Effect of Cucurbita ficifolia Bouché on glutathione level and glycosylated hemoglobin percentage in a Mexican rural population with type 2 diabetes.

ETHNOPHARMACOLOGICAL IMPORTANCE: Cucurbita ficifolia Bouché fruit is widely used in Mexican traditional medicine to treat type 2 diabetes (T2D) because it has been attributed with antioxidant and hypoglycemic properties in different experimental models and T2D patients. An imbalance in physiological glutathione (GSH) concentrations increases the susceptibility to developing complications associated with oxidative stress in T2D patients. AIM OF THE STUDY: To investigate the effect of C. ficifolia on the antioxidant properties of GSH, general health measurements, and biochemical parameters in a Mexican rural population, and to evaluate the changes in socio-affective scores of patients due to improvement in T2D. MATERIALS AND METHODS: Twenty-seven women diagnosed with T2D with poor glycemic control volunteered and were divided into two groups: C. ficifolia (0.5 g/kg of fresh pulp weight) with hypoglycemic pharmacotherapy, and another group with only hypoglycemic pharmacotherapy, for 12 weeks. We evaluated the effect of the fresh pulp of C. ficifolia on body mass index, blood pressure, glucose, glycosylated hemoglobin, cholesterol, triglycerides, and GSH. Expanding the study, we evaluated the quality of life, anxiety, and depression scores before and after the intervention. RESULTS: Treatment with the fresh pulp of C. ficifolia for 12 weeks reduced glycosylated hemoglobin, similar to the hypoglycemic pharmacotherapy group, and significantly increased GSH concentrations. The patients' moods did not change despite increased GSH concentrations and improved T2D control. CONCLUSIONS: The increased GSH concentrations due to the consumption of fresh pulp of C. ficifolia could help to protect against oxidative stress and extend therapeutic benefits in addition to the usual hypoglycemic drugs in patients with T2D.

Novel methodology for detecting and localizing cancer area in histopathological images based on overlapping patches.

Cancer disease is one of the most important pathologies in the world, as it causes the death of millions of people, and the cure of this disease is limited in most cases. Rapid spread is one of the most important features of this disease, so many efforts are focused on its early-stage detection and localization. Medicine has made numerous advances in the recent decades with the help of artificial intelligence (AI), reducing costs and saving time. In this paper, deep learning models (DL) are used to present a novel method for detecting and localizing cancerous zones in WSI images, using tissue patch overlay to improve performance results. A novel overlapping methodology is proposed and discussed, together with different alternatives to evaluate the labels of the patches overlapping in the same zone to improve detection performance. The goal is to strengthen the labeling of different areas of an image with multiple overlapping patch testing. The results show that the proposed method improves the traditional framework and provides a different approach to cancer detection. The proposed method, based on applying 3x3 step 2 average pooling filters on overlapping patch labels, provides a better result with a 12.9% correction percentage for misclassified patches on the HUP dataset and 15.8% on the CINIJ dataset. In addition, a filter is implemented to correct isolated patches that were also misclassified. Finally, a CNN decision threshold study is performed to analyze the impact of the threshold value on the accuracy of the model. The alteration of the threshold decision along with the filter for isolated patches and the proposed method for overlapping patches, corrects about 20% of the patches that are mislabeled in the traditional method. As a whole, the proposed method achieves an accuracy rate of 94.6%. The code is available at https://github.com/sergioortiz26/Cancer_overlapping_filter_WSI_images.

Advances and challenges in Bioinformatics and Biomedical Engineering: IWBBIO 2020.

This Supplement issue, presents five research articles which are distributed, mainly due to the subject they address, from the 8th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2020), which was held on line, during September, 30th-2nd October, 2020. These contributions have been chosen because of their quality and the importance of their findings. Those contributions were then invited to participate in this supplement for the following journals of BMC: BMC Bioinformatics and BMC Genomics. In the present Editorial in BMC journal, we summarize the contributions that provide a clear overview of the thematic areas covered by the IWBBIO conference, ranging from theoretical/review aspects to real-world applications of bioinformatic and biomedical engineering.

