The intricate and multifaceted nature of diabetes disrupts the body’s crucial glucose processing mechanism, which serves as a fundamental energy source for the cells. This research aims to predict the occurrence of diabetes in individuals by harnessing the power of machine learning algorithms, utilizing the PIMA diabetes dataset. The selected algorithms employed in this study encompass Decision Tree, K-Nearest Neighbor, Random Forest, Logistic Regression, and Support Vector Machine. To execute the experiments, two software tools, namely Waikato Environment for Knowledge Analysis (WEKA) version 3.8.1 and Python version 3.10, were utilized. To evaluate the performance of the algorithms, several metrics were employed, including true positive rate, false positive rate, precision, recall, F-measure, Matthew’s correlation coefficient, receiver operating characteristic area, and precision–recall curves area. Furthermore, various errors such as Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, and Root Relative Squared Error were examined to assess the accuracy of the models. Upon conducting the experiments, it was observed that Logistic Regression outperformed the other techniques, exhibiting the highest precision of 81 percent using Python and 80.43 percent using WEKA. These findings shed light on the efficacy of machine learning in predicting diabetes and highlight the potential of Logistic Regression as a valuable tool in this domain. Diabetes is a complicated and complex medical illness that has an impact on how the body utilizes glucose, a form of sugar that is the body’s primary fuel source. When our body is unable to create insulin or is unable to use the insulin that is produced by our bodies, diabetes results. According to statistics from 2017, 425 million people worldwide have diabetes. Diabetes causes 2 to 5 million deaths worldwide each year. By 2045, it is projected to grow to 629 million [Type 1 IDDM (Insulin-Dependent Diabetes Mellitus), also known as Type 1 diabetes, occurs when the body is unable to produce sufficient insulin, leading to a dependence on insulin injections for patients with this condition.Type 2 is otherwise called Non-Insulin-Subordinate Diabetes Mellitus (NIDDM). This kind of diabetes is caused when the body’s cells cannot utilize insulin appropriately.Type 3 is called gestational diabetes, which is the aftereffect of an expansion in glucose levels during incubation. These pregnant women have not been diagnosed with any kind of diabetes before. There are long-term complications associated with diabetes. Diabetes can likewise prompt a higher risk of different medical conditions.Data mining is the process of finding insights into data, which helps in the prediction of an event before it happens. This technique is used to find some useful information from the raw data provided. If these techniques are accurately analyzed and applied to datasets, they can predict events when appropriate algorithms are used on training and testing datasets. In the previous paper we studied and reviewed, the diabetes prediction system required a small dataset , whereasThe current study contributes to the existing literature by conducting a comprehensive analysis of six classification algorithms for diabetes prediction, shedding light on their relative performance and identifying Logistic Regression as the most precise approach. This study employs a diverse set of performance metrics and multiple software tools, demonstrating methodological versatility. Furthermore, it outlines plans for the development of web-based and Android applications for diabetes prediction and management, emphasizing the practical implications of the research. The commitment to ongoing research and algorithm refinement adds to the literature by promoting continuous innovation in healthcare-related machine learning applications, ultimately enhancing our understanding of the potential of machine learning in addressing diabetes and other healthcare challenges.In the Diabetes Using Classification Algorithms (PDCA) research paper, Deepti Sisodia et al. (2018) compared only three algorithms, namely Decision Tree, Naïve Bayes, and Support Vector Machine (SVM) in experiments performed by using Pima Indians Diabetes Dataset (PIDD). By applying different classification algorithms, the best and highest accuracy is from Naïve Bayes of at 76.30% .In the Predicting Diabetes Mellitus using Data Mining Techniques (PDMDMT) research paper by J. Steffi, Dr. R. Balasubramanian et al. (2018), the authors compared five algorithms, namely Artificial Neural Network (ANN), Naïve Bayes, Support Vector Machine (SVM), Logistic Regression, and C5.0 .As proven by the Improved J48 Classification Algorithms for the Prediction of the Diabetes research paper, Gaganjot Kaur and Amit Chhabra et al. (July 2014) used the J48 Decision Tree algorithm to improve the accuracy of the J48 algorithm and the C4.5. These algorithms were used with an actual accuracy of 73.8281% and the accuracy of the proposed algorithm is 99.87% [Madhusmita Rout, Amandeep Kaur et al. (2019) applied SVM, Naïve Bayes, Decision Tree, KNN, and Logistic Regression techniques to the proposed prediction model and made an accurate comparison of those algorithms. Experimental results show that the Logistic algorithm had an accuracy of 82.35%, which has proven to be better than the other applied algorithms [Varma, K. M., and Panda, B. S. et al. 2019) showed how the data mining classification algorithms C5.0, Logistic Regression, Naïve Bayes, Support Vector Machine, and Artificial Neural Networks are utilized to demonstrate genuine diabetes mellitus predictions. Thereafter, a near investigation is made between them, utilizing metric system. As a result of the research work, C5.0 and Logistic Regression were equally good based on their accuracy measurements of 74.63% and 74.67%, respectively [ showed hWu, H., Yang, S., Huang, Z., He, J., and Wang, X. et al. (2018) used the PIDD dataset with the K-Means and Logistic Regression algorithms to perform the task, which has the best accuracy of 95.42% .O. Dr. O., S. Dr. K., and B. Ramudu et al. (2020) “Used machine learning Techniques for diabetes prediction”. The study utilized several regression algorithms, including SVM, KNN, and Artificial Neural Networks (ANN), to analyze the PIMA diabetes dataset. The research was conducted using R-Studio software 4.0.2 version, and Logistic Regression was found to have the highest accuracy at 78% .Das H., Naik, B., and Behera, H. et al. (2018) investigated diabetes forecasts utilizing different order calculations, for example, J48 and Bayesian naivety, to analyze the infection rapidly. The proposed model aids patients and clinicians in saving time by creating reports rapidly from an information store. It was assessed utilizing WEKA and MATLAB programming. Further investigation can be conducted with a mixture of information-digging procedures for accurate and precise outcomes .Abdulhadi, N., and Al-mousa, A. et al. (2021) used different machine learning algorithms like Logistic Regression Classifier, Linear Discriminant Analysis (LDA), Linear Support Vector Machine, SVC (Support Vector Classifier) with Polynomial Kernel, Random Forest Classifier, and Voting Classifier to evaluate the different accuracies. The best ML algorithm among them is the Random Forest classifier, which achieves 82% accuracy on the PIMA diabetes dataset .Based on the study by Chou, C.-Y., Hsu, D.-Y., Chou, C.-H. et al. (2023) diabetes prediction was performed and evaluated by applying various machine learning algorithms, including Logistic Regression, Binary Neural Network, Decision Forest, and Decision Tree to evaluate model performance, accuracy, precision, recall, and the F-measure. The two-level augmented Decision Tree was the most efficient model, outperforming all other models on the PIMA diabetes dataset with an augmented patient ID under curve result of 0.991 . A compaWe encourage research data to be archived in data repositories wherever possible.We utilized the PIMA diabetes dataset from the UCI Machine Learning Repository for this study . The 768The dataset consists of 768 observations (rows), making it a moderate dataset for a machine learning study.Number of pregnancies;Plasma glucose concentration;Diastolic blood pressure;Triceps skinfold thickness;2 h serum insulin;Body mass index (BMI);Diabetes pedigree function;Age.There are eight attributes (columns) in the dataset, including:The binary variable being targeted indicates whether diabetes is detected or not. Generally, a value of 1 indicates the presence of diabetes, while a value of 0 indicates its absence.There is no significant gender distribution in the dataset. It mainly focuses on diabetes status and health-related features. Therefore, the dataset itself does not contain information on gender distribution.The dataset contains details of the diabetes status of 768 sample members, including whether they have diabetes or not. By multiplying the number of people with diabetes (target variable = 1) and dividing it by the total number of observations (768), it is possible to estimate the prevalence of diabetes in the dataset.Datasets may