are widely used in biopolymer-based materials, like alginate polysaccharides that is among the most versatile biodegradable polymers by microorganisms . Also, Pmolecule . Additioacteria) .As their potential uses as functional food, microalgae prebiotics have novel characteristics distinguished from the other edible sources against the digestion by the gastrointestinal tract. Therefore, they help to enhance the growth of the health-related beneficial bacterial microorganisms called probiotics in the lower part of the gastrointestinal tract or the colon . MeanwhiThe aims of this review was to present and highlight the potential uses of microalgae, as alternative prebiotic edible sources for human consumption. Furthermore, the advanced non-destructive techniques were summarized for facilitating the tracking of these functional molecules’ during its cultivation process.Arthrospira platensis, and Scenedesmus obliquus. They are well known in United States, China, and Japan market  , like Arn market , 19. Micn market , 20.Thalassiosira pseudonana) through its active hybrid membrane system performance let it be used in several advanced pharmaceutical field for optimizing their production of prebiotics like exopolysaccharides (EPS)  , as anties (EPS) .Scenedesmus sp. contains up to 41%, Porphyridium contains up to 57%, and Chlorella contains up to 26% of carbohydrates that contains many potential prebiotic bioactive. On the other hand, Spirulina Maxima contains up to 71% protein and Phaeodactylum sp. contains up to 57% of oil. Thus, microalgae, in general, could be used as perfect sustainable alternative food that have health benefits for humans , which are category of fatty acids containing more than one double bond in the backbone and which have high bioactivity against cholesterol levels in the human body , 28 Tab.in situ oxygen-generation by an engineered C. vulgaris to overcome hypoxia in wounds was reported by Qiao et al.  to the C. vulgaris surface which reduces macrophage uptake and systemic clearance of the C. vulgaris and was termed as “RBCM-Algae.” The significant effect of C. vulgaris was reported through its in situ generation of O2 in the human’s system using a natural photosynthation. In which, the increased oxygen level on the tumor areas by the RBCM-Algae resulted in a remarkably radiotherapeutic efficacy. Besides, the chlorophyll from the C. vulgaris was observed to produce ROS during laser irradiation adding to improved tumor cells’ apoptosis  through their high phenolic and flavonoid content. For instance, our recent study on four microalgae species confirmed their high potential as antioxidant agents . An innoo et al. , wherebypoptosis .C. vulgaris when injected into the body by either intravenous or intratumoral, could efficiently arrive at the tumor spot and improve oxyhemoglobin, as it shows an extended life span with increased stability during in vivo circulation, which exhibits excellent biocompatibility with tissue cells , topical dissolved oxygen (TDO), and topical gaseous oxygen (TGO)  . For exaen (TGO) . Based ot ulcers  29, 31), 31Synecin vitro colonic fermentation. In that study, they mentioned that A platensis P. Score was 6.93 ± 0.05 and C. vulgaris was 2.54 ± 0.02 which were significantly high compared to the control with -1.35 ± 0.04.Microalgae is an important source of prebiotics that defined as potential native and modified forms of polysaccharides (PS) such as xylooligosaccharides (XOS), galacto-oligosaccharides (GOS), alginate oligosaccharide (ALGOS), neoagaro-oligosaccharides (NAOS), galactans, arabinoxylans, and β-glucans . These mPrebiotic was described by the Association of Probiotics and Prebiotics (ISAPP) as a non-digestible carbohydrate group, such as short and long-chain β-fructans [Fructooligosaccharide (FOS) and inulin], and GOS that beneficially affects the human by stimulating the growth of probiotics bacteria , and release some essential metabolites that could improve the human gut health  . These bA. platensis extracellular products  are greatly contributing to the growth of the human gut microbiota. When these compounds are ingested they reach the colon without any change and then the microbial consortia of the human gastrointestinal promote their degradation. Shankar et al. . These compounds have the extension of homogalacturonan or rhamnogalacturonan I that could be acetylated at C2 or C3 that facilitate the linking with arabinose, galactose, and xylose sugars on the side chains and forming new structures of POSs. Also, GOS (produced from lactose sugar extension) is formed from galactose extension at C3, C4, or C6 from the originated sugar from lactose through enzymatic trans-glycosylation that form pentasaccharides with galactose in β(1→6), β(1→3), and β(1→4) to form different types of GOS such as trans-galactooligosaccharides (TOS) . Table 3In this section, we will discuss in brief the recent commonly used methods that could be applicable for microalgae prebiotic extraction. These techniques have benefits and limitations. The chemical hydrolysis/pre-treatment followed