Special Issue: New Advances in Bioinformatics and Biomedical Engineering Using Machine Learning Techniques, IWBBIO-2022.

Bioinformatics is revolutionizing Biomedicine in the way we treat and diagnose pathologies related to biological manifestations resulting from variations or mutations of our DNA [...].

TPH2-703 G/T polymorphism is associated with stress, depression, and psychosocial symptoms in mentally healthy Mexicans.

Tryptophan hydroxylase (TPH) catalyzes the rate-limiting step of serotonin synthesis. TPH2 is the brain-specific isoform of this enzyme, and genetic variations in the TPH2 gene have been shown to impact its transcription and enzymatic activity and are associated with mood disorders. In this study we focused on the rs4570625 (-703G/T) single nucleotide polymorphism of TPH2 gene. By using conventional polymerase chain reaction (PCR), we examined the effect of this polymorphism on stress, anxiety, and depression symptoms as well as quality of life, evaluated based on the Holmes-Rahe Inventory, the Beck Anxiety Inventory, the Beck Depression Inventory, and the World Health Organization Quality of Life - Short Version, respectively. We found that individuals with the homozygous recessive T/T genotype had lower stress and depression scores. In addition, the quality of life in the psychological health domain was better in males with the T/T genotype. These results suggest that T/T genotype could decrease the susceptibility to developing stress and depression in the Mexican population without a diagnosis for an emotional disorder.

Los estados hematológico y nutricional se asocian con el desarrollo de preeclampsia en una población mexicana / Hematologic and nutritional states are associated with the development of preeclampsia in a Mexican population

Introducción: la insuficiencia de nutrientes y elementos traza son factores de riesgo para el desarrollo de preeclampsia; estos nutrientes pueden provocar cambios en el estado hematológico que pueden usarse para la prevención de complicaciones durante la gestación. Objetivo: la presente investigación analizará el estado nutrimental y hematológico durante los 3 trimestres de gestación en mujeres gestantes con preeclampsia para evaluar su relación con esta alteración gestacional. Método: se realizó un estudio transversal, descriptivo y analítico en 78 gestantes que acudían a control prenatal, 11 de ellas diagnosticadas de preeclampsia. Los parámetros hematológicos fueron determinados por cartometría hemática. Se aplicó un cuestionario de frecuencia de consumo de alimentos para obtener el perfil de ingesta de nutrientes de las gestantes. Resultados: la presión arterial (PA) del grupo de normotensas fue significativamente menor en comparación con las gestantes con preeclampsia. El perfil hematológico varía durante la gestación y el valor de plaquetas fue significativamente más alto en las gestantes con preeclampsia. El estado nutrimental de las gestantes en el tercer trimestre tuvo mayor consumo de macronutrientes y micronutrientes comparado con el primero y el segundo trimestres de gestación; este mismo patrón se observó en las gestantes con preeclampsia. Las gestantes normotensas tuvieron un mayor aporte de minerales y de vitaminas con respecto a las gestantes con preeclampsia. Conclusión: los valores hematológicos se modifican durante cada trimestre de gestación. Las gestantes con preeclampsia tuvieron valores altos de plaquetas comparadas con las normotensas. Nuestros datos muestran un mayor aporte de minerales y vitaminas en las gestantes normotensas comparadas con aquellas que han desarrollado preeclampsia, por lo que se recomienda que la atención de las gestantes se acompañe de orientación nutrimental para prevenir complicaciones. (AU)