contain missing values, which can affect data analysis and modeling. Data preprocessing steps may be required to handle missing data.Generally, we know that the prediction process consists of our main attributes, which are called true positive (TP), true negative (TN), false positive (FP), and false negative (FN)  and are A classification model’s precision is a metric that assesses its capacity to correctly identify only the pertinent data points . MathemaBy using Equation (1), we can calculate the precision value .The recall is a metric that evaluates the ability of a model to identify all the relevant cases within a dataset . MathemaBy using Equation (2), we can calculate the recall value .The Mathews correlation coefficient (MCC) is more robust statistical measure than others, as it produces a higher score when the model achieves good outcomes across all four classes of the confusion matrix  relative to the size of the positive and negative components in the dataset . MathemaBy using Equation (3), we can calculate the MCC value .A true positive is an outcome where the model correctly predicts the positive value. Mathematically, the true positive can be calculated by the formula below:By using Equation (4), we can calculate the TPR value.In a classification problem, a true negative refers to an outcome where the model correctly predicts the negative class. Mathematically, the true negative can be calculated by using the formula below:By using Equation (5), we can calculate the TNR value.In a classification problem, a false positive is an outcome where the model incorrectly predicts the positive class. Mathematically, the false positive rate can be calculated by using the formula below:By using Equation (6), we can calculate the FPR value.A false negative occurs when the model predicts a negative class. False negatives are a type of error in which the model fails to detect a true positive instance. Mathematically, the false negative can be calculated by using the formula below:By using Equation (7), we can calculate the FNR value.The formula calculates the sum of the absolute differences between each predicted value and its corresponding true value and then divides by the total number of predictions to obtain the average error. The lower the MAE value, the better the predictive model is performing. Mathematically, the MAE can be calculated by using the formula below:By using Equation (8), we can calculate the MAE value.RMSE is the standard deviation of errors that occur when predicted on a dataset. This is similar to MSE (Main Squared Error), but the root of the value is considered when determining the accuracy of the model. Mathematically, the RMSE can be calculated by using the formula below :(9)MSE=1By using Equation (9), we can calculate the RMSE value .Relative Absolute error is expressed as a ratio, and a minor error is compared to the errors produced by a small model. Mathematically, the RAE can be calculated by using the formula below:By using Equation (10), we can calculate the RAE value.RRAE (Relative Ratio Absolute Error) is a metric that can be used to evaluate the performance of a predictive model. It is calculated by dividing the Mean Absolute Error (MAE) of the model by the error of the Zero-R classifier, which is a simple classifier that always predicts the most frequent class in the training data. The formula for RRAE isBy using Equation (11), we can calculate the RRAE value.https://github.com/wisalsafi/Diabetes-Prediction-Using-Machine-Learning-Algorithms-with-PIMA-Diabetes-Dataset/tree/main/ML%20Algoritms%20for%20Diabetes accessed on 7 July 2023.This experiment uses two sets of programming tools in WEKA , a combination of machine learning algorithms utilizing Python libraries such as MATLAB, Jupiter Notebook, and Scikit Learn Library. The result is generated from both programming tools which compares their results and graphs and calculates accuracy, error rate, F-measure, PRC , MCC (Matthew’s correlation coefficient), and ROC (receiver operating characteristic) areas. These experiments are performed using classification algorithms, for instance, Random Forest, Logistic Regression, K-Nearest Neighbors, Decision Tree, Naïve Bayes, and Support Vector Machine. The results and evolutions that we have performed and concluded, using both WEKA and Python, have been uploaded to GitHub, which is publicly accessible at The dataset used in the proposed work is the PIMA diabetes dataset, which consists of 768 instances. We performed our experiments and split the dataset by using a percentage split. “Percentage split” in machine learning often refers to dividing a dataset into separate subsets for training, validation, and testing. To assess the effectiveness of machine learning models and make sure they generalize successfully to new, unexplored data, segmentation is a crucial step. It is essential to choose a distribution that suits your particular situation, the size of your dataset, and the availability of the data. When the dataset is small, you can also use methods like cross-validation to obtain the most out of your data.The Decision Tree algorithm is utilized in this paper on the grounds that the Decision Tree calculation has the best precision proportion in the PIMA dataset and enjoys a few benefits, for example, it is not difficult to peruse and decipher, it is simple to get ready, and it requires less information cleaning. Decision Trees are regulated AI calculations that are utilized for both grouping and relapse strategies. Managed implies it requires a name for its execution. A Decision Tree is a supporting apparatus with a tree-like design that models potential results and assets. Decision Trees furnish a method for giving calculations contingent control articulations. They incorporate branches that address the dynamic advances that can prompt the endorsement of results . The DecDecision Trees have three main mathematical formulas, including information gain, entropy, and gain.Information gain is a measure that determines the change in entropy when a dataset is partitioned based on an attribute. It indicates the amount of new information that the attribute provides about the target variable. The Decision Tree construction process relies on information gain values to determine the sequence in which nodes should be split. The algorithm aims to maximize the information gain at each step, selecting the attribute that offers the greatest information gain as the first to be split. The value can be computed using the formula below:From Equation (12), we can calculate information gain.Entropy is a metric to compute impurities in certain attributes. It determines randomness in the data. Entropy can be calculated as follows:From Equation (13), we can calculate Entropy.The gain is a measure of impurity or purity used when constructing a Decision Tree in the CART algorithm. Gain can be calculated using the formula below:From Equation (14), we can calculate Gain.True Positive (TP): It is positive for both observed and predicted.False Positive (FP): It is negative for both observed and predicted.False Negative (FN): It is positive for observed, but negative for predicted.True Negative (TN): It is positive for predicted, but negative for observed.A confusion matrix is a table that summarizes the results of a classification model on a set of input data. It presents the count of correct and incorrect predictions for each class . The matTo calculate the accuracy score, the correct values are placed along a diagonal line from the top left to the bottom right of the matrix. Adding the values on this diagonal line (134 + 48 = 182) gives the accuracy of the model, which, in this case, is 79.13%.India’s PIMA diabetes dataset uses 760 instances, but here we experimented on the WEKA tool by using a percentage split and then taking a percentage share of 70%, which holds only 230 instances, and performing experiments that give us the best accuracy ratio, like the FPR , TPR (true positive ratio), Prec (precision), F-M (F-measure), and Rec .Logistic Regression is a supervised machine learning algorithm that models the likelihood of discrete outcomes when input variables are given. This helps in understanding the alliance between multiple independent variables and one dependent variable . It is ay is the dependent variable.