The presented study tracks the increase or decrease in the prevalence of seventeen different chronic non-communicable diseases in Serbia. This analysis considers factors such as region, age, and gender and is based on data from two national cross-sectional studies conducted in 2013 and 2019. The research aims to accurately identify the regions with the highest percentage of affected individuals, as well as their respective age and gender groups. The ultimate goal is to facilitate organized, free preventive screenings for these population categories within a very short time-frame in the future.The study analyzed two cross-sectional studies conducted between 2013 and 2019, using data obtained from the Institute of Public Health of Serbia. Both studies involved a total of 27801 participants. The study compared the performance of Decision Tree and Support Vector Regressor models with artificial neural network (ANN) models that employed two encoding functions. The new methodology for the ANN-L36 model was based on artificial neural networks constructed using a Latin square (L36) design, incorporating Taguchi's robust design optimization.The results of the analysis from three different models have shown that cardiovascular diseases are the most prevalent illnesses among the population in Serbia, with hypertension as the leading condition in all regions, particularly among individuals aged 64 to 75 years, and more prevalent among females. In 2019, there was a decrease in the percentage of the leading disease, hypertension, compared to 2013, with a decrease from 34.0% to 32.2%. The ANN-L36 model with Fuzzy encoding function demonstrated the highest precision, achieving the smallest relative error of 0.1%.To date, no studies have been conducted at the national level in Serbia to comprehensively track and identify chronic diseases in the manner proposed by this study. The model presented in this research will be implemented in practice and is set to significantly contribute to the future healthcare framework in Serbia, shaping and advancing the approach towards addressing these conditions. Furthermore, experimental evidence has shown that Taguchi's optimization approach yields the best results for identifying various chronic non-communicable diseases. Diabetes, unipolar depression, lung cancer, ischemic heart disease, and cerebrovascular disease are among the leading causes of mortality in the nation The significance and justification for utilizing analyses in public health research become evident through studies that provide insight into the mechanisms by which determinants of health influence health outcomes. For instance, trends in the incidence rates of cardiovascular diseases demonstrate that the risk of their occurrence increases with age For instance, systolic blood pressure is such a case. While most studies indicate that systolic blood pressure increases with age, research conducted on several isolated populations did not show such a trend Similarly, the increased incidence of lung cancer and liver cirrhosis in older age groups can, at least partially, be explained by their correlation with risky lifestyles. Studies have shown that both men and women are equally affected by chronic obstructive pulmonary disease (COPD), contradicting previous findings that had suggested the disease was more common in men. This shift can be attributed to the growing prevalence of smoking among women The surveillance of chronic non-communicable diseases (NCDs) and analysis of their prevalence serve multiple important purposes and objectives. These include timely health policy planning, identification of trends and risks, development of effective prevention strategies, early detection, and treatment. Such data are crucial for informed health policy planning and resource allocation based on priorities. Surveillance enables prevention and early diagnosis, improves patient outcomes, and reduces the burden of disease with global impact. The evaluation of intervention effectiveness allows for assessing outcomes and identifying areas for improvement. Moreover, it provides relevant information for raising awareness and public education, promoting healthy lifestyles, and supporting informed health decision-making. Through the analysis of disease prevalence and trends, the effectiveness of measures can be determined, and areas for further enhancement of strategies can be identified. The surveillance of chronic non-communicable diseases has a comprehensive impact on population health and well-being.In today's society, the prevalence of numerous stress-inducing factors contributes to the emergence and progression of diseases. Hence, alongside lifestyle modifications, regular preventive and diagnostic check-ups are crucial, and healthcare systems need to be enhanced with intelligent techniques. The healthcare industry incessantly seeks novel approaches to enhance the quality of healthcare services and improve the treatment outcomes for complex diseases. One area showing promise is Artificial Intelligence (AI), encompassing various branches such as Machine Learning (ML) algorithms and Artificial Neural Networks The creation of an innovative methodology that makes use of the experiment's sturdy construction and incorporates Taguchi's orthogonal vector plans highlights the novelty of this research One key advantage of this design construction lies in its ability to render the process insensitive to variation, while simultaneously ensuring that factors are neither lost nor duplicated N-of-1 study designs, diverse treatment profiles, and varying patient characteristics, establishing its versatility and wide-ranging applicability.It is crucial to note that the success of any chosen AI tool often hinges upon accurate predictions during the design stage. By leveraging Taguchi's orthogonal vector plans, the constructed ANN model holds the potential to generate realistic predictions for an extensive range of RO1: Introducing the new methodology with two coding functions for the ANN-L36 model for predicting the increase/decrease or constant dissemination of chronic non-communicable diseases from 2013 to 2019.RO2: Comparison of the new ANN-L36 model with ML algorithms and other ANNs based on different hidden layer functions. The remainder of the document is structured as follows: In Section The main research objectives that defined our study are as follows:2In this section, we will describe recent studies relevant to our research and present the authors' objectives and approaches for addressing medical data-related challenges. Specifically, we will discuss research conducted in the field of enhancing medical diagnosis and making various health predictions using various ML algorithms and ANNs. Using genetic algorithms, decision trees, and synthetic minority oversampling approaches, the authors of the research Two models were provided in a study published in Support Vector Machine (SVM) is widely recognized for its high classification accuracy in pattern classification. Researchers in An automated medical decision support system using a convolutional neural network (CNN) or EfficientNet implementation and ten layers of stratified cross-validation was one of the main goals of a study published in The researchers in The primary aims of the study A novel method for the prediction of five common chronic diseases such as breast cancer, diabetes, heart attacks, hepatitis, and kidney disease combining augmented artificial intelligence, specifically an Artificial Neural Network with Particle Swarm Optimization (PSO) is presented in the study Using AI and big data machine learning approaches, the researchers in In the study According to available literature and current understanding, no comparable studies have used ANN designs based on Taguchi's Orthogonal vector plans using two coding functions to predict chronic non-communicable diseases, despite the growing body of research in computer-aided medical diagnostics. Moreover, the accuracy found in this study is superior to that of any other scientific study. Furthermore, there are no similar studies using the most straightforward ANN constructed in this way, with a high convergence rate (less than eight iterations are required to perform the experiment).•Analyzing the acquired Mean Magnitude of Relative Error (MMRE) values.•Investigating various activation functions for the hidden layer of the ANN.•Identifying the most effective techniques for encoding and decoding input values, including approaches like the fuzzification/defuzzification methods.•Minimizing the number of necessary experiments while ensuring robust outcomes.•Conducting thorough testing and validation of the model across diverse datasets pertinent to chronic non-communicable diseases.To conclude, our goal is to explore which of the proposed combination of six distinct methodologies for the artificial neural network ANN-L36 gives the best results within the realm of medical diagnostics for chronic communicable diseases 3To accomplish the primary research objectives, we will outline the following in this section: ML algorithms such as Decision Tree and Support Vector Machine, and ANN with Radial Basis Function in standardized and normalized way will be showcased in subsection 3.1coming to diagnosis’, essentially classifying a patient's condition. Parameters such as max depth, min samples split, min samples leaf, max features, and min impurity decrease are used to fine-tune the algorithm. To explore the incidence rate of chronic non-communicable diseases by region, age group, and gender for the identified ‘leader disease’, the gain ratio formula, a variation on the information gain formula that reduces bias, is a better choice in this case.Decision tree classification is a rapid and effective method for categorizing datasets. The process involves two stages: training the classifiers with features from the proposed dataset and then testing their performance, typically measured in terms of accuracy. Creating sparse and dense regions in the data space is the fundamental principle behind decision trees. As long as the data isn't sufficiently homogeneous, the tree stops. After training, a decision tree capable of generating well-categorized predictions is returned The underlying concept beneath the SVM algorithm is the development of an optimum hyper-plane that can be used to classify patterns that are linearly separable. The ideal hyperplane is one that is selected from the collection of hyper-planes for pattern classification in a way that optimizes the margin or the distance between the hyperplane and the pattern's closest point. This algorithm's primary objective is to maximize the margin in order to enable accurate pattern classification. The better the pattern classification, the larger the margin. In the case of non-linear separable patterns, the supplied pattern is mapped into a new space, often one with a higher dimension, where it becomes linearly separable. The kernel function, Φ(x) can be used to map the given pattern into space with more dimensions, i.e., x → Φ(x). Adopting an alternative kernel function is essential for the SVM-based classification; common ones are linear, poly, rbf, and sigmoid The Radial Basis Function Artificial Neural Network (RBF ANN) is distinct from other ANN topologies because it uses a single hidden layer and computes the output in a different way. The hidden layer of an RBF ANN projects data into a higher-dimensional space, and its construction is based on the cover theorem. Consequently, there should be more neurons in the hidden layer than there are in the input layer. Either a linear activation function or no activation function at all may be used in the output layer. Different kinds of classifiers are frequently used for comparison. In this study, we compare RBF neural network classifiers trained with the symmetric Fuzzy Means algorithm and standard Support Vector Machine classifiers with Gaussian kernel functions 3.2Full Factorial Plan) P, where L is the number of levels of factor variation and P is the number of factors, when we have a big number of input factors (more than 6) and at a large number of levels (greater than 5) 11312 = 1 088 391 168 experiments. For instance, when utilizing three levels with 12 parameters and two levels with 11 parameters, the number of iterations needed is N = LP. Using a Taguchi orthogonal plan with 12 parameters (weight coefficients) at three levels and 11 parameters at two levels, only OA|36,211,312|= 36 experiments are needed. OA is the commonly used acronym to refer to Orthogonal Array in Taguchi's optimization method. In this research, OA will represent OA(ANNi), which signifies the actual risk calculated using the ANN-L36 orthogonal vector plan derived from Latin square extraction. Taguchi's robust design method reduces the number of experiments by 