No significant statistical difference was observed between the two groups in terms of IGF-1 and C (p > 0.05). Regarding hematological parameters, Hb, Ht, and mean corpuscular volume (MCV) were found to be significantly lower in the EG compared with the CG (p < 0.05). Moreover, the distance covered in the YYIR1 test was found to be significantly lower in soccer players within the EG compared with the CG (p < 0.05). Additionally, it was determined that there was no significant difference between the groups in terms of DSt and GAt values (p > 0.05). Lastly, significant differences were observed between the EG and CG in terms of anthropometric characteristics  (p < 0.05). The present study showed that the changes in evaluated characteristics might result from the complex influence of endocrine-disrupting chemicals, the content of which is high in the environment of the Aral Sea region. The results obtained may help monitor the health of athletes living in an environmentally unfriendly environment.The Aral Sea region (Uzbekistan) is infamous because of the ecological disaster characterized by the disappearance of the Aral Sea due to excessive uncontrolled water intake for agriculture needs. A new desert occurrence, soil and climate aridization led to pesticide and toxic metals environment pollution. The impact of environmental conditions in some areas of Uzbekistan on the health of soccer players is not as noticeable as, for example, the effectiveness of training, so it is not widely discussed in scientific papers. The aim of the present study was to study the features of endocrine status, hematology parameters  and hemoglobin (Hb)), and their influence on body composition and physical fitness performance in local young soccer players of the Aral Sea region as the territory of ecological disaster. The study involved 60 male soccer players aged from 18 to 22 years. Participants were divided into two groups: the experimental group (EG), which consisted of 30 soccer players living on the territory of the Aral ecological disaster region, and the control group (CG), which included 30 soccer players, natives of the ecologically favorable region of Uzbekistan. All volunteers had anthropometric measurements, concentrations of insulin-like growth factor-1 (IGF-1), total testosterone (TT), estradiol (E2), cortisol (C), RBC, and Hb count. Moreover, Yo-Yo Intermittent Recovery Test Level 1 (YYIRT1) and professional skills tests such as dribbling shuttle test (DSt) and goal accuracy test (GAt) were assessed. When comparing the CG group to the EG group, it was observed that the EG group exhibited statistically significantly reduced levels of TT and E2 ( Soccer is one of the most popular games in the world . In UzbeThe impact of environmental conditions in some areas of Uzbekistan on the health of soccer players is not as noticeable as, for example, the effectiveness of training, so it is not widely discussed in scientific papers. The southwestern territory of Uzbekistan, named the Aral Sea region, is infamous because of the ecological disaster that has existed there since the middle of the 20th century . ExcessiThe Aral ecological disaster has at least two complementary effects on the health of athletes: air pollution by microparticles and pollutants ,14 as weTaking into account previous research, which has found low levels of red blood cells (RBCs) and hemoglobin (Hb) in individuals with high plasma concentrations of organochlorine pesticides (OCPs) in the Aral Sea region ,33, thisGiven the potential for environmental pollutants to impact the oxygen-carrying capacity of blood, this study aimed to shed light on the impact of ecological disasters on young soccer players’ physical health and performance. By studying the effects of endocrine status and hematology parameters on body physique and physical fitness, this research may help identify ways to monitor and mitigate the health risks posed by negative environmental factors in the Aral Sea region.The participants were divided into two groups: The experimental group (EG) consisted of 30 soccer players, grown and since birth living on the territory of the autonomous Republic of Karakalpakstan (Uzbekistan) in the region of the Aral ecological disaster. The control group (CG) included 30 soccer players, natives of Khwarazm region of UzbekistanIn total, players from both groups trained for 216 h over 52 weeks in 2021/2022 from November to October. The entire cycle consisted of three periods: a «training period» that included only training (24.6 weeks from November 2021 to late April 2022), a «playing period» that had training and regular season amateur college team games (23.4 weeks from late April to early October), and a vacation (4 weeks from October to the beginning of the next cycle). All tests were performed at the end of April 2022 from weeks 23 through 25 of the training cycle.A total of 60 male soccer players aged from 18 to 22 years were recruited to participate in our study. Of them, 30 athletes were from a team based at Karakalpak State University named Berdakh , and another 30 participants trained on Urgench state university  team. The two teams, in equal proportions, comprised four goalkeepers, 20 defenders, 20 central midfielders, eight wingers, and eight attackers. The minimum sample size for our study was determined using G-power software version 3.1.9.7 . In accordance with our study’s design, an a priori analysis utilizing t tests was conducted to calculate the statistical power. Specifically, we employed the Wilcoxon–Mann–Whitney test (two groups) with the following parameters: α error probability (α err prob) = 0.05, power (1-β error probability) = 0.80, allocation ratio of 1, and effect size of 0.70 ,15,18. BParticipants started training at the age of 10–12 years. The training program consisted of three weekly workouts . All thirty students in the soccer departments of both universities volunteered for this study because the physical fitness assessment is an additional selection factor for professional teams. None of them reported any sports injuries, using any supplement that could affect growth and maturation, iron supplementation, or donated blood at the time of the investigation. The players followed the same nutritional and hydration protocol as during the soccer season. In all tests in this study, athletes participating in 80% of competitions and training for the whole season could not perform additional exercise/training regimens.The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the local ethics committee of the St. Petersburg State Pediatric Medical University . Informed consent was obtained from all individual participants included in the study.http://www.cawater-info.net/index_e.htm and https://visibleearth.nasa.gov/images/41377/aral-sea/41378l (accessed on 20 February 2023)  .During the study, participants attended a single outpatient visit where their medical history was recorded, and they underwent a comprehensive physical examination. Throughout the three-day study period, participants were instructed to refrain from any dietary disruptions, maintain proper hydration, avoid stress, and refrain from engaging in excessive physical activity. To reduce measurement variations, the same experienced investigator conducted all the evaluations. Physical fitness characteristics assessments were supervised by the main researcher and physical coach responsible for these athletes, considering the capabilities that they worked on for each match. We followed the guidelines of the American College of Sports Medicine  to ensurOn the first day of the experiment, two experienced investigators performed anthropometric measurements in the morning at a comfortable temperature  in the isolated, artificially illuminated medical room. Participants were dressed in light clothing. On the second day, the participants first warmed up for 15 min. Subsequently, they performed the Goal Accuracy Test and Dribble Shuttle Test . Moreover, there was a 3 min rest period between the two tests. Lastly, the participants were subjected to the Yo-Yo Intermittent Recovery Test Level 1 (YYIRT1). The measurement session was completed after the participants performed cool-down exercises following all the tests. All physical fitness characteristics evaluations were performed at approximately the same time of the day (between 4:00 and 5:00 pm) and in similar environmental conditions   on the gThe body physique parameters were measured according to the method proposed earlier ,36,37. TMeasurement of 23 parameters, including body mass, body mass index (BMI), stature, sitting height, circumference of limbs, leg length, size of chest, pelvis, large joints diameters, and skinfold thickness were performed on all participants. The stature and sitting height were assessed using mobile stadiometer SECA Model 217, Germany. Participants were unshod to measure the standing height (stature), with the heels, hips, shoulder blades, and back of the head as close as possible to the stadiometer, with feet placed beside each other. For sitting height, participants sat on a 50 cm bench and were instructed to bring their buttocks as close as possible to the stadiometer, holding their upper body straight and placing their hands on their feet, at which point, their height was assessed. The distance between the highest point of the head and the bench was calculated as sitting height. Body mass was measured with electronic medical scales SECA Model 217, Germany, to the nearest 100 g. BMI was calculated using the Quetelet formula. The leg length was calculated as stature minus sitting height. The anterior-posterior  and biacromial chest diameters (BACDs), as well as pelvic biiliac diameter, were measured using a large spreading caliper . The wrist, knee, elbow, and ankle diameters were measured by sliding caliper , measurement accuracy for both calipers was up to 0.1 cm. Non-elastic tape SECA Model 200, Germany, was used to take limb circumferences with 0.01 accuracy. The skinfold thickness was assessed using a professional caliper ET MEASURE model SK-101, China, with spring calibrated to produce equal pressure (0.01 kg/mm2) on both sides of the fold, and the accuracy of the measurement was 0.2 mm.The somatotype was assessed by the Heath–Carter method. Body somatotype the three-dimensional distance from a profile to the mean of all profiles  and height-to-weight ratio (HWR), according to Carter and Heath ,38. NumeThe endurance capacity of the players was measured by the YYIRT1 test . PlayersThe dribbling shuttle test and goal accuracy test assessed the professional performance skills used to control the examination included in the sport selection procedure for professional soccer teams in Uzbekistan.The DSt procedure is sketched and presented in The player needs to hit precisely the central zone (zero quadrants) of empty football goals of standard sizes from a distance of 30 m. As a result, the number of successful hits from 5 attempts was recorded.Venous blood samples were taken between 7:00 and 8:30 a.m. following an overnight fast. Blood samples were centrifuged at 1500 rpm for 10 min to obtain the serum. Hormonal (insulin-like growth factor 1 (IGF-1), total testosterone (TT), estradiol (E2), and cortisol ©) and hematological parameters (Red blood cells (RBC), Hemoglobin (Hb), hematocrit (Ht), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCH), mean corpuscular volume (MVC), and iron concentrations (Fe)) were registered. In this sense, IGF-1, TT, E2, and C concentrations in serum were measured by the direct solid-phase chemiluminescent enzyme immunoassay («sandwich» method) with commercial test kits using MR-96A Mindray microplate reader, Shenzhen Mindray Bio-Medical Electronics Co., Ltd., China. Regarding hematological parameters, in addition, RBC, Hb, Ht, MCH, and MCV were determined on a Mindray BC-20s hematology analyzer, Shenzhen Mindray Bio-Medical Electronics Co., Ltd., China. In addition, Serum Fe concentrations were measured by calorimetry using semi-auto chemistry analyzer BA-88A Mindray, Shenzhen Mindray Bio-Medical