β0, β1, β2, …, nβ are the regression coefficients for the intercept.X1, X2, X3, … and nX are the independent variables [ariables .Apply the sigmoid function on linear regression and the equation will bep is the predicted probability of the dependent variable being 1 [ being 1 .Random Forest is a machine learning algorithm that has evolved from a Decision Tree . Random RF (x) is the prediction of Random Forest for input x.Ft (x) is the prediction of t-th Decision Tree for input x.FT is the total number of Decision Trees in the Random Forest .KNN is a classification-focused supervised machine learning algorithm . K standFollowing The Naïve Bayes algorithm is a supervised machine learning algorithm. Naïve Bayes is based on the Bayesian theorem, which shows the probability of a hypothesis when the proof is provided. This algorithm also states that each feature of a particular dataset makes an equal and independent contribution to the problematic target class . The NaïP(H): The probability that hypothesis H is correct. P(S): Probability of S data. Previous probability of H .P(H|S): Probability of hypothesis H given data D.P(S|H): The probability that the S data is given the hypothesis H.SVM is a supervised machine learning algorithm, which is used for both classification and regression problem types. It can easily perform multiple categorical and continuous variables . SVM plat is the number of prepared models.i are zero.Derivatives of L P with respect to αThe vector w and the constant b define the hyperplane .We illustrate the values and graph of the MCC (Matthews correlation coefficient), which is between +1 and −1; the MCC perfect score is +1 and the worst score is −1 . The ROCWe mentioned some important errors in our research paper, which are shown in the form of tables and graphs. The true positive rate (TPR) and the true negative rate (TNR) are performance metrics used in machine learning, especially in the context of binary classification problems . They prDiabetes is a complex medical disease that affects how the body uses glucose, a form of sugar that is the body’s primary fuel source. Diabetes results when our body is unable to make insulin or is unable to use the insulin produced by our body. According to 2017 statistics, 425 million people worldwide are suffering from diabetes. Diabetes causes 2 to 5 million deaths worldwide each year. By 2045, this is expected to increase to 629 million . Type 1


The gastrointestinal (GI) microbiota is a complex and dynamic ecosystem whose composition and function are influenced by many internal and external factors. Overall, the individual GI microbiota composition appears to be rather stable but can be influenced by extreme shifts in environmental exposures. To date, there is no systematic literature review that examines the effects of extreme environmental conditions, such as strict isolation and confinement, on the GI microbiota.We conducted a systematic review to examine the effects of isolated and confined environments on the human GI microbiota. The literature search was conducted according to PRISMA criteria using PubMed, Web of Science and Cochrane Library. Relevant studies were identified based on exposure to isolated and confined environments, generally being also antigen-limited, for a minimum of 28 days and classified according to the microbiota analysis method (cultivation- or molecular based approaches) and the isolation habitat . Microbial shifts in abundance, alpha diversity and community structure in response to isolation were assessed.Regardless of the study habitat, inconsistent shifts in abundance of 40 different genera, mainly in the phylum Bacillota (formerly Firmicutes) were reported. Overall, the heterogeneity of studies was high. Reducing heterogeneity was neither possible by differentiating the microbiota analysis methods nor by subgrouping according to the isolation habitat. Alpha diversity evolved non-specifically, whereas the microbial community structure remained dissimilar despite partial convergence. The GI ecosystem returned to baseline levels following exposure, showing resilience irrespective of the experiment length.An isolated and confined environment has a considerable impact on the GI microbiota composition in terms of diversity and relative abundances of dominant taxa. However, due to a limited number of studies with rather small sample sizes, it is important to approach an in-depth conclusion with caution, and results should be considered as a preliminary trend. The risk of dysbiosis and associated diseases should be considered when planning future projects in extreme environments.https://www.crd.york.ac.uk/prospero/, identifier CRD42022357589. The Generally, the microbiota composition is dependent of environment and food intake in early childhood but is considered to be rather stable in adulthood , 13. HowCurrent literature supports changes in intestinal microbiota due to extreme environmental conditions such as extreme temperatures , 27, higIf environmental bacterial load influences the gut microbiota, what happens in case of poor bacterial diversity exposure? The effects of antigen-limited or poor environments on the human gut microbiota have to date received rather little attention. There is evidence that spaceflights can cause dysbiosis in humans, with a reduction in symbiotic microbes and a rise in opportunistic pathogens, affecting both microbial diversity and community structure significantly , 37. SevRoseburia, Prevotella, Lachnospira, and Phascolarctobacterium, while abundances of the genera Faecalibacterium, Parabacteroides, Bacteroides, Bifidobacterium, and Anaerostipes dropped. However, it remains unclear whether these effects result from exposure to an antigen-limited or antigen-poor environment or from microgravity, which has already been linked to changes in virulence factors, bacterial stress responses and biofilm formation  in such an environment on the composition, diversity and stability of the GI microbiota has not yet been conducted. Therefore, we aim to fill this gap in knowledge through a comprehensive review of the available literature and a systematic search for relevant studies. We ask for two questions:What is the effect of long-term residence (>28 days) in an antigen-limited or poor environment on the relative abundances of key bacterial taxa, and what happens to GI microbiota in terms of richness and biodiversity  and community structure?How reversible are the isolation-induced effects on the relative abundances, alpha diversity and community structure after the exposure to an antigen-limited or poor environment?2.2.1.nd 2022. The protocol of this systematic review is registered on the PROSPERO platform with the registration number CRD42022357589. The full search strategy was conducted in assistance with a specialized librarian and is documented in the supporting information  guidelines . To idenormation . It cons2.2.Inclusion criteria were established based on the five PICOS dimensions, i.e., participants, interventions, comparator, outcome and study design .Participants: Healthy adults, regardless of sex, age, or weight status, who had been under isolation conditions for at least 28 days were included.Intervention: Isolation in an environment with constant / reduced antigen exposure. Such isolation conditions are found (a) in space missions; (b) in isolation simulations, such as MARS-500, SIRIUS and Lunar Palace-1 or in bed-rest studies; (c) in extreme environmental conditions, such as in Antarctica, Arctic and Siberia. The intake of probiotics was allowed. Studies that focused on the use of antibiotics for preventing infectious and inflammatory diseases in humans were excluded.Comparator: Studies with or without control groups met eligibility criteria.Outcome: Assessment of the microbiota of the human GI tract.Study design: Randomized controlled trials or non-randomized controlled trials with any publication date and written in English, German and Russian. Only original articles were included.2.3.To identify eligible studies, the search results of the databases were combined, and the duplicates were removed. Two authors (BK and CS) independently screened titles and abstracts to identify relevant trials. Full-text articles were evaluated regarding their eligibility , with uncertainties being discussed between the authors (<3% cases). A third author (IM) was involved if the discrepancy persisted.Throughout the decades, the methods of microbial analysis changed from cultivation and cell counting to molecular-based approaches like next-generation sequencing, micro-arrays or quantitative polymerase chain reaction. Due to the huge diversity between the method procedures and the associated heterogeneity of the outcomes, the results were assessed separately from each other. The studies were classified into two groups according to microbiota analysis method:Group 1–Cultivation-based approaches for microbiota analysis.Group 2–Molecular-based approaches for microbiota analysis.Additionally, subgroups were created to provide a more homogenous summary of findings.Subgroup 1–Isolation caused by space missions.Subgroup 2–Isolation caused by spaceflight- or gravity simulators, e.g., MARS-500, bioregenerative life support systems (BLSS) or bed rest-studies.Subgroup 3–Isolation in a natural, earth-bound habitat, e.g., Antarctica.Certain experiments may have resulted in multiple publications concerning the GI microbiota. As the outcomes may differ in detail and description, all publications are listed in the tables. However, a summary of these studies is provided in the text and data evaluation sections.2.4.The following information was extracted from each included article for groups 1 and 2: study characteristics, conditions of isolation, methods of GI microbiota analysis and outcomes. Each study’s characteristics are reported using the original data and summarized in tabular form. Characteristics across the studies are presented as mean, minimum and maximum for sample size, age, body mass index (BMI) and study length.Primary