99.9999999669% (0.999999999669 = 1-(36/1 088 391 168)). Taguchi's orthogonal vector plan considers all parameters equally while taking a chosen subset of combinations and eliminating duplication. Additionally, they can be assessed separately from one another. Every level of a given parameter is observed to have an orthogonal vector plan. Every one of the (P-1) additional parameters is evaluated at least once at all L levels. The proposed ANN architecture in our experiment is ANN-L36. This architecture is based on the Taguchi orthogonal array L36 with 11 parameters on two levels L1 and L2, and 12 parameters on three levels L1, L2, and L3, where Wi, i = 1, …,12 We will introduce a novel ANN-based methodology in this subsection that was built using Taguchi's orthogonal vector plan. The number of parameters, weighting coefficients, and levels of each parameter determine Taguchi's robust experimental design in each orthogonal plane. When organizing as many tests as feasible in which all conceivable discrete values of each input component are combined, there are several strategies for establishing the dependence of the output on the input quantities using the FFP  respondents of both genders participated in the 2013 survey, and 13178 (47.4%) respondents of both genders participated in the 2019 survey. According to gender, 13190 (47.4%) were male respondents, and 14611 (52.6%) were female respondents. Observed by region: Belgrade region had 6760 (24.3%) respondents, Vojvodina region had 5925 (21.3%) respondents, Sumadija and Western Serbia region had 8659 (31.1%) respondents, and Eastern and Southern Serbia region had 6457 (23.2%) respondents. The respondents were of different age groups, from 15 to over 85 years old. Age groups' total share was as follows: from 15 to 24 years, 3241 (11.7%) respondents; from 25 to 34 years, 3590 (12.9%) respondents; from 35 to 44 years, 4140 (14.9%) respondents; from 45 to 54 years 4317 (15.5%) respondents; from 55 to 64 years 5268 (18.9%) respondents; from 65 to 74 years 4252 (15.3%) respondents; from 75 to 84 years 2510 (9.0%) respondents and over 85 years 483 (1.7%) respondents. The dataset consists of 17 chronic non-communicable diseases, where they are identified according to gender, region, and age groups for the 10 most prevalent diseases.4Within this section, we will showcase the outcomes derived from our study. It is evident from the findings that cardiovascular diseases encompass the most substantial portion of morbidity within the Serbian population. Hypertension is the most influenced in both periods, with 34.0% in 2013 and 33.2% in 2019. Lower back diseases are in second place, with 20.4% in 2013 and 19.55 in 2019. About half of the identified residents are affected by these two diseases, while the remaining 15 chronic diseases have the other half of the percentage share. The ranking goes as follows Cervical spine diseases with 13.1% in both years; High Cholesterol with 13.9% in 2013 and 12.6% in 2019; Coronary heart diseases with 11.5% in 2013 and 10.7% in 2019; Arthrosis with 10.4% in 2013 and 9.1% in 2019; Diabetes mellitus with 8.4% in 2013 and 8.5% in 2019; Allergies with 8.8% in 2013 and 8.0% in 2019; Depression with 6.8% in 2013 and 5.8% in 2019. The remaining chronic diseases are represented in a smaller percentage The prevalence of chronic non-communicable diseases in Serbian population in 2013 and 2019 was estimated using a univariate regression model. Significant variations in the observed periods were found using a multivariate regression model. In 2013, observed according to four regions; statistically significant differences were identified in the following diseases: Chronic lung diseases, Coronary heart diseases, Hypertension, Stroke, Lower back diseases, Allergies, Urinary track diseases, and High Cholesterol. In 2019, observed according to four regions, significant differences were identified in the following diseases: Myocardial infarction, Hypertension, Arthrosis, Allergies, Depression, and High Cholesterol. Statistically significant differences in disease prevalence were observed between the two time periods, particularly among different age groups. The diseases showing significant differences include Coronary heart diseases, Hypertension, Arthrosis, Diabetes mellitus, Allergies, Kidney diseases, Depression, and High Cholesterol. It's worth noting that in almost all of these diseases, there was a significant decrease in incidence by region. The highest percentage share of all diseases is present among residents in the underdeveloped regions of Sumadija and western Serbia and the regions of eastern and southern Serbia, and significantly lower in the more developed regions of Belgrade and the region of Vojvodina As expected, the results analyzed according to age groups show also statistically significant differences. In 2013, observed according to age groups, significant differences were identified in the following diseases: Chronic lung diseases, Coronary heart diseases, Hypertension, Stroke, Lower back diseases, Allergies, Urinary track diseases, and High Cholesterol. In 2019, observed according to age groups, significant differences were identified in the following diseases: Myocardial infarction, Hypertension, Arthrosis, Allergies, Depression and High Cholesterol. According to these two time periods concerning age groups, statistically, significant differences exist in almost all diseases, except for Stroke and Allergies. In almost all diseases, there was a significant decrease in incidence observed by age groups. The highest percentage share of all diseases is present in residents aged 64 to 75, the expected lowest in the age group 15 to 24 The obtained results analyzed according to gender, as expected, show statistically significant differences. In 2013, observed by gender, significant differences were identified in the following diseases: Chronic lung diseases, Coronary heart disease, Hypertension, Stroke, Lower back diseases, Allergies, Urinary track diseases, and High Cholesterol. In 2019, observed according to the gender, significant differences were identified in the following diseases: Myocardial infarction, Hypertension, Arthrosis, Allergies, Depression, and High Cholesterol. There are no statistically significant differences, except for the following diseases, observed according to these two time periods concerning gender: Coronary heart disease and Depression. In almost all diseases, there was a significant decrease in incidence observed by gender. A higher incidence of all diseases is present in the female part of the population compared to the male part observed in both periods “gain ratio” is primarily used in the field of machine learning, specifically in decision tree algorithms for attribute selection. It is employed to determine the relevance of a feature (attribute) in a dataset. Gain ratio is an improvement over information gain, addressing the bias that information gain has towards attributes with a larger number of distinct values. It's often used to help decision trees make more informed splits by considering the intrinsic characteristics of the attributes. In order to investigate the incidence rate of chronic non-communicable diseases, particularly focusing on the prominent ailment of Hypertension, across various factors including region, age group, and gender, the graphical depiction of the gain ratio is presented in The 2 robustly elucidates the correlation between the actual and estimated values.The robustness of the proposed methods and constructed models refers to their resistance or stability. Many inferences can be made from the correlation coefficients between the estimated and actual values in 2013 and 2019, which are shown in 5RO1: Introducing the new methodology with two coding functions for the ANN-L36 model for predicting the increase/decrease or constant spread of chronic diseases in the period from 2013 to 2019. All models showed that the incidence rate of almost all non-communicable diseases in Serbia is in decline and the percentage share of each individual disease is significantly lower in 2019 compared to 2013. Using the ANN-L36 model, it was additionally established that the incidence rate in the highly developed regions of Belgrade and Vojvodina is significantly lower than in the less developed regions of Sumadia and western Serbia, as well as eastern and southern Serbia. It was also determined that the percentage share of each chronic non-communicable disease is significantly higher in both observed periods (2013 and 2019). As expected, there are significant differences in the incidence rate according to age group, where the percentage of each disease is highest among the population aged 65 to 74 and the lowest among the population aged 15 to 24. It can be concluded that in the following period, the percentage share of each of the observed diseases will gradually decrease.RO2: Comparison of the new ANN-L36 model with ML algorithms and other artificial neural networks based on different hidden layer functions. By comparing the MMRE values for each of the used models, it can be concluded that ANN-L36 is the model with the slightest error that can precisely and accurately determine the incidence rate of chronic non-communicable diseases.Each region has a variable prevalence of chronic non-communicable diseases, which is inversely correlated with the region's level of development. The most prosperous regions of Belgrade, with an average of 2.4%, and Vojvodina, with 1.9%, have the lowest incidence rate of chronic non-communicable diseases, of which cardiovascular diseases lead with hypertension as the leading disease. In contrast to these two regions, the less developed regions of Sumadija and western Serbia, with an average of 2.6%, and the region of eastern and southern Serbia, with an average of 2.2%, have a higher incidence rate, but still with leading cardiovascular diseases. Observed by age groups, the research showed that the most vulnerable age groups with the highest incidence rate are from 75 to 84 years old, followed by the age group from 65 to 74. On the other hand, the respondents over 85 years old had a significantly lower percentage of disease on average in 2013, around 9.0%, while in 2019, it was 7.9%. As expected, the lowest percentage of the disease is in the age group of young people from 15 to 24 years old. All analyzes conclude that the female population gets sick significantly more than the male population in Serbia. In almost all chronic non-communicable diseases, the percentage of female respondents was 10.5% in 2013, and 4.6% in 2019 is significantly higher than that of male respondents, an average of 7.3% in 2013 and 3.3% in 2019. Based on the obtained results, the set research objectives of this study can be answered in the following way:6There haven't been any studies that provide data on all seventeen chronic non-communicable diseases in Serbia at the national level, based on what is currently known and the literature that is available. In developing countries like Serbia, the impact of cardiovascular diseases is the most prevalent, often leading to fatal outcomes such as heart attacks. For these reasons, it is imperative to change lifestyles, undergo preventive screenings, and improve the entire healthcare system. Prior to 2013, research indicated an increase in the number of chronic non-communicable diseases among the population in Serbia. However, this study demonstrates that overall healthcare system improvement can lead to better health outcomes for the entire society. As a result, a proportional reduction in the incidence of almost every chronic non-communicable disease in Serbia was observed between 2013 and 2019. These findings highlight the positive effect of organized preventive screenings, which need to be intensified and expanded to areas and age groups with higher disease prevalence. For instance, the most prevalent disease in Serbia, hypertension, showed a slight decrease from 34.0% to 32.2% during the observed period.Furthermore, the proposed models for regional and age group analysis can significantly contribute to the preventive healthcare system. These models enable rapid and efficient analysis of a large amount of collected data and identification of the most vulnerable regions, age groups, and genders. The most accurate model was the ANN-L36 Fuzzy algorithm with a relative error of 0.1% and correlation coefficients exceeding 0.98. Their application is not limited to medicine but can be utilized in various scientific fields such as neuroscience, pattern recognition, nuclear science, robotics, and others. With the continuous advancements in artificial intelligence diagnostics, the proposed approach can be used to develop tools that provide precise and rapid assessment of functionality necessary for diagnosing and treating different