by the alkaline extraction method is a possible methodology to obtain alginates the major polysaccharide in microalgae. The acid pre-treatment promotes the solubilization of the alginate and by alkaline extraction, soluble sodium alginate is formed and could be easily separated by the centrifugation process. However, this method is considered a time-consuming and complicated technique . Another2O2 could effectively decrease the hydrolysis time by acid that is used for targeting the plant polysaccharides by facilitating the lignin fraction, which increases the rate of reducing sugars production from plants -assisted extraction, which is based on sound wave migration that generates cavitations, leads to the disruption of cells and their walls . Comparem plants .Enzyme-assisted extraction has emerged as a promising tool to obtain extracts with attractive biological properties. The hydrolysis enzymes are effectively acting on the microalgae cell walls which facilitates the release of the intracellular prebiotics. Additionally, the enzymatic treatments have been used for the modification of obtained natural PS polymers that are used for the chemical structural evaluation of the novel prebiotic fibers and the enhancement of their biological activity, simplifying structural, and structure/function studies. The huge benefits of the enzymatic methods for the extraction of prebiotics are due to the decrease in the extraction time, the consumed energy, the used solvents, and the increase in the extracted yield . These aMicroalgae macromolecule analysis has been practiced for decades. A promising strategy for microalgae chemical assessment is using bioinformatics to provide a fast prediction tool for its carbohydrates, proteins, and even lipids. Nowadays, a potential limitation is the lack of reference structures of some microalgae proteins, and thus a wide range of analytical methods should be used for building a strong database based on these different analytical destructive or non-destructive techniques combined with chemometrics and other informatics methods.2 and C-OH deformations of its polysaccharides , C. vulgaris (CV), P. tricornutum (PT), and S. obliquus (SO) were fully scanned (400–1800 cm–1) by confocal Raman microspectroscopy. In which, by principal component (PC)1,2 and clustering heatmap, it was found that CV and SO were clustered as the same group compared to AP and PT for the wavenumber 1160 cm–1  have different characteristic ranges at different frequency regions of the FTIR spectrum. Therefore, it has the potential to be used for microalgal biochemical monitoring -α-d-glucans. Also, they mentioned that IR bands in the region of 800–950 cm–1 are very sensitive to anomeric configuration of glucose. The changes at 860–862 cm–1 indicates the changes in the α-configuration of glucose units. Thus in that study they distinguished the microalgae EPS structure from amylose close structure.Fourier transform infrared is a molecular spectroscopic technology that can identify the chemical groups based on their different absorbance of wavenumbers from 400–4000 cmnitoring . Generalnitoring . The FTInitoring . For insnitoring . Therefonitoring  mentioneo et al.  used FTISingle-cell electrochemical current by microelectrode has developed as an important technique for major studies of single-cell functionality . It is k–10 Amp) relation with the actual biochemistry of cells. Therefore, the continuous development of intracellular electrochemical detection and its relationship with chemical invasive methods and other non-invasive methods like micro-Raman spectroscopy should be more solidified.However, specific care is needed to maintain a very small current response , covered with sensing polymeric film, and two interdigital transducers for input and output are commonly used for acoustic sensors chemical composition devices . In whicArthrospira plantensis microalgae biofilm on microfluidic chip. They mentioned that SAW allowed the optimization of the deposition process of microalgae biofilm for sensitive detection of microalgae elements. In that study, they characterized the toxicity of A. plantensis microalgae heavy metal (Cd2+ and Hg2+) with a detection limit 10–12 M . Mass spectrometers are composed of an ion source, a mass analyzer, and a detector, and various designs and modes of action are available for different applications. Electrospray ionization (ESI) technique  and matrAdvanced sequencing methods combine sequencing experiments of fractionated polysaccharides and oligosaccharides that are treated with hydrolyzed enzymes to generate smaller fractions and the database search could obtain the structure of the novel prebiotics obtained from microalgae . Also, tS. elongatus showed the presence of rhamnose, xylose, arabinose, fructose, sucrose, and maltose as identified by HPLC analysis. In the tested EPS of the S. elongatus, presence of rhamnose sugar indicates the component of rhamnolipid, one of the glycolipid may contribute to biosurfactant property. These metabolic bioactive phenomena is increasing the functionility