outcomes concerning the GI microbiota were alpha diversity (richness and biodiversity), community structure/beta diversity and significant shifts in the abundance of individual microbial taxa . Signif2.5.For the included studies, a risk of bias assessment was performed using Risk of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool . As onlyThe rating ranged between “Low risk,” “Moderate risk,” “Serious risk” and “Critical risk” of bias. The authors declare that “Low risk” corresponds to the risk of bias in a high-quality trial, however, due to the limited number of studies found, no study was excluded for risk of bias.3.The literature search process for identification of eligible studies is shown in 3.1.A detailed overview of the characteristics for the single trials is presented in n = 15), otherwise from American (n = 3) or by multiple institutions working together (n = 1). Shifts in microbial abundance as a consequence of residency in space (n = 4) or long-term confinement in natural, terrestrial habitats (n = 2) have been reported. However, the alterations of the human GI microbiota by isolation was mainly studied in spaceflight- and gravity simulation facilities (n = 13 via 9 different units). There are several publications on two of these experimental units: In the case of the Mars-500 experiment, 3 publications were identified (2). The isolation intervention lasted on average 120 days (covering 30–520 days) and was accompanied by pre- and/or post-intervention measurements in >65% (n = 13). In 10 experiments, the diet was very tightly controlled and based on a typical space diet. Furthermore, 4 studies investigated the possibility of maintaining the GI microbiota composition by providing the volunteers with supportive probiotics or prebiotics for either regular or intermittent intake. Although all research groups collected stool samples to study the microbiota composition, no study reported stool frequency and/or consistency.The 19 included studies ranged from 1964 to 2021 and data was mainly published by Asian researchers  Escherichia, spore-forming bacteria, Proteus, Bacteroides, Enterococcus, Klebsiella, Citrobacter, Shigella, Corynebacterium and, in simulated emergency situations, changes in Clostridium perfringens were detected. One study found that the Bacteroides were the most numerous and stable, whereas severe fluctuations occurred in the transit microbiota (mentioned Staphylococcus and yeast)  . Anotherd yeast) . Finallyd yeast) . Due to n = 3; biodiversity n = 3), to remain stable  or to increase . Richness and biodiversity were not reported separately in 3 studies (n = 7) with a tendency towards convergence observed repeatedly  shifts in abundances were observed.At the phylum level abundances predominantly remained stable following the confinement; in all but 4 of the 13 included studies (Bacteroides (spp.), Eubacterium (spp.), Faecalibacterium (spp.), Lactobacillus (spp.), Prevotella (spp.), Alistipes (spp.), Blautia (spp.), Lachnospira, Ruminococcus (spp.), Bifidobacterium (spp.) and Clostridium (spp.) were reported to be either increasing or decreasing in their relative abundances in-mission. Across all studies, abundance shifts were predominantly associated with the phyla Bacillota (67.5%) and Bacteroidota  using molecular-based techniques to analyze microbiota changes.Three other studies , 37, 40 Beta-diversity has consistently been described as dissimilar in-mission , 37, 40,n = 7 subjects , Faecalibacterium (rather reducing) and Prevotella . Lachnospira and Ruminococcus also frequently showed significant changes in their abundance during the intervention, but these were very heterogeneous with tendencies towards reduction. One study  were stationed on a research station in Antarctica for 3 months in the study of Jin et al.  over the course of a 30-day cruise. Here, it needs to be considered, that this study is different from the other with extreme environments due to the large samples size. Probiotics were administered to some but not all of the sailors in this study. None of the described study models caused an increase or decrease in the abundance of phyla; even with the administration of probiotics to sailors, no differences were found. However, the administration of probiotics indicated that the impact of a long sea voyage on the intestinal microbiota were significant as beta diversity distances were significantly larger in the placebo group than in the probiotic group. The result was confirmed by alpha-diversity, which showed no significant differences among groups, but a sharp decline between probiotic and placebo groups while on mission. Only shifts in abundances at the species level were found, whereby the ratio between declines and increases was rather balanced with tendencies towards more increases.The study by Zhang et al.  determin3.4.Studies in space and simulation units were more similar in both genera and species profiles, while in the natural habitats the shifts were reported to be more diverse . HoweverStreptococcus species shifted rather consistently in abundance during the intervention  and does not lead to increased similarity in the GI microbiota among subjects. A systematic review examining the human GI microbiota during long-term space missions confirms our results suggesting that space travel may lead to microbial dysbiosis and metabolic changes in the human gut, including a drop in alpha diversity . SimilarIn contrast, hardly any significant changes in community structure were observed. Overall, the community structures remained heterogeneous and dissimilar between individuals, although a convergence of the GI microbiota was reported in several cases. Surprisingly, the effect of convergence was stronger in placebo groups compared to groups supplemented with probiotics . This fiAlistipes, Bacteroides, Bifidobacterium, Faecalibacterium and Prevotella, although there was no clear direction of development for these either. Alterations in intestinal genus levels is often present in case of intestinal microbiota dysbiosis, even more, a dysbiotic abundance of those genera is associated with several diseases  and descriptive levels. To counter this problem, subgroup analyses were performed which reduced heterogeneity to some degree. Despite differences in analysis techniques, habitats, study designs and frameworks for well-conducted studies, all studies were rather highly controlled, which is also reflected in the risk of bias. Here, the ROBINS-I tool for assessing risk of bias in non-randomized studies of interventions, recommended by the Cochrane Handbook, was used.One of the main issues in the studies reviewed was the extremely low sample sizes, making it challenging to conduct quantitative analyses at the individual study level. Additionally, there were inconsistencies in the study protocols and characteristics, making comparisons between studies difficult. To address these limitations, future studies should aim to increase their sample sizes to enhance statistical power. Furthermore, most of the studies did neither analyze immunological/biochemical parameters in parallel to the microbiota analysis or, the data were published separately and not reported in context. To better understand the effects of long-term isolation on the human GI microbiota, researchers should consider internal and external factors, such as nutrition, genetics, and the immune system. This will enable a clearer differentiation between the effects of isolation and those stemming from other variables. Finally, this systematic review is the first of its kind, providing new insights into the effects of isolation on the human GI microbiota.5.Overall, our review highlights the complexity of the relationship between the human GI microbiota and its environment. While extreme conditions can affect the composition of the GI microbiota, the internal factors that have shaped the microbiota over time appear to be the primary drivers of its composition and function in response to isolation in antigen-deprived conditions. Maintaining and/or strengthening the host’s fitness and immunity through diet, pre- and probiotics, and favorable lifestyle factors may have a positive impact on the human GI microbiota, especially under extreme conditions, and promote GI health and prevent disease.The original contributions presented in the study are included in the article/IM and BK: conceptualization. BK and CS: methodology. BK, CS, and JK: formal analysis. BK (80%) and IM (20%): writing–original draft preparation. IM, PE, JP, JD, and CL: writing–review and editing. BK: visualization. IM: supervision. All authors have read and agreed to the published version of the manuscript.Homo symbiosus. All authors thank the Open Access Publishing Fund of Tübingen University for support.IM acknowledges support by the German Aerospace Center (DLR), grant number 50WB1920. This publication work was supported in part by the European Commission in the context of ERC-2017-AdG N°788191- The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Arrhythmias are one of the most common causes of loss of flying privileges for both military and civilian pilots in the Western World, and atrial fibrillation (AF) is one of the most common arrhythmias worldwide. Aircrew, and particularly pilots, are subject to a unique and exacting working environment, especially in high-performance military aircraft. This manuscript analyzes AF cases in German military aircrew from both a clinical and occupational perspective to point out specific characteristics in this comparatively young, highly selected, and closely monitored group, and to discuss AF management with the aim of a return to flying duties.The digital information systems of the German Air Force Centre of Aerospace Medicine (GAFCAM) were searched for aircrew  with the diagnosis of AF. Evaluation results for underlying disease, AF characteristics, important clinical findings, and occupational decisions were analyzed in the light of current clinical guidelines and aeromedical regulations.In a 34-year period, between March 1989 and January 2023, 42 aircrew with at least one episode of AF were registered, all of them were male. The median age at initial diagnosis was 47 years . The median follow-up period was 5.35 years. 