The development of medical diagnostic models to support healthcare professionals has witnessed remarkable growth in recent years. Among the prevalent health conditions affecting the global population, diabetes stands out as a significant concern. In the domain of diabetes diagnosis, machine learning algorithms have been widely explored for generating disease detection models, leveraging diverse datasets primarily derived from clinical studies. The performance of these models heavily relies on the selection of the classifier algorithm and the quality of the dataset. Therefore, optimizing the input data by selecting relevant features becomes essential for accurate classification. This research presents a comprehensive investigation into diabetes detection models by integrating two feature selection techniques: the Akaike information criterion and genetic algorithms. These techniques are combined with six prominent classifier algorithms, including support vector machine, random forest, k-nearest neighbor, gradient boosting, extra trees, and naive Bayes. By leveraging clinical and paraclinical features, the generated models are evaluated and compared to existing approaches. The results demonstrate superior performance, surpassing accuracies of 94%. Furthermore, the use of feature selection techniques allows for working with a reduced dataset. The significance of feature selection is underscored in this study, showcasing its pivotal role in enhancing the performance of diabetes detection models. By judiciously selecting relevant features, this approach contributes to the advancement of medical diagnostic capabilities and empowers healthcare professionals in making informed decisions regarding diabetes diagnosis and treatment. In recent years, there has been a significant surge in research and development in the field of artificial intelligence. This growth can be primarily attributed to advancements in the physical devices utilized in this domain and the remarkable increase in processing power of computer systems. Various areas have benefited from these advances in artificial intelligence, including the medical, automotive, aerospace, and educational sectors –8. AdditOne area that has significantly benefited from technological advancements and artificial intelligence is the field of medicine, particularly in relation to medical diagnosis. Machine learning techniques have been used to create applications that serve as support for medical professionals in making disease diagnosis decisions. These algorithms use large volumes of data obtained from patients through clinical studies, enabling accurate diagnoses. Such applications have become increasingly common in various medical specialties, including cancer detection, heart disease, eye diseases, and chronic conditions. As pointed out by Alcalá-Rmz et al. , diabeteMachine learning algorithms analyze a dataset consisting of features and, based on observations of prelabeled data, are aimed at classifying new data into predefined classes. Random forest, naive Bayes, and artificial neural networks are among the algorithms commonly used to generate diabetes detection models –14, amonF1 score, precision, and area under the ROC curve (AUC).As pointed out by Alcalá-Rmz et al. , it is pBased on the dataset used by Alcalá-Rmz et al. , the twoThe motivation behind this work arises from the growing significance of accurate diagnosis in the medical field, particularly in the detection of diabetes. Machine learning algorithms have demonstrated promising results in generating disease detection models; however, the key challenge lies in optimizing the input data by selecting the most relevant features. In this research, the integration of feature selection techniques, namely, the Akaike information criterion and genetic algorithms, is aimed at comparing and evaluating the performance of different classifier algorithms in detecting diabetes. The primary objective is to enhance the diagnostic capabilities of healthcare professionals and improve the accuracy of diabetes diagnosis, consequently leading to better treatment outcomes for patients.This paper is organized as follows: Related Work presents previous studies on the utilization of machine learning classifier algorithms and feature selection techniques in generating classification models for disease detection, with a specific focus on diabetes. Background provides an overview of the feature selection methods employed, namely, AIC and GA, as well as the implemented algorithms for generating classification models, including SVM, RF, kNN, GB, ET, and NB. Materials and Methods outlines the materials and methods utilized in this study. Experiments and Results describes the conducted experiments and presents the obtained results. Finally, Discussion and Conclusions provides a comprehensive discussion and draws conclusions, along with highlighting points to be considered for future research.In recent years, the rapid advancement of technology, particularly in the field of artificial intelligence, has led to the development of technological applications aimed at addressing various challenges in different domains. One domain that has significantly benefited from these advancements is healthcare, where numerous applications have been proposed and implemented to facilitate medical diagnosis , 16. TheIn the field of medical diagnosis, machine learning techniques have been extensively used, leading to the development of numerous applications. Bhavsar et al.  conducteSimilarly, Battineni et al.  conducteDiabetes is among the diseases that have received attention in the development of applications and classification models focused on its detection, utilizing machine learning methods. In their work, Chou et al.  conducteOther work that employs machine learning techniques for the detection of diabetes was conducted by Kangra and Singh , where vThe architecture of an artificial neural network for the automatic classification of diabetic and nondiabetic patients through clinical and paraclinical features in Mexico is proposed by Alcalá-Rmz et al. . The autLike those mentioned above, there have been many proposed works that make use of machine learning techniques focused on the detection of diabetes. Different classifier algorithms have been used and combined to improve the results in the performance of the models. Another important aspect to consider in the development of classification models is the dataset used, as the performance of the system often depends on this, not only in the accuracy of the classification but also in the performance of the model, since the more features the dataset has, the longer the classification model may take to process. Considering this, feature selection techniques help to optimize datasets, identifying the most relevant ones.Regarding the use of feature selection techniques in diabetes classification and detection models, Hu et al.  implemenF1 score, ROC curve, and execution time. The results demonstrated classification accuracy ranging from 68% to 99%, depending on the selected features or feature subsets as inputs to the model.In the study conducted by Haq et al. , a machiThe study undertaken by Sneha and Gangil  focuses F1 measure, precision, and recall of 1.00, 0.92, and 1.00, respectively. These findings highlight the effectiveness of the proposed model in accurately identifying early signs of diabetes.The research presented by Olabanjo et al.  introducThe summarized results, as shown in F1 score, precision, and AUC ROC being employed for assessment.Based on the literature review, several machine learning algorithms have been utilized in the development of diabetes detection models, and feature selection techniques have been incorporated in some studies. Building upon these findings, this research article introduces two feature selection methods, namely, the Akaike information criterion (AIC) and genetic algorithms (GA), to generate classification models. The dataset used in this study consists of clinical and paraclinical features of both diabetic and nondiabetic patients, following the approach employed by Alcalá-Rmz et al. . NotablyTo ensure the robustness and credibility of the research findings, the evaluation methodology incorporates the Wilcoxon test. The inclusion of the Wilcoxon test enables the assessment of the statistical significance of variations between the diabetic and nondiabetic groups within the dataset. This statistical analysis plays a vital role in validating the efficacy of the classification models for diabetes diagnosis , 37. By In summary, the present study builds upon previous research in the field of diabetes detection by incorporating machine learning algorithms and feature selection techniques. However, this work extends beyond existing studies by introducing the Wilcoxon test to evaluate the statistical significance of the results. By integrating this statistical analysis, the research ensures the reliability and robustness of the classification models developed for diabetes diagnosis. Consequently, this integration enhances the practical utility and impact of the research, contributing to the advancement of knowledge in the field of diabetes detection and diagnosis.The feature selection process involves the application of various techniques to obtain a subset of features from a larger original feature set. This subset comprises only the most relevant features, selected based on specific criteria . In the Forward selection: the forward selection method begins with an empty model and gradually incorporates variables one by one. At each step, the variable that produces the greatest improvement to the model is added. The process continues until a predefined stopping rule is met or until all variables have been included in the model . AUC can be interpreted as the probability that, given a pair of samples consisting of one positive and one negative instance, the model correctly classifies them. A higher AUC value corresponds to better model performance .F1 score is a comprehensive metric that combines precision and recall, offering a balanced evaluation of a model's performance. It is especially valuable when there is an uneven distribution of positive and negative instances. The F1 score takes into account both false positives and false negatives, treating both types of errors equally. It proves useful in scenarios where there is a trade-off between precision and recall, aiming for a harmonious blend of the two. The F1 score is mathematically defined in [The fined in (6)F1=2∗Precision measures the proportion of correctly classified positive instances out of all instances predicted as positive by the