Electronics Co., Ltd., Shenzhen, China. The inter-assay coefficient of variations and sensitivity were: 4.3–7.8% and 0.15 ng/mL for IGF-1; 4.4–9.3% and 0.33 nmol/L for TT; 6.1–8.5% and 6.0 pg/mL for E2; 3.8–8.1% and 8.7 nmol/L for C; and 4.5–9.2% and 2.0 µmol/L for Fe.A comparison of anthropometric, hematological parameters, hormonal, and Fe concentrations, distance in YYIR1, DSt time, and numbers of ball touching between participants in the EG and CG was conducted using the Mann–Whitney U-criterion. The decision to use a non-parametric test was made after examining the data using the Shapiro–Wilk test, which indicated a deviation from normal distribution. Categorical variables, the numerical ratio of the distribution effective shots on GAt in the EG and CG, were analyzed using Fisher’s exact test for feature conjugation tables 5 × 2.Where the samples analyzed had statistically significant differences, a standardized effect size Cohen’s d was calculated using standard formula . The Cohp < 0,05. All continuous data are presented as arithmetic mean (µ) and 95% confidence intervals (CI). Categorical data were presented as fractions with 95% CI.The calculations were performed using the statistical data processing software Past version 2.17, Norway, Oslo, 2012, StatXact-8 statistical data processing algorithm with Cytel Studio software package version 8.0.0 and SPSS software . An Excel application from Microsoft Office 2010 Exploratory Software for Confidence Intervals (ESCI-JSMS), Melbourne, Australia, 2001, was used to calculate Cohen’s d. The results were considered to be significant at p < 0.05). According to this, compared with the CG group, the EG group was found to have significantly lower levels of TT  and E2 . No statistically significant difference was found between the two groups regarding IGF-1 and C (p > 0.05).As shown in p = 0.002, effect size: 0.88, large effect), Hb , Ht , and MCV  values between the two groups. These parameters were found to be significantly lower in the EG compared with the CG. Moreover, no significant differences were found between the EG and CG groups in other hematological parameters such as Fe and MCH (p > 0.05). Additionally, the distance in the YYIR1 test  was statistically significantly lower in soccer players in EG compared with CG. Finally, it was observed that the values related to DSt  did not differ significantly between the groups at a statistically significant level (p > 0.05)  .p > 0.05). Based on these results, it can be concluded that the presence of effective shots is independent of the region of residence of study participants.As shown in p = 0.01, effect size: 0.64, moderate effect), body weight , BACD , APCD , PBD , wrist diameter , knee diameter , elbow diameter , ankle diameter , suprailiac skinfold , and calf back skinfold  of the CG were significantly higher than those of the EG. Moreover, only leg length was found to be significantly higher in the CG group compared with the EG group in terms of anthropometric characteristics . Lastly, there were significant differences in ectomorph  and mesomorph somatotype component  between EG and CG groups. Additionally, it was observed that soccer players in the EG predominantly exhibited mesomorph somatotype component (medium effect size) and ectomorph somatotype component compared with the CG group (When CG group .The scientific literature has predominantly focused on the impact of air pollution on the physical condition of athletes, including effects on aerobic performance, hematological parameters, and strength characteristics of soccer players ,44,45,46In our study, we sought to observe a homogeneous group of soccer players, considering their gender, age, level of training, experience, training schedule, and level of fatigue. We found no high plasma cortisol concentrations or differences in participants from either group. Increased cortisol concentration is a valid marker of overtraining in athletes ,48,49. TProlonged physical activity increases IGF-1 ,30 and tSoccer is a rigorous sport in which the players must exercise and perform at greatly uneven intensities distributed over time in a highly discontinuous manner ,52, YYIRParticipants from EG compared with players from the CG had a decreased amount of RBC and Hb, combined with normochromia, equal plasma iron content, and a tendency to microcytosis. Thus, they are assumed to have no impairment of hemoglobin synthesis, the efficiency of which depends on iron intake, all other things being equal. Less total hemoglobin in them is transported in fewer small erythrocytes, so the saturation of each erythrocyte is equivalent. The value of total Hb in participants in EG depends on the number of RBC, that is, on erythropoiesis, but not on the Hb saturation of each erythrocyte. If the number of RBCs in athletes from both groups were not different, with equal MCH we would obtained the same Hb content in them. It is known that Hb, RBC, Ht, MCV, and MCH do not change in soccer players during the competitive season .Currently, the mechanisms of the effects of pesticides and pollutants on hematopoiesis are not fully understood. However, analysis of the literature suggests a direct and sex steroid-mediated pathway of reduced RBC production under the action of OCPs ,33. Low Testosterone normally promotes erythropoiesis by direct effects on the hematopoietic stem cell ,63 or byDecreased RBC in peripheral blood may be a consequence of damage to hematopoietic cells and mature erythrocytes because oxidative stress increases lipid peroxidation . IntenseIt should be noted that the effect size of these changes in athletes from the environmentally disadvantaged region was not high. The distance covered when performing YYIRT1 correlates not only with hematological parameters but also the efficiency of the test is determined by the compensatory reactions of blood circulation and respiration, which develop in trained athletes . IncreasThus, the presumed endocrine-disrupting and hematopoiesis-reducing effects of unfavorable environmental factors were not critical for the changes in the physical fitness characteristics of the examined soccer players. We observed the most pronounced effect of the possible anti-androgenic and anti-estrogenic effects of long-term persistent OCPs on volunteers’ body structure and shape.It is known that the formation of an anthropometric profile in soccer players is a consequence of the influence of two interrelated components: targeted selection of young people for sports ,4,7,40, Sitting height is a convenient and valid anthropometric parameter for assessing child maturity in pre-pubertal and early pubertal life ,40,49,72Soccer players are characterized by a mesomorph body type ,74 with The data obtained in work indicate a delay of maturation in the pre-pubertal period and in puberty in soccer players living in the conditions of the Aral ecological disaster. The anthropometric features revealed may be a consequence of the endocrine-disrupting effects of OCPs. We obtained lower TT and E2 values in soccer players from EG compared with CG volunteers with equal IGF-1 concentration. OCPs are known to decrease the rate of maturation due to anti-androgenic ,21,22,23Large joint diameters are used as an anthropometric marker of bone mass . IncreasDespite the significant findings and potential implications, several limitations to this study should be considered. Firstly, the study only included male soccer players; therefore, the results may not be generalizable to female athletes or athletes in other sports. Additionally, the sample size was relatively small, which may limit the study’s statistical power and reduce the findings’ representativeness.Secondly, the study was cross-sectional, meaning it only provided a snapshot of the athletes’ health status and physical performance at a single time. As such, it is not easy to conclude causality or the long-term effects of environmental factors on the health and performance of athletes. Thirdly, the study only assessed a few ecological factors and did not consider potential confounding variables that could influence the results. For example, the athletes’ dietary habits, lifestyle, and other environmental exposures were not measured, which may have affected the study’s findings.Finally, the study did not investigate the potential mechanisms underlying the observed effects of environmental factors on the athletes’ health and performance. Further research is needed to identify the specific chemicals or toxins responsible for the observed effects and elucidate the biological pathways through which they act.In fact, for a depth analysis of the factors that concurred for justifying the changes, it would be necessary to describe and quantify the training process and load during the period and use this as a moderator. We consider this a bias, and the generalization of the findings should be avoided. Future studies should consider monitoring the training load and use this sum of training exposure as a possible co-variable. Despite that, both groups competed in the same category, and age, stature, body mass, and body mass index were controlled to observe the previous difference. It is also expected that the training process for these categories can be relatively similar without major differences in the overall load imposed or advanced training methods. Moreover, our study did not consider exposure time to a specific training goal. This means that in some cases, higher exposure to cardiorespiratory training may have more impact in one group than in another. Thus, future studies should consider exposure time to a given training method as a possible co-variable.In addition, future research can include an observational study in which actions involving decision making are obtained and the normal behavior of the footballer is evaluated, such as vertical and horizontal passes, comparing by context, as well as increasing the information on the fluctuation of physical conditioning during the season, when the air quality is good, and after several weeks/months, when this quality is not favorable. This study is one of the pioneering investigations conducted in Spain concerning this particular issue and focused on youth football players. This type of study may help in the improvement and control of the physical–biological performance of football players in training stages, especially from the sports medicine perspective.The present study objectively characterizes the physical development and performance of local young soccer players in the Aral Sea ecological disaster region, focusing on their anthropometric profile, hormonal regulation, and oxygen-carrying capacity. The findings reveal that these athletes display ectomorph somatotype features, including a low sitting height, flattened chest, narrow pelvis, smaller skinfold thickness, thin bones, and an increased ectomorph and decreased mesomorph component of Heath–Carter somatotype. It was suggested that these physical development features may be influenced by environmental endocrine-disrupting substances that reduce the content of sex steroids in athletes from the observation group. Furthermore, the negative environmental factors in the Aral Sea region are likely responsible for the soccer players’ decreased levels of RBC and Hb. This decline may have reduced oxygen-carrying capacity and decreased endurance performance during the YYIRT1 test. However, while hormonal changes, blood cell count, and somatotype features were affected, there was no significant influence on the professional skills of the examined soccer players.These findings are significant as they provide insights into the impact of environmental factors on athletes’ physical development and performance. This information can be valuable for monitoring the health status of athletes living in regions with ecological issues. The results of this study also contribute to the limited literature on this topic, and further research is needed to explore the complex relationships between environmental factors, physical development, and athletic performance.