19 of them (45%) were pilots. The breakdown of events and occurrence was found to be: single (23), paroxysmal (16), persistent (2), permanent (1). In 27 aircrew (64%) AF terminated spontaneously. Long-term recurrence prevention was variable with catheter ablations in 8 cases. 36/42 aircrew were returned to flight status with restrictions, while 6/42 were permanently disqualified from flying.Management of AF in military aircrew requires a comprehensive approach regarding the flight environment as well as clinical guidance. Aeromedical disposition should be case-by-case based on aeromedical regulations, individual clinical findings, and specific occupational requirements in this challenging field of work. Cardiovascular disease is the most common reason for loss of flying privileges for both military and civilian pilots in Western Europe, and cardiac arrhythmias are the main disqualifier , 21, 26.The pathophysiology of AF is characterized by a complex interplay of triggers, perpetuators, and substrate development . Risk faAircrew, and particularly pilots, are subject to a unique and exacting working environment, especially in high-performance military flying . In addiThis manuscript decribes the incidence, course, management, and occupational consequences of AF in German military aircrew over a 34-year period. AF cases in this highly selected and regularly screened collective are chacterized in comparison with the normal population. Evaluation for underlying disease and treatment of AF are compared to the recommendations by current clinical guidelines, highlighting changes in the management of AF during the observation period. Occupational decisions are discussed in the light of aeromedical regulations by different authorities.The digital information systems  of the German Air Force Centre of Aerospace Medicine (GAFCAM) were searched for aircrew  with the diagnosis of AF in a 34-year period, between March 1989 and January 2023. Search criteria were International Classification of Disease (ICD)-10 codes I48.0, I48.1, I48.2, and I48.9. In addition, paper records were searched for relevant information including medical reports by the treating physicians. All German military pilot applicants, licensed non-pilot aircrew applicants, and air traffic controller (ATCO) applicants are medically screened at the GAFCAM. Active-duty German Air Force, Army and Navy pilots and non-pilot aircrew undergo periodic medical examination (PME) at GAFCAM to identify any potential flight disqualifying health conditions as per the German flight requirements. Those found without disqualifying conditions are then deferred for 3 years from GAFCAM, but are evaluated by local flight surgeons annually, up to the age of 40. Pilots above 40 years of age are examined annually at the GAFCAM. Licensed non-pilot aircrew and ATCO are examined annually by the local flight surgeon regardless of their age.In cases of documented AF, aircrew were examined for cardiac and non-cardiac causes with echocardiography, Holter monitoring, exercise ECG (when in sinus rhythm), and laboratory investigations including electrolytes and thyroid hormones. Those over 40 years of age were examined for coronary artery disease (CAD). This was done by invasive coronary angiography in the early years, within the last decade evaluation of the coronary arteries was mainly performed by computed tomography coronary angiography (CTCA).Statistical analyses were conducted using IBM SPSS Statistics for Windows 24 . Data analysis was primarily descriptive. Because the Kolmogorov-Smirnov test showed that none of the nominal scale parameters was normally distributed, median with minimum and maximum values were calculated.According to the regulations of the North Rhine Medical Association, the responsible authority for this study, a vote of the ethics committee was not necessary for this retrospective analysis.2 . The median follow-up period was 5.35 years . Most of the analyzed individuals had either a single episode of AF or paroxysmal AF. Only two had persistent, and one had permanent AF. The latter wanted to participate in aeromedical evacuation (AE) missions. AF patterns, Aircrew roles, and underlying or concomitant disease are shown in Table In a 34-year period, 42 aircrew with at least one episode of AF were registered, all of them were male. The median age at initial diagnosis was 47 years , the median body mass index (BMI) was 25.8 kg/mMore than half of AF cases in this study were single episodes with identifiable precipitants in more than 50% of these. These precipitants were three cases of mental stress, four cases of infections including one myocarditis, one episode during an exercise ECG, one during a centrifuge run, two episodes after alcohol use, one of them in combination with an opulent meal, and one case of post-interventional AF after an occlusion of an atrial septal defect. One aircrew had a syncope when driving his car and was then diagnosed with AF. None of the aircrew in our cohort was reported to have noticed AF symptoms at work or during flying.Three of five affected high-performance jet pilots, who were older than 50 years of age at initial diagnosis, flew irregularly to keep up their license. 13 non-pilot aircrew were not licensed aircrew and flew irregularly, eight of them were physicians (flight surgeons and AE personnel) with an average age of 52.5 years at initial diagnosis. The symptomatology of AF was mild in most individuals, while in some cases with short AF episodes the symptomatology remained unclear. Four aircrew were known to be asymptomatic with AF being an incidental finding. In total, 28 aircrew were symptomatic with AF, two additional individuals reported unspecific symptoms including weakness and dyspnea in one case, and subsultus in the other one. Six aircrew with AF were asymptomatic, in another six aircrew symptomatology was not reported.The most frequent underlying risk factor of AF was hypertension. Of the four CAD cases, two received percutaneous coronary interventions (PCI) with stenting, the others had coronary artery stenoses not leading to ischemia.Most of the AF episodes in our cohort terminated spontaneously. Twelve were cardioverted electrically or pharmacologically. In one case electrical cardioversion was not successful, so this case was declared as permanent AF. For long-term treatment, beta receptor blockers were administered to more than half of all individuals. Class I or III antiarrhythmic drugs according to Vaughan Williams classification (drugs affecting sodium or potassium channels) were used for long-term recurrence prevention in four individuals, catheter ablation in eight individuals. All of them received pulmonary vein isolation (PVI) as primary procedure, one of them in combination with a left atrial roof line. Two of the ablated individuals underwent subsequent ablations at four and five years after the first ablation, respectively. In one of these cases, another procedure is currently discussed due to a recent recurrence. In three individuals of our collective, AF was associated with typical atrial flutter. They received an ablation of the cavotricuspid isthmus. The earliest catheter ablations for AF or atrial flutter were performed in 2006. In one individual, an associated atrial tachycardia was ablated, in another one an accessory pathway.2DS2VASc , diabetes, stroke (doubled), vascular disease, age 65 to 74 and sex category ) score [2DS2VASc score of 2 or more, two of them had sustained a transient ischemic attack, one had hypertension plus diabetes. 