model. It is particularly important in scenarios where false positives can have significant consequences. High precision indicates a low false-positive rate, meaning that positive predictions are highly likely to be correct. Precision is defined as the ratio of true positives (TP) to the sum of true positives and false positives (FP), as shown in (7)PreciSupport vector machine (SVM) is a linear model commonly used for classification and regression problems –63. It iSupport vectors are the data points that are closest to the hyperplane in a support vector machine (SVM) model. If these points were removed, the position of the hyperplane would be altered, highlighting their significance. The hyperplane, which represents the threshold for class division, becomes more certain in its classification as the data points move further away from it.This classifier was developed by Breiman . Its perk samples from a training dataset that are closest to the unknown samples. To achieve this, the Euclidean distance is calculated between a given set of queries and the input data, enabling the identification of the k-nearest input points for each query. The output for the unknown samples is then determined by computing the average of the input features based on the initial k samples [This method has found extensive applications in various statistical contexts. It employs a nonparametric approach, where the key idea is to identify a group of  samples .Gradient boosting (GB) is a highly popular machine learning algorithm commonly applied to tabular datasets. It offers exceptional capability in discovering nonlinear relationships between the model target and features while also exhibiting versatility in handling missing values, outliers, and categorical features with high cardinality without requiring any special preprocessing. GB can be effectively employed for both classification , 69 and k features from the entire feature set. Each tree then selects the best feature to split the data based on a mathematical criterion, typically the Gini index. This process of using random feature subsets results in the creation of multiple decorrelated decision trees. During the construction of the forest, the Gini importance of each feature is calculated as the normalized total reduction in the selected mathematical criterion for feature selection [k features according to the user's preference.The extremely randomized trees classifier (extra trees classifier) is an ensemble learning technique that combines the results of multiple independent decision trees to produce classification outcomes . It sharelection . This Gielection . FeatureAs discussed in the study by García-Domínguez et al. , BayesiaA Bayesian network classifier is comprised of a directed acyclic graph (DAG) in which each node represents a random variable and the edges denote the probabilistic dependencies between them . Each noC given a set of observed features X1, X2, ⋯, Xn isIn the context of classification, a Bayesian network classifier is aimed at estimating the posterior probability of a class based on a given set of observed features or attributes. This estimation is achieved by applying Bayes' theorem and selecting the class with the highest posterior probability as the predicted class label. The general formula for computing the posterior probability of a class P(C) represents the prior probability of class C, P represents the conditional probability of the observed features given class C, and P denotes the marginal probability of the observed features.Here, C can be decomposed using the assumption of local independence, which states that each feature is conditionally independent of its nondescendants given its parents. This decomposition leads to the following equation:To classify a new instance, the classifier calculates the posterior probabilities for each class and selects the class with the highest probability. This process is known as maximum a posteriori (MAP) estimation. The probability of the observed features given class Xi) represents the set of parent nodes of feature Xi.Here, Parents(P(Xi|Parents(Xi)), the classifier can use various techniques such as maximum likelihood estimation or Bayesian estimation. These estimates can be derived from the training data.To estimate the conditional probabilities In the context of classification problems, a dataset consists of instances with associated class labels and a set of features or attributes. The classifier uses the dataset to learn the conditional probabilities and build the Bayesian network model. The features in the dataset represent the characteristics that describe each instance and play a crucial role in the classification process.In summary, a Bayesian network classifier uses Bayesian networks and Bayes' theorem to estimate the posterior probabilities of classes given observed features. It leverages the principles of local independence and employs maximum a posteriori estimation to classify new instances.The Wilcoxon test, also known as the Wilcoxon signed-rank test, is a nonparametric statistical test used to compare paired or independent samples. It is particularly useful when dealing with data that violates the assumptions of normal distribution or when working with ordinal or nonparametric data. In the context of classification datasets, the Wilcoxon test plays a crucial role in evaluating the significance of differences between two sets of measurements or variables.The Wilcoxon test is designed to assess whether there is a statistically significant difference between two groups or conditions. It is well-suited for situations where the sample size is small, the data does not follow a normal distribution, or the data consists of ordinal rankings. The test is based on the ranks of the observations rather than their actual values, making it robust to outliers and violations of normality assumptions.di = xi − yi, where xi and yi are the paired observationsCalculate the differences between paired observations: di|Rank the absolute values of the differences |Assign ranks to the differences, ignoring the signsW+) and the sum of the ranks for negative differences (W−)Calculate the sum of the ranks for positive differences , the null hypothesis is rejected, and it is concluded that there is a significant difference between the two groups.The The Wilcoxon test has been widely used in various fields, including healthcare, biology, and social sciences. Several studies have demonstrated its effectiveness in comparing variables and identifying significant differences in classification datasets. For example, Smith and Johnson  applied The Wilcoxon test is a powerful statistical method for comparing paired or independent samples, making it a valuable tool for analyzing classification datasets. Its robustness to violations of normality assumptions and its ability to handle ordinal or nonparametric data contribute to its widespread use in various research fields. In our study, we employ the Wilcoxon test to assess the significance of differences in feature distributions between diabetic and nondiabetic patients, enhancing the reliability and validity of our findings.The dataset was obtained from information of Mexican patients at the general hospital “Centro Médico Siglo XXI” and is the same as the one used by Alcalá-Rmz et al. , where iAs described in the study by Alcalá-Rmz et al. , a totalIn order to enhance performance, it is often necessary to normalize the input variables of many machine learning algorithms. The purpose of normalization is to rescale the values of numeric columns in the dataset to a common scale without distorting the relative differences in value ranges or losing information. The effectiveness of these algorithms heavily relies on the quality of the input data. Consequently, normalizing the data has a beneficial effect on improving data quality and, consequently, the classification algorithm's performance, as discussed by D. Singh and B. Singh .In this study, the MinMaxScaler class from the scikit-learn library  in PythoThe objective of the feature selection process is to identify the subset of features that contribute the most information to the classification performed by the classifier algorithm. This process is aimed at eliminating features that do not effectively distinguish the classes to be classified. By performing feature selection, a reduced dataset is obtained, containing a smaller number of features compared to the original dataset. This reduced dataset is then used as input for the implementation of the classifier algorithm in the classification model.In this study, the classification process focuses on two classes based on the patient's disease status: 0 representing nondiabetic patients (control) and 1 representing diabetic patients (cases). The feature selection process, implemented using both the Akaike criterion information and genetic algorithms, was carried out using the R programming environment . To faciThe feature selection process in this study involved the implementation of two distinct techniques: the Akaike criterion information and genetic algorithms. Each technique resulted in the generation of two separate subsets of features, obtained through the respective selection method. Additionally, a third subset, comprising all the features of the dataset, was included to provide a benchmark for comparing the efficiency of the feature selection methods. These three subsets of data were subsequently used as input for the classifier algorithms to generate the classification models.In this study, a total of six classifier algorithms were considered: support vector machines (SVM), random forest (RF), k-nearest neighbors (kNN), gradient boosting (GB), extra trees (ET), and naive Bayes (NB). For each algorithm, a classification model was generated using three different data subsets: the feature subsets obtained through the Akaike criterion information and genetic algorithms, as well as the full feature set comprising all the clinical and paraclinical features. Consequently, a total of 18 classification models were created to evaluate their performance in classifying diabetic and nondiabetic patients based on these features. The classification models were implemented using the R programming language.To assess the statistical significance of differences between diabetic and nondiabetic patients, the Wilcoxon test was conducted on a comprehensive dataset consisting of 19 clinical and paraclinical features. The dataset was partitioned into two distinct groups: “cases,” comprising patients diagnosed with diabetes, and “controls,” comprising patients without diabetes. Due to the unequal number of records