Sankey diagrams illustrated that lean persons displayed healthy DBs and low sitting time, while those with excessive fat had non-healthy DBs spent more time sitting. The variables that effectively distinguished between the groups include active transport and leisure time domains alongside low-intensity PA, represented by walking intensity and healthy dietary behaviors. The first three variables participated significantly in the optimal discriminant subset . The discriminant power of the optimal subset (contained four above-mentioned variables) was average (Wilk’s Λ = 0.755) and determined that weak relationships between PA domains and DBs resulted from heterogeneous behaviors and mixed patterns of behaviors. Identifying the trajectory of the frequency flow through specific PA and DBs allowed for well-designed tailored intervention programs to improve healthy habits in adolescents. Therefore, identifying the set of variables that discriminate the most between lean, normal, and excessive fat groups is a suitable target for intervention. The practical achievements are canonical classification functions that can be used to classify (predict) participants in groups based on the three the most discriminating PA and DB variables.Physical activity (PA) and dietary behaviors (DBs) are crucial determinants of body mass composition. This work is a continuation of the previous study of PA and DBs patterns in late adolescents. The main aim of this work was to assess the discriminant power of PA and dietary behaviors and to identify the set of variables that discriminated participants with low, normal, and excessive fat the most. The results were also canonical classification functions that can allow the classification of individuals into adequate groups. A total of 107 individuals  participated in examinations, which used the International Physical Activity Questionnaire (IPAQ) and Questionnaire of Eating Behaviors (QEB) to assess PA and DBs. The participants self-reported body height, body weight, and BFP, with the accuracy of the data confirmed and empirically verified. Analyses included the metabolic equivalent task (MET) minutes of PA domains and intensity, and indices of healthy and non-healthy DBs, calculated as a sum of the intake frequency of specific food items. At the beginning, Pearson’s r-coefficients and chi-squared tests were calculated to study various relationships between variables, while the main considerations were based on discriminant analyses conducted to determine the set of variables with the most power to distinguish between lean, normal, and excessive body fat groups of participants. Results showed weak relationships between PA domains and strong relationships between PA intensity, sitting time, and DBs. Vigorous and moderate PA intensity related positively to healthy behaviors (r = 0.14, r = 0.27,  Excessive body fat results from several factors, which include weak physical activity (PA), a sedentary lifestyle, and poor dietary behaviors (DBs). Obesity is still growing in prevalence  and is cMany studies have focused on the relationship between PA and DBs ,9,10,11,PA can be considered through the prism of domains, intensity levels, and global nutrition indicators. However, a gap exists in the literature as no studies have examined the relationship between PA and DBs and their combined effects in body mass composition. In addition, studies attempting to indicate the variables most related to body mass composition, particularly lean, normal, and excessive body fat, are lacking. Furthermore, no studies have identified the PA and DB variables that discriminate the most between groups of adolescents with different levels of body fat. Despite many works showing PA and DB patterns using principal component analysis (PCA), approaches using analysis of variance (ANOVA) or regression models ,17,18 arControl functions of PA and dietary habits used for regulating body weight as a whole, and body fat in particular, are well documented ,20,21. SThe most efficient intervention programs created for the fight against obesity combine PA and dietary recommendations ,23. HoweThis work constitutes the second part of a study on the effects of PA and DBs on body mass composition in late adolescents. A detailed description of the sample size estimation, study design, participant recruitment, data collection, data handling, input of missing data, validation, and assessment of the consistency between self-reported and empirically measured body weight and BFP, as well as the Yeo–Johnson power transformation of data to a normal distribution, were published previously . Herein,The Research Bioethics Committee of the Faculty Senate of the Wroclaw University of Health and Sport Sciences approved the study (consent numbers 33/2018 and 13/2022). The study followed the ethical principles for medical research involving human subjects contained in the Declaration of Helsinki published by the World Medical Association.Power analysis conducted for clustering procedures, presented in the first part of the study, indicated a requirement for a minimum of 60 males and 60 females. However, the final analysis included 107 participants.Participants included 107 healthy individuals ) recruited in 2022 from students in their 1st year of study at the Faculty of Physical Education and Sport at Wroclaw University of Health and Sport Sciences. A flowchart describes the sampling procedure .n = 25), those who met exclusion criteria (n = 9), and those who did not answer the questionnaires (n = 6), 107 participants took part in the study.Of 275 students recruited to the university in 2022, 147 took part in the examinations. After eliminating students rejected from the university  project, which evaluates PA, DBs, attitudes towards health, lifestyle, intrinsic and extrinsic risk factors for injuries during PA, and risk factors for overweight and obesity.Sports field studies students completed online questionnaires using Google Forms immediately after an academic lecture (Human Anatomy taught by the author) during the 2022 academic year. The author of this article conducted recruitment, data collection, and data entry. The study used the Polish version of the International Physical Activity Questionnaire (IPAQ) (long version)  and the Anthropometrical and body composition measurements used standard procedures and included two body height measurements with an accuracy of 0.1 cm using an anthropometer . A bioelectric impedance method assessed body weight and BFP using an InBody230 device .A statistical approach assessed the reliability of the self-reported BFP and body weights used in the analyses. A comparison of the slopes and intercepts of simple regression analyses of the empirical and self-reported data confirmed the reliability of the latter .n = 13). The propensity for a data point to be missing was random, also known as missing completely at random (MCAR) [http://www.rstudio.com/ (accessed on 15 November 2022)) with the mice package (v.3.14.0).Although there were no missing anthropometrical, body composition, or PA data, there were missing data for the QEB questionnaire (m (MCAR) ,30. All The Shapiro–Wilk test evaluated the normality of data distribution, with the variables identified as non-normally distributed transformed to a normal distribution shape using the Yeo–Johnson power transformation . Transfocirclize package in R [2). Graphically, trajectories of the number of males and females flowing from PA domains or intensity, through sitting quintiles, and DBs, to body composition categories, were presented as Sankey diagrams. Sankey diagrams are not a statistical method, but visually depict a flow from one set of values to another. In this work, Sankey diagrams present the interrelationships between different subcategories of participants. The graphs were prepared in Google Sheets using the ChartExpo add-on.The procedures of a discriminant analysis need knowledge about simple associations between variables and relations between subgroups. Therefore, as a first stage of analysis, Pearson’s product–moment correlation coefficients were calculated. Graphically correlation matrices were presented in chord diagrams using the age in R . Chord dBFP used in Sankey diagrams was categorized according to the American Journal of Clinical Nutrition, which states that males should have 8–19% body fat and females should have 21–32%. A BFP of less than 8% in males is considered a fat deficit, with 8–14% considered lower normal (1st), 14–19% upper normal (2nd), and above 19% considered excessive fat. Meanwhile, less than 21% body fat is a deficit in females, with 21–27% considered lower normal (1st), 27–32% upper normal (2nd), and above 32% considered excessive.2 for each variable in relation to the rest of the variables. Tolerance is the proportion of a variable’s variance not accounted for by other independent variables in the equation. Raw and standardized coefficients (β) of the canonical discriminant functions were calculated. Raw values are used to build discriminant equations, while β–values allow for a comparison of the discriminant power between variables. Coefficients of the classification functions were calculated in addition. These can be used to predict subjects’ group membership. At the end, hierarchical cluster analysis (HCA) assessed the similarities and dissimilarities between BFP categories, with Mahalanobis distances and the Ward method of linkage used to agglomerate the set of variables that best discriminated between groups.The main approach was based on discriminant analysis (DA). This method allows for identifying the set of variables that distinguish between the four BFP categories the most, which, unlike analysis of variance, allows for multivariate analyses. Discriminant analysis procedures allow for building canonical discriminant functions that are the linear combination of independent variables that will discriminate between the categories  in a perfect manner . DA alsop-value equal to 0.05 to determine the significance level, and all calculations (except those using RStudio) employed Statistica 13.0 .All statistical tests and procedures used a p = 0.027 and p = 0.036, respectively). In addition, a lower value for total sitting time in males was very close to significance (p = 0.076). Comparing PA and DB patterns, several similarities and dissimilarities emerged between both sexes. One of the common phenomena included the relationship between some unhealthy PA and DBs . Moreover, the preliminary analysis suggested a possible combined effect of such behaviors on body composition. Therefore, this work analyzed the multivariate relationship between PA and dietary behaviors and their impact on body fat percentage in-depth.The basic characteristics of the males and females were presented previously . Sex dif2 tests. Results were illustrated with chord and Sankey diagrams. The second stage, as a main analysis, was descriptive discriminant analysis conducted in order to identify a set of variables.The analysis in this work was divided into two parts. The first stage for discriminant analysis was to study the interrelationship between PA domains, sitting time, and dietary behaviors. The approach was based on simple Pearson correlations and χInitial examinations included the interrelationships between PA domains and PA intensity, with sitting time and dietary behaviors, by calculating Pearson-product correlation matrices and producing chord diagrams . Meanwhip > 0.05). On the other hand, sitting