diseases. This can greatly assist professionals and medical teams in overcoming challenges frequently encountered in the field of medical diagnostics. By employing ML algorithms and ANN architecture, our proposed approach offers an efficient and effective solution for identifying the most vulnerable regions and age groups affected by diseases, ultimately leading to improved patient outcomes.Finally, comprehensive efforts to monitor and identify chronic diseases in the way suggested by this study have not been carried out on a national scale in Serbia. The model introduced in this research is poised for practical implementation, offering a substantial contribution to Serbia's forthcoming healthcare framework. This contribution will play a pivotal role in refining and advancing the approach to tackling these conditions. Moreover, empirical evidence has compellingly demonstrated that Taguchi's optimization approach outperforms other methods in pinpointing a range of chronic non-communicable diseases.6.1The limits of missing data must be taken into account when applying Decision Trees, Support Vector Regressors, and Artificial Neural Networks to forecast chronic non-communicable diseases based on national medical datasets. The dataset's missing values have the potential to generate biases and reduce the predictive models' accuracy. Incomplete patient records, resulting from missing data, can impede the comprehensive analysis and prediction process, as these models rely on complete data for accurate predictions.Furthermore, missing data can lead to reduced sample sizes, compromising the statistical power of predictive models. Models' ability to identify meaningful relationships and patterns linked to chronic non-communicable diseases may be hampered by smaller sample sizes, which would restrict the applicability of their conclusions to the whole population. Additionally, imputing missing values introduces challenges, as the chosen imputation methods may introduce assumptions and uncertainties that impact the accuracy and reliability of predicted outcomes.WHAT-IF simulations and look for hidden patterns in the data. By leveraging the capabilities of deep learning models, researchers can potentially address the challenges associated with missing data, enhancing the accuracy, reliability, and generalizability of predictive models for chronic non-communicable diseases.In light of these drawbacks, deeper learning models should be the main focus of future studies that use various national medical datasets to forecast chronic non-communicable diseases. Recurrent neural networks (RNNs), one type of deep learning model, have demonstrated promise in handling missing data and discovering intricate patterns from large-scale datasets. Particularly, some RNN types, such as Fuzzy Cognitive Maps, may carry out different DeclarationsNo funds, grants, or other support were received.Research Ethics Committee from the Institute of the Public health “Dr. Milan Jovanovic - Batut”, protocol code: 7959/1. Data and/or Code availability: The raw dataset used for this study is under a Non-Disclosure Agreement (NDA) and is, therefore, unavailable to the public. All patients participated in the national study via random sampling, voluntarily and anonymously, and their consent was obtained. Personal data related to study participants is not available.Nevena Rankovic: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Conceptualization. Dragica Rankovic: Writing – review & editing, Writing – original draft, Visualization, Supervision, Methodology, Investigation, Formal analysis, Conceptualization. Igor Lukic: Writing – original draft, Resources, Investigation, Formal analysis, Data curation. Nikola Savic: Writing – review & editing, Validation, Resources, Investigation, Data curation. Verica Jovanovic: Writing – review & editing, Validation, Resources, Data curation.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.


Both low skeletal muscle mass and delirium are prevalent in older hospitalized patients, while their associations are unclear. This systematic review and meta-analysis aim to investigate the associations between low skeletal muscle mass and the incidence of delirium in hospitalized patients.  The PubMed, Web of Science, and Embase were searched for relevant studies published before May 2022, and we conducted this systematic review and meta-analysis according to the PRISMA and MOOSE guidelines. The summary odds ratios (OR) and 95% confidence intervals (CI) were estimated, and subgroup analyses were also conducted according to the age and major surgeries.  Finally, nine studies with 3 828 patients were included. The pooled result showed no significant association between low skeletal muscle mass and the incidence of delirium . However, sensitivity analysis suggested that one study caused a significant alteration of the summary result, and the meta-analysis of the remaining 8 studies showed that low skeletal muscle mass was significantly associated with an 88% increased incidence of delirium . Furthermore, subgroup analyses indicated that low skeletal muscle mass was associated with a higher incidence of delirium in patients ≥75 years old or undergoing major surgeries instead of those <75 years old or without surgeries, respectively.  Hospitalized patients with low skeletal muscle mass might have higher incidence of delirium, particularly in those of older age and undergoing major surgeries. Therefore, great attention should be paid to these patients. Sarcopenia is a progressive and generalized skeletal muscle disease that is characterized by the loss of skeletal muscle mass and function , 2, and Delirium, an acute state of brain failure, which is defined as an acute and fluctuating impairment in attention, awareness, and cognition –10. A reSkeletal muscle, one of the largest metabolic organs, is thought to influence cognitive functions . HoweverThis systematic review and meta-analysis were registered at PROSPERO (CRD42022330979).We conducted and reported this systematic review and meta-analysis according to the PRISMA and MOOSE guidelines –19, and All studies retrieved from the electronic databases were reviewed by two authors independently, and the disagreements were resolved by discussion. The inclusion criteria were: (1) observational studies about the hospitalized patients; (2) the skeletal muscle mass of each patient was assessed by CT, BIA or other methods; (3) the relevant data about the associations between skeletal muscle mass and delirium were reported; and (4) the studies were published in English. editorials, reviews, abstract, and animal studies were excluded from this systematic review and meta-analysis. For each included study, the quality was evaluated based on the Newcastle-Ottawa-Scale (NOS) .For each included study, the data were extracted by two authors independently, and the extracted data were as follows: the name of the first author and the year of publication; the country where the study was conducted; the design of the study; the assessment of skeletal muscle mass and the definition of low skeletal muscle mass; the assessment of delirium; the study population and number of patients; and the incidence of delirium or the estimates of effect size with 95% confidence intervals (CI) about the associations between skeletal muscle mass and the incidence of delirium.Q statistic [P value of the Q test <0.1 or the I2 value ≥50% meant a statistically significant heterogeneity. The stability of the summary result was evaluated by sensitivity analysis, which was performed by omitting one study at a time during repeated analyses. If the removal of a study leads to a significant bias in the summary effect size and 95% CI, the study will be eliminated [P value of less than 0.1 was considered statistically significant publication bias. When publication bias existed, the trim-and-fill method would be used to estimate an adjusted summary OR and 95% CI and eliminate publication bias [In this systematic review and meta-analysis, the software STATA version 15.0 was used for all statistical analyses. Considering the assessment of skeletal muscle mass and the definition of low skeletal muscle mass were inconsistent among the included studies, all the summary odds ratios (OR) and 95% CI were estimated with a random-effects model. The heterogeneity was assessed by tatistic , and a Piminated , 23. TheFrom the three electronic databases and references of related studies, we identified 1,979 studies. After reviewing the title, abstract, and full text, nine studies , 24–28 wThese 9 studies contained a total of 3,828 patients from America, Italy, Japan, South Korea, and the Netherlands. Only one study included patients with a mean age of 59.4 years old, and the other studies included patients with a mean or median age of over 60 years old. The skeletal muscle mass was assessed by CT or MRI in 6 studies and by BIA in 2 studies, and only one study assessed the muscle mass according to the calf circumference. Besides, patients in 7 studies underwent kinds of major surgeries. The detailed information of included studies was shown in Pheterogeneity=0.02) (P=0.02), and a higher BMI could be associated with an increased risk of postoperative delirium [For the associations between low skeletal muscle mass and the incidence of delirium, the pooled result of 9 included studies showed no significant association existed  . Howeverty=0.02) . Considedelirium –31. To dPheterogeneity=0.86) (P=0.05)  . However(P=0.05) . Therefo(P=0.05) .Pheterogeneity=0.84; Pheterogeneity=0.37; Pheterogeneity=0.58; Pheterogeneity=0.84; In subgroup analysis, we observed significant associations between low skeletal muscle mass and incidence of delirium in both patients with or without major surgeries, but low skeletal muscle mass was associated with a higher incidence of delirium in patients undergoing major surgeries (OR 2.21, 95% CI 1.08 to 3.34; To our knowledge, this study is the first systematic review and meta-analysis investigating the associations between low skeletal muscle mass and the incidence of delirium in hospitalized patients. The initial meta-analysis of the whole nine studies did not yield a significant association between low skeletal muscle mass and the incidence of delirium. Therefore, we performed a sensitivity analysis and removed the study by Pernik et al.  which coSkeletal muscle is one of the largest metabolic organs of the human body, and it is also an important endocrine organ that secretes myokines in autocrine, paracrine, and endocrine manners for communicating with other systems , 33. TheLow skeletal muscle mass and delirium also have several common risk factors including increased age, malnutrition, and severe illness , 3, 8, 9Considering the associations between skeletal muscle and delirium, the ways of improving skeletal muscle mass might be used for preventing delirium. For age-related sarcopenia, only physical exercise instead of nutritional intervention alone or specific drugs is recommended as the primary treatment of skeletal muscle loss . FurtherThe strengths of this study were that we first conducted this systematic review and meta-analysis about the associations between low skeletal muscle mass and incidence of delirium. Through the main meta-analysis and subgroup analyses, we found that low skeletal muscle mass was significantly associated with an increased incidence of delirium in hospitalized patients, particularly in those undergoing major surgeries or ≥75 years old. Our results could be helpful to identify the hospitalized patients with a high risk of delirium.There were also several limitations of this study. First, the number of included studies was relatively small, and we found a significant existence of publication bias. To compensate for this limitation, we conducted a trim-and-fill analysis, and the adjusted pooled result remained constant. Second, the included studies did not have a consistent assessment and definition of low skeletal muscle mass and delirium, which could increase the risk of bias. However, limited by the small number of included studies, subgroup analyses about this problem were hard to conduct, and further studies with a large sample and consistent assessment of this issue are required in the future. Third, some studies indicated that a higher BMI could be associated with an increased risk of postoperative delirium –31, indiIn conclusion, low skeletal muscle mass is associated with an increased incidence of delirium in hospitalized patients, particularly in those of older age and undergoing major surgeries. Therefore, great attention should be paid to these patients. Considering the limitations of this systematic review and meta-analysis, further prospective observational studies about the associations between skeletal muscle mass and delirium are required in the future.