in the two groups, a subsampling technique was employed to ensure equal group sizes.p value, evidence was obtained regarding the presence of significant differences between the two groups in terms of the selected features. The p value served as a measure of the probability of obtaining the observed differences by chance, enabling the assessment of the statistical significance of the results.The Wilcoxon test, a nonparametric statistical test, was implemented using Python and specialized statistical libraries, such as SciPy . This teThe Wilcoxon test was performed on the complete dataset using Python, which validates the findings and conclusions drawn from the classification models. This statistical test provides a rigorous assessment of the differences between diabetic and nondiabetic patients, effectively eliminating the possibility that the classification results are purely due to random chance. The use of the Wilcoxon test, in conjunction with Python and specialized statistical libraries, enhances the reliability and validity of the classification models employed for diabetes diagnosis.Akaike information criterionGenetic algorithmsThis section presents the results obtained from the experimentation conducted to generate classification models for diabetic and nondiabetic patients using clinical and paraclinical features. Two distinct feature selection methods were independently implemented to compare their effectiveness in creating classification models. The methods implemented are as follows:Support vector machineRandom forestk-nearest neighborsGradient boostingExtra treesNaive BayesBy using the selection methods, two subsets of features were generated, which will be described in subsequent sections. To assess the effectiveness of the feature selection methods, a third subset consisting of the full set of features from the original dataset was included for comparison. With these three feature subsets established, a classification model was created for each implemented classifier algorithm. The classifier algorithms used in this study were as follows:A comprehensive analysis and comparison of the efficiency in classifying diabetic and nondiabetic patients using clinical and paraclinical features was conducted. A total of 18 classification models were generated by combining the three defined subsets of features with the six implemented classifier algorithms. This allowed for a thorough evaluation of the classification performance across different feature sets and algorithms.No Feature Selection. One subset of features used was the original set containing all the features of the datasetAkaike Information Criterion. The subset of features obtained through the feature selection process with the Akaike criterionGenetic Algorithm. The subset of features obtained through the feature selection process with genetic algorithmsTo compare the performance of the classification models, three different subsets of features were used to generate them:The Akaike information criterion was employed for the feature selection process, using the strategy known as backward elimination. This approach begins by considering an initial model that encompasses all the features, and its AIC (Akaike information criterion) is calculated. As the process advances, features with the lowest correlation are successively eliminated, leading to the generation of new models and the calculation of their respective AIC values. The removed features in each model are cumulative. Model number 1 includes all features except one (WHR), model number 2 has 2 features removed (WHR and DIASTOLIC.BP), model 3 has 3 features removed , model 4 has 4 features removed , and finally, model 5 has 5 features removed . The best model is determined by the lowest AIC value. The original dataset consists of 19 features, while the subset of features generated through the feature selection process using the Akaike information criterion comprises 14 features. This represents a 27% reduction in the size of the dataset utilized for generating the classification models for diabetic and nondiabetic patients.To generate and compare the classification models for diabetic and nondiabetic patients, a second feature selection process was conducted on the original dataset. This process utilized genetic algorithms, specifically the Galgo genetic algorithm in conjunction with the random forests classifier algorithm. The parameters of the model are detailed in The gene frequency represents the count of times a feature has appeared in the models, while the gene rank indicates the stability and frequency of each feature within the models, sorted by rank. Figures The original dataset contains 19 features, and the subset of features generated through the feature selection process using genetic algorithms consists of 5 features. This results in a 73% reduction in the size of the dataset used for the generation of the classification models for diabetic and nondiabetic patients.k = 10 was employed. The evaluation of the classification models was based on various metrics, including accuracy, sensitivity, specificity, F1 score, precision, and AUC. These metrics, along with the feature selection methods and classifier algorithms, allowed for the identification of the most effective model in distinguishing patients with diabetes. Additionally, the analysis determined the subset of features that best described the presence of the disease in a patient from the original dataset.The classification models for diabetic and nondiabetic patients were generated using the subsets of features obtained through the applied feature selection methods and the selected classifier algorithms. To validate the models, a k-fold cross-validation approach with Initially, the classification models were created using the complete set of features available in the dataset. The performance of these models was evaluated using various metrics, and the results for each implemented classifier algorithm are presented in To assess the effectiveness of the feature selection methods in the creation of classification models, the subset of features obtained through the application of the Akaike criterion was used. The following models were generated using this feature subset, and the results for the analyzed metrics are presented in Finally, the last set of classification models was generated using the feature subset obtained through genetic algorithms as the feature selection method. The results for the analyzed metrics in each of the classification models generated in this stage are presented in The Wilcoxon test was conducted to compare the two groups of the complete dataset consisting of 19 features: cases (diabetic patients) and controls (nondiabetic patients). The primary objective of this test was to evaluate the statistical significance of the differences observed between the two groups.p values. Both tables contain 19 entries, corresponding to the 19 features of the dataset.The initial lengths of the cases and controls groups were 520 and 499, respectively. After adjusting for group length, both groups were reduced to a length of 499 through subsampling techniques. The results of the Wilcoxon test are presented in p values are extremely low, indicating that the likelihood of obtaining such differences by chance is highly improbable. Therefore, these results support the validity and reliability of the classification models, further confirming the robustness of our findings.These results provide crucial insights into the significance of the differences observed between diabetic and nondiabetic patients for each feature. The obtained By using the Akaike criterion as a feature selection technique, a reduction of 27% in the dataset size was achieved, keeping only 14 features out of the original 19. The selected features, which efficiently describe whether a patient has diabetes or not according to this method, are shown in Table 5By applying genetic algorithms as a feature selection technique, a reduction of 73% in the number of features from the original dataset was achieved, resulting in only 5 selected features out of the initial 19. The frequency and rank of the genes (features) in the models determined by the genetic algorithm implementation are shown in Figures Table 7The reduction achieved in the number of features through the implementation of the described feature selection methods is significant, particularly in the case of genetic algorithms. This reduction in the dataset size has a generally positive impact on the performance of the systems. By working with a smaller amount of data, several advantages can be obtained, including reduced processing time and lower energy consumptionAlthough the implementation of feature selection techniques on the original dataset resulted in a reduction of 27% and 73% using the Akaike information criterion and genetic algorithms, respectively, it is important to note that in certain applications of the classification model, maximizing classification accuracy is often preferred over minimizing the amount of processed data. This is particularly relevant in the medical field, as is the case here. Therefore, for the generation of the classification models, the complete set of features was also considered a reference. This approach allows for finding a balance between the model's performance and the amount of data used for analysisThe objective of this study is to compare the performance of six machine learning algorithms in combination with two feature selection techniques for generating classification models of diabetic and nondiabetic patients using clinical and paraclinical features. The implemented classifier algorithms include SVM, RF, kNN, GB, ET, and NB, while the feature selection techniques utilized are the Akaike information criterion and genetic algorithms. Initially, classification models were created using the complete feature set of the dataset and subsequently compared with models generated using feature subsets obtained through the feature selection techniques. A total of 18 classification models were generated, and their performance was compared. Based on the results obtained from the feature selection methods, the following can be concluded:F1 score, and precision. These models demonstrate a strong ability to accurately classify both positive and negative instancesThe random forest (RF), gradient boosting (GB), and extra trees (ET) models consistently perform well across multiple metrics, including high AUC, specificity, sensitivity, accuracy, F1 score, and precision. It shows a balanced performance in correctly classifying both positive and negative instancesThe SVM model also performs