13 aircrew were anticoagulated, five with vitamin K antagonists, three of those only temporarily. Eight were anticoagulated with direct oral anticoagulants (DOAC), four of those temporarily. Temporary anticoagulation after catheter ablation for at least two months is mandatory due to the thrombogenicity of the ablation lesions. The first individual anticoagulated with a DOAC received dabigatran in 2011, the others were administered with DOACs 2015 or later.The CHA)) score  in our cAccording to German military regulations, aircrew with AF are usually unfit for flying . After aWe analyzed all cases with AF in the German Armed Forces over a 34-year period from a clinical and occupational perspective. To the best of our knowledge, this is the largest analysis of AF in military aircrew in Europe. The United States Air Force Aerospace Waiver Guide Compendium mentions a five-year review by the Aeromedical Information Management Waiver Tracking System through 2020 including 76 cases of AF/atrial flutter, 10 of which were disqualified. According to the same source, there is an Aeromedical Consultation Service Atrial Fibrillation Working Group currently following 168 cases . In 2013Aeromedical concerns of AF include palpitations, dizziness, shortness of breath, presyncope, syncope, exercise intolerance, haemodynamic instability, and stroke risk. The loss of atrial contribution to cardiac output, loss of atrioventricular synchrony, and the rapid ventricular response during an episode may impair cardiac performance, especially during exertion, and can be acutely distracting or incapacitating .Aeromedical regulations for aircrew with AF are complex and vary between civilian and military licensing authorities of different countries. According to European Union regulations  professiAccording to the regulations issued by the US Federal Aviation Authority (FAA) in most cases a deferral and a FAA decision is required. The aeromedical examiner (AME) has to provide detailed information by a Non-Valvular Atrial Fibrillation Initial or Recertification Status Report. In case of a positive decision a Special Issuance is issued by the FAA .In the German Armed Forces all aircrew with atrial fibrillation with or without catheter ablation are unfit for flying . Based oIn the US Air Force, a history of atrial fibrillation is disqualifying for all flying classes and retention. The one exception is a single episode of atrial fibrillation clearly associated with a reversible cause. Additionally, the use of maintenance medications for the treatment or prevention of major rhythm disturbances including atrial flutter or atrial fibrillation requires a waiver for retention and all flying classes. A history of catheter ablation is also disqualifying for all flying classes . WaiversAll these regulations focus on several criteria which are important for disposition. First of all, underlying or concomitant cardiac or non-cardiac disease that may be incompatible with flying has to be excluded by thorough evaluation. The detection of other arrhythmias associated with AF is also important. As some of the regulations allow for flying in case of a single episode with an identifiable cause, triggers or precipitants of single AF episodes have to be identified and the likelihood of recurrence estimated. As AF is a progressive disease , 24, it In Europe, symptom severity is often characterized using the European Heart Rhythm Association (EHRA) symptom scale , 30. Sym2DS2VASc score [2DS2VASc score results are dynamic and change over time, regular reevaluations are required.Common stroke risk factors are summarized in the clinical risk-factor-based CHASc score . In our Sc score  and compSc score  should aDOACs have entered EASA regulations in 2019, before only vitamin K antagonists were allowed for anticoagulation. In a meta-analysis DOACs have shown to be non-inferior in the prevention of stroke compared to warfarin, but they were associated with a 10% reduction of all-cause mortality, a non-significant (14%) reduction in major bleeding risk, significant (52%) reduction in intracranial haemorrhage, but 25% increase in gastrointestinal bleeding [For more than ten years now, catheter ablation has been accepted as a first-line treatment for rhythm control alternative to drug treatment. It is often preferred by aircrew as many side effects of antiarrhythmic drugs are incompatible with flying , 11. AftThe commercialization of space is moving forward and will increasingly provide opportunities for older and less selected individuals to fly into space. Many of these individuals will be at increased risk of cardiovascular disease or arrhythmia. Knowledge about the pathophysiology and triggers of AF under the conditions of such flights as well as management of the arrhythmia will therefore be of increasing importance. Further studies on AF in environments comprising acceleration forces, possible hypoxia etc., and including older individuals and a higher ratio of females are warranted.There are some strengths and limitations of our study that have to be mentioned in this manuscript. One of the strengths is the comprehensive analysis of a large number of aircrew over a long duration. In addition to the very long observation time, it is also a strength that there is only one center in the German Armed Forces for aeromedical assessment of military aircrew of all services . So, the analysis included every aircrew with AF within the last 34 years. On the other hand, it is a limitation that the total number of aircrew examined in the 34-year period is not exactly known. Therefore, the incidence cannot be calculated, but it is certainly much lower than in the general population. Another limitation is that only German data were analyzed. Combining data from other countries’ military flying health data repositories might bolster the sample size. Additionally, this study only provided data on male aircrew, as only males were affected by AF during the observation period. It is not clear if databases of licensing authorities worldwide, e. g. the FAA, have been reviewed to exclude or include females as part of their comparisons and critical thoughts.In conclusion, the incidence of AF in the comparatively young, male, highly selected, and medically monitored group of aircrew is, as expected, much lower than in the general population. Management of AF in military aircrew requires a comprehensive approach regarding military aviation environment as well as clinical guidance. Aeromedical disposition should be case-by-case decisions based on aeromedical regulations, individual clinical findings, and specific occupational requirements in this challenging field of work.


Transcultural validation studies of depression scales are rare in Morocco. The Center for Epidemiologic Studies Depression Scale (CES-D) is commonly one of the most common and frequently used screening instruments for depressive symptoms, but the scale has not, up to date, been validated in dialect of Arabic in Moroccan contexts. Given the importance of assessing and preventing depressive symptoms in our Moroccan context, this study aims to validate the CES-D, translated, and adapted to the dialect of Arabic and Moroccan culture, in a sample with substance use disorder.N = 140). Then, this structure was confirmed in the validation sample (N = 205) using confirmatory factor analysis (CFA).The data were analyzed in two successive phases. First, exploratory factor analysis (EFA) was used to assess the factor structure in the pilot sample  and average variance extracted (AVE = 0.64–0.66), respectively. The square roots of AVE were higher than factor-factor pairs correlations, and the Heterotrait-Monotrait ratio (HTMT) of correlations values was less than 0.85, indicating acceptable discriminant validity.Overall reliability and both convergent and discriminant validity tests indicated that the Moroccan dialectal Arabic version of the CES-D had a good performance and may serve as a valid tool for measuring the severity of depression in people with substance use disorder. Mental disorders can lead to significant physical, emotional, and social health issues, and can greatly impact the daily lives of those affected , 2. TheyThe co-occurrence of mental disorders and substance use disorders (SUD) compounds the existing challenges in mental health; indeed, substance use disorders also have a high prevalence among individuals with mental disorders –11. ThisDepression, as defined by the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-V), is characterized by a constellation of symptoms, including sadness, loss of interest in or pleasure in nearly all activities, and other physical and psychological symptoms. These symptoms may include changes in appetite or weight, sleep disturbances, fatigue, feelings of worthlessness or excessive guilt, difficulty concentrating, and recurrent thoughts of death or suicide. The DSM-V requires the presence of at least five of these symptoms for a minimum duration of two weeks in order to diagnose major depressive disorder. These symptoms must also cause significant impairment in social, occupational, or other areas of functioning .Untreated depression can persist for an extended period and impact daily activities, such as academic and social functioning . DepressAccording to the World Health Organization (WHO), depression affects a staggering 280 million people worldwide, making it the primary cause of disability globally and a significant risk factor for suicide. According to estimates, depression affects 3.8% of the population, including 5% of adults  and 5.7% of individuals over the age of 60 .Depression's prevalence varies across countries and regions globally. A study conducted in 18 countries  found that