of microalgae EPS as prebiotic sources.Chromatography enables researchers to separate components of a mixture, identifying their properties, and determining their amounts , 118. PrOther chromatographic examples include ion-exchange chromatography, which allows separation based on the charge of molecules. Elution is carried out by changing the ionic strength of the mobile phase, either by modifying pH or increasing salt concentration. On the other hand, reverse phase chromatography is based on repulsive hydrophobic forces from the interactions between a polar carrier solvent that separate the microalgae macromolecules and the non-polar stationary phase into the column. Another chromatographic example is Anion Exchange Chromatography that could monitor the hydrolysis processes on the microalgae lignocellulose, and the target long-chain polysaccharides .Alsenani et al.  combinedIn conclusion, microalgae chemical composition plays an important role in its novelty applications as alternative nutritious prebiotic sources. The advancement of commonly used extraction methods and the analytical techniques for measuring microalgae species’ chemical composition could increase their applicability and potential uses in the field of bioactive prebiotic application and functional evaluations. Which, enhances their application in pharmaceuticals, functional food, and all related scientific fields. These advancements could include the discovery of new microalgae bioactive prebiotics, as well as the study of the real-time functional activity of microalgae exopolysaccharides, oligosaccharides during microalgal cultivation, as a critical approach to be developed for microalgae biotechnology of these functional compounds. Furthermore, nanoprobe microelectrodes with gold and other nanoparticles such as silver have become one of the most important approaches to verifying the functionality of various microalgae  as well as evaluating their physiological status. As a result, both conventional and spectroscopic methods could be used to validate the health benefits of microalgae health functional potential and their bioactive components benefits.MG: conceptualization, studied the literature, drafted, and edited the manuscript. MT, YZ, and FF: helped in the writing process. XL and BC: helped in the writing process and the project administration. YH: supervision, writing, and reviewing. All authors edited, proofread, and accepted the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Using hierarchical multiple regression and Hayes' PROCESS Macro by SPSS 21.0, and structural equation modeling by AMOS 23.0, support was found for both mediation and moderation effects. Results showed that entrepreneurial leadership significantly positively affects entrepreneurial performance by mediating with tacit knowledge sharing and job embeddedness. Moreover, career growth opportunities moderate the serial mediating effect of tacit knowledge sharing and job embeddedness between entrepreneurial leadership and entrepreneurial performance. This study provides theoretical guidance for entrepreneurial leadership to improve entrepreneurial performance.The purpose of this study is to explore the impact of entrepreneurial leadership on entrepreneurial performance in start-ups. Specifically, a moderated serial mediation model was developed to investigate the mediating role of tacit knowledge sharing and job embeddedness and the moderating effect of career growth opportunities. Data was collected from 376 start-up employees  The rapid development of the Internet has given rise to a boom in entrepreneurship. According to the Global Entrepreneurship Monitor's 2018/2019 global report, “79.3% of respondents think entrepreneurship is a good career choice”  refers to the combination of forces that prevent individuals from leaving their jobs  are opportunities provided by managers for employees to enhance their job competence and expertise, allowing the employees more room for development and responsibility at work. They are a kind of stimulation and motivation for employees  were male, and 240 (63.8%) were female. Respondents were mainly between 21 and 40 years old (96.5%). The majority of respondents had a bachelor's degree or higher (79.5%), and most respondents had a monthly salary of between ¥3,000 and ¥8,000 (60.9%). Respondents came from various companies, most of which had fewer than 100 employees (75.5%).The maturity scales published in national and international journals were used in this study. All English scales were translated using a standard translation back-translation procedure to avoid semantic ambiguity to the greatest extent possible. Second, given the sensitivity and covert nature of corporate surveys, all variables in this study were appropriate for use in an informal setting and were measured by anonymous self-assessment. All items were assessed using Likert 7-point scales, ranging from 1 (strongly disagree) to 7 (strongly agree).Because the EL scale developed by Gupta et al.  had manyJE was measured using the seven-item scale developed by Crossley