well, achieving high scores in AUC, specificity, sensitivity, accuracy, F1 score, and precision compared to the other models. It may have higher rates of false positives and lower overall accuracy compared to the top-performing modelsThe naive Bayes (NB) model achieves moderate performance with relatively lower scores in specificity, accuracy, F1 score. It may have higher rates of false positives and lower overall accuracy compared to the other modelsThe k-nearest neighbor (kNN) model shows relatively lower performance in terms of specificity, accuracy, and In summary, the random forest (RF), gradient boosting (GB), and extra trees (ET) models exhibit strong overall performance across multiple metrics, while the SVM model also performs well. The naive Bayes (NB) model achieves moderate performance, and the k-nearest neighbor (kNN) model shows relatively lower performanceBased on the findings derived from the exploration of classification models for distinguishing between diabetic and nondiabetic patients, utilizing six distinct classification algorithms, and employing the entire dataset encompassing 19 features, the subsequent conclusions can be established:F1 score, and precision, even when using the reduced 14-feature subset. These models demonstrate the advantage of feature selection, as they maintain their strong classification abilities with a smaller set of featuresThe random forest (RF), gradient boosting (GB), and extra trees (ET) models maintain their high performance across multiple metrics, including AUC, specificity, sensitivity, accuracy, F1 score, and precision when using the reduced feature subsetThe SVM model also maintains a high level of performance, with consistently high scores in AUC, specificity, sensitivity, accuracy, F1 score, and precision. However, it still achieves moderate performance overallThe naive Bayes (NB) model shows a slight decrease in performance compared to the other models, particularly in specificity, accuracy, F1 score, and precision when using the reduced feature subsetThe k-nearest neighbor (kNN) model exhibits the lowest performance among the models, with lower scores in specificity, sensitivity, accuracy, In summary, the random forest (RF), gradient boosting (GB), and extra trees (ET) models maintain their strong classification performance even with a reduced feature subset. The SVM model also demonstrates robust performance, while the naive Bayes (NB) model shows a slight decrease in performance. The k-nearest neighbor (kNN) model performs relatively weaker compared to the other models. These findings highlight the effectiveness of feature selection in reducing the dimensionality of the dataset while maintaining good classifier performanceFor the classification models that use the subset of features obtained through the Akaike criterion (14 features), the following can be concluded:F1 score, and precision. This highlights the advantage of feature selection in reducing the dimensionality of the dataset while preserving good classifier performanceThe random forest (RF), gradient boosting (GB), and extra trees (ET) models maintain their strong classification performance even with the significantly reduced 5-feature subset. These models consistently achieve high scores in various metrics, including AUC, specificity, sensitivity, accuracy, F1 score, and precision when using the reduced 5-feature subsetThe SVM model also maintains a relatively high level of performance, with consistently good scores in AUC, specificity, sensitivity, accuracy, F1 score, and precision compared to the other models. However, it still achieves moderate performance overallThe naive Bayes (NB) model performs slightly lower in terms of specificity, accuracy, F1 score, and precision when using the reduced 5-feature subsetThe k-nearest neighbor (kNN) model exhibits the lowest performance among the models, with lower scores in specificity, sensitivity, accuracy, In summary, the random forest (RF), gradient boosting (GB), and extra trees (ET) models demonstrate their robustness by maintaining their strong classification performance even with a highly reduced feature subset. The SVM model also maintains good performance, while the naive Bayes (NB) model shows slightly lower performance. The k-nearest neighbor (kNN) model performs relatively weaker compared to the other models. These findings emphasize the effectiveness of feature selection in reducing the dimensionality of the dataset while preserving or even improving classifier performanceFor the classification models that use the subset of features obtained through genetic algorithms (5 features), the following can be concluded:The results of the Wilcoxon test reveal significant differences between the case group (diabetic patients) and the control group (nondiabetic patients) concerning the dataset features. This indicates that the selected features play a relevant role in distinguishing between the two groups. Selecting appropriate features is essential for generating accurate and effective classification models for diabetes detectionThe statistical significance obtained through the Wilcoxon test provides robust validation for our classification models. The results support the effectiveness of the models in distinguishing between diabetic and nondiabetic patients using the selected features. This reinforces confidence in the utility and applicability of our models in diabetes detection for future clinical scenariosThe inclusion of the Wilcoxon test in our study has enabled a comprehensive assessment of statistical significance and validity of the results obtained. This test has been instrumental in ruling out random chance as the cause of observed differences between the groups and supporting the robustness of our findings. The incorporation of this statistical test strengthens the quality and reliability of our study and its conclusionsFrom the Wilcoxon statistical test performed on the complete dataset, the following can be concluded:F1 score, and precision, further validates the effectiveness of the approach employed in this investigation.In conclusion, the effectiveness of feature selection techniques in conjunction with classification algorithms has been demonstrated in this study. The reduction of the dataset to a smaller subset of features while maintaining strong classification performance highlights the efficiency of the approach employed. The results obtained, which are comparable to or surpass the performance reported in related studies, emphasize the superiority of the feature selection methods utilized in this research. By leveraging feature selection, the study successfully extracted the most relevant and discriminative features, resulting in accurate and efficient classification models. The robustness of these models, as evidenced by metrics such as AUC, specificity, sensitivity, accuracy, Furthermore, the advantages of feature selection in terms of model interpretability and efficiency are demonstrated in this study. Through the reduction of dataset dimensionality, both computational efficiency and interpretability of the models are improved by focusing on the most informative features. The findings emphasize the significance of feature selection in classification tasks. The integration of feature selection methods with classification algorithms results in compact yet powerful models that exhibit comparable or superior performance to related studies. This further highlights the potential of the approach employed and emphasizes the crucial role of feature selection in optimizing classification outcomes.Further investigation of feature selection techniques: although the Akaike information criterion and genetic algorithms were utilized in this study, other feature selection methods can be explored. Techniques such as recursive feature elimination, principal component analysis, and lasso regression could be considered to evaluate their impact on the performance of classification modelsIntegration of domain knowledge: incorporating domain expertise and prior knowledge can potentially improve the feature selection process. Collaborating with medical professionals or domain experts to identify relevant features or incorporating additional clinical variables could enhance the accuracy and interpretability of the classification modelsEvaluation on larger datasets: conducting experiments on larger datasets can provide a more comprehensive understanding of the performance and scalability of the classification models. By including a broader range of patient samples, the generalizability of the models can be further assessedIncorporation of ensemble methods: ensemble methods, such as model stacking or boosting, can be explored to further enhance the classification performance. Combining the predictions of multiple classification models can potentially improve the accuracy and robustness of the overall systemExternal validation and clinical application: it is crucial to validate the developed models on independent datasets or in real clinical settings. Conducting external validation studies with different patient populations and healthcare settings can provide valuable insights into the generalizability and real-world applicability of the classification modelsBased on all of the above, some points considered for future work are as follows:


