Inflammation and weight change related to neurocognitive and functional impairment in diabetes and psychiatric disorders.

INTRODUCTION: Obesity is a global pandemic associated with various cardio-metabolic and psychiatric disorders. Neurocognitive and functional deficits have been associated with several somatic and psychiatric disorders. Adiposity-related inflammation has recently emerged as a key risk factor for neurocognitive and functional impairments. This prospective transdiagnostic study aimed to investigate the role of adiposity-related inflammatory markers in neurocognitive and functional outcomes associated with weight changes. METHODS: Peripheral blood inflammatory and oxidative stress biomarkers and neurocognitive and functional performance were assessed twice over 1 year in 165 individuals, including 30 with schizophrenia, 42 with bipolar disorder, 35 with major depressive disorder, 30 with type 2 diabetes mellitus (T2DM), and 28 healthy controls. Participants were stratified by body mass index into categories of type 2 obesity (T2OB; n=30), type 1 obesity (T1OB; n=42), overweight (OW; n=53), and average weight (NW; n=40). Mixed one-way analysis of covariance and linear and binary logistic regression analyses were performed. RESULTS: Compared with NW, T2OB and T1OB were significantly associated with impaired neurocognitive and functional performance (p<0.01; η2p=0.06-0.12) and higher levels of C-reactive protein and platelets (PLT) (p<0.01; η2p=0.08-0.16), with small-to-moderate effect sizes. IL-6, IL-10, and PLT were key factors for detecting significant weight changes in T1OB and T2OB over time. Regression models revealed that inflammatory and oxidative stress biomarkers and cellular adhesion molecules were significantly associated with neurocognitive and functional performance (p<0.05). DISCUSSION: Obesity is characterized by neurocognitive and functional impairments alongside low-grade systemic inflammation. Adiposity-related inflammatory biomarkers may contribute to neurocognitive and functional decline in individuals with T2DM and psychiatric disorders. Our data suggest that these biomarkers facilitate the identification of specific subgroups of individuals at higher risk of developing obesity.

Inflammation and lipid metabolism as potential biomarkers of memory impairment across type 2 diabetes mellitus and severe mental disorders.

INTRODUCTION: Neurocognitive impairment is a transdiagnostic feature across several psychiatric and cardiometabolic conditions. The relationship between inflammatory and lipid metabolism biomarkers and memory performance is not fully understood. This study aimed to identify peripheral biomarkers suitable to signal memory decline from a transdiagnostic and longitudinal perspective. METHODS: Peripheral blood biomarkers of inflammation, oxidative stress and lipid metabolism were assessed twice over a 1-year period in 165 individuals, including 30 with schizophrenia (SZ), 42 with bipolar disorder (BD), 35 with major depressive disorder (MDD), 30 with type 2 diabetes mellitus (T2DM), and 28 healthy controls (HCs). Participants were stratified by memory performance quartiles, taking as a reference their global memory score (GMS) at baseline, into categories of high memory (H; n = 40), medium to high memory (MH; n = 43), medium to low memory (ML; n = 38) and low memory (L; n = 44). Exploratory and confirmatory factorial analysis, mixed one-way analysis of covariance and discriminatory analyses were performed. RESULTS: L group was significantly associated with higher levels of tumor necrosis factor-alpha (TNF-α) and lower levels of apolipoprotein A1 (Apo-A1) compared to those from the MH and H groups (p < 0.05; η2p = 0.06-0.09), with small to moderate effect sizes. Moreover, the combination of interleukin-6 (IL-6), TNF-α, c-reactive protein (CRP), Apo-A1 and Apo-B compounded the transdiagnostic model that best discriminated between groups with different degrees of memory impairment (χ2 = 11.9-49.3, p < 0.05-0.0001). CONCLUSIONS: Inflammation and lipid metabolism seem to be associated with memory across T2DM and severe mental illnesses (SMI). A panel of biomarkers may be a useful approach to identify individuals at greater risk of neurocognitive impairment. These findings may have a potential translational utility for early intervention and advance precision medicine in these disorders.