et al. . A reliaTKS was measured using a three-item scale from Lee's  study onIn this study, the scale used by Van Veldhoven and Dorenbosch  was seleA four-item EP scale was create based on the classic scale of Chandler and Hanks , with apScholars have noted that the age and salary of employees, the number of employees at a company, and company tenure have a significant impact on EP .Because the EL, TKS, JE, CGO, and EP data come from the same source, the possibility of a common method variance existing between the five constructs could not be disregarded. Firstly, the Harman single factor method was used to test common method variance , JE , CGO , and EP . The results of variable correlation analysis preliminarily verify the research hypotheses of this paper. The AVE from all variables (except EP) is >0.5, which is an acceptable value . Furthermore, a structural equation model was used to test this hypothesis. The main path coefficient (γ) of this model is shown in p < 0.001). Therefore, hypothesis 1 is accepted.Following the suggestion of Baron and Kenny , hierarcp < 0.001; M3, β = 0.794, p < 0.001). When both independent (EL) and mediating variables (TKS and JE) were added to the analysis of EP, both EL , TKS  and JE  showed a significant positive association on EP. Hayes' (2013) SPSS PROCESS Macro (model 6) was used to obtain a preliminary idea of the direct vs. indirect associations in the model. b = 0.257, SE = 0.058, 95% CI = , excluding 0].Model 83 was used in SPSS PROCESS Macro Hayes,  to test b = 0.050, SE = 0.026, 95% CI = ], medium  and high  ]. The serial mediating association of TKS and JE between EL and EP was low , medium  and high . None of these confidence intervals include 0, so hypothesis 2, hypothesis 3, and hypothesis 4 were confirmed. Furthermore,  0.168)] . The medIn this study, participants expressed positive attitudes toward EL. It was confirmed that EL could improve EP and the impact pathways between EL and EP were explored. This study examined the mediating role of TKS between EL and EP. EL actively encourages employees to seize opportunities and promote knowledge sharing practices  EL has a positive association with EP. (2) TKS and JE can mediate EL and EP, and TKS and JE both play a serial mediating role. (3) CGO is a moderator of the positive association between EL and TKS, and can moderate the mediating effect of TKS on EL and EP and the serial mediating association of TKS and JE on EL and EP. In summary, based on the resource-based view and SET theory, this study explored the mediating role of TKS and JE and the moderating role of CGO. The mechanism of EL's influence on EP in the Chinese context is comprehensively analyzed. Start-ups should actively encourage employees to share tacit knowledge, embed employees in the organization, and provide sufficient CGO. This study enriches the literature on EL and hopes to stimulate scholars' thinking.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article. The patients/participants provided their written informed consent to participate in this study.BP and SJ: conceptualization, methodology, formal analysis, and writing—original draft preparation. BP: investigation and funding acquisition. BP and WS: writing—review and editing. BP and ZT: supervision. All authors have read and agreed to the published version of the manuscript.This research was supported by the National Natural Science Foundation of China, Grant Number 71804119 and China Postdoctoral Science Foundation, Grant Number 2019M663482.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Background and objectives: To investigate the risk factors for emphysematous cystitis (EC) compared to those of acute cystitis (AC) to increase clinicians awareness of the possibility for the aggravation of patient status. Materials and methods: We retrospectively reviewed a total of 54 patients who were hospitalized with a diagnosis of EC by abdominal computed tomography (CT) scan from 2006 to 2020. The control group included 92 patients who were hospitalized for the treatment of AC in the same period. We sought to identify the clinical features and predisposing diseases, such as age, sex, diabetes mellitus (DM), hypertension (HTN), cerebrovascular accident (CVA), chronic kidney disease (CKD), neurogenic bladder (NB), history of urinary tract infection (UTI), and emphysematous pyelonephritis (EPN), that were associated with the development of EC. Results: The median (interquartile range (IQR)) age of the patients with EC was older than that of the patients with AC (78.5 (15.3) years (range: 52–100) vs. 70.0 (26.5) years (range: 28–97 years)). Sepsis and mortality occurred only in the EC group . The univariate analysis of predisposing factors revealed that age, DM, HTN, CVA, CKD, and NB were significantly associated with EC. In the multivariate analysis, DM , CKD , NB  were associated with EC. Conclusions: The results of this study revealed that DM, CKD, and NB were significant risk factors for EC. The tendency toward sepsis and high mortality underscore the need for careful observation while