Aloe vera-based hydrogels have emerged as promising platforms for the delivery of therapeutic agents in wound dressings due to their biocompatibility and unique wound-healing properties. The present study provides a comprehensive overview of recent advances in the application of Aloe vera-based hydrogels for wound healing. The synthesis methods, structural characteristics, and properties of Aloe vera-based hydrogels are discussed. Mechanisms of therapeutic agents released from Aloe vera-based hydrogels, including diffusion, swelling, and degradation, are also analyzed. In addition, the therapeutic effects of Aloe vera-based hydrogels on wound healing, as well as the reduction of inflammation, antimicrobial activity, and tissue regeneration, are highlighted. The incorporation of various therapeutic agents, such as antimicrobial and anti-inflammatory ones, into Aloe vera-based hydrogels is reviewed in detail. Furthermore, challenges and future prospects of Aloe vera-based hydrogels for wound dressing applications are considered. This review provides valuable information on the current status of Aloe vera-based hydrogels for the delivery of therapeutic agents in wound dressings and highlights their potential to improve wound healing outcomes. Aloe vera-based hydrogels and recent advances in Aloe vera-based hydrogels for wound dressing applications.Medicinal plants have been used since ancient times. It has even been estimated that nearly 80% of the world’s population relies on traditional herbal medicine for primary health care . Herbal Aloe vera (AV) belongs to the Liliaceae family, of which the best-known species is Aloe Barbadensis Miller, and has been used for thousands of years in traditional medicine ..AV mucilAV, aiming to improve the healing process, was designed to mimic both layers of the skin [None of the dressings available on the market today are fully capable of reproducing all the characteristics of native skin. An asymmetric bilayer membrane with a top dense polycaprolactone layer that provides mechanical support and a bottom porous layer of chitosan and the skin . The resAV inner gel products. AV used in dietary supplements appears to be safe [AV whole leaf extract and latex [AV whole leaf extract in humans can cause adverse clinical effects: skin irritation, hives, cramping, and diarrhea to those who are allergic to plants in the lily family, for example, onion and tulips [No serious adverse reactions were demonstrated following the topical application of  be safe . The inn be safe . Howevernd latex ,201. Topd tulips ,203,204.It is important to apply modern delivery techniques to develop affordable products based on efficacious traditional natural medicines for wound healing and to improve their therapeutic effect.AV to improve the local retention period.Further research is needed to ensure that these formulations reach the pharmaceutical market. Chemotherapy treatments for cancer are associated with the presence of ulcers in the oral mucosa that causes pain, bleeding, and difficulty swallowing or speaking. There is no effective standard treatment, and few studies have been published on the therapeutic effects of natural products such as Future treatments may arise from medicinal plants, which have fewer side effects and improved bioavailability for the wound-healing process. In addition, in the future, a great challenge is represented by the development of an intelligent treatment that presents anti-inflammatory, antimicrobial, and antioxidant cumulative properties for the treatment of all types of wounds. Furthermore, the commercialization and use in preclinical research and clinical practice of natural products used in wound healing must be increased significantly to discover the potential of these products, considered natural bioactive molecules, in the treatment and regeneration of skin tissue. Future research should be considered to find new natural bioactive compounds related to their usage in the wound-healing process and their ability to act as substitutes for existing antibiotics.AV-based hydrogels, it is possible to develop multifunctional biomaterials that provide sustained release of agents, promote wound healing, reduce inflammation, and prevent or treat microbial infections. However, it is important to note that the specific formulation and efficacy of such hydrogels may vary depending on the therapeutic agents chosen, their concentration, crosslinking method, and other factors. Extensive research and testing  are required to optimize the formulation and ensure its safety and effectiveness for clinical use and to promote human well-being worldwide.By incorporating therapeutic agents into


The plant vascular system is a vital part of healthy plants and is composed of two primary tissue types: phloem and xylem. The phloem transports carbohydrates from the source leaves to where they are needed for growth and development, while the xylem delivers water and mineral ions from the roots to the rest of the plant. These two types of vascular tissues form a complex and essential network that supports the overall functioning of the plants. Despite its importance, the plant vascular system is vulnerable to colonization by phytopathogenic bacteria, including wall-less Mollicutes, walled phloem-limited bacteria, walled xylem-restricted bacteria, and other bacteria that invade the plant through different pathways. The infection of plants by these bacteria can often lead to stunted growth, wilting, necrosis, flower deformation, tumors/galls, and even plant death, resulting in serious economic losses to commercial crops and ornamental trees.Candidatus Liberibacter asiaticus (CLas), and black rot disease caused by the bacterium Xanthomonas campestris pv. campestris.In this Research Topic, we aim to highlight the current state of research and understanding of the mechanisms by which plant vascular-colonizing bacteria cause diseases. The contributing articles delve into Huanglongbing (HLB), which is caused by the bacterium Huang et al. sheds light on the population diversity of CLas strains in citrus trees. The authors used a prophage gene-based typing system and machine learning tools to establish two CLas geno-groups and found evidence of at least two different introductions of CLas strains into Mexico. In addition, the study raises important questions about the origin, transmission, and management of HLB, which has important implications for citrus growers and researchers around the world and could help to develop new management strategies against HLB, such as the use of resistant varieties, the development of early detection methods, or the deployment of natural enemies of the insect vector. The study also highlights the importance of international cooperation in addressing the HLB challenge. For example, the findings of this study demonstrated the close relatedness between CLas strains in Mexico and the United States, underscoring the need for coordinated efforts to prevent the disease from spreading across borders.Citrus crops are important in many regions, and their production is threatened by HLB disease caused by CLas, which seriously affects the livelihoods of farmers and the citrus industry. The study by Cifuentes-Arenas et al. investigated the effects of HLB on the yield and fruit quality of five-year-old Sicilian lemon trees in São Paulo State, southeastern Brazil, over a five-year period (2017-2021). The study found that the symptoms of HLB increased over time, and it took an estimated 10 years for symptoms to occupy over 90% of the tree canopy. Compared to healthy trees, trees with symptoms on 20%, 50%, and 80% of their canopy experienced significant reductions in both yield and fruit quality, with an average reduction in production of 18%, 38%, and 53% respectively. Additionally, the fruits produced by the symptomatic branches of lemons were lighter, and the number of dropped fruits was not related to the symptom severity. The study also observed that the flushing on symptomatic branches started earlier compared to healthy branches of lemon and orange trees. This early flushing is likely an attempt by the tree to compensate for the loss of photosynthetic capacity due to HLB infection, but it also leads to a faster progression of symptoms and a greater decline in production.Carvalho et al. provides important insights into the potential of late-season sweet orange selections for diversifying citrus orchards and mitigating the risks of pest and disease outbreaks in humid subtropical regions. In a long-term field experiment conducted in northwestern Paraná State, Brazil, the authors assessed the horticultural characteristics of 19 late-season sweet orange selections under endemic conditions of citrus canker and HLB. The findings demonstrate the importance of diversifying citrus orchards through the cultivation of resistant and late-season sweet orange selections, which can help increase productivity and profitability. The study also highlights the need for further research to evaluate the performance of these selections under different environmental conditions and to develop sustainable methods to control citrus canker and HLB.Another study conducted by However, more research is needed to fully understand the underlying mechanisms of HLB and to develop effective management strategies. This includes studying genetic and ecological factors that influence the spread and impact of the disease, as well as exploring innovative technologies and methods for controlling insect vectors and reducing the spread of CLas. The research community, policymakers, and industry stakeholders need to work together to support research efforts and implement the best available strategies to manage this devastating disease.Xanthomonas campestris pv. campestris (Xcc) is a significant problem in Brassica crops, such as oilseed rape (Brassica napus L.), causing substantial yield losses worldwide. However, our understanding of the molecular mechanisms underlying Brassica’s resistance to black rot remains limited. Yang et al. compared the gene expression profiles of two EMS (ethyl methanesulfonate)-mutagenized B. napus lines that exhibited contrasting levels of resistance to Xcc with their susceptible progenitor. The findings revealed a steady number of differentially expressed genes (DEGs) in the susceptible line ZS9mXccS-1 at different time points of infection, whereas the resistant line ZS9mXccR-1 showed a gradual increase in DEGs throughout the course of infection. Furthermore, the authors used a weighted gene co-expression network analysis (WGCNA) to identify hub genes involved in immunity. Multiple defense-related hub genes were highlighted, including the cell surface receptor genes CRK11 and BIR1, and the associated downstream regulatory genes WRKY11 and PBL30. Additionally, the KEGG analysis of DEGs belonging to two distinct co-expression modules revealed enriched pathways associated with defense, including Ca2+ signaling, receptor-mediated immunity, and phytohormone balance. These results provide new insights into the molecular mechanisms of resistance to black rot disease in Brassica crops. The findings of this study might facilitate the development of improved breeding strategies and the identification of key genes and pathways involved in resistance to Xcc in Brassica crops.In addition, black rot disease caused by the bacterium The published articles in this Research Topic not only shed light on the mechanisms of disease caused by vascular colonizing bacteria in plants but also address the challenges and opportunities facing this area of research. As a result, these findings are expected to provide valuable guidance for future research and policy decisions. These pathogens have a significant impact on agriculture, horticulture, forestry, and natural ecosystems, so it is important that researchers, scientists, stakeholders, and policymakers work together to address these challenges.The authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.