Rheumatoid arthritis (RA) is an autoimmune disease and closely associated with both genetic and environmental factors. Volatile organic chemicals (VOC), a common environment pollutant, was associated with some autoimmune diseases, while whether VOC exposure or which VOC leads to RA is yet clarified.A cross-sectional study using data from the 6 survey cycles  of NHANES program was performed. The RA or non-arthritis status of participant was identified through a questionnaire survey. The quantile logistic regression method was used for correlation analysis between VOC metabolites (VOCs) in urine and RA. The covariates included age, gender, race, educational level, marital status, total energy intake, physical activity, smoking, hypertension, diabetes, urine creatinine, albumin and marihuana use.A total of 9536 participants (aged 20 to 85) with 15 VOCs, comprising 618 RA and 8918 non-arthritis participants, was finally included for analysis. Participants in the RA group showed higher VOCs in urine than that in the non-arthritis group. A positive association between 2 VOCs  and RA was detected in the model 3, which was independent of all the covariates. The relative parent compounds of the two VOCs included N,N-Dimethylformamide and acrolein.These findings suggested that the VOC exposure significantly associated with RA, providing newly epidemiological evidence for the establishment that environmental pollutants associated with RA. And also, more prospective studies and related experimental studies are needed to further validate the conclusions of this study. Rheumatoid arthritis (RA), as one of the most prevalent chronic inflammatory diseases, is characterized with systemic autoimmune disorders and inflammatory synovitis, affecting 0.5–1.0% of the population over the world . DespiteThe effect of environment pollutants on the risk of RA seems to have been answered in the affirmative, and even some studies have begun to explore the underlying molecular mechanism . NeverthVolatile organic compounds (VOC) are common organic pollutants in the air, which could be produced by both human industry activity and natural sources . CompareHerein, we carried out this cross-sectional study aimed to investigate the association between RA and the exposure to specific volatile organic compounds metabolites (VOCs) in urine, which could specifically reflect the degree of exposure to a particular VOC parent. To our best knowledge, this is the first study to systematically investigate whether the exposure to specific VOC is associated with RA, which would provide new epidemiological evidence about the association of VOC exposure and RA, and help us develop more precise prevention and control measures against RA.https://www.cdc.gov/nchs/nhanes). Performed very two years, the NHANES program is a series of population-based surveys designed to investigate the nutrition and health status of children, as well as adults in the USA. The participants included in this program was sampled using the multistage stratified sampling method combined with oversampling for certain subgroups, such as people older than 60 and people of other races in the USA. Various data including demographic data, examination data, laboratory data and questionnaire data were obtained from the participants. The investigation protocol of NHANES program was reviewed and approved by the National Center for Health Statistics (NCHS), a division of Centers for Disease Control and Prevention (CDC). Before inclusion, all the participants received an informed consent form setting out details of this program, and would sign their consent form. In this study, we used the data of the volatile organic compounds metabolites in urine to reflect the exposure to volatile organic compounds. A total of 6 survey cycles of the NHANES program  was used and combined for association analysis between VOC exposure and RA.In this study, all the data was obtained from the National Health and Nutrition Examination Survey (NHANES) program  column, with 15 mM ammonium acetate and acetonitrile as the mobile phases, was applied for chromatographic separation. In brief, the eluent from the column is firstly ionized using an electrospray interface to generate and transmit negative ions into the mass spectrometer. Then the comparison of relative response factors  with known standard concentrations yields individual analyte concentrations (https://wwwn.cdc.gov/nchs/data/nhanes/2015-2016/labmethods/UVOC_UVOCS_I_MET.pdf). More details of the laboratory method detecting urine VOCs could be found in the website (https://wwwn.cdc.gov/nchs/data/nhanes/2015-2016/labmethods/UVOC_UVOCS_I_MET.pdf).The quantitative detection of VOCs in human urine was performed using ultra performance liquid chromatography coupled with electrospray tandem mass spectrometry (UPLC-ESI/MSMS) as described in previous studies . The AcqThe RA or non-arthritis status of participant was identified through a questionnaire survey. In brief, participants will firstly be asked ‘Did a doctor or other healthcare professional ever tell you that you have arthritis?’. If the answer was “yes,” a follow-up question “What type of arthritis was it?” would be asked. According to the answers of the 2 questions, participants were grouped into the RA or non-RA subgroups. If the participant reported “no” for the first question, the participant was grouped into the non-arthritis group. If the participant reported “yes” for the first question and “RA” for the second question, the participant was grouped into the RA group.2). Then the participants could be further separated into 3 subgroups, including normal weight (BMI < 25), overweight (25≤BMI < 30), and obesity (BMI≥30) , body mass index (BMI), dietary information , life behavior characteristics , concurrent disease , kidney function  and marijuana use of participants were collected and set as the covariates for statistical analysis. All the participants included in this study were over 20 years old. The race information included 5 groups . The education level was divided into 6 subgroups , and the marital status was separated into 7 states . The BMI information of each participant was obtained from the examination data, which was calculated by dividing weight by the square of height (kg/m(BMI≥30) . The inf(BMI≥30) . Accordip < 0.05 was identified as statistically significant in all the statistical analysis. The R software (https://www.r-project.org/), with the help of EmpowerStats (https://www.empowerstats.com), were used together for statistical analysis.The correlation analysis between exposure to VOCs in urine and RA was performed in this cross-sectional study. Firstly, all the included participants were grouped into RA or non-arthritis subgroups according to their arthritis evaluation. Then the comparation analysis of baseline data between RA and non-arthritis subgroups was carried out by using the Chi-square test for categorical data and Kruskal-Wallis test for quantitative data respectively. Meanwhile, the concentration of various urine VOCs between RA and non-arthritis subgroups was compared by the Kruskal-Wallis test. And also, the concentration of various urine VOCs in the RA population with different smoking status was compared by the Kruskal-Wallis test. Next, 3 quantile logistic regression models with different covariates were constructed to analyze the association between the urine VOCs and RA. In the quantile logistic regression analysis, the concentration of all VOCs is divided into 4 concentration intervals according to the concentration from low to high, and the number of people in each concentration interval is roughly equal. The non-adjust model was firstly constructed to directly analyze the association between urine VOCs and RA, and the other 2 model were subsequently applied for further analysis. The model 1 was adjusted by age, gender, race, educational level, marital status, urinary creatinine and albumin. The model 2 was adjusted by age, gender, race, educational level, marital status, total energy intake, physical activity, smoking, hypertension, diabetes, urinary creatinine and albumin. The model 3 was adjusted by age, gender, race, educational level, marital status, total energy intake, physical activity, smoking, hypertension, diabetes, urinary creatinine, urinary albumin and marijuana use. In addition, the non-linear relationship between RA and 15 VOCs was analyzed using the smooth curve fitting method. Subgroup analysis was performed to analyze the association between VOCs and RA in population identified with both non-users smoking and non-users marijuana status. Due to the combination of multiple survey cycles, the baseline data and VOCs comparation results between RA and non-arthritis groups were adjusted by sampling weight. The sensitivity analysis was performed through restricting the association analysis in population without smoking and marijuana use history. In addition, multiple covariates were included in this study for quantile regression analysis. A As shown in The comparation results of baseline or covariates data between RA and non-arthritis subgroups were summarized in the The comparation results of urine VOCs between the 2 subgroups was summarized in the The comparation results of urine VOCs between the 3 subgroups in the RA population was summarized in the The quantile logistic regression method was used to analyze the association between VOCs and RA. Firstly, the concentration of each VOCs was divided into 4 quantiles from low to high with equal sample size. The incidence of RA in the quantile 1 (Q1) was set as the reference, and the corresponding odds value (OR) was obtained by comparing the incidence of the other quantiles (Q2-Q4) with that of Q1. The concentration distribution of the 15 VOCs in urine was summarized in the As shown in In this study, we performed a cross-sectional study to systematically investigate the association between the exposure to specific VOC and risk of RA. The VOC concentration in human could be tested in blood, urine, breath and even sweat. However, direct testing of VOC in blood, urine or breath may yield inaccurate results due to the volatilization characteristic of VOC. Although the primary route of human exposure to VOC is through lung inhalation , VOC in N,N-Dimethylformamide, Ethylbenzene, styrene) to the 9 VOCs were summarized according to previous studies. After adjusted by all the covariates, there were 6 VOCs remained to be associated with RA at different concentration quantiles, including CEMA, 3HPMA, DHBMA, AMCC, PGA and MA. Most of these parent compounds have been identified as hazardous environmental pollutants by the United States Environmental Protection and Agency (U.S. EPA).In order to further analyze the association between exposure to VOC and RA, 3 quantile regression models with different covariates were constructed. After analysis, 9 VOCs were identified to be positively associated with RA at different concentration quantiles, including CEMA, 3HPMA, AAMA, CYMA, DHBMA, HMPMA, AMCC, PGA and MA. As shown in Among these VOCs, the 3HPMA stand out because a statistically significant OR was detected over the whole quantiles of all the 3 models. Except for 3HPMA, CEMA was another metabolite of acrolein, which was also detected positively associated with RA in this study. Acrolein is ubiquitous in nature and living environments and widely applied as an intermediate to produce acrylic acid, which possesses super-absorbent properties and used as raw materials for diapers, paints and coatings , 28. TheN,N-Dimethylformamide and Ethylbenzene, styrene. And all these VOC were firstly found to be positively associated with the RA. The acrylamide has been widely used for producing polyacrylamide polymers applied as flocculants, and it was reported that the exposure to acrylamide was associated with the impairment of central and peripheral nervous system , a division of Centers for Disease Control and Prevention (CDC). Before inclusion, all the participants received an informed consent form setting out details of this program, and would sign their consent form.TL finished the writing of the manuscript. TL and JY complete the data analysis. HQ made the tables and figures. YH and JY reviewed and revised the manuscript. YH repeated the data analysis and supervised the whole process. All authors contributed to the article and approved the submitted version.