treating patients with EC with the risk noted above. Emphysematous urinary tract infection (UTI), including emphysematous pyelonephritis (EPN) and emphysematous cystitis (EC), is characterized by gas-forming infections in related organs. While most sources of the gas are bacteria, several cases of EC were reportedly induced by chemicals . Among gThe first case of pneumaturia, which was most likely a symptom of EC, was reported in 1671 . The incWhile acute cystitis (AC) is a common disease, EC is very rare and can be fatal. Therefore, clinicians must discriminate AC from EC. However, when a clinician encounters a patient with AC in an outpatient clinic, it may be difficult to determine whether to perform imaging tests such as CT or to start aggressive treatment for EC.The aim of our study was to investigate the risk factors for EC compared to those of AC and to help clinicians to prepare for the possibility of the aggravation of patient status.We retrospectively reviewed patients who were hospitalized diagnosed with EC based on abdominal CT findings at Kyung Hee University Hospital at Gangdong, Seoul Medical Center and Kyung Hee University Medical Center between 2006 and 2020. The inclusion criteria for the EC group were patients over 20 years of age who were admitted with dysuria, hematuria, and pyuria in whom urine examination was performed and gas formation around the bladder was confirmed by CT . PatientWe sought to identify the clinical features and predisposing diseases such as age, sex, diabetes mellitus (DM), hypertension (HTN), cerebrovascular accident (CVA), chronic kidney disease (CKD), neurogenic bladder (NB), history of urinary tract infection (UTI), and EPN associated with the development of EC. Species identified in urine culture, sepsis, and mortality rates were also compared between the AC and EC groups.p values and relative ratios were calculated for the risk factors. p values < 0.05 were considered significant. In addition, multivariate model analysis was performed to determine the significance of EC and AC.Data were analyzed using SPSS Statistics for Windows, version 17.0 . Proportion comparisons for categorical variables were performed using Pearson’s Chi-squared, and Fisher’s exact. Escherichia coli was the most common causative agent in both the AC and EC groups , followed by Klebsiella pneumoniae. Extended-spectrum beta-lactamase (ESBL)-producing bacteria were more frequently identified in the EC patients (36.5%). In the AC group, the incidence and mortality of sepsis were both zero, whereas among the 54 EC patients, 26 (48.1%) were diagnosed with sepsis and 6 died (11.1%) ) age of patients with EC was older than that of the patients with AC (78.5 (15.3) years (range: 52–100) vs. 70.0 (26.5) years (range: 28–97 years)). Although the proportion of women was high in both the AC and EC groups , the difference was particularly noticeable in the AC group. Five underlying diseases, including HTN, DM, CVA, CKD and NB, were present at a low proportion in the AC patients but prevalent in the EC patients, particularly DM  and HTN . UTI history showed similar rates in both the AC and EC groups (18.9% vs. 16.7%). No patients with EPN were detected in the AC group, whereas 10.2% of EC patients were diagnosed with EPN. In the urine culture tests,  (11.1%) .p = 0.193 and OR 0.859; 95% CI 0.326–2.261; p = 0.814, respectively). However, age , DM , HTN , CVA , CKD , and NB  were significantly associated with EC. In the multivariate analysis, DM , CKD , and NB  were significantly associated with EC  0.600; 95% confidence interval (CI) 0.291–1.235;  with EC .This study collected information on EC patients from three institutions and analyzed their demographic data, underlying diseases, history, and causative bacteria, and compared them to those of the AC control group to identify the risk factors that discriminated EC with symptoms similar to those of AC. As the number of patients diagnosed with EC is small, the number of related studies is correspondingly small worldwide and retrospective risk factor analysis studies are also scarce.The 2021 European Association of Urology guideline on urological infections recommends that the diagnosis of uncomplicated cystitis is first based on the history of lower urinary tract symptoms, urinalysis and urine culture are performed, and antimicrobials therapy is applied. However, no detailed information on emphysematous cystitis or differentiation points from acute cystitis are specified.Escherichia coli (58%) and Klebsiella pneumoniae (21%). The authors emphasized that if risk factors such as DM and complicated UTI are present and there is no response to standard treatment, EC should be suspected and a CT scan is recommended for the assessment of the disease severity and extent [Thomas et al. studied 135 EC patients reported in the literature between 1956 and 2006. They reported an average patient age of 61.9 years and that 64% of patients were female and 66.7% had DM. The overall mortality rate was 7% and the major pathogens were Escherichia coli (54.4%). The patients were