More and more people are suffering from cancer due to the problems of food safety, environmental pollution and increasing pressure. In the year of 2020, near 20 million people are suffering from the cancer all over the world, and this number is increasing continuously. Thus, it is imminent to develop novel strategies to alleviate the hazard of cancer for human being.Micro RNAs are small non-coding RNAs that participate in the post-transcriptional regulation of gene expression by affecting the stability and translation of transcripts. They are transcribed by RNA polymerase II. The enzyme produces pre-miRNAs blocked by polyadenylation. In addition to tumor-associated proteins and their coding genes, noncoding RNA have been identified as key regulators of many biological processes. MiRNAs have been shown to be involved in the pathogenesis of many diseases, including different types of cancer, arteriosclerosis, ischemic stroke and liver fibrosis with differential expression regulation of miRNAs in various disease mechanisms. Novel high-efficiency methods can be developed to specifically detect cancer by the screening of the most specific mRNA and model diagnosis; new therapeutic drugs could be designed to treat a variety of tumors or neurodegeneration through the regulation of ferroptosis concentration and potentially increasing the effectiveness of these therapies by miRNAs. Therefore, the aim of this study is to develop novel mRNA that can regulate the expression of tumor-associated genes and to develop diagnostic biomarkers for diverse types of tumors. Meanwhile, it is necessary to evaluate the specificity of miRNA-regulated tumors and to develop miRNA-targeted therapies for tumorgenesis.1. The molecular mechanism of miRNAs in penicillin-induced persistent pathogen infection and carcinogenesis.2. The application of circulating mRNA in the diagnosis of breast cancer.3. miRNA regulates iron concentration to induce the apoptosis of cancer cells.Previous studies have focused on the regulatory mechanisms of miRNAs in tumor cells, the diagnostic biomarkers of tumors, and the interfering with relevant disease pathology through the emerging role of miRNAs in the regulation of ferroptosis. The four articles in this Research Topic “Volume II: Tumorigenesis regulated by miRNAs” are mainly involved in the following aspects:Integrating lncRNAs and mRNAs Expression Profiles in Penicillin-Induced Persistent Chlamydial Infection in HeLa Cells used microarrays to identify lncRNA and messenger RNA that were differentially expressed between Chlamydia infection and uninfected cells . A network of these differentially expressed genes (DEGs) was constructed to elucidate the relationship between lncRNA and mRNA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis with differentially expressed genes were also performed. Furthermore, the lncRNA-miRNA-mRNA competing endogenous RNAs (ceRNAs) network. The relative expression levels of four mRNAs were validated by quantitative real-time PCR, the results of which were correlated with the microarray results. In addition, the integration of protein-protein interaction network was constructed and hub genes were identified. These findings provide a new perspective on the molecular mechanisms of penicillin-induced persistent chlamydial infection. The results of LncRNA-miRNA-mRNA networks indicated that the alterations in lncRNA may affect the mRNA transcription and protein translation of vital pathways during the consecutive chlamydialin fection. The findings provide newly discovered information on the critical role of lncRNA in persistent Chlamydia infection, which may be useful to know more about the functions of lncRNA. This study showed only an overview of differentially expressed lncRNA and miRNAs by the predication of some tumorgenesis related potential genes and pathways via bioinformatics. Further studies need to be carried out to identify the function of the differentially expressed lncRNAs which are candidates for novel diagnostic biomarkers for chlamydial infection.For instance, the article AreviewontheroleofmiR-671in human disorders illustrates that the MiR-671 gene was involved in the pathogenesis of many diseases , however, different studies have revealed opposite roles for this miRNA MiR-671. The roles of upregulation and downregulation of MiR-671 under different mechanisms of tumorgenesis were reviewed from three aspects: cytology, animal experiments and human studies. The results in this study indicate that miR-671 is a miRNA that can affect the expression of many target mRNAs in various signaling pathways involved in different human diseases. However, the future application of miR-671 targeted therapy in cancer treatment needs further investigation of in vitro and in vivo regulatory mechanism of miR-671.The same miRNA shows different regulatory mechanisms in different cancer types. The article A circulating mir-19b-based model in diagnosis of human breast cancer collected plasma samples from BC patients and normal controls , and determined the differentially expressed miRNAs in the plasma of BC patients. A binary Logit model was used to develop a miRNA diagnostic model, and plasma samples collected from patients with other types of tumors to were used to determine the specificity of this model in BC diagnosis. It was discovered that 5 circulating miRNAs were significantly upregulated in the plasma of BC patients and MiR-19 shows the highest specificity. In combination with miR-16 and miR-106a, a miR-19b-based 3-circulating miRNA model was employed for further validation. Taken the samples together, the model showed 92% of sensitivity and 90% of specificity in the diagnosis of BC. However, further validation using a larger cohort of patients with BC and other tumor types is indispensible for future clinical use of these miRNAs in breast cancer diagnosis.Due to the problem of environmental pollution, food safety and lifestyle, breast cancer (BC) is the most common cancer among women. In 2020, The number of women suffering from breast cancer surpassed the number of lung cancer for the first time, breast cancer has become the leading cause of cancer-related deaths among women worldwide. Nevertheless, the 5-year survival rate for women with early-stage breast cancer is 95 percent. In consequence, early detection of diagnostic biomarkers for BC is one of the most effective ways to reduce thereof mortality. At present, breast cancer is mainly diagnosed through imaging examination, including ultrasound, magnetic resonance imaging and magnetic resonance imaging. There is an urgent need to develop more efficient approaches for specific detection due to the lack of reliable specific biomarkers for the diagnosis of BC. The article Emerging role of miRNAs in the regulation of ferroptosis have shown that microRNAs are involved in regulating the concentration of ferroptosis . MiR-675, miR-93, MiR-27a, MiR-34a, and miR-141 have been shown to affect ferroptosis metabolism, antioxidant metabolism, and lipid metabolism, thus affecting all key mechanisms during ferroptosis enrichment. MiRNAs are a small group of non-coding transcripts that specifically bind to their target transcripts and induce their degradation or inhibit their translation. In addition, many miRNAs can affect ferroptosis concentration through indirect pathways. Since ferroptosis enrichment can eradicate cancer cells independently of apoptosis, these miRNAs can induce cancer cell death in different ways. The bioinformatics tool can facilitate the identification of miRNAs involved in ferroptosis concentration and the development of ferroptosis concentration-related miRNA therapies to treat a variety of tumors or neurodegeneration. The combination of miRNA-based therapies with conventional or targeted anticancer therapies is expected to improve the effectiveness of these therapies. Because cancer cells are heterogeneous in terms of miRNA markers, it will be necessary to provide a miRNA profile for each patient before the applications of these new approaches in the clinic.Ferroptosis is involved in the initiation and progression of many diseases and is regarded as a hotspot of investigations on the treatment of disorders. The article in vivo and in vitro function demonstration and clinical application in diagnosis and treatment, thereby promoting the application of miRNAs in the development of novel anti-tumor drugs.Previous studies have shown that many miRNAs are found to be associated with cancer, either as tumor suppressors or as oncogenes. Some miRNAs have been discovered to be overexpressed in cancer compared with the corresponding normal tissue, thus suggesting their potential as tumorgenesis related biomarkers. However, the application of these specifically expressed miRNAs in clinical detection and pre-treatment requires more comprehensive screening, alignment, and tumorgenesis regulatory mechanism investigation by cytology, animal experiments, and human experiments. More and more novel targets for cancer cells, such as claudin 18.2, which is a kind of protein responsible for the intercellular molecules communication and highly expressed in the surface of cancer cells due to the destruction of tight junction in cancer cells. Therefore, this new target protein has attracted the attentions of researchers and pharmaceutical companies to develop new anti-cancer drugs such as Zolbetuximab and Osemitamab. Moreover, new miRNAs targeting this kinds of new cancer target proteins can be considered in future study, which needs the extensive screening of the corresponding miRNAs, the