time was negatively related to unhealthy DBs (r = −0.16), which suggests a minor relationship between time spent sitting and the frequency of eating unhealthy foods. PA leading to leisure time and active transport are the domains most related to healthy eating behaviors , indicating that more PA supports healthy behaviors.p < 0.05), while walking (low intensity) was also positively related (r = 0.14). The strongest, negative, and significant relationship with sitting time presented PA vigorous intensity .p = 0.785) with small non-healthy behaviors. Healthy behaviors are related to lower sitting time (41%—quantiles Q1 and Q2), while negative behaviors mostly to higher sitting time (44%—quantiles Q4 and Q5). However, differences in proportions were not significant . Persons with excessive fat, to a large degree, prefer non-healthy dietary behaviors, and they are related to more time spent sitting. On the other hand, tracking yellow lines leading from sex to excessive fat persons, as well as green lines leading to lean persons, showed that more males are included in excessive fat categories, while females mostly agglomerated in lean categories. Differences were statistically significant .2 = 0.13, p = 0.938). Indeed, most participants with excessive fat preferred non-healthy eating (56.0%) and moderate-intensity PA. (42.9%). Differences in proportions were not significant .Based on the results so far, it was reasonable to look for a set of PA and DB variables that differentiate individuals with different levels of adiposity the most.Standardized mean values with 95% CI are presented in active transport and leisure time domains, low-intensity PA , and healthy dietary behaviors.The results of discriminant analysis are limited to the indication of the set of variables that most discriminate between the four groups of participants. In-depth analysis of the discriminating functions separating groups of participants, their eigenvalues, structural coefficients, and predicted classifications were omitted. The set of variables best differentiating between the groups contained 2 for each variable in relation to the rest of the variables. Tolerance is the proportion of a variable’s variance not accounted for by other independent variables in the equation. All three PA domains were statistically significant, while dietary behaviors were not. All four variables explained 25% of the between-group variance, which confirmed Wilks’ lambda (Wilk’s Λ = 0.755). The first three variables were found in all earlier steps of analysis, indicating their high discriminatory power. Nonetheless, healthy DBs replaced unhealthy behaviors in the second last step and the vigorous PA domain in the previous step. The first and the second discriminant functions , which separated groups in the best way, explained overall 89% of the between-groups variance. The raw coefficients of the functions presented in β =−1.15) and low intensity represented by the walking domain (β = 1.42). The second function is determined mainly by leisure time (β = 0.91). All three PA variables occurred more diagnostic than dietary healthy behaviors. The first function separates mainly participants who are active commuters from those who use privet or public transport, while the second function distinguishes between physically active and non-active during leisure time. ANOVA one-way tests for each variable confirmed the highest and statistically significant discriminant power of the active transport  and the lowest and nonsignificant (but quite close to significance) of the healthy dietary behaviors .lean) can be written as: lean—1st classification function; at—PA value (MET-min/week) from active transport domain; lt—PA value (MET-min/week) from the leisure time domain; walk—PA value (MET-min/week) from walking domain; hdi—scores from healthy dietary behaviors.Based on the best subset four canonical classification functions were calculated, and four classification equations are presented in Moreover, HCA allowed for a visual assessment of the similarities between the four groups of participants based on the four variables in the best-discriminating set, the results of which are presented in active transport, leisure time, low-intensity PA represented by walking intensity, and healthy dietary behaviors. As a practical result, the canonical discriminant functions were determined. They can be used for classification of individuals into groups with lean, normal, and excessive fat mass.This work mainly aimed to identify the set of variables that most discriminated between participants separated into subgroups based on BFP. In addition, an initial part of the main analysis was to assess interrelations between PA domains, intensity, time spent sitting, and healthy and non-healthy DBs. The study demonstrated a minor relationship between PA domains, sitting time, and DBs. On the contrary, stronger associations were observed for PA intensity and DB. Sankey diagrams provided a well-illustrated trajectory of the frequency flow of participants through specific PA and DB areas and clearly showed that the participants with excessive fat displayed non-healthy DBs and spent more time sitting. Individuals with a lean body mass preferred vigorous-intensity PA, while persons with excessive body fat preferred low or moderate-intensity PA. The main achievement, received with discriminant analysis, was to identify the set of four variables that most effectively distinguished between lean, normal, and excessive fat groups of participants. The set contained The initial stage of analysis was to assess the associations between PA and DB variables and the relationship between subgroups of participants preferring specific PA and DB domains. It occurred that healthy DBs increased modestly in proportion to increased leisure time and active transport. Moreover, an even stronger relationship was observed between healthy DBs and vigorous-intensity PA. Meanwhile, sitting time was related negatively to unhealthy DBs. These results are in agreement with Mitchell et al. , who stuMore persons with healthy DBs were moderately or vigorously physically active, with less than 30% preferring low-intensity PA. This is in line with observations before the pandemic, which suggested that 27.5% of young adults do not achieve the levels of PA necessary for health . In addiactive transport, leisure time, walking intensity (lowintensity), and healthy dietary behaviors. HCA of the groups with different BFP confirmed similarities between lean and lower normal groups and between upper norm and excessive fat groups, separately, as well as dissimilarities between these clusters.The main stage of analysis was to identify the set of variables that might efficiently discriminate participants between subgroups previously separated based on body fat percentage . This kind of analysis, which is a novel approach to the problem, has two main benefits. The first is a set of variables identified as the most discriminated subgroups. This set can be treated as variables worth targeting to prevent overweight . The second one is the determination of canonical discriminatory functions for classifying individuals into groups differing in BFP. To the authors’ best knowledge, no works in the field of PA and DB relationships have attempted to solve the aforementioned problem. Most analyses aimed at assessing the variability between groups of subjects with different PA levels, body composition, or DBs used ANOVA or various regressions. This approach made it possible to highlight individual variables for which groups significantly differed and identify predictors of dependent variables. The multidimensional approach indicated the four-element subset that best differentiates the selected groups. The subset contained p < 0.05). This is convergent with results suggesting significant risk factor for being overweight and obese included no frequent leisure time physical activity [Participants  who are active commuters, active during leisure time even with low-intensity and healthy eating were more likely lean or have normal body fat than participants who are not related to these domains. The coexisting components found in this study are partially in line with other authors. Most studies have shown a beneficial effect of leisure time on body weight and body fat mass, which was confirmed in systematic reviews ,45. Leisactivity . Howeveractivity . Regardlactivity . This shactivity . Nonetheactivity ,51.Our own results, consistently with others, indicated the importance of active commuting as a factor related to body mass composition. Many studies indicate that using active transport in work, school, or for private purposes constitutes an essential element of overall individual PA . The greA healthy diet is a foundation for health and well-being, while an unhealthy diet is a risk factor for various diseases . The algA practical result of many studies is the set of regression equations to predict the value of dependent variables such as body weight components based on significant predictors. These equations can be used in various groups of people. Usually, different kinds of regression are used. In this article, the practical achievement was to determine the classification functions, which can be used to classify individuals based on a set of variables the most discriminated participants into 4 groups of BFP. Although our own methodology is quite different to regression analyses and classification equations cannot be directly compared or discussed with regression equations, our own results are consistent across identified variables. Taking into account the studied problem, the most often indicated predictors of overweight and obesity were the intensity of PA and healthy dietary behaviors (particularly consuming fruits and vegetables). Both domains were negatively related to body weight or body fat  ,67. MoreLimitations of this study include its cross-sectional design, which limited causal inference analysis. Another limitation is the inclusion of self-reported data. Despite empirical verification of the data reliability, the error resulting from such data acquisition is less robust than measuring directly. Moreover, collecting data through a web-based survey can affect accuracy. Nonetheless, a better response is expected from an online survey as respondents are more likely to complete it.In conclusion, weak relationships between PA domains and DBs resulted from heterogeneous behaviors and mixed behavior patterns. Identification of the trajectory of the frequency flow through specific PA and DB areas allows for well-designed, tailored intervention programs that can promote healthy habits in adolescents. Thus, identifying the set of variables that most discriminate between lean, normal, and excessive fat groups is a reliable target for developing interventions. Intervention programs should use a combination of increased physical intensity, especially during leisure time, and healthy eating. It is also advisable to promote active transport on foot or by bicycle. Authorities and policy-makers could include a prescription for lowering body fat in education through a combined program of nutrition and PA, which could shape people’s awareness of the need to change their lifestyle habits. A practical achievement are the equations classifying the individuals based on a set of four variables (PA and DB variables) into four groups with different levels of body fat. These equations can be useful in conducting interventions.