treated primarily with conservative management, including antibiotics . Ten of the 136 patients with EC died, corresponding to a mortality rate of 7.4%. Thus, despite the relatively low mortality rate of the EC compared to the EP, a high level of suspicion should be maintained to promote successful and conservative management [Schicho et al. reported a mortality rate of 7.4% for EC in a total of 136 patients between 2007 and 2016. The average patient age was 67.9 ± 14.2 years. In 21 patients (15.4%), emphysematous infections of other organs were simultaneously detected, most commonly EPN. The primary pathogen identified was Similar to the previously described studies, the incidence of sepsis in this study was 48.1% and the mortality rate was as high as 11.1%. CT is an essential test for EC as its mortality rate differs from that of general AC; thus, it is important to identify the risk factors. Several studies have identified DM as a significant risk factor ,4,7,8,11Toyota et al. reported that two-thirds of the EC patients had DM compared to 10% of the general population. The high concentration of sugar in the tissue creates a favorable environment for microorganisms . The ferEC patients present symptoms ranging from asymptomatic to abdominal and urinary pain. In particular, CKD patients with decreased renal function may be asymptomatic. Yokoo et al. reported an EC complication in a patient undergoing hemodialysis and suggested that, as patients with a long history of dialysis often complains of asymptomatic hematuria, careful observation and examination are necessary . In 2018Quint et al. published two EC-related case reports suggesting the high probability of developing EC in patients with underlying diabetes, neurogenic bladder, and chronic UTI. They also suggested the need to address the underlying diseases to ensure treatment success . In NB, Our study has several limitations. Since this research is a retrospective investigation, the study on factors that can influence the development of EC other than the previously identified variables is unfeasible. The control (AC) group included only hospitalized patients. The symptoms of AC are not generally serious; thus, outpatient management is typical and cases requiring hospitalization often include gross hematuria or complaints of severe discomfort. Also, physicians from different institutions may have different hospitalization criteria for cystitis patients. Thus, selective bias may have occurred in the selection of the control group. Another drawback is that laboratory findings, such as white blood cells, hemoglobins and C-reactive protein (CRP), were not analyzed in this study. Significant results may be obtained by comparing the blood and urine exams between the two groups. Later, prospective, and large-scale studies are needed to address this shortcoming.The comparisons of EC and AC in this study identified DM, CKD, and NB as risk factors for EC. Thus, the treatment of general cystitis in patients with these risk factors requires active evaluations such as CT scans and suspicion of EC to prevent the development of serious conditions such as sepsis.


Background: The shock index, pediatric age-adjusted (SIPA), defined as the maximum normal heart rate divided by the minimum normal systolic blood pressure by age, can help predict the risk of morbidity and mortality after pediatric trauma. This study investigated whether the SIPA can be used as an early index of prognosis for non-traumatic children visiting the pediatric emergency department (ED) and were directly admitted to the intensive care unit (ICU). We hypothesized that an increase in SIPA values in the first 24 h of ICU admission would correlate with mortality and adverse outcomes.Methods: This multicenter retrospective study enrolled non-traumatic patients aged 1–17 years who presented to the pediatric ED and were directly admitted to the ICU from January 1, 2016, to December 31, 2018, in Taiwan. The SIPA value was calculated at the time of arrival at the ED and 24 h after ICU admission. Cutoffs included SIPA values >1.2 (patient age: 1–6), >1.0 (patient age: 7–12), and >0.9 (patient age: 12–17). The utility of the SIPA and the trends in the SIPA during the first 24 h of ICU admission were analyzed to predict outcomes.Results: In total, 1,732 patients were included. Of these, 1,050 (60.6%) were under 6 years old, and the median Pediatric Risk of Mortality score was 7 (5–10). In total, 4.7% of the patients died, 12.9% received mechanical ventilator (MV) support, and 11.1% received inotropic support. The SIPA value at 24 h after admission was associated with increased mortality , MV support , inotropic support , and a long hospital length of stay (HLOS) . Persistent abnormal SIPA value was associated with increased mortality , MV support , inotropic support , and a long HLOS . Patients with abnormal to normal SIPA values were associated with decreased mortality , while patients with normal to abnormal SIPA values were associated with increased mortality .Conclusions: In non-traumatic children admitted to the ICU from the ED, increased SIPA values at 24 h after ICU admission predicted high mortality and bad outcomes. Monitoring the trends in the SIPA could help with prognostication and optimize early management. The shock index (SI), defined as the ratio of heart rate (HR) to systolic blood pressure (SBP), is widely used as a predictor in patients presenting with trauma or acute hemorrhage , 2. StudThere is an age-specific normal limit of vital signs in pediatric patients. The normal range of SI values may differ across different age groups. Hence, the SI, pediatric age-adjusted (SIPA), defined as the maximum HR divided by minimum SBP from an age-specific normal limit, was developed  16–19).