Vacuoles are organelles in plant cells that play pivotal roles in growth and developmental regulation. The main functions of vacuoles include maintaining cell acidity and turgor pressure, regulating the storage and transport of substances, controlling the transport and localization of key proteins through the endocytic and lysosomal-vacuolar transport pathways, and responding to biotic and abiotic stresses. Further, proteins localized either in the tonoplast (vacuolar membrane) or inside the vacuole lumen are critical for fruit quality. In this review, we summarize and discuss some of the emerging functions and regulatory mechanisms associated with plant vacuoles, including vacuole biogenesis, vacuole functions in plant growth and development, fruit quality, and plant-microbe interaction, as well as some innovative research technology that has driven advances in the field. Together, the functions of plant vacuoles are important for plant growth and fruit quality. The investigation of vacuole functions in plants is of great scientific significance and has potential applications in agriculture. The vacuoles of plant cells are multifunctional organelles that display strong plasticity during plant growth and development. Lytic vacuoles (LVs) function as reservoirs for ions and metabolites , and are crucial for general cell homeostasis Andreev, . VacuoleVacuole functions are tightly connected with vacuolar proteins, many of which are embedded in the lipid monolayer vacuolar membrane, referred to as the tonoplast. The tonoplast is an important physical barrier that separates the acidic vacuolar lumen compartment from the cytoplasm. Tonoplast-specialized proton pumps, channel proteins, ion transporters, and enzymes located in the tonoplast are essential for the normal function of the vacuole.The biogenesis and function of plant vacuoles have been topics of interest for decades. Advances in live imaging technology have resulted in constant updates to the field of vacuole-related research. For example, recent studies have shed light on the role of the vacuole in plant embryo development and patterning, through regulating cell division in the embryo , a gene encoding a key factor in embryo development and somatic embryogenesis. Overexpression of LEC2 triggers the development of embryos in plant leaves, which allows for a relatively clear view of vacuole morphology  and fluorescence correlation spectroscopy technologies, among others  Marty, . LVs arePSVs mainly occur from the late embryonic developmental stage to the seed germination stage, and function to store proteins and important minerals during seed filling ) of archaea. In addition, evolutionary analyses indicate that the plant tonoplast has a wide range of origins, and that the proteins positioned on it show strong homology with those in plant cell membrane systems   marker line. This hypothesis was tested by specifically blocking various steps of the vacuolar transport pathway and tracking the VHA-a3 signal and vacuole morphology. In that study, the initiation of LVs, including transportation of the tonoplast proton pump and important lipids, appeared to be independent of key proteins in the vacuolar transport system, Rab5 and Rab7. Moreover, the initiation of LVs did not occur in the region containing the Golgi apparatus. It was postulated that the precursors of LVs form in an area enriched with sterols, directly shed from the ER. At the very beginning, vacuole precursors were empty, subsequently gradually expanding to accommodate highly acidic fluid. This process appears to be related to autophagosomes; however, there is no experimental evidence for the involvement of a typical autophagy process in the initiation of LVs Biogenesis of protein storage vacuolesAnother hypothesis is that LVs in plant cells are independent and separate from each other. Cui et al.  PSVs form de novo, while those of Arabidopsis thaliana form via functional reprogramming of LVs  seeds, PSVs in root cells were converted into LVs in two different ways: de novo biogenesis and functional reprogramming genesis. Both types of genesis were observed in epidermal, exodermal, endothelial, and vascular cells , and clathrin Vacuole-related cell growth and pattern formationSecondary metabolism and secondary metabolites are typical characteristics of plants and some microorganisms, and are the result of adaptation to the external environment during evolution. Most secondary metabolites are produced in the cytoplasm. Since some metabolites are toxic to even the plant itself, they are preferentially stored inside vacuoles, where they are isolated from other cellular compartments. Vacuolar-related secondary metabolic processes are widely involved in plant growth and development. The vacuole undergoes regular changes in growth and morphology during the production and secretion of colored nectar, which contains secondary metabolites, such as alkaloids, terpenes, and cyclic olefin ether glycosides Contribution of vacuoles to fruit qualityVacuole distribution has an essential role in embryonic development and pattern formation. Studies on  Jensen, , leading Jensen, . Very re Jensen, . Compare Jensen, .(3)ContGossypium hirsutum) plays a key role in the timing of pollen release and the normal accumulation of nutrients, such as starch, in the female gametophyte  dysfunction changes the dynamics of the central vacuole. This leads to incorrect positioning of the male gametophyte, which ultimately affects pollen development and disrupts pollen germination  help to prevent the aggregation of small PSVs in aleurone cells  can significantly promote the accumulation of malic acid in apple callus and tomato fruit  is a major cultivated fruit crop worldwide. The processes involved in the induction of grape berry ripening have been intensively investigated, with particular focus on the vacuole, since it occupies more than 99% of the total intracellular volume in grape berry  and/or abiotic stress . In this regard, vacuoles are key organelles in maintaining ion homeostasis and stabilizing the intracellular environment. Thus, vacuoles help plants to cope with environmental fluctuations, particularly water scarcity   under cold stress , use the transmembrane electrochemical potential gradient generated by V-ATPase and V-PPase to sequester toxic Na+ in the vacuole, thereby reducing its harmful effects on cells. In addition to tonoplastic transporters, proteins in the vacuole lumen are critical for plant stress resistance , and plays an important role in heat shock-induced PCD Vacuole functions in response to biotic stressOther membrane transporters, such as the tonoplast NaHerbaspirillum rubrisubalbicans, negatively affects rice plant growth by suppressing V-ATPase activity and increasing ethylene content  through membrane remodeling, actin rearrangement, microtubule movement, and adjustment of the autophagy system   can actually enter plant cells and form symbiotic relationships with plants . During this process, the central vacuole provides growth space for the microbe, while transporters in the tonoplast provide the necessary ions; however, vacuole functions in the interactions between plants and microorganisms remain unclear. How plant endophytes survive in different organs and plants requires further study. Hence, vacuoles play important roles in multiple physiological processes and the investigation of vacuole functions in plants is of great scientific significance and has potential applications in agriculture.Plants tolerate various stresses (including abiotic and biotic stress) in different environments by changing their metabolic processes, which can reduce quality and decrease yield. Vacuoles are also crucial in plant resistance to abiotic stress or bacterial invasion. Although a variety of bacteria and fungi can infect plants, only a few (partial


Antimicrobial resistance (AMR) is a global and increasing threat to human health. Several genetic determinants of AMR are found in environmental reservoirs, including bacteria naturally associated with widely consumed fermented foods. Through the food chain, these bacteria can reach the gut, where horizontal gene transfer (HGT) can occur within the complex and populated microbial environment. Numerous studies on this topic have been published over the past decades, but a conclusive picture of the potential impact of the non-pathogenic foodborne microbial reservoir on the spread of AMR to human pathogens has not yet emerged. This review critically evaluates a comprehensive list of recent experimental studies reporting the isolation of AMR bacteria associated with fermented foods, focusing on those reporting HGT events, which represent the main driver of AMR spread within and between different bacterial communities. Overall, our analysis points to the methodological heterogeneity as a major weakness impairing determination or a causal relation between the presence of AMR determinants within the foodborne microbial reservoir and their transmission to human pathogens. The aim is therefore to highlight the main gaps and needs to better standardize future studies addressing the potential role of non-pathogenic bacteria in the spread of AMR. Salmonella, Listeria, and Escherichia coli as incidental food contaminants cannot be excluded , and limiting the time span to articles published between 2018 and 2023. This initial search retrieved 120 research articles, but we restricted the final list to 49 articles following an accurate reading of the text, as they appeared to be the most relevant for the aims of the present review. We then focused in particular on those reporting HGT events, which represent the main driver of AMR spread within and between different bacterial communities.The fermented food matrices analyzed in the articles, along with their geographical origin, are reported in Overall, the great majority of studies reported the identification of AMR bacteria in animal-based fermented food products, including both dairy and meat/fish sources a. HoweveWhen compared to the geographical distribution of the studies , the preAs shown in the The power of molecular approaches is increasingly being exploited to investigate AMR in foodborne bacteria, but the identification of AMR determinants by genome sequencing alone is not sufficient to assign the AMR phenotype to a specific strain or isolate, as it does not provide information on the actual expression of the detected gene(s). As mentioned above, four of the analyzed articles did not test phenotypic resistance to the antibiotics corresponding to the detected AMR determinants, whose presence was identified exclusively by sequence comparison. On the other hand, 21 of the articles reporting phenotypic resistance of the selected isolates to one or more antibiotics did not proceed to the molecular identification and characterization