Methionine sulfoxide (MetO) has been identified as a risk factor for vascular diseases and was considered as an important indicator of oxidative stress. However, the effects of MetO and its association with moyamoya disease (MMD) remained unclear. Therefore, we performed this study to evaluate the association between serum MetO levels and the risk of MMD and its subtypes.We eventually included consecutive 353 MMD patients and 88 healthy controls (HCs) with complete data from September 2020 to December 2021 in our analyzes. Serum levels of MetO were quantified using liquid chromatography-mass spectrometry (LC–MS) analysis. We evaluated the role of MetO in MMD using logistic regression models and confirmed by receiver-operating characteristic (ROC) curves and area under curve (AUC) values.p < 0.001 for all). After adjusting for traditional risk factors, serum MetO levels were significantly associated with the risk of MMD and its subtypes . We further divided the MetO levels into low and high groups, and the high MetO level was significantly associated with the risk of MMD and its subtypes . When MetO levels were assessed as quartiles, we found that the third (Q3) and fourth (Q4) MetO quartiles had a significantly increased risk of MMD compared with the lowest quartile .We found that the levels of MetO were significantly higher in MMD and its subtypes than in HCs (In this study, we found that a high level of serum MetO was associated with an increased risk of MMD and its subtypes. Our study raised a novel perspective on the pathogenesis of MMD and suggested potential therapeutic targets. RNF213 variants suggested the genetic etiology in MMD (Moyamoya disease (MMD), as a chronic intracranial vasculopathy, was unknown for its etiology. MMD was characterized by the stenosis or occlusion at the terminal portion and main branches of internal carotid artery (ICA) associated with the formation of anomalous collateral circulation on angiography . MMD wasy in MMD . Our prey in MMD . These fy in MMD . HoweverIn recent years, with the further study of multi-omics technologies, researchers have found that the ultimate effect of gene or protein regulatory action was to cause changes in metabolites in body . MetabolTherefore, this study aimed to demonstrate the correlation between MetO and the risk of MMD, contributing to the exploration of novel risk factors and the identification of potential therapeutic targets in MMD.In this prospective study, we consecutively included MMD patients from September 2020 to December 2021 and collected the clinical data. All patients were diagnosed as MMD with digital subtraction angiography (DSA) according to the 2012 Japanese diagnostic criteria . Minors RNF213 p.R4810K variant with primer designation of RNF213-4810F 5’-GCCCTCCATTTCTAGCACAC-3′ and RNF213-4810R 5’-AGCTGTGGCGAAAGCTTCTA-3′. Neurological status was assessed by the modified Rankin Scale (mRS) at admission and divided into two groups . The Suzuki stage of MMD patients was defined as the more severe side.All baseline characteristics, including demographic data (age and gender), medical history , clinical features  were collected by independent chart reviews. Fasting peripheral blood samples from all participants were used for routine and biochemical blood tests, as shown in p value<0.05 was considered statistically significant.The SPSS software (version 26.0) and R project (version 3.6.3) were used for all statistical analyzes. Continuous data were compared using t-tests or Mann–Whitney U tests between two groups. Kruskal-Wallis tests or one-way ANOVA were used for comparisons among multiple groups. Continuous data were presented as mean with standard deviation (SD) or median with interquartile range (IQR). Categorical variables were compared with Pearson chi-square tests, Fisher exact tests, and Kruskal-Wallis tests, and categorical variables were presented as frequencies. Spearman correlation tests were used to assess the association between clinical characteristics and MetO quartiles. We have performed three logistic regression models to analyze the role of MetO in MMD and its subtypes, including Crude model, Model 1, and Model 2, which have been described in previous studies . Crude mp > 0.05). However, rates of hypertension, diabetes, hyperlipidemia, smoking, and drinking were significantly higher in the MMD group than in the HC group . Regarding clinical features, the MMD group had significantly higher levels of SBP (p < 0.001), DBP (p = 0.001), and BMI (p = 0.005). Additionally, levels of inflammatory biomarkers were significantly higher in the MMD group, including WBC count, NEUT count, NLR, SII, and MHR . There were also more risk factors in the MMD group, including significantly higher levels of TG, Hcy, and lower levels of HDL-C . In addition, the MMD group had significantly higher levels of MetO compared to the HCs  and 88 age-matched HCs were eventually included for analysis. Baseline variables were compared between HCs and MMD patients . There wp = 0.015), indicating a higher risk for ischemia. Levels of MetO were significantly higher in both ischemic-type and hemorrhagic-type MMD groups than in the HC group .The MMD patients were categorized into two groups based on the median level of MetO, including low-MetO group and high-MetO group. The clinical characteristics of MMD patients in both groups have been shown . Althougp = 0.006). Moreover, patients with higher levels of MetO were associated with higher levels of BMI, UA, MHR, and lower levels of HDL-C and ApoA .We also compared the clinical variables of MMD patients based on the quartile levels of MetO . The resp < 0.001; p < 0.001). In Model 2, MetO was also found to increase the risk of MMD . The ROC curves revealed that the predictive accuracy of Model 2 , Model 1 , and Model 2 , Model 1 , and Model 2 . The AUC value of ROC curves increased with the development of the models , Model 1 , and Model 2 . The AUC of ROC curves in Crude model, Model 1, and Model 2 were 0.671, 0.792, and 0.889, respectively , Model 1 , and Model 2 . The AUC of ROC curves in Crude model, Model 1, and Model 2 were 0.711, 0.754, and 0.831, respectively  result in the oxidation of Met to MetO, including two stereoisomers, S-MetO and R-MetO . The incThe link between MetO and cerebrovascular disease has been shown. A recent large prospective study on metabolomic characteristics of ischemic stroke found a significant association between MetO and incident stroke, with higher levels being associated with increased risk . AnotherMMD was considered to result from a combination of inflammation, genetic, and other factors. Previous evidence showed that levels of oxidative stress in endothelial colony-forming cells were significantly increased in MMD patients . By elimOxidative stress was generally accompanied by inflammation responses characterized by the production of ROS and the release of inflammatory cytokines. MetO has been shown to induce the activation of M1/classical macrophage activation, alter the extracellular nucleotide metabolism, and promote the increase of macrophage ATPase/ADPase activity . The Metin vitro and in vivo.Our previous study has shown that several factors, such as ALB, BMI and HDL-C, were associated with the risk of MMD . In thisIn summary, our study revealed that the levels of MetO was increased in MMD patients, and we identified MetO as an independent risk factor for MMD and its subtypes. These findings suggested that MetO reductases might serve as potential therapeutic targets. Our study provided a novel perspective on the risk of MMD.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethics Committee of Beijing Tiantan Hospital, Capital Medical University. The patients/participants provided their written informed consent to participate in this study.JL illustrated all the results and drafted the manuscript. PG and CZ performed all the statistical analyzes. QH, CL, CT, and YuZ collected the data. JW, QZ, RW, and YaZ revised the manuscript. DZ and JZ conceived and designed this research. All authors have read and approved the final version of the manuscript.This study was supported by the National Natural Science Foundation of China (81701137), the National Key Research and Development Program of China (2021YFC2500502), Beijing Municipal Organization Department Talents Project (2015000021469G219), and Beijing Municipal Administration of Hospitals’ Mission Plan (SML20150501).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.


Artemisia is one of the important alternative treatments for many diseases, as well as the prevention of the effect of oxidizing substances that cause damage to the various organs of the body, including the liver and kidneys. The kidney and the liver are considered the body's most critical organs, and their functions in storage, metabolism, detoxification and elimination of medications, and their metabolic products make them target structures for “drug-induced” harm. The goal of this investigation was to see if Artemisia extract might protect hepatic and renal tissues from diclofenac-induced damage.a total of 40 adult Wistar rats were separated equally into four groups randomly. The rats of the control group got only distilled water orally without medicine or therapy, while those in the second group administrated 100mg/kg/day of Artemisia orally for one month. The third group received 10mg/kg/day of Diclofenac (DF) orally. The fourth group received 10mg/kg/day of DF and 100mg/kg day of Artemisia orally. After one month, kidney parameters  and liver parameters (aspartate aminotransferase (AST), alanine aminotransferase (ALT), and alkaline phosphatase (ALP)) were measured.the results revealed increasing in the kidney  parameters and liver parameters  in the group treated with diclofenac compared to the control group while they decreased significantly (p≤0.05) in diclofenac + Artemisia group comparing to diclofenac group.we conclude from these results that Artemisia may have a role in reducing the toxic effect of diclofenac on kidney and liver by decreasing the liver enzymes and kidney criteria in the blood. The aim of the present study is to evaluate the role of Artemisia to reduce the toxic effect of diclofenac on liver and kidney. Because of the abundance of active principles provided by nature during millions of years of evolution, medical valuable plants, serve a substantial role in modernistic medicine. Plant compounds, also known as phytochemicals, have a wide range of physiologically active, beneficial effects and help plants protect themselves against insects, bacteria, viruses, and other predators. To achieve the intended pharmacological effect, these phytochemicals operate on many pathways at the same time, either alone or in combination. Many medical valuable plants or herbs are venerable by traditional medical traditions  because of their therapeutic abilities, and plants account for more than 40% of modern pharmaceuticals -3. ModerDiclofenac (DF) is one of the drugs that belong to the family of nonsteroidal anti-inflammatory drugs (NSAIDs), and it is used to decrease pain, fever, and inflammation . DiclofeStudy size and design: forty male Wistar albino rats, the weight ranged (230-250 g) and the age ranged (2-3 months), were used. The rats were separated into four groups in plastic cages, fed a regular food, and given tap water and exposed to 12-hour light-dark cycle for 28 days . Each separated group comprising 10 animals: 1) Control group: the rats administrated only distilled water, no diclofenac nor Artemisia, for one month; 2) Artemisia group: from the first to the last day of the experiment, rats were given 100 mg/kg Artemisia daily orally through needle gavage; 3) diclofenac group: rats were given 10 mg/kg diclofenac orally every day from the first to the last day of the experiment; 4) Artemisia + diclofenac group: a dosage of 100 mg/kg of Artemisia and 10 mg/kg of diclofenac was given orally to rats via gavage every day from the first to the last day of the trial.Setting: the current study was carried out in the animal house of College of Pharmacy, University of Kerbala, Karbala City which lies in the middle of Iraq for the period from January 5th, to March 3rd, 2022. The animal house is equipped with all the necessary means to preserve the experimental animals and unify the external conditions such as cages of the same sizes, heating and cooling devices, air vacuums and thermometers, in addition to the necessary tools used for feeding and drinking water. The animal