–19.16–19There is a paucity of data on this subject in non-traumatic pediatric patients. One study conducted in 2020 indicated that febrile children with elevated SIPA values on the initial emergency department (ED) visit were associated with ICU admission following an unscheduled ED revisit within 72 h . ConsideTo investigate whether the SIPA can be used as an early index of prognosis for non-traumatic children visiting the pediatric ED and were directly admitted to the ICU, this study compared the association of the SI and SIPA with clinical characteristics, mortality, and adverse outcomes. Moreover, we analyzed the utility of trends in the SIPA in predicting mortality and adverse outcomes in the first 24 h of ICU admission. We hypothesized that an increase in SIPA values in the first 24 h of ICU admission would correlate with adverse outcomes in non-traumatic children.This retrospective observational study was conducted at the EDs of three medical institutes  located in northern and southern Taiwan. There were 35,000, 11,000, and 30,000 annual pediatric ED visits, respectively. The study was approved by the Institutional Review Board of the Chang Gung Medical Foundation . Patient and physician records and information were collected from the research database in the studied medical foundation and anonymized prior to the analysis.We enrolled children aged 1–17 years who presented to the pediatric ED and were directly admitted to the ICU between January 1, 2016, and December 31, 2018. Patients aged <1 year were excluded because the normal range of vital signs varied drastically by month and the normal range of SIPA values in this population was not clearly categorized in previous studies. Patients who experienced traumatic injury, who had out-of-hospital cardiac arrest, were discharged against medical advice, or were transferred to other hospitals after admission were also excluded. We calculated the SI and SIPA values from medical records documented in the research database for the included patients at the time of ED arrival and 24 h after admission. These values were then categorized as either abnormal  or normal based on the normal range of vital signs in different age groups that were adopted in recent studies , 25, 27.The primary outcome was mortality at ICU admission. The secondary outcomes included mechanical ventilator (MV) support, inotropic agent support, and the HLOS. To further investigate the utility of using the SIPA as a tool in clinical monitoring, we used the trends of the SIPA value at admission and 24 h later to correlate with clinical outcomes. In this step of the analysis, four different trends of the SIPA were analyzed: normal to normal, normal to abnormal, abnormal to normal, and abnormal to abnormal SIPA values during the first 24 h.U test for continuous variables and chi-square analysis for binomial variables were performed to determine the clinical characteristics and outcomes that were correlated with the SI and SIPA. Logistic regressions assessing the association of clinical outcomes with the SIPA value and the trends in the SIPA during the first 24 h were performed after adjusting for confounding factors. Statistical significance was defined as a two-sided p-value < 0.05. All statistical analyses were conducted using the IBM SPSS Statistics for Mac (version 26).The Mann-Whitney During the study period, 1,732 patients were included in the study. Of these, 1,050 (60.6%) were under 6 years old, 1,010 (58.3%) were male, and the median PRISM score was 7 .p < 0.001] and SIPA , while there was no significant difference between the normal and abnormal SI groups . Regarding clinical outcomes, neither abnormal SI nor abnormal SIPA values at admission were significantly correlated with in-hospital mortality, MV support, and inotropic support. An abnormal SIPA value was associated with increased HLOS .The association of SI and SIPA with clinical appearance and outcomes is shown in p < 0.001] and SIPA  groups than in the abnormal group. Abnormal SIPA values at 24 h were associated with all measured outcomes, including high mortality , MV support , inotropic support , and a long HLOS , but not with a high rate of mortality or other secondary outcomes. In contrast, SIPA values at 24 h after admission were associated with increased mortality , MV support , inotropic support , and HLOS . Furthermore, delta SI was associated