the average lifetime and 12-month prevalence estimates of major depressive episodes were 14.6% and 5.5% in the 10 high-income nations, respectively, and 11.1% and 5.9% in the eight low- to middle-income nations, respectively. In high-income countries, the average age of onset is 25.7, while in low- to middle-income nations, it is 24.0 . ExpertsMorocco, a low- and middle-income country, grapples with the significant health challenge posed by depression . RegrettTo effectively support individuals with comorbid depression and SUD in Morocco, it is imperative to utilize valid, reliable, and culturally adapted tools for diagnosing depression. Early identification and intervention can significantly reduce the disease burden and lower the risk of depression –43. A usThis measure has been used across age groups, countries, and in both community and institutionalized samples. The initial CES-D testing as well as subsequent sample testing have demonstrated strong psychometric properties as a screening tool , 47–49. Although a classical Arabic version of the CESD-R (Center for Epidemiologic Studies Depression-Revised) exists , there hThe original version of the CES-D was translated from English into a dialectal Arabic. The latter was reviewed by an expert group of psychiatrists, epidemiologists and linguists and finally translated back into English by two independent translators who were unfamiliar with the CES-D. English language specialists reviewed the back translation and made corrections based on their feedback. The final dialectal Arabic version was chosen by the committee after it was judged satisfactory. Thereafter, 20 participants were invited to complete and provide feedback on the scale during a pilot test of the latter. Nothing was found to be unclear or difficult to grasp. Because of this, following the pilot test, no changes were made.It should be noted that among the problems encountered during cross-cultural adaptation are problems concerning words that did not have a precise equivalent in the Moroccan dialect, problems related to the limited nuances in the expression of the state of mood in the Moroccan dialect, or problems related to the variation in the expression of the state of mood from one region to another. For items that had no equivalent in dialectal Arabic, we had to use sentences that could describe the situation in parentheses next to the term in classical Arabic or use synonyms.A consecutive series of people who were seeking substance abuse treatment and attending routine follow-up appointments at the addictology center in Fez City were recruited. All participants were informed of the purpose of the study.Inclusion criteria: being a current user of at least one psychoactive substance, diagnosed with substance use disorder (SUD) by a psychiatrist using the MINI interview, having not yet entered the withdrawal period, being able to communicate and complete the CES-D, in addition to providing demographic information, and agreeing to participate in the study by providing written consent as required by the current legislation.Exclusion criteria: not being a current user of any psychoactive substance, being in withdrawal, not being able to communicate and complete the CES-D, in addition to providing demographic information, and not agreeing to participate in the study.The CES-D is a 20-item self-report measure used to assess the presence and severity of depressive symptoms in the past week. The items are measured on a four-point Likert scale. Response options range from 0 to 3 for each item , with a total score between 0 and 60. The original factor structure included four factors: depressed affect ; positive affect ; somatic complaints ; and interpersonal difficulties  , 44. ResIn phase 1, the 20-item CES-D  was piloted with 140 participants between February 2021 and July 2021. In phase 2, the modified 16-item CES-D (version 2) was distributed to 205 participants between September 2021 and July 2022.N = 140) using EFA by principal axis factoring as a method of extraction and oblimin rotation. The selection of the extracted factors was decided on the basis of two different criteria: only factors with an eigenvalue greater than 1 and elements that had a factor loading greater than 0.40 were kept [Statistical data analyses were performed on the Jasp and R programs: the exploratory factor analysis (EFA) was performed on the Jasp program 0.17.1 version, and the confirmatory factor analysis (CFA) was performed with the packages "psych", "semTools", and "lavaan" of the R program. The CES-D items in the whole sample were first analyzed by descriptive statistics. Then, the structure and internal consistency of the CES-D were tested. The assessment of factorability was based on the Kaiser–Meyer–Olkin (KMO) test and Bartlett’s sphericity test . The facere kept –63. The ere kept . The fitN = 140) was analyzed by exploratory factor analysis, while the second one (N = 205) was tested by confirmatory factor analysis. The two samples presented similar demographic characteristics (Table The study population consisted of two samples of people who were seeking substance abuse treatment at the addictology center in Fez City. The first sample N = 40 was anN = 140). The sampling adequacy for performing the analysis was verified through the Kaiser–Meyer–Olkin test. The total KMO value was 0.90, suggesting excellent sampling adequacy. Bartlett’s test of sphericity  indicated that inter-item correlations were sufficiently large to perform EFA.The underlying factor structure of the CES-D was examined by analyzing the data from the first sample  for the combination of somatic and depressive symptom items . The secThe most commonly used measures of internal consistency are Cronbach's alpha and CR, which measure reliability based on observed item correlations. The alpha values need to be at least 0.70 and ideally above 0.80 to be considered good consistency . The relConvergent validity is an assessment for measuring the level of correlation between multiple agreed-upon indicators of the same structure. To determine convergence validity, the CR and the AVE should be considered , 67. TheDiscriminant validity refers to the extent to which the constructs differ from each other empirically. It also measures the degree of difference between overlapping constructs . DiscrimThe bolded values are the square root of AVE of each dimension, whereas the other values are the inter-correlation among the latent factor dimensions Table . The higIt appears from Table The fit statistics for the CFA model were χ2 = 187.39, CFI = 0.96, TLI = 0.95, RNI = 0.96, RMSEA = 0.06, SRMR = 0.04, and χ2/df = 1.86  in two samples of Arab women , 101. InIn support of these findings, Uluşahin et al. conducted a cross-cultural study on depressive symptoms among outpatient samples in both British and Turkish populations. In the Turkish sample, the first common component responsible for the greatest variability was the somatization factor, while in the British sample, it was the component that reflected fundamental depressive symptoms . In his In our study, we obtained a Cronbach's alpha of 0.90 for the 16-item scale derived from CFA, with alphas ranging between 0.88 and 0.93 for the three factors extracted from this analysis. These findings demonstrate excellent internal consistency for the scale, indicating high reliability. Furthermore, the CES-D scale has consistently shown good reliability across various sociocultural contexts. For example, Brett D Thomb et al. (2008) found an alpha of 0.88 , α = 0.8The three-factor measurement model demonstrated an excellent to satisfactory fit. CFA measures were used to assess the validity and reliability of the features. However, the validity of the measurement operation is severely restricted due to the location, timing, and utilization of the scores, particularly in studies with small sample sizes. Results from research conducted in one location with a specific population may be challenging to generalize to another sample from a different location and/or demographic.Overall, this study is the first time that a dialectal Arabic CES-D version has been validated among Moroccans with SUD. We investigated its psychometric properties among a sample of 205 Moroccans with SUD using CFA to examine its factor structure. However, this study has some limitations that should be highlighted. The sampling was conducted in a single addictology center and targeted a small sample size of patients with SUD. In addition, the sample is small and contains patients of different socio-demographic characteristics. Hence, these findings cannot, however, be extrapolated to all Moroccan patients with substance disorders from different regions. Therefore, additional research on larger samples of various populations, as well as longitudinal surveys, are needed to evaluate the scale's predictive validity for psychosocial outcomes. This version of 16 items is a quick, valid, and reliable instrument to identify depressed people or those at risk for developing depression, to allow for intervention, prevent, and/or treat depression early in order to decrease disease burden, and decrease the risk of depression.