Introduction: nutrient and trace element deficiency are risk factors for the development of preeclampsia; these nutrients induce changes in the hematologic state, which can be used to prevent complications during pregnancy.Objective: this research will analyze the nutritional and hematological status during the 3 trimesters of gestation in pregnant women with preeclampsia to evaluate its association with gestational alterations.Method: a cross-sectional, descriptive, and analytic study was conducted on 78 pregnant women who attended a prenatal control clinic, 11 of whom were diagnosed with preeclampsia. The hematological parameters were determined through hematometry. To get the nutritional intake profile of the pregnant women, a frequency of food consumption questionnaire was used.Results: the normotensive group´s arterial pressure (PA) was significantly lower than that of pregnant women with preeclampsia. The hematological profile changed during pregnancy, and platelet value was much higher in preeclamptic women. The nutritional status of pregnant women in the third trimester consumed more macronutrients and micronutrients than in the first and second trimesters, and this pattern was observed in pregnant women with preeclampsia. When compared to pregnant women with preeclampsia, normotensive women received more minerals and vitamins.Conclusion: hematological values are modified during each trimester of gestation, and pregnant women with preeclampsia had high platelet values compared to normotensive women. Our data show a greater intake of minerals and vitamins in normotensive pregnant women as compared to those who developed preeclampsia; therefore, it is recommended that pregnant women be provided with nutritional guidance in order to prevent pregnant complications. (AU)

[Hematologic and nutritional states are associated with the development of preeclampsia in a Mexican population]. / Los estados hematológico y nutricional se asocian con el desarrollo de preeclampsia en una población mexicana.

Introduction: Introduction: nutrient and trace element deficiency are risk factors for the development of preeclampsia; these nutrients induce changes in the hematologic state, which can be used to prevent complications during pregnancy. Objective: this research will analyze the nutritional and hematological status during the 3 trimesters of gestation in pregnant women with preeclampsia to evaluate its association with gestational alterations. Method: a cross-sectional, descriptive, and analytic study was conducted on 78 pregnant women who attended a prenatal control clinic, 11 of whom were diagnosed with preeclampsia. The hematological parameters were determined through hematometry. To get the nutritional intake profile of the pregnant women, a frequency of food consumption questionnaire was used. Results: the normotensive group´s arterial pressure (PA) was significantly lower than that of pregnant women with preeclampsia. The hematological profile changed during pregnancy, and platelet value was much higher in preeclamptic women. The nutritional status of pregnant women in the third trimester consumed more macronutrients and micronutrients than in the first and second trimesters, and this pattern was observed in pregnant women with preeclampsia. When compared to pregnant women with preeclampsia, normotensive women received more minerals and vitamins. Conclusion: hematological values are modified during each trimester of gestation, and pregnant women with preeclampsia had high platelet values compared to normotensive women. Our data show a greater intake of minerals and vitamins in normotensive pregnant women as compared to those who developed preeclampsia; therefore, it is recommended that pregnant women be provided with nutritional guidance in order to prevent pregnant complications.

Introducción: Introducción: la insuficiencia de nutrientes y elementos traza son factores de riesgo para el desarrollo de preeclampsia; estos nutrientes pueden provocar cambios en el estado hematológico que pueden usarse para la prevención de complicaciones durante la gestación. Objetivo: la presente investigación analizará el estado nutrimental y hematológico durante los 3 trimestres de gestación en mujeres gestantes con preeclampsia para evaluar su relación con esta alteración gestacional. Método: se realizó un estudio transversal, descriptivo y analítico en 78 gestantes que acudían a control prenatal, 11 de ellas diagnosticadas de preeclampsia. Los parámetros hematológicos fueron determinados por cartometría hemática. Se aplicó un cuestionario de frecuencia de consumo de alimentos para obtener el perfil de ingesta de nutrientes de las gestantes. Resultados: la presión arterial (PA) del grupo de normotensas fue significativamente menor en comparación con las gestantes con preeclampsia. El perfil hematológico varía durante la gestación y el valor de plaquetas fue significativamente más alto en las gestantes con preeclampsia. El estado nutrimental de las gestantes en el tercer trimestre tuvo mayor consumo de macronutrientes y micronutrientes comparado con el primero y el segundo trimestres de gestación; este mismo patrón se observó en las gestantes con preeclampsia. Las gestantes normotensas tuvieron un mayor aporte de minerales y de vitaminas con respecto a las gestantes con preeclampsia. Conclusión: los valores hematológicos se modifican durante cada trimestre de gestación. Las gestantes con preeclampsia tuvieron valores altos de plaquetas comparadas con las normotensas. Nuestros datos muestran un mayor aporte de minerales y vitaminas en las gestantes normotensas comparadas con aquellas que han desarrollado preeclampsia, por lo que se recomienda que la atención de las gestantes se acompañe de orientación nutrimental para prevenir complicaciones.