MICEmi: A method to identify cognitive endophenotypes of mental illnesses.

BACKGROUND: Characterizing neurocognitive endophenotypes of mental illnesses (MIs) could be useful for identifying at-risk individuals, increasing early diagnosis, improving disease subtyping, and proposing therapeutic strategies to reduce the negative effects of the symptoms, in addition to serving as a scientific basis to unravel the physiopathology of the disease. However, a standardized algorithm to determine cognitive endophenotypes has not yet been developed. The main objective of this study was to present a method for the identification of endophenotypes in MI research. METHODS: For this purpose, a 14-expert working group used a scoping review methodology and designed a method that includes a scoring template with five criteria and indicators, a strategy for their verification, and a decision tree. CONCLUSIONS: This work is ongoing since it is necessary to obtain external validation of the applicability of the method in future research.

Public stigma profile toward mental disorders across different university degrees in the University of Valencia (Spain).

Background: A large proportion of studies carried out in recent years in different populations have shown that stigma toward mental disorders is highly prevalent. In the present study we conducted a comprehensive assessment of stigma to describe and compare stigma toward mental disorders in students enrolled in five different university degrees. Methods: Three hundred and twenty-five students from the University of Valencia (Spain), attending the second term of their first-degree courses in the faculties of medicine, psychology, teaching, economics, and data science participated in this cross-sectional study. Stigma was measured using: the Reported and Intended Behavior Scale (RIBS), the Scale of Community Attitudes toward Mental Illness (CAMI), the Attribution Questionnaire (AQ-27), and the Knowledge about Mental Illness test (KMI). Results: We found different patterns of stigma according to gender, the fact of knowing or living with a person with mental disorders and the university degree studied. Overall, women show fewer stigmatizing attitudes than men but similar stereotypes and prejudice toward people with mental disorders. However, the pattern of results across degrees is more complex. Overall, students of medicine, psychology and teaching showed fewer stigmatizing attitudes than students of economics and data science but differences between degrees were more subtle in stereotypes and prejudice toward people with mental disorders. Conclusion: Our study suggests the existence of different profiles of stigma in relation to mental disorders in university students. These profiles varied in relation with the degree being studied, gender and already knowing or living with a person with mental disorders.

Online polarization and cross-fertilization in multi-cleavage societies: the case of Spain.

The impact of the social media (SM) has been seen on the one hand as the cause of large exacerbation of negative messages, responsible for massively harmful societal phenomenon against democracies. On the other hand, recent studies have begun to look at how these online channels were able to provide a new impulse in human communication. The novelty of our work resides on analysing several axes of polarizations related to different societal topics. We believe our approach to reflect a more complex society, differing from the recent literature, which has considered a unique left-right dichotomic cleavage. Our methodology consists of extracting topics from the priority themes of the SM debate, using BERT language processing techniques and TF-IDF model. Our results show situation of social media interactions in a multidimensional space does exist. We highlight how social media behaviours, polarization and cross-fertilization differ as upon concrete topics. We argue therefore the 'mega-identity partisanship' which differentiate US online users in two different spaces cannot be extended for the rest of countries taking as first evidence the case of Spain. Further research should extend our conclusions for a possible generalization.

Immune-Inflammatory Biomarkers Predict Cognition and Social Functioning in Patients With Type 2 Diabetes Mellitus, Major Depressive Disorder, Bipolar Disorder, and Schizophrenia: A 1-Year Follow-Up Study.