house is also managed by a number of skilled technicians who have experience in dealing with laboratory animals.Variables: the current study was experimental and depended on laboratory rats. They were randomly separated into four groups, control healthy rats, rats administrated diclofenac, rats administrated Artemisia, and rats administrated both Artemisia and diclofenac. All lab animals were exposed to the same conditions such as temperature, feeding, and light-dark period.Drug dose preparation: dose of diclofenac, 10mg/kg.bw [mg/kg.bw , was preAs an example, the dosage for 240 gm rat = 240/1000 = 0.24ml of stock solution which equal to 10mg/kg.bw.Plant extract preparation: the air-dried herb of Artemisia annua powder (100 g) was combined with one liter of heated distilled water, preserved at 60°C for 15 minutes, and afterward the mixture was filtered three times by filter paper. The resultant solution was left to dry at 37°C until it left behind a 62 percent w/w concentrated residue. The stock solution was preserved at -20°C until it was used, and normal saline was used to make up the diluted concentration [ntration .Serum collection: at the end of the experiment, two ml of blood was taken from each rat through a syringe, and then placed in a test tube and left for half an hour to clot, then the test tubes were transferred to the centrifuge, where the tubes were centrifuged at a speed of 4500 rpm for 10 minutes to separate serum from coagulant blood, which was subsequently frozen until biochemical examinations were done.Laboratory measurements: biochemical tests for kidney function including aspartate aminotransferase (AST), alanine aminotransferase (ALT), and alkaline phosphatase (ALP) in addition to liver function including albumin, creatinine, and urea were done for all groups of the study  groups. A Dri-Chem-NX500 auto-analyzer was used to measure biochemical analyses in blood samples . Dry-Chem analyzer was a dry chemistry analyzer that can carry out a variety of clinical chemistry test parameters. It includes an integrated auto-pipetting system, required no calibration or water, made setup and maintenance simple.Bias: the current study was an experimental study conducted on 40 male rats under controlled conditions in the laboratory, where the rats were selected of close ages and weights and from the same breed, and randomly divided into four groups and exposed to the same environmental conditions such as temperature, lighting period, and nutrition throughout the experiment period.Data analysis: statistical Package for the Social Sciences (SPSS) for windows operating system was used for statistical analysis. All data were reported as a Mean ± SE. The statistical analysis by using a “One-way analysis of variances” was done to examine the statistical significance of differences between the control and other groups .Ethics issues: the experiment of the present study was done according to instructions of animal ethics committee in Pharmacy Faculty, University of Kerbala, where the study was done, clearance number (Ph3423).Participant: the current experimental study was conducted on 40 male rats under controlled conditions in the laboratory. The rats were randomly assigned into 4 groups, each group containing 10 rats. All rats were close in age and weight and of the same breed. At the end of the experiment, the kidney functions represented by creatinine, albumin, and urea criteria, in addition to the liver functions represented by alanine and catalase enzymes, were measured and the following results were revealed.Kidney function:Liver function:2O2, and it provides excellent protection by enhancing the antioxidant defense system and decreasing the generation of reactive oxygen species (ROS) [+2 (e.g. heme) and producing reactive oxygen species (ROS), which can be cytostatic or cytotoxic. Reactive oxygen species can cause cellular injury through the peroxidation of membrane phospholipids, the activating of pro-apoptotic mechanisms, and the instability of genomic and mitochondrial deoxy-ribonucleic acid (DNA) [The toxicity of the liver and kidney, which is based on distinct pharmacological mechanisms, has a wide range of consequences resulting from cell destruction and causing acute or chronic functional abnormalities in the respective organs . So, thees (ROS) ,27. Arteid (DNA) . Artemis+2(like heme) and making ROS, which can either stop the growth of cells or kill them. ROS can damage cells by making it easier for membrane phospholipids to oxidize, by turning on pro-apoptotic pathways, and by making genomic and mitochondrial DNA less stable [The Artemisia plant has the unusual characteristics of modulating ROS. Sometimes in cases, they have a strong effect on hydroxyl ions and hydrogen peroxide as antioxidants and radical scavengers. They also may offer good protection by making antioxidants work better and reducing the production of ROS ,27. Artes stable . Some ofs stable . Our fins stable .et al. [et al. [et al. [Parasuraman et al.  and Bark [et al.  discover [et al. ,30. Kuma [et al.  discover [et al. ,26,32.In addition, the actions of enzymatic and non-enzymatic antioxidants in liver and kidney cells are inhibited. As a result, any medicinal reagent with antioxidant activity can reduce cellular damage produced by ROS and can be upgraded to be a medicated attitude to avoid DF harmful effects . A greatLimitations: in the current study, we used only adult and healthy male rats, while female rats were excluded also, low active rats were excluded.We can conclude from this study that Artemisia may have hepatorenal protective and preventive effects against diclofenac-induced liver and kidney damage. Finally, the results of this investigation showed that Artemisia may have preventive effect against liver and kidney cell damage caused by a variety of drugs, including nonsteroidal anti-inflammatory drugs (NSAIDs). However, further research is needed in humans to validate the efficacy, therapeutic effects, adverse effects, and exact dosing.Herbal medication is a good choice for the treatment of different disorders caused by chemical drugs.Artemisia has a surprising effect in reducing the toxic effect of nonsteroidal anti-inflammatory drug on liver and kidney.


Human exposure to pollutants in indoor environments is a relevant health concern. Long-term monitoring data in indoor environments are largely missing due to a lack of adequate measuring devices. Biomonitoring  can provide useful information on indoor air quality and effects. Given their ability to intercept pollutants from the atmosphere, lichens and mosses are commonly used as outdoor biomonitors of atmospheric pollution by potentially toxic elements. Their application in indoor environment is recent but represents a promising output of the scientific research. In this review, indoor biomonitoring studies carried out using mosses and lichens have been compared, and critical issues and open matters have been underlined, as well as future perspectives related to their fruitful application in indoor environments. This review highlights the peculiarities of each study and the need for the development of shared harmonised protocols.Biomonitoring in indoor environments is a recent application, and so far, indoor air quality (IAQ) has been investigated only in a few cases using photosynthesising biomonitors. On the whole, 22 studies have been selected and reviewed, being specifically focused on the assessment of IAQ using biomonitors, such as lichens (9 papers), mosses (10), or their combination (3). In general, indoor samples face an altered light regime, ventilation, and a reduced hydration, which should be taken into consideration during the design and implementation of indoor monitoring. This review highlights critical issues (and some solutions) related to sample devitalisation (moss), hydration during exposure, preparation of the exposure device (mostly lichen and moss bags), duration of the exposure, post-exposure treatments, assessment of the vitality of the samples, as well as data elaboration and interpretation. This review evidences the feasibility and usefulness of lichen/moss monitoring in indoor environments and the need to develop standardised protocols. There in health . This isn health .Despite awareness concerning the problem of IAQ, long-term monitoring measurements in indoor environments are largely missing due to a lack of adequate measuring devices . In factSince biomonitoring of IAQ is a fairly recent application, several matters still must be addressed to adapt the (outdoor) biomonitoring technique to indoor conditions. In this review, selected papers involved in indoor biomonitoring using mosses and lichens have been compared, and critical issues and open matters have been underlined, as well as future perspectives related to the use of biomonitors in indoor environments.A systematic review was carried out with English terms in WOS, Scopus, as well as Google Scholar and finally integrated using a free search. The catch terms “indoor(s)”, “air quality”, “IAQ”, “air pollution”, “biomonitoring”, “heavy metals”, and “mosses” or “lichens” were combined to select suitable papers.After duplicate removal, title screening, and abstract revision, the single papers were classified by type of biomonitor and ordered according to the year of publication. Only papers with full text in English have been included. Twenty-two studies have been selected and deeply examined, being specifically focused on monitoring IAQ using lichens (nine papers) and mosses (ten) alone, or in combination (three). With one exception ;Methodological aspects related to the monitoring devices  or to tProtection of cultural heritage .Among the reviewed literature, the main research topics can be summarised as follows:Twenty-one papers considered the content of major/trace elements in mosses/lichens : some ofWith the exception of the study by da Silva et al.  (in BrasFlavoparmelia caperata. Samples were collected from a clean environment and exposed for two months (April–June 2010) indoors (classrooms) and outdoors (courtyards) of the studied primary schools. Lichens, once set to suitable bark pieces, were displayed inside trays and exposed in the classrooms, while those placed outside were bound to tree branches, in both cases at about 1.80 m from the floor. Accumulation data were interpreted in terms of exposed to control (EC) ratios [Canha et al.  carried ) ratios . EnrichmThe study by Canha et al.  represenF. caperata and according to the site and identified the contribution of sea salt spray and industrial pollution as further outdoor sources for indoor depositions.The work by Canha et al.  consistsPseudevernia furfuracea as biomonitor of trace elements  and polycyclic aromatic hydrocarbons (PAHs) in five selected primary schools in Latium : one in a highly urbanised area and four in rural settings. Lichen bags  were exposed for two months (February–April 2014), and indoor (classrooms) and outdoor (gardens in the schools) environments were compared (EC ratios were used for data interpretation). Higher outdoor concentrations of trace elements and PAHs were found in the urban area (affected by traffic emissions), and EC ratios overall reflected a bioaccumulation for all investigated elements. On the other hand, indoor/outdoor ratios were <1, indicating that indoor air was only partially affected by outdoor pollutants, except for Cd in the urban area and for Hg and PAHs in the rural area. Traffic emissions were less relevant in the rural areas, and moreover, an explication for higher indoor PAHs could be that windows were generally closed during the winter season, especially in rural areas (the weather was colder than in the urban area).Protano et al.  used theEvernia prunastri. Lichen bags were placed for two months indoors and outdoors in a school and a private house for each study area. Samples exposed indoors were regularly sprayed (not washed) with distilled water (up to three times per week). Lichens exposed outdoors significantly accumulated (EC ratio) most of the investigated elements  in the urban area, while only a few  were accumulated in the rural area.During a citizen science experiment that involved teachers and students, Paoli et al.  assesseda fluorescence emission) was not affected.Independently of the outdoor concentrations, the indoor values were overall similar, both in rural and urban buildings. An indoor accumulation occurred for a few traffic-related elements , but on the whole, IAQ in the schools