Determination of the Severity and Percentage of COVID-19 Infection through a Hierarchical Deep Learning System.

The coronavirus disease 2019 (COVID-19) has caused millions of deaths and one of the greatest health crises of all time. In this disease, one of the most important aspects is the early detection of the infection to avoid the spread. In addition to this, it is essential to know how the disease progresses in patients, to improve patient care. This contribution presents a novel method based on a hierarchical intelligent system, that analyzes the application of deep learning models to detect and classify patients with COVID-19 using both X-ray and chest computed tomography (CT). The methodology was divided into three phases, the first being the detection of whether or not a patient suffers from COVID-19, the second step being the evaluation of the percentage of infection of this disease and the final phase is to classify the patients according to their severity. Stratification of patients suffering from COVID-19 according to their severity using automatic systems based on machine learning on medical images (especially X-ray and CT of the lungs) provides a powerful tool to help medical experts in decision making. In this article, a new contribution is made to a stratification system with three severity levels (mild, moderate and severe) using a novel histogram database (which defines how the infection is in the different CT slices for a patient suffering from COVID-19). The first two phases use CNN Densenet-161 pre-trained models, and the last uses SVM with LDA supervised learning algorithms as classification models. The initial stage detects the presence of COVID-19 through X-ray multi-class (COVID-19 vs. No-Findings vs. Pneumonia) and the results obtained for accuracy, precision, recall, and F1-score values are 88%, 91%, 87%, and 89%, respectively. The following stage manifested the percentage of COVID-19 infection in the slices of the CT-scans for a patient and the results in the metrics evaluation are 0.95 in Pearson Correlation coefficient, 5.14 in MAE and 8.47 in RMSE. The last stage finally classifies a patient in three degrees of severity as a function of global infection of the lungs and the results achieved are 95% accurate.

Arsenic exposure and non-carcinogenic health effects.

Inorganic arsenic (iAs) exposure is a serious health problem that affects more than 140 million individuals worldwide, mainly, through contaminated drinking water. Acute iAs poisoning produces several symptoms such as nausea, vomiting, abdominal pain, and severe diarrhea, whereas prolonged iAs exposure increased the risk of several malignant disorders such as lung, urinary tract, and skin tumors. Another sensitive endpoint less described of chronic iAs exposure are the non-malignant health effects in hepatic, endocrine, renal, neurological, hematological, immune, and cardiovascular systems. The present review outlines epidemiology evidence and possible molecular mechanisms associated with iAs-toxicity in several non-carcinogenic disorders.

Association Between Socio-Affective Symptoms and Glutathione and CD4 and CD8 Lymphocytes in College Students.