Background: Systemic, low-grade immune-inflammatory activity, together with social and neurocognitive performance deficits are a transdiagnostic trait of people suffering from type 2 diabetes mellitus (T2DM) and severe mental illnesses (SMIs), such as schizophrenia (SZ), major depressive disorder (MDD), and bipolar disorder (BD). We aimed to determine if immune-inflammatory mediators were significantly altered in people with SMIs or T2DM compared with healthy controls (HC) and whether these biomarkers could help predict their cognition and social functioning 1 year after assessment. Methods: We performed a prospective, 1-year follow-up cohort study with 165 participants at baseline (TB), including 30 with SZ, 42 with BD, 35 with MDD, 30 with T2DM, and 28 HC; and 125 at 1-year follow-up (TY), and determined executive domain (ED), global social functioning score (GSFS), and peripheral blood immune-inflammatory and oxidative stress biomarkers. Results: Participants with SMIs and T2DM showed increased peripheral levels of inflammatory markers, such as interleukin-10 (p < 0.01; η2 p = 0.07) and tumor necrosis factor-α (p < 0.05; η2 p = 0.08); and oxidative stress biomarkers, such as reactive oxygen species (ROS) (p < 0.05; η2 p = 0.07) and mitochondrial ROS (p < 0.01; η2 p = 0.08). The different combinations of the exposed biomarkers anticipated 46-57.3% of the total ED and 23.8-35.7% of GSFS for the participants with SMIs. Limitations: Participants' treatment, as usual, was continued without no specific interventions; thus, it was difficult to anticipate substantial changes related to the psychopharmacological pattern. Conclusion: People with SMIs show significantly increased levels of peripheral immune-inflammatory biomarkers, which may contribute to the neurocognitive and social deficits observed in SMIs, T2DM, and other diseases with systemic immune-inflammatory activation of chronic development. These parameters could help identify the subset of patients who could benefit from immune-inflammatory modulator strategies to ameliorate their functional outcomes.

Prediction of the risk of cancer and the grade of dysplasia in leukoplakia lesions using deep learning.

OBJECTIVES: To estimate the probability of malignancy of an oral leukoplakia lesion using Deep Learning, in terms of evolution to cancer and high-risk dysplasia. MATERIALS AND METHODS: A total of 261 oral leukoplakia lesions with a mean of 5.5 years follow-up were analysed from standard digital photographs. A deep learning pipeline composed by a U-Net based segmentation of the lesion followed by a multi-task CNN classifier was used to predict the malignant transformation and the risk of dysplasia of the lesion. An explainability heatmap is constructed using LIME in order to interpret the decision of the model for each output. RESULTS: A Dice coefficient of 0.561 was achieved on the segmentation task. For the prediction of a malignant transformation, the model provided a sensitivity of 1 with a specificity of 0.692. For the prediction of high-risk dysplasia, the model achieved a specificity of 0.740 and a sensitivity of 0.928. CONCLUSION: The proposed model using deep learning can be a helpful tool for predicting the possible malignant evolution of oral leukoplakias. The generated heatmap provides a high confidence on the output of the model and enables its interpretability.

Specific metabolic syndrome components predict cognition and social functioning in people with type 2 diabetes mellitus and severe mental disorders.

OBJECTIVE: Obesity and metabolic diseases such as metabolic syndrome (MetS) are more prevalent in people with type 2 diabetes mellitus (T2DM), major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ). MetS components might be associated with neurocognitive and functional impairments in these individuals. The predictive and discriminatory validity of MetS and its components regarding those outcomes were assessed from prospective and transdiagnostic perspectives. METHODS: Metabolic syndrome components and neurocognitive and social functioning were assessed in 165 subjects, including 30 with SZ, 42 with BD, 35 with MDD, 30 with T2DM, and 28 healthy controls (HCs). A posteriori, individuals were classified into two groups. The MetS group consisted of those who met at least three of the following criteria: abdominal obesity (AO), elevated triglycerides (TG), reduced high-density lipoprotein cholesterol (HDL), elevated blood pressure (BP), and elevated fasting glucose (FPG); the remaining participants comprised the No-MetS group. Mixed one-way analysis of covariance and linear and binary logistic regression analyses were performed. RESULTS: Cognitive impairment was significantly greater in the MetS group (n = 82) than in the No-MetS group (n = 83), with small effect sizes (p < 0.05; η²p = 0.02 - 0.03). In both groups, the most robust associations between MetS components and neurocognitive and social functioning were observed with TG and FPG (p < 0.05). There was also evidence for a significant relationship between cognition and BP in the MetS group (p < 0.05). The combination of TG, FPG, elevated systolic BP and HDL best classified individuals with greater cognitive impairment (p < 0.001), and TG was the most accurate (p < 0.0001). CONCLUSIONS: Specific MetS components are significantly associated with cognitive impairment across somatic and psychiatric disorders. Our findings provide further evidence on the summative effect of MetS components to predict cognition and social functioning and allow the identification of individuals with worse outcomes. Transdiagnostic, lifestyle-based therapeutic interventions targeted at that group hold the potential to improve health outcomes.