was not affected by outdoor conditions. The vitality of indoor exposed samples  have been exposed for two months (between October and December 2017) inside the cabin of 10 volunteer’s cars (smokers and non-smokers): the bioaccumulation of heavy metals, nicotine content, and thallus vitality (by chlorophyll a fluorescence emission) have been investigated. Two different lichen bags have been placed within the cabin of each car . Prior to the exposure, the samples were washed via sequential immersions (three times) in deionised water, while during the exposure, the samples were not sprayed. After two months in smokers’ cars, lichens accumulated relevant amounts of metal(loid)s  and nicotine. The exposure decreased the photosynthetic activity of the thalli by 60% in comparison with non-smokers’ cars. Exposed to control ratios revealed an indoor uptake also for Cu and Sb in non-smoker’s cars, caused by traffic pollution.Paoli et al.  demonstrE. prunastri and Ramalina farinacea) were exposed using the lichen bag technique for 3 months. Ecophysiological parameters  and the accumulation of metal(loid)s  were measured. Since Pb bullets were used within the shooting range, an overall contamination from Pb was expected. In fact, based on their Figure 1, a significant uptake of Pb was detected in E. prunastri, in terms of exposed to control ratios  were reported in both species, accompanied by a significant alteration of membrane permeability in E. prunastri .Sujetovienė and Česynaitė  investigi et al. ), reflecCladonia verticillaris) for 20, 40, 60, and 90 days (November 2018–January 2019) and recorded the content of photosynthetic pigments (as well as chlorophyll degradation) in the transplants. The variability in indoor light (not uniform among the investigated environments) appeared as a relevant driver for chlorophyll modifications, likely confounding the effects of CHOH contamination.The study by da Silva et al.  focused E. prunastri) were exposed for about 3 months (from October 2020 to the beginning of January 2021) at increasing distances from the closest road, highly concerned with particulate matter from vehicular traffic. An outdoor/indoor mixed sampling design was applied. The concentrations of Al, Ba, Cd, Cr, Cu, Fe, Ni, Sb, Sn, and Zn were investigated in the transplants, together with the magnetic properties. The magnetic properties of the transplants  showed that the bioaccumulation of magnetite-like particles decreased exponentially with the distance from the road [Winkler et al.  investigthe road . The expAt the beginning of the nineties, Al-Radady et al. ,29 carriSphagnum spp.), comparing, respectively, devitalised (by HNO3) hydrated and dry mosses for 30 days. In fact, a common practice using moss bags is the preliminary devitalisation of the material, as also carried out in this research. They demonstrated that keeping the moss hydrated (with deionised water supplied by a simple device made by a capillary mat) improved its collection efficiency (of dry depositions), both indoors and outdoors. In the second study, the attention was focused on indoor pollution by Pb, selecting six houses in the areas of Bradford and Manchester (UK): besides measuring overall dust and Pb depositions outdoors and indoors (and in relation to the season), the study involved a transplant experiment with devitalised and irrigated moss bags (Sphagnum spp.), which were exposed for four weeks during 1990 (1–2 m from the floor in selected rooms). Noteworthy, the results highlighted peaks of depositions in proximity to the windows (in relation to wind flow) and a decrease within a few meters inside the investigated rooms, with wide variation according to the position of the room.In the first paper, they evaluated the feasibility of measuring metal deposition rates  both indoors and outdoors using irrigated moss bags (Hylocomium splendens to investigate emissions from road traffic within a tunnel in Wien (Austria). Moss samples were taken from a remote area in the Alps and exposed in September 2003 for four weeks by means of wooden frames (10 cm × 10 cm), covered with a thin plastic net with a mesh size of 1 cm × 1 cm. Mosses were not dried before the exposure and not sprayed during the exposure. Selected elements  and PAHs were analysed using ICP-AES, AAS, and gas chromatography/mass spectrometry detection (GC-MSD). Enrichment factors were used for data interpretation. Concentrations were significantly higher in moss bags exposed within the tunnel  than along busy roads outside tunnels.Zechmeister et al.  used theH. splendens for 49 days within a working environment (office) in the Czech Republic. Samples were taken from a clean area; then, after washing in distilled water, half of the material was devitalised  and exposed at about 2 m from the floor, while the other half was not devitalised but kept hydrated during the exposure in a sampling device set up for this purpose. Trace elements  were evaluated on a weekly basis. Hydrated samples featured higher levels of Sb, Si, and to a lesser extent, Pb, while no difference appeared for Cu and Hg.Motyka et al.  exposed Sphagnum girgensohnii). Samples were collected in May 2011 from a remote area near Dubna, cleaned from extraneous material , and gently air-dried. Moss bags were prepared and exposed for 10 weeks during autumn. The study included a multi-pollutant assessment of air contaminants  and an evaluation of health risks for employees. The results from the moss monitoring refer to 10 elements , whose values were interpreted in terms of absolute concentrations. The moss bags at the garage entrance accumulated higher amounts of metals than those exposed in the interior, likely in the form of dry particulate matter. Noteworthy, it was hypothesised that the lower relative element enrichment within the garages can be due to the dry indoor environment, which limited both moss physiological activity and further (ionic) element uptake.Vuković et al.  investigPleurozium schreberi in Poland. The study consisted of a comparison of samples  exposed for three months inside kitchens (smoking areas) and bedrooms (non-smoking areas). Heavy metals associated with tobacco smoke were assessed  with AAS, and noteworthy, hair samples from smokers and non-smokers were also analysed. Relative accumulation factors (RAFs) were used for data interpretation. Mosses exposed in smoking areas showed higher RAFs  than samples exposed in non-smoking areas. Some elements increased even in bedroom samples, suggesting the movement of pollutants to the supposed “clean” area. As expected, higher heavy metal concentrations were determined in hair samples from smokers.Rajfur et al.  monitoreHypnum cupressiforme). For this purpose, they selected six rural and six urban sites in the area of Naples , where coupled (indoor/outdoor) exposure of samples was carried out for 12 weeks (May–July 2017). Indoor samples were exposed in bedrooms or living rooms (2 m above the floor), while outdoor samples were exposed on the respective balconies (not protected from wind and rain).Capozzi et al.  comparedFor data evaluation, “elements were considered enriched when in 60% of the sites, post-exposure concentration exceeded pre-exposure concentration plus two folds the standard deviation” . OverallP. schreberi) for 8 weeks (May–July 2008) indoors and outdoors in 20 selected households. The sampling devices were similar to those described in Zechmeister et al. [Zechmeister et al.  investigr et al. . OutdoorH. cupressiforme) for 12 weeks (March–June 2019) in triplicate. Indoors, the bags were exposed in bedrooms or living rooms (2 m above the floor), while outdoors, the bags were exposed from the first to the third floor . Element concentrations were higher in the moss-bags exposed outdoors. The results revealed a similar accumulation profile, while some differences were related to the specific environments . The use of indoor/outdoor ratios (mostly lower than 0.75) suggested that the indoor pollution was strongly affected by outdoor conditions.Sorrentino et al.  investigSphagnum fallax, P. schreberi, and Dicranum polysetum) for 90 days (14 November 2020–14 February 2021) using the moss bags method. The content of 25 elements was measured, as well as chlorophyll a fluorescence emission . Relative accumulation factors (RAFs) were used for the interpretation of element concentrations. Most of the investigated elements originated from outdoor depositions; however, the results also suggested higher indoor uptake for Al, Ba, Cr, and Fe .Świsłowski et al.  investigPleurozium spp., Polytrichum spp., and Rhytidiadelphus spp.) and the lichen P. furfuracea exposed in moss and lichen bags in an underground parking in the town of Prešov (Slovakia). Samples were collected in June 2016 from a remote area. Prior to the exposure, the material was cleaned from impurities, washed in distilled water, and then air-dried (60 °C for 24 h). The samples were exposed for 6 weeks in 2017 . Fourteen elements were investigated , and relative accumulation factors (RAFs) were used for data interpretation. A significant uptake was detected in the case of Fe, Mn, Ni, and Zn in the parking lot. The overall accumulation capacity followed the order Pleurozium spp. > Rhytidiadelphus spp. > P. furfuracea > Polytrichum spp., but since it changed depending on element and taxon, the study highlighted the usefulness of combining different biomonitors to obtain a clearer picture of indoor air pollution.Demková et al.  comparedHypogymnia physodes) and moss bags (Dicranum scoparium) for one month in 2017. Samples were collected in July 2017 from a remote area. Prior to the exposure, the material was cleaned from impurities, washed in distilled water, and then air-dried at room temperature. The study also included a comparison of hydration treatments: in each room, one batch of samples was sprayed (once a week); the other batch was left untreated. A group of elements with potential toxicological concern was investigated , and relative accumulation factors (RAFs) were used for data interpretation.Demková et al.  assessedUnder the experimental conditions, hydration of the samples did not influence the metal uptake. Higher concentrations were found in the moss, namely for Al, Cr, Cu, Fe, Ni, Pb, and Zn, while the lichen accumulated much more Cd and Mn. Laboratories were more contaminated  than offices; pc rooms showed the highest values of Cd and Cu.2) [0 in the atmosphere posing a potential health risk for the employees of the herbaria—as shown, e.g., by Oyarzun et al. (2007) [P. furfuracea, the moss H. cupressiforme, and bark pieces of Pinus nigra collected in a remote area were transplanted to the inside of selected rooms of the herbarium of the University of Florence  to assess Hg contamination after 3 and 6 weeks of exposure. The results revealed a significant accumulation in all biomonitors, with peaks of 0.656 μg/g in the lichen and 0.533 μg/g in the moss, suggesting indoor-air contamination [A noteworthy example of biomonitoring applied to indoor environments is a pilot study carried out to evaluate the residual contamination caused by mercury bichloride (HgCl2) . Such a . (2007)  in Spainmination .The assessment of IAQ in work and life environments is of paramount importance to estimate the total risk of exposure for humans and to identify relevant pollution sources . BiomoniE. prunastri (four), P. furfuracea (three), R. farinacea (one), H. physodes (one), and the soil lichen C. verticillaris (one). The foliose lichen F. caperata (often used as outdoor biomonitor) has been used in Portugal (one). Fruticose species are in general easy to collect, transplant and prepare for the analyses, and the thallus has a wide surface/volume ratio, considered as well suited to intercepting ambient particles. This holds true also in indoor environments. However, which part of the thallus has to be taken for measuring elements in indoor monitoring has been explicitly reported only in Paoli et al. [E. prunastri) were selected for the analysis and that this choice is foreseen by the protocols generally applied in the field of biomonitoring with lichens (at least for fruticose lichens). As a rule of thumb, the outermost portions of the thalli should be selected, being better exposed and, hence, able to intercept pollutants. In the case of mosses, the selection of apical segments (ca. 3–4 cm) excised from the shoots in H. splendens [H. cupressiforme [S. girgensohnii [P. schreberi [Accounting for the fact that the research by Canha et al.  