Background: The prevalence of anxiety and depression in young students is associated with biosocial factors and scholastic stress. However, few studies have evaluated emotional-affective symptoms that are related to the immune system and antioxidant parameters in young individuals without diagnoses of affective disorders. Aim: This study aims to assess the relationship between emotional-affective symptoms and glutathione concentrations and CD4 and CD8 lymphocyte counts in college students. Methods: College students (n = 177) completed standardized psychometric instruments, including the Perceived Stress Scale, Hamilton Anxiety Scale, Beck Depression Inventory, Familiar Social and Friends Support Scale, and Rosenberg Scale. Blood samples were biochemically analyzed. Analyses of variance were conducted between four groups according to symptom severity. Results: A considerable prevalence of stress, anxiety, and depression symptoms was observed and negatively correlated with self-esteem and socio-familiar support. Perceived stress was sexually dimorphic. Although biochemical parameters were within reference ranges, glutathione, CD4, and CD8 tended to be lower in participants with anxiety and depression symptoms, which may be of predictive value. Conclusion: The relationship between antioxidant/immune parameters and socio-affective scores is latent in undiagnosed college students who might develop affective disorders. The findings suggest that during the initial development of affective disorders, stress management strategies should be implemented to help college students cope with the academic load and monitor negative changes in their physiological state.

Main findings and advances in bioinformatics and biomedical engineering- IWBBIO 2018.

In the current supplement, we are proud to present seventeen relevant contributions from the 6th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2018), which was held during April 25-27, 2018 in Granada (Spain). These contributions have been chosen because of their quality and the importance of their findings.

Towards Improving Skin Cancer Diagnosis by Integrating Microarray and RNA-Seq Datasets.

Many clinical studies have revealed the high biological similarities existing among different skin pathological states. These similarities create difficulties in the efficient diagnosis of skin cancer, and encourage to study and design new intelligent clinical decision support systems. In this sense, gene expression analysis can help find differentially expressed genes (DEGs) simultaneously discerning multiple skin pathological states in a single test. The integration of multiple heterogeneous transcriptomic datasets requires different pipeline stages to be properly designed: from suitable batch merging and efficient biomarker selection to automated classification assessment. This article presents a novel approach addressing all these technical issues, with the intention of providing new sights about skin cancer diagnosis. Although new future efforts will have to be made in the search for better biomarkers recognizing specific skin pathological states, our study found a panel of 8 highly relevant multiclass DEGs for discerning up to 10 skin pathological states: 2 healthy skin conditions a priori, 2 cataloged precancerous skin diseases and 6 cancerous skin states. Their power of diagnosis over new samples was widely tested by previously well-trained classification models. Robust performance metrics such as overall and mean multiclass F1-score outperformed recognition rates of 94% and 80%, respectively. Clinicians should give special attention to highlighted multiclass DEGs that have high gene expression changes present among them, and understand their biological relationship to different skin pathological states.

Leukemia multiclass assessment and classification from Microarray and RNA-seq technologies integration at gene expression level.

In more recent years, a significant increase in the number of available biological experiments has taken place due to the widespread use of massive sequencing data. Furthermore, the continuous developments in the machine learning and in the high performance computing areas, are allowing a faster and more efficient analysis and processing of this type of data. However, biological information about a certain disease is normally widespread due to the use of different sequencing technologies and different manufacturers, in different experiments along the years around the world. Thus, nowadays it is of paramount importance to attain a correct integration of biologically-related data in order to achieve genuine benefits from them. For this purpose, this work presents an integration of multiple Microarray and RNA-seq platforms, which has led to the design of a multiclass study by collecting samples from the main four types of leukemia, quantified at gene expression. Subsequently, in order to find a set of differentially expressed genes with the highest discernment capability among different types of leukemia, an innovative parameter referred to as coverage is presented here. This parameter allows assessing the number of different pathologies that a certain gen is able to discern. It has been evaluated together with other widely known parameters under assessment of an ANOVA statistical test which corroborated its filtering power when the identified genes are subjected to a machine learning process at multiclass level. The optimal tuning of gene extraction evaluated parameters by means of this statistical test led to the selection of 42 highly relevant expressed genes. By the use of minimum-Redundancy Maximum-Relevance (mRMR) feature selection algorithm, these genes were reordered and assessed under the operation of four different classification techniques. Outstanding results were achieved by taking exclusively the first ten genes of the ranking into consideration. Finally, specific literature was consulted on this last subset of genes, revealing the occurrence of practically all of them with biological processes related to leukemia. At sight of these results, this study underlines the relevance of considering a new parameter which facilitates the identification of highly valid expressed genes for simultaneously discerning multiple types of leukemia.