Manganese is a trace element with many critical physiological functions, which should be supplied to animals and humans through diet. Since goose meat is eaten in many countries worldwide, this study aimed to systematically review the content of this element in goose meat and its relation to the recommended intake at the level of adequate intake (AI) and nutrient reference values-requirements (NRV-R). Already 100 g of goose meat can cover the daily AI per Mn for a wide range of adults, depending on the thermal treatment used. Placing information on the content of Mn in goose meat and the percentage of NRV-R on the packaging may be valuable information for the consumer in terms of making food choices to diversify the diet. Consumption of goose meat containing manganese may be justified in people struggling with mental disorders , lipid (hypercholesterolemia), and carbohydrate metabolism (reduced glucose tolerance), in whom reduced concentration of this element has been confirmed in blood.Manganese is a trace element with essential physiological functions that should be supplied to animals and humans through diet. Goose meat is prevalent in many regions of the world. Therefore, the aim of the study was a systematic review  of the content of Mn in raw and cooked goose meat and their relation to the recommended intake at the level of adequate intake (AI) and the nutrient reference values-requirements (NRV-R). The literature analysis shows that the content of Mn in goose meat depends on the breed, type of muscles, the presence of skin, and the cooking method used. AI level recommendations for Mn intake range from 0.003 to 5.50 mg/day, depending on the country, age, and gender. Consumption by adults (regardless of sex) of 100 g of domestic or wild goose meat covers the daily AI per Mn in various percentages, depending on the type of muscles (more Mn in leg muscles), presence of skin (more Mn in skinless muscles), and thermal treatment . Placing information on the Mn content in goose meat and the percentage of NRV-R on the packaging may be valuable information for the consumer in making food choices to diversify the diet. There are few studies on the content of Mn in goose meat. Therefore, it is reasonable to conduct research in this area. Manganese (Mn) is one of the essential minerals in animal and human nutrition. It is classified as a micronutrient or trace element. Manganese (Mn) is an essential dietary mineral for mammals and is a component of metalloenzymes such as arginase, glutamine synthetase, and pyruvate carboxylase . BecauseHumans obtain manganese from the air, water, and food. Plant sources have much higher manganese concentrations than animal sources. Whole grains , rice, and nuts  contain the highest amounts of manganese. Chocolate, tea, mussels, clams, legumes, fruit, leafy vegetables (spinach), seeds , and spices  are also rich in manganese ,6.Manganese is mainly absorbed as Mn(II), and absorption is reported to be below 10% of ingested manganese. Manganese uptake in the intestine is mediated by high-affinity metal transporters, such as divalent metal transporter-1 , which is also involved in transporting other metals . AbsorbeGeese are waterfowl consumed in some regions of the world, especially Asia, some countries of Europe, and the USA . The quaDeficiency of Mn in young birds causes perosis . Manganese in plant materials is available in 50–60%, but its bioavailability in poultry feed mixtures is reduced in excess calcium and phosphorus. Because its content and bioavailability in plant feed fluctuate significantly, it is often added to premixes in the form of inorganic trace minerals (ITM), for example, oxides, carbonates, chlorides, and sulphates. Manganese has a low potential for toxicity due to its poor intestinal absorption and efficient biliary elimination. Still, it can interact with several other dietary nutrients, such as Zn and Fe, by competing with Fe for intestinal absorption sites or reducing Fe and Zn tissue concentrations . HistoriThe manganese content in goose feed, its chemical form, and bioavailability may be reflected in its range in goose meat. In the literature, there are few published research results on the content of Mn in goose muscles in terms of its consumption and, more often, in biomonitoring as a bioindicator of environmental pollution ,15. In aThis systematic review aims to (1) analyze the original animal studies from 1980–2022 on the manganese content of raw and culinary processed goose, (2) present chosen recommendations for adequate intakes (AI) for manganese (mg/day) depending on age and sex; (3) compare manganese in goose meat content with adequate intake and nutrient reference values-requirements for humans.The review was prepared following PRISMA guidelines to ensure the transparency of the research conducted . An elecThe AGRO database was used as an additional source . AGRO biThe screening was made in the areas: title, abstract, keywords (Scopus), and topic . The search and selection process was performed by two reviewers working independently and in parallel. The results from each database were exported to files with the appropriate extension of CSV or excel, and summaries were created for each database containing publication information, including abstracts. The files are included in The manganese content (mg/kg of tissue) in raw goose muscles is shown in ®. The mean Mn content in breast muscles was 1.6 mg/kg dry mass (DM) and did not differ significantly between with skin and without skin (1.5 vs. 1.7 mg/kg DM).Considering the presence of skin, data from the U.S. Department of Agriculture)  show thaAlopochen aegyptiacus) are eaten. In the Eastern James Bay Cree of Quebec in Canada, goose (Branta canadensis) is traditionally eaten by local rural communities. A study by Geldenhuys et al. [Game geese are also a source of energy and nutrients for the population in many parts of the world. For example, in South Africa hunted (during spring and autumn hunting) Egyptian geese  the highest Mn content in skinless WBC muscles compared to PF muscles (2.7 vs. 1.1 mg/kg DM). However, in muscles with skin, significantly (p ≤ 0.01), the highest content of Mn was found in grilled muscles (3.8 mg/kg DM), compared to raw muscles and other methods of heat treatment . In terms of Mn, the interaction between the type of breast muscle (with or without skin) and the heat processing method was statistically significant (p ≤ 0.001). However, the authors did not note significant differences in this ingredient retention, regardless of the muscle type (with or without skin) and the applied heat treatment.Goluch et al.  studied Considering the wild goose, Geldenhuys et al.  found 1 Balance studies have suggested that an intake of 0.74 mg/d should be sufficient to replace daily manganese losses . IntakesIn 1993, the EU Scientific Committee for Food suggested 1–10 mg/d to be an acceptable intake of manganese . The WorThe European Food Safety Authority (EFSA) concluded that data is insufficient for deriving average requirements (ARs) or population reference intakes (PRIs) for manganese. EFSA proposed an adequate intake (AI) at 3 mg/day for adults based on observed mean intake from a mixed diet in the EU as stated in dietary reference values (DRVs) for manganese. An AI of 3 mg/day is proposed for adults, including pregnant and lactating women. For infants aged 7 to 11 months, an AI of 0.02–0.5 mg/day is suggested, reflecting the wide range of manganese intakes that appear to be adequate for this age group. The main contributor to dietary manganese intake is cereals (57%), followed by fruit, vegetables, nuts, and coffee/tea . The socThe Scientific Committee on Food (SCF) could not set any observed adverse effect level (NOAEL) because no relevant dose-response animal studies were found. Consequently, SCF did not assign a tolerable upper intake level (UL) for manganese.In some countries, the intake of manganese is not often estimated. In the EU, adults’ estimated mean manganese intakes range from 2 to 6 mg/day, with most values around 3 mg/day. Estimated mean manganese intake ranges from 1.5 to 3.5 mg/day in children and 2 to 6 mg/day in adolescents . The PolIn contrast, the Spanish population’s estimated daily manganese intake was 2.372 mg/day. The population of the island of El-Hierro presented the highest intake (2.717 mg/day), and the one in Fuerteventura (1.986 mg/day) showed the lowest intake . ResultsFor the consumer purchasing food products, the information on the label, which relates to energy and nutritional value, is essential. This information should also include the reference value of the daily intake (NRV). These recommendations are based on the best available scientific knowledge of the daily amount of energy or nutrient needed for good health . The nut®. However, considering wild goose meat, more AI will cover 100 g of raw muscle from Egyptian Geese (1.2–3.33%). In many countries, various thermal treatment of goose meat is used, which is associated with changes in the content of minerals and their retention [® breast muscles grilled with skin (7.6–21.1%). Also, the consumption of 100 g of cooked wild Egyptian goose breast meat will cover a significant percentage (10.9–33.3%) of the daily AI. The higher Mn content in fried and pan-roasted meat is due to the specific nature of these treatments, which do not require water, allowing for greater water retention. It has already been shown that during frying, the meat surface temperature quickly reaches 115–120 °C or above 120 °C, and the protein forms a solid layer on the surface of the meat, thanks to which the loss of soluble substances such as inorganic salts is reduced [etention ,35,36. A reduced .® goose grilled breast meat with skin covers 19% of the consumer’s NRV-R.Taking into account the marking of products with the NRV-R value , raw gooA deficiency of manganese in the diet can cause changes in the osteoarticular system. Research has now established that it is the most efficient divalent cation in activating the glycosyltransferase enzymes, essential in the chondroitin sulfate formation, the major polysaccharide of the cartilage synthesis of cartilage and bone collagen, as well as in bone mineralization. Women with osteoporosis have been shown to have lower serum Mn levels than women with normal bone mineral density .A link between low dietary Mn and impaired insulin secretion and glucose metabolism has also been shown . Mn is aManganese is considered an essential and critical nutrient, and its deficiency or overabundance may cause depressive disorders. People with depression were observed to have lower urinary manganese concentrations or MnSOD activity compared to the control group . SeveralThe primary source of manganese in the human diet is tea and plant products. Diversifying the diet with goose meat is also worthwhile because, unlike plant food, it does not contain substances that diminish bioavailability, such as fiber or phytates. As a natural source of this element, goose meat does not increase exposure to overconsumption, as can happen when consuming dietary supplements. As little as 100 g of goose meat can cover the daily AI for Mn for a wide range of adults, depending on the heat treatment used.Consumption of goose meat containing manganese may be justified in people suffering from mental disorders , lipid (hypocholesterolemia), and/or carbohydrate (reduced glucose tolerance) disorders, in whom reduced concentration of this element has been confirmed in the blood. Placing information on the content of Mn in goose meat and the percentage of NRV-R on the packaging may be valuable information for the consumer in terms of making food choices to diversify the diet. Since there are few studies on the content of Mn in goose meat, it is reasonable to research the content of the impact of various thermal processing techniques on its concentration and retention.