with increased mortality , MV support , inotropic support , but not with HLOS.We performed a regression analysis on the correlation of SIPA values at admission, 24 h after admission, and delta SI with the measured outcomes. After adjusting for age, sex, and patient severity, SIPA values at admission were associated with increased HLOS [1.282 days, 95% confidence interval (CI): 0.240–2.324, p = 0.001), MV support , inotropic support , and HLOS . In contrast, patients with normal SIPA values in the first 24 h were associated with a decreased HLOS . Patients with abnormal to normal SIPA values were associated with decreased mortality . In contrast, patients with normal to abnormal SIPA values were associated with increased mortality  and increased MV support .The results of the SIPA trends in terms of patient outcomes are shown in Studies have proven the SIPA to be a useful tool for predicting the risk of morbidity and mortality in the pediatric population experiencing trauma , 18, 24.Moreover, there was a significant difference in PRISM scores between the normal and abnormal SIPA groups at admission. This finding indicates that the SIPA more accurately identifies a patient's severity than the SI unadjusted for age. Interestingly, elevated SIPA values at admission were only associated with an increased HLOS, whereas elevated SI values at admission were not associated with any measured outcomes. This discrepancy could be a consequence of early intervention and management, which may alter patient outcomes. Children presenting to the ED with initially unstable conditions receive aggressive resuscitation and a high level of care; thus, children with abnormal SIPA values at admission can avoid adverse outcomes. Additionally, the average age of the patients was high in both the normal SI and SIPA groups. This may be related to the limited physiological ability of compensation in response to disease progression in young children.Nordin et al. demonstrated that the change in SIPA values in the prehospital period predicts the need for transfusions, ICU admission, and mortality in pediatric trauma patients . A studyDelta SI can also be used to predict adverse outcomes. As shown in The main advantage of SIPA is that the values can be rapidly calculated at the bedside. The required parameters are easily obtained from routine vital signs. The SIPA should be integrated into electronic medical records and automatically calculated in real time according to HR and SBP. The utility of SIPA monitoring may allow early recognition of children with poor outcomes, and be an indicator for early implementation of aggressive treatment. Further research is needed to evaluate the improvement in early management among children admitted to the ICU after SIPA monitoring.This study has several limitations. First, it had a retrospective study design; thus, all data collection depended on the information documented in the medical records. Second, this study was conducted at the ED of three medical institutions. We did not analyze interfacility differences in practice, such as ICU admission criteria or indications for inotropic agent administration. However, we believe that this would have a minimal effect on our results, as all the studied hospitals have similar systems. Third, the sample population was limited to children aged 1–17 years. The applicability of these findings to patients younger than 1 year is unknown and should be the focus of future work. Finally, we used non-invasive SBP measurements for SI calculation in all patients included. Accurate measurement of vital signs in young children is challenging. The measurement of vital signs may differ among nursing staff who are unaccustomed to caring for children. Nevertheless, the effect should be minimal, as this would have affected both the calculated SI and SIPA values equally. The usefulness of the SIPA should be tested in large prospective cohorts of non-traumatic children. Additional resuscitation metrics  may be incorporated for a precise prediction of prognosis.In conclusion, elevated SIPA values at 24 h after ICU admission predicts high mortality and bad outcomes. Monitoring the trends in the SIPA could provide information for predicting adverse outcomes in non-traumatic children visiting the pediatric ED, directly admitted to the ICU. It is practicable to add the SIPA to HR and SBP, allowing for the prognostication and optimization of early management.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The study was approved by the Institutional Review Board of the Chang Gung Medical Foundation . Patient and physician records and information were collected from the research database in the studied medical foundation and anonymized prior to the analysis.I-MC and Y-HH contributed to the conception and design of the study. P-CC performed the statistical analysis and data interpretation. K-CH, YY, C-JL, and F-JC contributed to drafting the article and reviewing the literature. All authors contributed to the manuscript revision and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.