Evaluation of plasma arsenicals as potential biomarkers of exposure to inorganic arsenic.

Exposure to inorganic arsenic (iAs) remains a global public health problem. Urinary arsenicals are the current gold-standard for estimating both iAs exposure and iAs metabolism. However, the distribution of these arsenicals may differ between the urine and target organs. Instead, plasma arsenicals may better represent internal dose and capture target organ exposure to arsenicals. Drinking water iAs, plasma and urinary arsenicals were quantified in individuals living in the Zimapan and Lagunera regions of Mexico. The relationship between drinking water iAs and plasma arsenicals was examined using both Spearman correlations and multivariable linear regression models. In addition, the distribution of arsenicals in plasma and urine was examined and the association between plasma and urinary arsenicals was assessed using both Spearman correlations and multivariable linear regression models. Levels of iAs in drinking water were significantly associated with plasma arsenicals in unadjusted and adjusted analyses and the strength of these associations was similar to that of drinking water iAs and urinary arsenicals. These results suggest that plasma arsenicals are reliable biomarkers of iAs exposure via drinking water. However, there were notable differences between the profiles of arsenicals in the plasma and the urine. Key differences between the proportions of arsenicals in plasma and urine may indicate that urine and plasma arsenicals reflect different aspects of iAs toxicokinetics, including metabolism and excretion.

Multi-Objective Genetic Algorithms to Find Most Relevant Volumes of the Brain Related to Alzheimer's Disease and Mild Cognitive Impairment.

Computer-Aided Diagnosis (CAD) represents a relevant instrument to automatically classify between patients with and without Alzheimer's Disease (AD) using several actual imaging techniques. This study analyzes the optimization of volumes of interest (VOIs) to extract three-dimensional (3D) textures from Magnetic Resonance Image (MRI) in order to diagnose AD, Mild Cognitive Impairment converter (MCIc), Mild Cognitive Impairment nonconverter (MCInc) and Normal subjects. A relevant feature of the proposed approach is the use of 3D features instead of traditional two-dimensional (2D) features, by using 3D discrete wavelet transform (3D-DWT) approach for performing feature extraction from T-1 weighted MRI. Due to the high number of coefficients when applying 3D-DWT to each of the VOIs, a feature selection algorithm based on mutual information is used, as is the minimum Redundancy Maximum Relevance (mRMR) algorithm. Region optimization has been performed in order to discover the most relevant regions (VOIs) in the brain with the use of Multi-Objective Genetic Algorithms, being one of the objectives to be optimize the accuracy of the system. The error index of the system is computed by the confusion matrix obtained by the multi-class support vector machine (SVM) classifier. Principal Component Analysis (PCA) is used with the purpose of reducing the number of features to the classifier. The cohort of subjects used in the study consisted of 296 different patients. A first group of 206 patients was used to optimize VOI selection and another group of 90 independent subjects (that did not belong to the first group) was used to test the solutions yielded by the genetic algorithm. The proposed methodology obtains excellent results in multi-class classification achieving accuracies of 94.4% and also extracting significant information on the location of the most relevant points of the brain. This suggests that the proposed method could aid in the research of other neurodegenerative diseases, improving the accuracy of the diagnosis and finding the most relevant regions of the brain associated with them.

Multiclass classification for skin cancer profiling based on the integration of heterogeneous gene expression series.