Interpersonal problems are consistently identified with psychopathology that often has its onset in adolescence. Most of the commonly used instruments in child and adolescent psychiatry target non-interpersonal problems. The Inventory of Interpersonal Problems (IIP) is a widely studied and utilized instrument in the adult mental health field.This study aimed to examine the psychometric properties of the IIP (circumplex version) used with a clinical adolescent population. Method: Sixty-two adolescents (13-17 years) who received treatment in a child and adolescent mental health clinic (CAMHS) were included in the study. To establish reliability and validity, we conducted confirmatory factor analyses, internal consistency, and validity analyses.Confirmatory analyses did not show optimal model fit. However, other indices like CFI and TLI were promising. The reliability of the eight scales was in the same range as previous studies and acceptable. There were expected significant correlations between IIP-C scales and the broadband scales of Youth Self-report (YSR).The pioneer nature and its clinical focus are strengths however, there is a need for more research. The promising results are encouraging, and future research could also explore how best to bring the instrument into the digital age. Interpersonal problems are consistently identified with psychopathology such as major depression, anxiety, alcohol and drug dependence, and maladjusted personality –4. TheseThe IIP was developed in the crucible of clinical work with people seeking psychotherapy. Horowitz et al.  noted thOver the years, researchers keen on using the measure created various versions of the IIP to best suit their needs. One line of development was motivated by a need for a clinic-friendly measure that was short and brief –10. In aAnother line of development was influenced by the research-based traditions of interpersonal relationships and the circumplex model , 18. CirTwo sources provide guidelines for what psychometric instruments are recommended for CAMHS in Norway: the Norwegian Directorate of Health, and the Norwegian Association for Child and Adolescent Psychiatry. Guidelines propose assessing a wide range of factors that cover anamnestic information, symptoms, somatic functioning, cognitive abilities, i.e., intelligence, family and social network, and functioning . The Youopened promising new directions of research in such areas as attachment theory, clinical judgement, depression, and psychotherapy”  treated in CAMHS. We are primarily interested in examining if the IIP-C results from an adolescent population yield the same factor structure as we see in an adult clinical population. To establish validity, we use a commonly used tool in CAMHS - the Youth Self Report (YSR), and its two broadband syndrome scales of internalizing and externalizing problems. We expect to see a correlation pattern where IIP-C dominant scales will be significantly correlated with the externalizing scale. Conversely, IIP-C submissive scales will be significantly correlated with the internalizing scale. Additionally, the YSR scale “social problems” was of particular interest due to its implicit concordance with interpersonal content. We expected social problems to have high correlation with all of the IIP-C scales.The study was part of a larger study about parent involvement in usual clinical practice. The regional ethics committee (REK-III), University of Bergen approved the project. All the participants and parents of the participants under 16 years of age consented to participate in the study. All new referrals to the clinics received printed information regarding the research study either before or shortly after the first consultation at the clinic. The primary therapist was present during the assessments and answered questions the adolescents may have had. Since the IIP-C was not used in adolescents before, we first asked a panel of six therapists and another panel of five adolescents who were currently receiving in-patient treatment, to provide feedback regarding the instrument. We were interested in their assessment of the comprehension level, instructions, burden of time to complete the assessments and its level of difficulty. Based on their feedback and recommendation, no changes were made to the Norwegian translation.A total of 62 adolescents between 14-18 years of age participated in the study. The participants were referred to and treated in outpatient clinics in CAMHS. Information regarding referral reason was available for 70% (n = 44) of the clinical sample. Forty percent (n = 18) was referred due to being sad/depressed 16 % (n = 7) for behavioral problems. The others were spread over a variety of referral reasons like anxiety/phobia (2%), school problems (7%), hyperactivity (9%), eating disorders (9%), autistic symptoms (7%) and (10%) were referred for various other reasons. We had only two exclusion criteria - psychosis and clinician evaluated developmental disorder, i.e., IQ < 70. The mean age of the sample was 15.7 years (SD= 1.04), and 69 % were girls. Fifty-six per cent of the adolescents lived with both parents, 32 % had permanent residence with their mothers, and a small group (2.4 %) commuted between the mother and father. The rest (9.6 %) lived in adoptive homes and residential care facilities.It is hard for me to (for example) trust other people and keep things private from other people.” The second 25 items start with “things I do too much” (for example),”I fight with other people too much,” and “I am too sensitive to criticism”. The items form eight scales that are projected on to the interpersonal circumplex. The IIP-C scales have their own abbreviations which are as follows: Domineering/Controlling (PA), Vindictive/Self-centered (BC), Cold/Distant (DE), Socially inhibited (FG), Nonassertive (HI), Exploitable (JK), Overly Nurturant (LM), and Intrusive/Needy (NO). The IIP-C was translated into Norwegian . The first 39 items begin with “orwegian  and repoThe YSR questionnaire consists of 112 items that adolescents report on a 3-point Likert scale . There are three scales assessing the competence , eight narrow-band syndrome scales . The two broadband scales are made up of Anxious/Depressed, Withdrawn/Depressed and Somatic Complaints  and Rule-breaking behavior and Aggressive behavior . The alpha coefficients for the syndrome scales range from .71 - .95 . The NorScale scores were computed as the mean of the items in the actual subscale . We baseχ225 = 51.073; RMSEA = .131 (.079 - .182); CFI = .918; TLI = .908; SRMR= 0.281. The RMSEA exceeded the acceptable level of .08, implying that the estimated model was significantly different from the theoretical model. However, CFI and TLI were above > .90 and SRMR was below .80, indicating that the model may hold some promise.The fit indices were I am too independent, and item 29, it is hard for me to put another's needs before my own. Deleting these items did not affect the alpha coefficient of the total IIP-C scale. However, removing item 44 from the subscale increased the α coefficient of PA by .04, and removing item 29 increased the α coefficient of BC by .02, which are small-to-medium changes and therefore, the items were retained in subsequent analyses.Table The intercorrelations of the IIP-C scales are summed up in table The eight scales were rotated to converge maximally with the theoretical locations to obtain and a visual representation of their location in the interpersonal circumplex. Figure p < 0.00, suggesting a certain degree of overlap between the instruments. Table There was a moderate correlation between the YSR total syndrome scale  and the IIP-C total score , r(62) = 0.59, The overall aim of the current study was to examine the psychometric properties of the IIP-C in a clinical sample of adolescents treated in specialty mental health services in Norway. This is the first study that examined the psychometric properties of the IIP-C used with a clinical adolescent population. Analyses included examining model fit, reliability of the scales, the factor structure, and validity by comparing it with a commonly used instrument in the child and adolescent mental health field—the Achenbach’s Youth Self Report.The RMSEA did not confirm good model fit. However, the other three indices were more promising. The CFI, TLI and SRMR are incremental or approximate fit indexes, which means that they are estimates of increments from the baseline model to the hypothesized model. The literature suggests several cut-off guidelines. One frequently used guideline proposed by Hu & Bentler  recommenOur goal is to use the IIP-C to assess adolescents’ interpersonal problems in a clinical setting, where there are currently no such measures. We, therefore, also conducted conventional reliability and validity analyses. The Cronbach’s alpha estimates of our study were .69 to .84, which was in the similar range reported by the clinical group in Norwegian adult study , which wExamining the pattern of Cronbach’s alpha for the IIP-C scales, the highest alpha values were for the scales DE, FG, HI and JK and the lowest for scales NO and PA. This is a pattern we also noticed with other circumplex versions of the IIP for instance, the original study by Alden et al. , the NorAccording to the theory of circumplex structures, there should be high correlations between adjacent scales and negative correlations between scales directly opposite each other . The scaCorrelation between the IIP-C and YSR provided support for concurrent and discriminant validity. Thematically, the YSR internalizing scale captures withdrawal from social relationships and heightened experience of being lonely. The IIP-C scales on the submissive end of the circumplex capture lack of assertion, social inhibition and overly accommodating and compliant. As expected, we found a high correlation between, for instance, FG, HI and JK and internalizing problems. Likewise, the YSR externalizing scale that signifies behavioral and disruptive problems strongly correlated with the IIP-C dominant scales BC, PA and NO that reflect interpersonal problems with domineering, controlling and intrusion. These high correlations provide support for concurrent validity. Conversely, the internalizing and externalizing scales had low correlations with their theoretical opposites (see Table The correlation between the LM (overly nurturant) and internalizing scale was unexpectedly high, even higher than the expected correlation between HI and internalizing problems. However, the same correlations based on ipsatized scales showed that HI had a higher correlation with internalizing problems than LM. This suggests that general distress played a role in the correlation between LM and internalizing problems. The content of LM captures the experience of doing too much for others at high personal cost to oneself. There is an implied sense of self-sacrifice and disappointment. Interestingly, this experience is more highly correlated with internalizing problems than the experience of being submissive, socially disconnected and inhibited. A longitudinal study showed that peer rejection in middle childhood is a pathway that puts adolescents on a trajectory to internalizing problems . CoupledThe strengths of this study are its pioneer nature and clinical focus. Nevertheless, there are several limitations. The low sample size limited our ability to conduct additional analyses, for example, post-hoc model fitting and conduct split-sample analyses like EFA with a sub-sample and CFA with another sub-sample. The sample is also a specific clinical group primarily in treatment for internalizing problems, mainly depression. However, while this is a representative referral reason for adolescents in CAMHS Norway , a large