of AMR genes. In both cases, the interpretation of the data in terms of the potential effects of gene transfer to other bacteria is impaired. Overall, simultaneous detection of phenotypic and genotypic AMR, which represents the most informative approach, should be considered a standard procedure. However, as shown in tetA, B, K, L, M, O, S, W) and erythromycin  resistance genes, confirming the results reported in previous studies in foodborne LABs [As for the molecular identity of the detected AMR determinants, there was a clear prevalence of tetracycline  focused on a single (or very few) specific AMR species/strains that were pre-selected for further characterization among the foodborne isolates, thus impairing full comprehension of the biodiversity associated with AMR within the food matrix. Finally, two of the studies employed metagenomic analysis of the whole microbial community within the food samples, which was not accompanied by the isolation of AMR bacteria. In these latter studies, the identification of the dominant species in each food sample and the corresponding presence of AMR genes were thus performed independently based on sequence analysis. Although highly informative of the fine structure of the microbial community, this approach does not allow for the precise assignment of AMR determinants to specific taxonomical entities. Lactiplantibacillus was the most represented genus hosting AMR determinants, closely followed by Enterococcus. Pediococcus, Lactobacillus, Streptococcus, Lactococcus, and Lacticaseibacillus, which are commonly found within the fermented food microbiota, were also frequently isolated. Accordingly, Staphylococcus and Latilactobacillus, which are mostly represented in meat-based foods that were a small minority of the products analyzed in all countries, were identified in only three studies. Further analysis of the occurrence of each AMR genus within the three major food matrices  confirmed this observation  did not verify the transferability of AMR genes, although two of them ,28 mentiSince HGT represents a key element in the spread of AMR genes among bacteria, we analyzed in depth the nine articles reported in E. faecalis JH2-2). However, the results of in vivo mating were described only in terms of the positive recovery of transconjugants without providing the corresponding transfer frequencies, while in vitro conjugation resulted in a transfer frequency of 10−6. We cannot therefore compare the results obtained with the two experimental approaches. As for the transfer frequencies reported in the nine articles, they were highly variable among the different studies, spanning from 10−1 to 10−9. Overall, low frequencies were observed in most cases, ranging from 10−6 to 10−7, but some articles reported higher transfer frequencies (10−1–10−2), especially in food mating systems [E. faecalis strain JH2-2. However, several other different pathogens were used as recipients, for example, S. aureus, K. pneumoniae, E. coli, and L. monocytogenes. In one case, a LAB strain was used as a recipient  or Gram-negative , human pathogens, or opportunistic pathogens. In all cases, the donor strains were represented by foodborne LAB, whose isolation from the different food matrices is described in All nine articles summarized in  systems . Such an systems ,33). Ove8647, in ), but alLactobacillus genus and by Enterococcus faecalis and faecium species, followed by Pediococcus pentosaceus and Staphylococcus saprophyticus. Concerning the choice of specific resistance determinants, transfer of tet and erm genes was prevalent, in particular tetM and ermB, respectively. This could be related to the frequent association of such genes with the conjugative transposon Tn916, conferring high transmission capacity [tet and erm genes carried by the same isolates but not associated with transposons or other conjugative elements did not show any transfer to the recipient strains [As shown in capacity ,76. Inde strains ,64.E. faecalis strains in two articles performing in vitro mating on filter or solid agar [P. pentosaceus strain OBK05 to four different Gram-positive pathogens was also shown by mating assays within a cheese matrix and was hypothesized to be mediated by a plasmid. However, the corresponding resistance gene was not identified but was only supposed to be plasmid-encoded since plasmid curing impaired the acquisition of the resistant phenotype by the recipient strains [tet and erm genes: for example, the ermB, tetM and tetS genes were successfully transferred from L. delbrueckii ssp. bulgaricus strain R6 and Lactiplantibacillus plantarum strain R41 to Listeria monocytogenes, but when using 18 other donor strains no transfer of the same genes was observed toward the same recipient [tetK was successfully transferred from the Staphylococcus saprophyticus strain KM1053 to two out of four Enterococcus/Staphylococcus recipients [E. faecalis and E. faecium donors to the recipient E. faecalis strain JH2-2 [msrC was reported to be mobilized from E. faecalis and E. faecium donors to the E. faecalis strain JH2-2 recipient by filter mating [P. pentosaceus isolates to the same E. faecalis JH2-2 recipient within food matrices were unsuccessful [tet and erm genes that were shown to be associated with plasmids or transposons (Tn916) and to be capable of horizontal transfer [tetL, harbored by the E. faecium strain UC7251, which was not transferred to Gram-negative recipients, probably due to the non-conjugative nature of the plasmid, which was lacking a complete conjugation apparatus [Aminoglycoside and streptomycin resistance genes were shown to be capable of HGT in lid agar ,64. Hori strains . Noteworecipient ; tetK wacipients , while iccessful . Aside fccessful ,45,64, wtransfer ,43,60,64Another methodological aspect that should be better standardized concerns the techniques used to confirm the transconjugants. Indeed, phenotypic AMR as the only confirmation method is often unreliable, and, as suggested by other authors, the combination of molecular analysis with phenotypic tests is recommended . Among tIn conclusion, our attempt to perform a comparison of the relevant literature published in the past five years and reporting the isolation and molecular characterization of foodborne AMR bacteria points to the methodological heterogeneity as a major weakness preventing a causal relationship between the presence of AMR determinants within the microbial reservoir associated with fermented foods and their transmission to human pathogens. This important limitation affects, in turn, the possibility of assessing the actual risk posed by the foodborne environmental reservoir for human health, which represents the basis for developing policies aimed at preventing the uncontrolled spread of AMR.Effective action to tackle the spread of AMR requires constant monitoring. Within the EU, the European Food Safety Authority (EFSA) coordinates annual monitoring of AMR in pathogenic bacteria from food and livestock and provides scientific advice on risk assessment models and possible strategies to counteract the environmental increase of AMR bacteria . The useAt a global level, the AMR Inter-Departmental Working Group established by the Food and Agricultural Organization (FAO) in 2015 has recently delivered an updated action plan for the years 2021–2025 with several important objectives, including strengthening surveillance and research on AMR spread, promoting responsible use of antimicrobials, and enabling good practices. The document highlights the utmost importance of closing the gaps in current knowledge on the mechanisms of AMR spread and the urgency of addressing this growing global threat in all countries through a coordinated, multisectoral, One Health approach in the context of the UN 2030 Agenda for Sustainable Development .Within this context, the International Union of Microbiological Societies (IUMS) recently launched a call to action directed to all microbiological societies worldwide, highlighting the need to develop science-based policies aimed at preventing the spread of AMR while also preserving the microbial ecosystem and its biodiversity by developing sustainable solutions to control infectious agents . The oveFrom a policy viewpoint, tackling AMR as a One Health issue represents an extremely important step to raise stakeholders’ awareness and elicit a global response from the food and agricultural sectors in the fight against AMR. Moreover, the active involvement of inter-governmental organizations such as FAO, WHO, and OIE, which are supported worldwide, ensures the alignment of strategic and financial efforts to sustain science-based and international guidance in the fight against AMR spread.https://www.esfri.eu/glossary). The initial and crucial efforts addressed by the scientific community toward infrastructural development were therefore aimed at standardizing SOPs and pipelines to produce datasets that could meet the FAIR  principles. These standardization efforts have impacted microbiological research as well [From the scientific viewpoint, further investigations are needed to close the knowledge gaps, but their usefulness relies on the possibility to compare the results of studies performed in different settings on different foods and microbial environments, which requires a high degree of methodological standardization, pointing to the urgent need for aligning standard operating procedures (SOPs) and pipelines. Efforts in this direction have been in place at the European and international levels since the first decade of this millennium, resulting in the development of analytical protocols for the quantification of AMR in food-grade bacteria. Factors affecting the results of phenotypic AMR assays, such as the composition of the growth medium, the inoculum size, and the time and temperature of incubation, have been identified, and the experimental protocols for determining the minimum inhibitory concentration (MIC) for antibiotics are presently applied with fully standardized procedures. Reference cut-off values to discriminate resistant and susceptible strains have also been uniquely defined in a document published and periodically updated by EFSA . Highlig as well ,85,86. F