However, in the group of 40 diabetics, this association was not observed. Upon multivariable regression analysis, the independent association of D/PCr with peritoneal albumin  and protein  loss was found only in non-diabetics in whom ultrafiltration also appeared as a significant predictor of peritoneal protein loss . A high comorbidity grade, older age, and low serum albumin were associated with mortality, but both peritoneal protein and albumin loss as well as D/Pcr were not determinants of survival. Baseline peritoneal protein and albumin loss was not associated with DM and did not predict survival. The clinical significance of the absence of association between fast peritoneal transport status and peritoneal protein flux in diabetics should be evaluated in a prospective study comprising a greater number of diabetics with evaluation of overhydration as a main inducing variable of protein leak.Increased peritoneal protein loss has been associated with the fast transport of small molecules, diabetes mellitus (DM), and a reduced survival in patients on peritoneal dialysis (PD), although some studies did not confirm the association with survival. In this single-center retrospective study, we investigated the relationship of baseline peritoneal albumin and protein loss with transport status, comorbidities including DM, and survival in 106 incident PD patients during the period of July 2005–June 2014. Five-year survival rate was determined using Cox-regression analysis. There were not significant differences in D/Pcr or peritoneal protein and albumin loss between diabetics and non-diabetics. In the group of 66 non-diabetics, high and high-average transporters for creatinine had higher values for both peritoneal protein (11.85 ± 6.77 vs. 7.85 ± 4.36 g/day;  It is well known that peritoneal dialysis (PD) causes a considerable amount of albumin and protein loss from the body, reaching the value between 5 g and 15 g per day . Since hPeritoneal protein clearance (PPCl) was shown to be predominantly associated with local but not systemic inflammation . Local pThe relation of peritoneal albumin and protein loss to comorbidity, especially to diabetes mellitus (DM), has not been precisely elucidated. Although most studies have shown a positive correlation between PPCl or peritoneal protein loss and DM, others have not confirmed the association ,13,14,15Bearing in mind that differences between the studies relate to whether greater peritoneal albumin and protein loss at the start of PD are associated with the presence of DM and comorbidity and whether it influences survival. In this one-center study, we aim to reanalyze the mentioned parameters in a cohort of patients starting PD.A hundred and six incident PD patients over the age of 18 years were included in this retrospective, single-center cohort study during the period of July 2005–June 2014. All procedures were in accordance with the Helsinki Declaration. The study protocol was approved by the Ethics Committee of University Clinical Center of Serbia (number of protocol: 170/1 of 18 July 2019). Exclusion criteria were peritonitis episode, pregnancy, breastfeeding, any systemic infection in the first six months of PD, albumin infusions immediately before and at the time of peritoneal protein loss determination, and patients with any missing data of the variables determined for analysis.Data were collected from patient medical histories. All the patients had a baseline peritoneal solute clearance, e.g., peritoneal equilibration test (PET) according to the method of Twardowski et al. , peritonSerum albumins were measured by the bromocresol purple (BCP) colorimetric method assay . Proteins in serum were determined by the biuret method on the same analyzer . Albumins in peritoneal effluent were measured by using the bromocresol green colorimetric assay (BCG), which forms a green complex with albumin . Proteins in urine and peritoneal effluent were determined by the colorimetric method using pyrogallol red combined with molybdate . Concomitantly, other biochemical parameters ) were assessed and included in the study. Baseline data such as demographics, cause of renal failure, and comorbidity were collected at the beginning of the study. Due to missing values for BMI and HbA1c, these parameters were not analyzed. The Davies comorbidity index was scored based on the presence of seven comorbid conditions: malignancy, ischemic heart disease, peripheral vascular disease, left ventricular dysfunction, diabetes mellitus, systemic collagen vascular disease, and any other condition severe enough to have an impact on survival in the general population . The num2). PPCl and PACl were expressed as mL/day and calculated using the following formula: PPCl = 24 h dialysate protein loss/serum protein, PACl = 24 h dialysate albumin loss/serum albumin.To estimate low-molecular weight solute transport, PET was performed using a 2.27% glucose concentration and a 2 L volume exchange as a standard 4 h dwell period. At the completion of the 4 h dwell period, the D/Pcr was calculated, and the patients were classified as slow (S), slow-average (SA), fast-average (FA), and fast (F) transporters. Kt/Vurea was calculated by the direct measurement of urea in the 24 h collected urine and peritoneal dialysate effluent. RRF was calculated as the mean of creatinine and urea clearance corrected for the body surface area . All p-values were two-sided and p-values < 0.05 were considered statistically significant.Values were expressed as the means ± standard deviation for continuous variables and as percentages for categorical variables. Normality was tested by the Shapiro–Wilk test. For continuous variables with a normal distribution, differences were tested with the p < 0.007). In the group of patients with DM, greater values were measured for proteinuria , PACl , weekly CrCl , and the mean arterial pressure (MAP) . There were not any differences in peritoneal albumin and protein loss, PPCl, or serum proteins. The presence of a medium and high comorbidity grade measured by the Davies score was greater in diabetics (p = 0.000).The patients were divided into two groups according to the presence of DM . Among tThe association of peritoneal protein and albumin flux with D/PCr was different regarding the presence of DM. The diabetics had no significant differences in both peritoneal protein and albumin loss or AlbCl and ProtCl depending on the transport categories for small solutes .p = 0.000), PACl , peritoneal protein loss , and peritoneal albumin loss . Concomitantly, non-diabetic F and FA transporters had significantly lower values of serum albumins  and serum proteins .In the group of non-diabetics, F and FA transporters compared to S and SA transporters had significantly greater values of PPCl  and protein  loss (p = 0.029) and peritoneal protein loss with ultrafiltration  were found. A significant association in diabetics was observed for peritoneal protein loss with diuresis  and with the Davies comorbidity grade .The correlation analysis between peritoneal protein and albumin loss and the studied parameters, depending on the presence of DM, confirmed a significant association with small solute transport for peritoneal albumin  loss  just in p = 0.008) and peritoneal protein loss  (p = 0.000), while MAP was found to be significantly associated with peritoneal albumin loss .When stepwise multivariate regression analysis was performed using peritoneal albumin and protein loss as the dependent variable, none of the investigated parameters showed a significant association in diabetics. However, in non-diabetics, D/PCr remained the only mutual parameter significantly associated with both peritoneal albumin loss  . AdditioDuring a period of observation of 60 months, 35 deaths were recorded. In the univariate Cox proportional-hazards model, older age, lower serum albumin values, higher CRP values, and the Davies comorbidity grade were associated with mortality . PeritonThe main study results were that increased peritoneal protein and albumin loss at the initiation of PD treatment had no significant influence on survival of patients treated with PD and were not associated with overall comorbidity expressed by the Davies score. Until now, the results of numerous studies have not been in agreement; some of them have confirmed the association of a high peritoneal protein flux with mortality and comorbid conditions, in particular peripheral vascular disease ,4,7,19, Another hypothesis regarding the association of fast transport status and a greater peritoneal albumin loss was challenged in our study. In addition, our results did not confirm previous findings indicating a higher prevalence of fast transporters for small molecules and a greater peritoneal protein loss among diabetics, compared to non-diabetics ,20,21,22Irrespective of the contradictory findings concerning the association of increased peritoneal permeability and DM, numerous studies have unequivocally shown a greater peritoneal albumin loss in fast transporters ,4,5,8. IEven a small increase in the large pore area independently of the small pore area, especially in the presence of an increased ECW and arterial pressure, would result in an increased peritoneal albumin and protein flux with no greater implications for small molecule transport rate. Indeed, it has been shown in diabetics that the large amount of peritoneal protein loss during PD is due to high plasma passage rates through the large pores, with a substantial amount of these proteins consisting of gamma globulins and alpha 2 macroglobulins ,13. RegaUnlike the rest of the population, diabetics do not show an association of a high transport status for small molecules with a greater peritoneal protein and albumin loss. Since no association of peritoneal protein and albumin loss was found with comorbidity at all (not only with diabetes) nor was the effect on survival observed, this finding in diabetics, in order to define the clinical significance, requires additional testing primarily in a prospective study conducted in a larger number of patients taking into account a more precise assessment of the ECW.