led to ti et al. ,26. Theyplendens , that ofssiforme , the greensohnii , and livchreberi ,22 was sPleurozium spp. (mostly P. schreberi) (four), Sphagnum spp. (four), H. cupressiforme (three), H. splendens, and Dicranum spp. (two).The studies with mosses were chiefly carried out with 3 [Concerning sample treatment, referring to general protocols available for bioaccumulation (outdoor) studies with lichens and mosses has been suggested, where such aspects have already been faced , allowi3  and refe3 ; further3 , referri3 ; litter 3 ; and in 3 ,27), eveIn general, the time span between collection and exposure should be as short as possible. If the samples are not prepared within seven days, it is recommended to store them dry in a freezer . AccounThere is a need for standardised procedures in biomonitoring indoor environments. The highest variability is probably the position and treatment of the transplants: Vuković et al.  mentioneIndoor sampling devices have usually been exposed between 1 and 3 m (garages) from the ground/floor, mostly at 2 m. Referring to the available data , the exposure devices consisted of lichen and/or moss bags in 12 out of 22 papers, with 2–10 g of lichen/moss material, dimensions from 10 cm × 10 cm up to 20 cm × 20 cm, and mesh sizes of 0.5–1 cm for lichens and 0.2–1 cm for mosses. Canha et al.  exposed The time of exposure in indoor environments ranged between 3 and 12 weeks . Most ofBeyond pollution, lichen and moss metabolism is basically related to water availability and light irradiance . Indoor E. prunastri, after 8 weeks) in schools and houses was comparable to that of the nearby samples exposed outdoors. Hence, such a time span was considered adequate to detect a signal upon element uptake but avoiding a physiological alteration of the samples. A similar result was obtained when exposing E. prunastri inside non-smokers’ cars: the vitality was not statistically different from that of control samples, and relevant alterations in the photosynthetic activity were measured in samples that travelled in smokers’ cars, which were caused by smoke and not by the stay inside the car [a fluorescence emission) in their mosses exposed indoors for 12 weeks (and not hydrated). Sujetovienė and Česynaitė [a fluorescence emission and membrane integrity as well as oxidative stress in indoor exposed samples, while Canha et al. [F. caperata exposed in schools, measuring higher values of electric conductivity  in some of their indoor samples. The interaction between pollutants and the indoor conditions was evident in the study by da Silva et al. [V/FM [Paoli et al.  reported the car . On the  the car  reportedesynaitė  reporteda et al. ,14 obsera et al. : indoor a et al.  indicatel. [V/FM , and/or l. [V/FM .With mosses and lichens being poikilohydric organisms, hydration of the thallus highly depends on water availability in the surrounding environment. This aspect is particularly relevant in an indoor environment. In most studies, living samples have been exposed and retrieved at the end of the transplant without further treatments . OtherwiPost-exposure treatments include drying; cleaning from extraneous materials (mandatory for outdoor samples); and eventually, storage of the material . It is Data interpretation has been generally based on common procedures applied for outdoor biomonitoring with lichens and mosses. Concerning lichens, all studies but one  investigRecently, Cecconi et al.  proposedIn order to disentangle major and trace metal pollution sources between indoor and outdoor environments, indoor/outdoor ratios (I/O) were often used, allowing for establishing (or at least trying to) the outer or internal sources for the investigated elements ,21,23,24This review of the existing literature pointed out the need for the development of standardised protocols also for indoor biomonitoring because, so far, IAQ has been investigated only in a few cases using mosses and lichens as biomonitors. Since mosses and lichens are not just passive sorbents, the importance of the vitality of the selected biomonitor has been highlighted, which should be considered as an advantage and not a limitation for the use of living organisms to detect air pollution effects. In general, indoor samples face an altered light regime, ventilation, and a reduced hydration, which should be taken into consideration when designing indoor monitoring studies.E. prunastri, while the studies with mosses were chiefly carried out with Sphagnum spp., Pleurozium spp. (mostly P. schreberi), or Hypnum cupressiforme.As a summary, in the case of lichens, the species mostly used was the fruticose lichen All lichen-based studies employed living samples, while devitalisation was a common practice in moss monitoring. The sample treatment (prior and after the exposure), the time span between collection and exposure, as well as the analytical methods should refer to general protocols available for bioaccumulation in outdoor studies with lichens and mosses, where such aspects have been already faced.The correct position and the treatment of the transplants during their stay still remain open matters: indoor sampling devices have usually been exposed between 1 and 3 m from the ground/floor, mostly at 2 m; however, the position was arranged according to the peculiarities of each study and the availability of adequate support for the exposure. The exposure devices usually consisted of lichen and/or moss bags, with 2–10 g of lichen/moss material, dimensions from 10 cm × 10 cm up to 20 cm × 20 cm, and mesh sizes of 0.5–1 cm for lichens and 0.2–1 cm for mosses. Alternatively, wooden frames or bark pieces were used as exposure device.The time of exposure in indoor environments usually ranged between 3 and 12 weeks: most of the studies with mosses adopted a time span of 10–12 weeks, while in the case of lichens, 8–9 weeks were deemed as a suitable period for monitoring IAQ.During their stay in indoor environments, living samples were exposed and retrieved without further treatments; alternatively, they were hydrated by periodic spraying with water (lichens), once or twice a week, or irrigated continuously (mosses) with deionised water supplied by a capillary mat. Non-destructive tests  can be used to assess the vitality of the samples, discriminating between the effects of indoor pollution and those of exposure conditions.Data interpretation has been generally based on common procedures applied for outdoor biomonitoring with lichens and mosses, which could be further developed, integrated, and adapted to indoor monitoring. In order to disentangle major and trace metal pollution sources between indoor and outdoor environments, I/O ratios are often used.As a concluding remark, in spite of some critical issues, this review evidenced the feasibility and usefulness of lichen/moss monitoring in indoor environments.


Segurilab and Previne Salud laboratories performed the tests. The dataset contains about 400 clinical cases. Each patient agreed to participate in the study by sharing the results of their PCR (reverse transcription polymerase chain reaction) tests and CBC (complete blood count). CBC test measured several components and features of the blood, including red blood cells, white blood cells, hemoglobin, hematocrit, and platelets. The shared data are intended to provide researchers with input to analyze various events associated with the diagnosis of Covid-19 linked to potential diseases identified in the components measured in the CBC test. These data are helpful for pattern analysis of blood components in modeling prediction and clustering problems. The components measured in the complete blood count and CRP together can be helpful for the analysis of different medical conditions using machine learning algorithms.In this work, we present the complete blood count data and PCR test results of a population of Ecuadorians from different provinces, primarily residing in the Andean region, especially in Quito. PCR was the standard test to detect Covid-19 during the pandemic since 2020. The data were obtained between March 1st and August 12th, 2021.  Specifications Table•The data provided favor the identification of medical conditions by analyzing patterns in the values of the CBC test associated with the results of the infection caused by SARS-Cov-2.•The shared blood component values and COVID-19 results can be used for research separately or in children up to older adults of both genders.•These data are suitable for training and benchmarking machine learning algorithms, focusing on prediction, and clustering techniques.•The CBC test data can serve as input for other investigations that attempt to discover the comparative ethnic characterization of Latin American populations using data from an Ecuadorian population as a reference.•The data can motivate the characterization of a patient's condition, through measurements of blood components, in association with age and gender.•Governments can use the CBC as a tool for COVID-19 diagnosis in scenarios of insufficient tests. Hemogram results from asymptomatic and symptomatic individuals could be used as an alternative to detect infections reducing the application of costly tests such as the PCR test and thereby saving economic resources.1The dataset was created to allow researchers to analyze the prediction and clustering of the CBC test values, together with the PCR test results, within the machine learning framework.2The data set presents information from PCR as a commonly used test for diagnosing SARS-Cov-2 infection by obtaining a positive or negative result for Covid-19. The CBCovid19EC dataset includes values from the CBC test, describing blood components measured in men and women from 7 to 82 years of age. The blood biometry tests were performed in private laboratories in Quito, with samples taken from patients from different provinces of Ecuador. The patients agreed to share their data by participating in this study. In total, 377 records included data on age, gender, and date of sample collection for PCR and CBC examination. In the processed data, an identifier feature is included for each case, the positive or negative result for COVID-19, available in the PCR characteristic, the date of the CBC test and the PCR test sample collection, sex, age, and twenty characteristics referred to the blood components measured in the blood count test. The raw data includes provincial characteristics such as the patient's place of origin, the laboratory, and the test type. These data were excluded from the processed data.In In In 3CBCovid19EC dataset shares information from PCR (reverse transcription polymerase chain reaction) and CBC (complete blood count) tests. The complete blood cell count (CBC) is one of the most frequently ordered laboratory tests The SegurilabPreviene SaludThe tests were performed by the laboratories There were 251 cases of negative results for Covid-19, in contrast to less than 40% of samples, represented by 126 positive cases. In addition, 141 women and 236 men participated in this study, with an average age of 37 years in both cases. To identify the average values of the blood components measured in the CBC test, these were grouped with the characteristics of sex and PCR.In In In summary, there are more samples with negative than positive values. The values of the blood components that present differences in the negative and positive results for Covid-19 are: Basophils, Eosinophils, Leukocytes, Lymphocytes, Monocytes, Neutrophils, Plaletets, and RBCs, while the blood components: Hematocrit, Hemoglobin, MCV, MCH, MCHC, MPV, MCV, and RDW are represented in similar or balanced values for both PCR results.The review committee approved the study at the Technical University of Manabí with protocol number CBI-UTM-20-11-20-ROA. Every Laboratory informed the study motivation and obtained patients' willingness before practicing the test. In the case of underage participants, informed consent was obtained from parents or legal guardians.R. Ordoñez-Avila: Data curation, Writing – original draft, Visualization, Validation, Writing – review & editing. J. Parraga-Alava: Conceptualization, Methodology, Supervision, Writing – review & editing. J. Meza Hormaza: Supervision, Writing – review & editing. L. Vaca-Cárdenas: Validation, Writing – review & editing. E. Portmann: Validation, Writing – review & editing. L. Terán: Validation, Writing – review & editing. M. Dorn: Validation, Writing – review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.