Most of the research studies developed applying microarray technology to the characterization of different pathological states of any disease may fail in reaching statistically significant results. This is largely due to the small repertoire of analysed samples, and to the limitation in the number of states or pathologies usually addressed. Moreover, the influence of potential deviations on the gene expression quantification is usually disregarded. In spite of the continuous changes in omic sciences, reflected for instance in the emergence of new Next-Generation Sequencing-related technologies, the existing availability of a vast amount of gene expression microarray datasets should be properly exploited. Therefore, this work proposes a novel methodological approach involving the integration of several heterogeneous skin cancer series, and a later multiclass classifier design. This approach is thus a way to provide the clinicians with an intelligent diagnosis support tool based on the use of a robust set of selected biomarkers, which simultaneously distinguishes among different cancer-related skin states. To achieve this, a multi-platform combination of microarray datasets from Affymetrix and Illumina manufacturers was carried out. This integration is expected to strengthen the statistical robustness of the study as well as the finding of highly-reliable skin cancer biomarkers. Specifically, the designed operation pipeline has allowed the identification of a small subset of 17 differentially expressed genes (DEGs) from which to distinguish among 7 involved skin states. These genes were obtained from the assessment of a number of potential batch effects on the gene expression data. The biological interpretation of these genes was inspected in the specific literature to understand their underlying information in relation to skin cancer. Finally, in order to assess their possible effectiveness in cancer diagnosis, a cross-validation Support Vector Machines (SVM)-based classification including feature ranking was performed. The accuracy attained exceeded the 92% in overall recognition of the 7 different cancer-related skin states. The proposed integration scheme is expected to allow the co-integration with other state-of-the-art technologies such as RNA-seq.

Statistical Analysis of the Main Configuration Parameters of the Network Dynamic and Adaptive Radio Protocol (DARP).

The present work analyses the wireless sensor network protocol (DARP) and the impact of different configuration parameter sets on its performance. Different scenarios have been considered, in order to gain a better understanding of the influence of the configuration on network protocols. The developed statistical analysis is based on the method known as Analysis of Variance (ANOVA), which focuses on the effect of the configuration on the performance of DARP. Three main dependent variables were considered: number of control messages sent during the set-up time, energy consumption and convergence time. A total of 20,413 simulations were carried out to ensure greater robustness in the statistical conclusions. The main goal of this work is to discover the most critical configuration parameters for the protocol, with a view to potential applications in Smart City type scenarios.

Optimizing multiple sequence alignments using a genetic algorithm based on three objectives: structural information, non-gaps percentage and totally conserved columns.

MOTIVATION: Multiple sequence alignments (MSAs) are widely used approaches in bioinformatics to carry out other tasks such as structure predictions, biological function analyses or phylogenetic modeling. However, current tools usually provide partially optimal alignments, as each one is focused on specific biological features. Thus, the same set of sequences can produce different alignments, above all when sequences are less similar. Consequently, researchers and biologists do not agree about which is the most suitable way to evaluate MSAs. Recent evaluations tend to use more complex scores including further biological features. Among them, 3D structures are increasingly being used to evaluate alignments. Because structures are more conserved in proteins than sequences, scores with structural information are better suited to evaluate more distant relationships between sequences. RESULTS: The proposed multiobjective algorithm, based on the non-dominated sorting genetic algorithm, aims to jointly optimize three objectives: STRIKE score, non-gaps percentage and totally conserved columns. It was significantly assessed on the BAliBASE benchmark according to the Kruskal-Wallis test (P < 0.01). This algorithm also outperforms other aligners, such as ClustalW, Multiple Sequence Alignment Genetic Algorithm (MSA-GA), PRRP, DIALIGN, Hidden Markov Model Training (HMMT), Pattern-Induced Multi-sequence Alignment (PIMA), MULTIALIGN, Sequence Alignment Genetic Algorithm (SAGA), PILEUP, Rubber Band Technique Genetic Algorithm (RBT-GA) and Vertical Decomposition Genetic Algorithm (VDGA), according to the Wilcoxon signed-rank test (P < 0.05), whereas it shows results not significantly different to 3D-COFFEE (P > 0.05) with the advantage of being able to use less structures. Structural information is included within the objective function to evaluate more accurately the obtained alignments. AVAILABILITY: The source code is available at http://www.ugr.es/~fortuno/MOSAStrE/MO-SAStrE.zip.