Sexual health means coordination and adaptation of physical, emotional, intellectual and social aspects of human beings. One of the variables that affect sexual function and sexual satisfaction is health literacy. The aim of this study was to investigate the relationship between health literacy level and sexual function in married women in Qazvin health centers.In the cross-sectional study, 340 married women were selected from four health centers in Qazvin, Iran, in 2020. These selected centers were chosen randomly from 26 health centers. Participants were included in the study by using the proportional selection method based on the sample size of the all health centers. Data collection tools include three questionnaires: demographic information, The Health Literacy Questionnaire (HELIA), and Female Sexual Function Index (FSFI). Data were analyzed using SPSS 24 software. A significance level of P < 0.05 was considered for statistical analyses.The highest and lowest scores of dimension’s sexual function are satisfaction, pain, and lubricant, respectively. The level of women’s health literacy in Qazvin was inadequate and borderline (56.4%). Each of the sexual function dimensions had significant positive correlations with health literacy (P < 0.001). There was a significant relationship between health literacy level with age, education, and occupation (P < 0.05). According to linear regression analysis, with the increase in years of marriage, sexual function is decreased (P < 0.02).Health literacy was inadequate in more than half of the study sample and health literacy was significantly associated with sexual function. Educational programs were necessary in order to promote women’s health literacy in health centers. The level of knowledge and awareness of the population towards their health status and effective factors are very important. Women have the most important role in society and family , 2. SexuSexual disorders are prevalent in men and women and need intervention , 6. SexuMost medical treatments for sexual function are not proven , 15. TheHealth literacy is a person’s capacity to obtain, interpret, and understand the basic information about health services that is necessary for appropriate decision- making . In otheIn this cross- sectional study, 340 married women were selected from four health centers in Qazvin, Iran, in 2020. These selected centers were chosen randomly from 26 health centers. Participants were included in the study by using the proportional selection method based on the sample size of all health centers in Qazvin. The sample size calculated based on the study of Sahebalzamani  and et aInclusion criteria included the following:• Married women over 18 years old • Having active health record in health centers • Having Literate.And the exclusion criteria included the following: • Women with a history of mental illness and medication  • Disinclination to participate in the study.At first, the purpose of the study was explained to the participants. A three-part questionnaire containing demographic information , Health Literacy Questionnaire (HELIA) and Women’s Sexual Function Index (FSFI) were completed by participants. The only native Iranian tool is the Iranian Adult Health Literacy Questionnaire or Health Literacy for Iranian Adults (HELIA) developed by Montazeri et al. . The desThe questionnaire has been used in many studies abroad and has shown a high degree of internal consistency and reliability, as well as validity . EvaluatData were entered into SPSS version 24. According to the non-parametric distribution of samples in the statistical population, parametric tests such as Mann-Whitney, Kruskal-Wallis, and Spearman correlation tests were determined. The linear regression analysis was used to predict sexual function by applying demographic variables and dimensions of health literacy. Significance level of less than 0.05. was considered.most of the participants’ education and job were diploma (59.7%) and unemployment (73.2%), respectively. The age of 183 individuals (53.8%) was 20–40 years old, and the age of 144 individuals (42.4%) was older than 40 years old. Among participants, 112 (32.9%) individuals had more than 20 years of marriage.According to the results, 79 individuals had inadequate literacy (23.6%), 110 (32.8%) borderline, 82 (24.5%) participants had adequate literacy, and 64 participants (19.1%) had excellent literacy. The scores of health literacy in selecting women include decision making , understanding , appraisal , access to information , and reading  respectively. The highest scores for sexual function are satisfaction  and desire  and the lowest scores are for pain  and lubrication .The results showed the significant relationship between demographic characteristics and scores of health literacy with sexual function P < 0.001) (Table  (Table 1There was a strong and positive correlation between the years of marriage (r=-0.386), age r=-0.288) and years of education (r = 0.59) of the participants with health literacy (P < 0.001). There was a significant relationship between sexual function and the occupational status of women (P < 0.001). The highest score for sexual function was observed in employed participants (22.94). Education level had a statistically significant relationship with the sexual function score (P < 0.001). There was a significant negative correlation  between the years of marriage and the age of the participants with sexual disorders, respectively. It was shown that there was a significant and positive correlation between total health literacy and dimensions of sexual function , age, years of marriage, and education as independent variables had a significant relationship with sexual function (P < 0.001). Access to information could lead to an increase in the chance of sexual function by 0.29, but in the multivariate model, it led to a decrease of 0.29, but it was not statistically significant (P < 0.42). In the multivariate model, only years of marriage had a significant relationship with sexual function (P < 0.02). With the addition of one year to the years of marriage, sexual function decreased by 0.25 Table .Health literacy affects people’s behavior, how they use information and services, and their physical and mental conditions. These variables also affect sexual function and satisfaction. Good sexual performance between couples includes a regular desire to engage in sexual activity, sexual arousal and orgasm, and increased marital satisfaction . Health In the present study, the percentage of health literacy levels in the range of inadequate to very high were 21.8%, 31.3%, 27.8%, and 19.1%, respectively, which shows that the level of women’s health literacy in Qazvin is inadequate. Ghanbari et al. . Another’s study, desire and arousal functions were the most prevalent sexual functions [One of the goals of health centers could be to improve women’s access to health-related questions, such as through easier access to physicians and employees of the health sector, or by increasing public awareness of the existence of credible sources such as brochures, IVRs, and publications and encouraging them to replace these sources with unreliable ones. Among the types of sexual function in this study, the highest score is related to sexual satisfaction, and the lowest is related to lubrication and pain. In Rosenunctions . Desire unctions , but lubunctions . Also, Munctions  concludeunctions .Based on the results of linear regression, a significant relationship was observed between years of marriage and sexual function after adjusting other variables. In this study, the increase in the number of years of marriage has led to sexual dysfunction. Contrary to these results, the study of Dehghankar et al. showed that with the increase in the years of marriage, the level of sexual satisfaction increased . The difIn this study, a significant relationship was observed between health literacy and sexual function. This relation was confirmed in Moghadam and Sahebalzamani reports. They found that health literacy affects attitudes, mental status, health-related behaviors, and consequently physical health status. In addition, health literacy can affect the use of information and sexual function in couples and thus have a positive effect on quality of life , 39.In this regard, it can be said that health literacy can affect couples’ moods and attitudes toward sex and marriage, and, therefore, can affect their sexual satisfaction and increase the probability of pregnancy , 41.Although this research has achieved its goals, there are inevitable limitations. First, this cross-sectional study was performed in only 4 health centers. Second, the variables under study depend on the socio-cultural environment. Therefore, to generalize the results to large groups in different areas, it is recommended that studies be conducted in other locations with larger samples. Also, meta-analysis of existing data can be done to better understand this field.It was concluded from this study that health literacy was inadequate in more than half of the study population and that health literacy was significantly associated with sexual function. In order to promote women’s health literacy, programs can be designed, implemented, and evaluated. Health literacy education must be one of the goals of the health centers. It is suggested that simple and educational materials be designed and used for women with average health literacy.


Giardia duodenalis and Enterocytozoon bieneusi are etiological agents of enteric diseases characterized by diarrhea that can progress to chronicity in humans, especially in children and in immunocompromised patients. This study aims to assess the genetic pattern of G. duodenalis and E. bieneusi detected in vegetables and fruits commercialized in Maputo markets, Mozambique and determine their public health importance. Eight study points were sampled: a farmer zone, a wholesale, four retail markets, and two supermarkets in Maputo city, where eight types of horticultural products were purchased. Using nested-PCR methods, 2.8% (9/321) and 1.3% (4/321) of samples monitored were positive for G. duodenalis and E. bieneusi, respectively. Based on the analysis of the β-giardin and ITS rRNA sequences of G. duodenalis and E. bieneusi detected, respectively, four different sequences of G. duodenalis  all from assemblage B and three genotypes of E. bieneusi  from group 1. These microorganisms were found and characterized for the first time in horticultural products in Maputo markets. All identified G. duodenalis and E. bieneusi display high genetic similarity within their β-giardin and ITS rRNA sequences, respectively, having been clustered into assemblages and genotypes with high zoonotic transmission potential. Our study may represent a relevant step in the understanding of these intestinal pathogens in association with fresh vegetables and fruits for human consumption, for a better and broader “One Health” approach. Giardia duodenalis and Enterocytozoon bieneusi are etiological agents of acute and chronic disorders including diarrhea, malabsorption, and severe debilitation illnesses in humans and animals, particularly in children and immunocompromised patients  or animals (zoonotic transmission) or even ingestion of contaminated water : A, B, C, D, E, and F can infect humans (mostly A and B) and other animals, and assemblages C to H have a narrower host range , parsley (Petroselinum crispum), Portuguese cabbage (Brassica Oleracea costata), pointed white cabbage (Brassica oleracea capitata), carrot (Daucus carota), tomato (Solanum lycopersicum), green pepper (Capsicum annuum), and lettuce (Lactuca sativa cea). The products were placed in individual plastic bags, encoded with a unique number and the date of collection, and transported in a container at 4°C. Several detaching and concentrating G. duodenalis cysts and E. bieneusi spores protocols reported in the literature were reviewed  and in the dry seasons (August to October) of 2019, in eight points of study, and according to the markets' typology, namely, a producer zone , one wholesale market (Zimpeto), four retail markets , and two supermarkets (LMC and LSP) in Maputo city. In each selected study point, we randomly purchased three specimens of each of the eight types of raw and fresh horticultural products, namely, coriander  were transported to the laboratory for processing. At the laboratory, we first weighed the fruits and vegetables according to their sizes: for the large ones, we weighed 150–250 g , and for the small ones, we weighed 80–100 g . Then, they were washed in 200 ml and 50 ml of a solution of NaCl (0.85%) in distillate water, for large and small fruits/vegetables, respectively, in a plastic bag. They were shaken vigorously on an orbital shaker  at 150 rpm for 20 min and brined overnight. The next morning, the solution was collected in 50 ml Falcon tubes and centrifuged at 8,000 × G. duodenalis and E. bieneusi, using the QIAamp Fast DNA Stool Mini Kit according to the manufacturer's instructions. The extracted DNA (100 μl/sample) was kept at −20°C.Genomic DNA was extracted from concentrated vegetable/fruit extracts for the molecular detection of bg) gene was used to detect G. duodenalis. The primary reactions were carried out using the primers GiaFW1 (5′-AAGCCCGACGACCTCACCCGCAGTGC-3′) and GiaRV1 (5′-GAGGCCGCCCTGGATCTTCGAGAC GAC-3′) for the 753-bp fragment amplification. For the secondary reactions, the primers GiaFW2 (5′-GAACGAACGAGATCGAGGTCCG-3′) and GiaRV2 (5′-CTCGACGAGCTTCGTGTT-3′) were used to amplify the 511-bp fragment.A nested-PCR analysis  region of the rRNA gene, and its genotyping was conducted by sequence analysis of amplified fragments of the ITS rRNA gene  and ClustalX  programs to determine G. duodenalis and E. bieneusi genetic variability.The products of the expected size were purified using a PureLink™ Quick Gel Extraction and PCR Purification Combo kit , as described on the manufacturer's instructions, and were submitted to Sanger sequencing in both directions on an ABI3100 automated sequencer  at STABVIDA Company . Three PCR products of each positive sample obtained from different PCRs were submitted to DNA sequencing. The authors analyzed all three sets of sequencing chromatograms obtained for each of the positive samples. Next, the individual sequences were subsequently aligned against reference sequences from the GenBank database using the BLASTN  and 1.3% (4/321), respectively, of the samples monitored. According to the distinct seasons, G. duodenalis was found in three (1.96%) samples  in the rainy season and in six (3.6%) samples  in the dry season. Enterocytozon bieneusi DNA was detected in one (0.7%) sample (pointed white cabbage) in the rainy season and in three (1.8%) samples  in the dry season, among four different markets. Giardia duodenalis was detected in products from four markets and one supermarket, and E. bieneusi was found in products from three markets and in products from the same supermarket as Giardia. The distribution of G. duodenalis assemblages and E. bieneusi genotypes identified in the horticultural products among the markets monitored, during dry and rainy seasons, in Maputo City is summarized in After nested-PCR analysis, G. duodenalis and E. bieneusi, respectively, using nested-PCR methods. The authors analyzed the three sets of sequencing chromatograms obtained for each positive sample, which were found to be consistent, indicating the reliability of the identified new sequences. No instances of mixed infection were identified.DNA Sanger sequencing was successfully achieved for all nine and four specimens diagnosed as positive for G. duodenalis bg sequences all from assemblage B in the samples studied. One of them was a known bg sequence, and three were novel bg sequences. Of the nine sequences obtained, three  had 100% similarity with the G. duodenalis nucleotide sequence MK033096, one (BGMZ7) constitutes a novel sequence (BgMZ2) and differs from the previous three by a single nucleotide at 356 (G→ A) displaying 99.81% homology with sequence MK033096. The remaining five sequences generated in this study are also new G. duodenalis bg sequences and have 99% homology with G. duodenalis nucleotide sequences previously reported: three  had one substitution to isolate KY320578 at 456 (C→ T), and two  had three substitutions to isolate MG736246 at 99 (T→ C), at 136 (A→ G), and at 356 (G→ A).Sequencing and alignment of our nine nucleotide sequences with others of known identity deposited in the GenBank database showed the presence of four different E. bieneusi ITS sequences characterized in the four positive samples obtained in this study revealed high genetic diversity: two (EBMZ86 and EBMZ95) sequences with the genotype KIN-1 , one  of the sequences characterized is a novel genotype displaying 99.71% homology with KIN-1 and Peru8 genotypes, with one substitution at 139 (T→ C) and one substitution at 159 (C→ T), respectively. The remaining novel one  had 99.44% similarity with KIN-1 and had two substitutions at 124 (G→ A) and at 182 (G→ A).The four To obtain information on the β-giardin and ITS rRNA sequences in this study and the respective reference sequences published in previous studies, two phylogenetic trees were constructed.G. duodenalis sequences from this study were clustered within assemblage B and formed three genetic clusters comprising species mostly infecting humans and several other domestic and wild mammals  all from assemblage B and three E. bieneusi genotypes (two novel and one known) from group 1 were characterized for the first time in fruits and vegetables from Maputo markets in Mozambique.Fresh vegetables and fruits, despite the health benefits of their human consumption, can be an important source of some foodborne pathogenic microorganisms, including G. duodenalis human infection are observed in developing countries in comparison to the developed regions  and the methodology used  when compared with developed countries , Costa Rica (Ziehl-Nielsen stain), and Poland (staining or with fluorescence in situ hybridization). The average prevalence of the reported contamination among these studies was estimated as 3.6%  was detected in the vegetables and fruits analyzed in this study. Although this low rate may be associated with the difficulty in recovering these tiny microsporidia spores from the horticultural products analyzed, limiting their molecular detection, this finding may also constitute a positive indicator of safer products commercialized in these markets compared to others from different regions.As observed for uce Lee, , and theuce Lee, . More reG. duodenalis was observed in fresh vegetables purchased in four municipal markets and E. bieneusi in fresh vegetables purchased in three markets and one supermarket. The higher occurrence of these pathogenic species in the main markets (open-air markets with limited or even absent proper sanitary conditions) than in the supermarkets monitored was expected and is certainly associated with their specific structural and environmental characteristics, which contribute to the contamination of food products in those former markets. However, in the other markets of different typologies surveyed, namely, in Central da Baixa market  and especially in the supermarket that have all the guaranteed resources, availability of water, market stalls and toilets, transport, handling and display of products for their safety, the produce contamination by one or both pathogenic species observed were not expected. The vegetables and fruits handled during preparation for display in the supermarket or even by the consumer can be one of the factors for the contamination that we observed . Usually, high contamination rates of lettuce and other types of leafy vegetables  are also reported by other authors due to the uneven external surface of their leaves, which enables the easy entry and adherence of the G. duodenalis assemblages (A–H) described to date, the zoonotic assemblages A and B were commonly detected  have been identified in humans  display 99% of similarity to other bg sequences deposited in the reference database. The four different bg sequences identified were very similar with a mild difference between them and were relative to the reference sequences only from one to three single-nucleotide polymorphisms (SNP). Phylogenetic analysis of all nine G. duodenalis isolates, from the vegetables and fruits included in this study, indicates that all of them clustered closer to other sequences , figuring out their host specificity and zoonotic potential. Molecular studies on E. bieneusi worldwide stated that the occurrence of genotypes from Groups 1  and 2  are the most commonly identified  or those who are in close contact with infected individuals; through the use of animal or human feces as manure for farmland; or direct access to infected cattle and other animals to crops. Indirect contamination of horticultural products at the production zone can occur by using irrigation water contaminated with feces; mixing pesticides; or washing horticultural products, hands, or equipment  by the molecular techniques, it does not mean for sure that the products were not contaminated at this point. For instance, G. duodenalis, like other intestinal parasites, were detected in this zone by the authors  that were randomly purchased in triplicate from the most popular and frequently visited markets in Maputo city, during the two main seasons of the year. By employing molecular tools, we not only demonstrated the presence of potential zoonotic assemblages/genotypes of G. duodenalis and E. bieneusi among the fresh fruits and vegetables examined but also highlighted the usefulness of these tools in tracing the sources of food contamination.Our research, to the best of our knowledge, represents a groundbreaking study on the detection of Cryptosporidium and Giardia in fresh leafy green vegetables and berry fruits. However, this method is neither suitable for routine analysis or monitoring in a hazard analysis plan and critical control points (Chalmers et al., G. duodenalis. If a multi-copy marker, such as the SSU rRNA gene, had been selected, it would have increased the sensitivity of detecting parasite cysts in vegetable product extracts. Instead, we utilized the bg locus, which is a single-copy gene but strikes a balance between very specific detection and genetic discrimination. While these methodology choices were made based on the available resources and technical conditions in the field, certainly, they may have led to an underestimation of both the positivity rate for Giardia and E. bieneusi reported in this study.However, our study has certain limitations associated with the methodology we adopted, particularly the protocols used for recovering cysts/spores from horticultural product extracts and the molecular tools employed. These limitations are primarily due to the unavailability of certain technical resources, such as a fluorescent microscope, and the constraints of working in the field. The only standardized method currently available for detecting intestinal protist parasites in food matrices is ISO 18744:2016, which was developed and optimized for the detection and enumeration of Even though it was not within the scope of this study, the absence of available technical conditions represents another limitation. Specifically, we were unable to conduct viability and infectivity assays on the transmissive stages of the detected protists in the horticultural products. Consequently, by solely focusing on the detection and genotyping of these microorganisms without assessing their viability and infectivity, the true impact of these agents on public health may have been underestimated. However, it is important to note that the transmissive stages of these important pathogens were found in raw products that had not undergone any cooking or freezing processes prior to ingestion by humans or other animals. Considering the high resistance of cysts and spores to adverse environmental conditions, as well as the endemic nature of both microorganisms in the country, these findings undoubtedly suggest concerns regarding public health and warrant further investigation in future studies.G. duodenalis and E. bieneusi in horticultural products collected in dry and rainy seasons from different typology markets in Maputo city, Mozambique. In addition, this study suggests the presence of potential zoonotic assemblages or genotypes of these two important pathogens in vegetables and fruits, commercialized in the markets most used by the population of the city and therefore can be a source of human infection by gastrointestinal parasitic pathogens. Another major concern is the fact that many people tend to consume these horticultural products raw, in salads, and other typical street food available in markets and/or nearby. Although no viability or infectivity assays were conducted to assess the actual impact of these protists on public health, this study suggests a risk associated with consuming raw horticultural products contaminated with these pathogenic intestinal species. It emphasizes the importance of implementing improved safety practices throughout the production, transport, and handling of these products. These measures should be adopted by producers, vendors, and consumers to minimize the potential for foodborne outbreaks.To the best of our knowledge, this study presents the first data on the detection and genetic characterization of the pathogenic The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.ML and OM: conceptualization, project administration, and supervision. CS, ML, SA, and OM: investigation. CS, SA, LX, and ML: methodology. CS, ML, and LX: software. CS and ML: writing—original draft. LX and OM: writing—review and editing. All authors have read and agreed to the published version of the manuscript.