Transdiagnostic neurocognitive deficits in patients with type 2 diabetes mellitus, major depressive disorder, bipolar disorder, and schizophrenia: A 1-year follow-up study.

BACKGROUND: Neurocognition impairments are critical factors in patients with major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ), and also in those with somatic diseases such as type 2 diabetes mellitus (T2DM). Intriguingly, these severe mental illnesses are associated with an increased co-occurrence of diabetes (direct comorbidity). This study sought to investigate the neurocognition and social functioning across T2DM, MDD, BD, and SZ using a transdiagnostic and longitudinal approach. METHODS: A total of 165 participants, including 30 with SZ, 42 with BD, 35 with MDD, 30 with T2DM, and 28 healthy controls (HC), were assessed twice at a 1-year interval using a comprehensive, integrated test battery on neuropsychological and social functioning. RESULTS: Common neurocognitive impairments in somatic and psychiatric disorders were identified, including deficits in short-term memory and cognitive reserve (p < 0.01, η²p=0.08-0.31). Social functioning impairments were observed in almost all the disorders (p < 0.0001; η²p=0.29-0.49). Transdiagnostic deficits remained stable across the 1-year follow-up (p < 0.001; η²p=0.13-0.43) and could accurately differentiate individuals with somatic and psychiatric disorders (χ² = 48.0, p < 0.0001). LIMITATIONS: The initial sample size was small, and high experimental mortality was observed after follow-up for one year. CONCLUSIONS: This longitudinal study provides evidence of some possible overlap in neurocognition deficits across somatic and psychiatric diagnostic categories, such as T2DM, MDD, BD, and SZ, which have high comorbidity. This overlap may be a result of shared genetic and environmental etiological factors. The findings open promising avenues for research on transdiagnostic phenotypes of neurocognition in these disorders, in addition to their biological bases.

Is processing speed a valid neurocognitive endophenotype in bipolar disorder? Evidence from a longitudinal, family study.

BACKGROUND: Substantial evidence supports the existence of neurocognitive endophenotypes in bipolar disorder (BD), but very few longitudinal studies have included unaffected relatives. In a 5-year, follow-up, family study, we have recently suggested that deficits in manual motor speed and visual memory could be endophenotype candidates for BD. We aimed to explore whether this also applies to processing speed. METHODS: A sample of 348 individuals, including 163 BD patients, 65 unaffected first-degree relatives (BD-Rel) and 120 genetically unrelated healthy controls (HC), was assessed with the Digit Symbol Substitution Test (DSST) on two occasions over a 2-year period (T1, T2). DSST values were controlled for age, years of education, occupational status, and subsyndromic mood symptoms. Differences between groups were evaluated with ANCOVAs. RESULTS: At T1 BD performed significantly worse than HC (p < 0.001; Cohen's d = 1.38) and BD-Rel (p < 0.001; Cohen's d = 0.82). BD-Rel showed an intermediate performance with significant differences with HC (p < 0.01; Cohen's d = 0.50). Similarly, at T2 BD performed significantly worse than HC (p < 0.001; Cohen's d = 1.44) and BD-Rel (p < 0.01; Cohen's d = 0.51). BD-Rel performance was intermediate and significantly lower than that of HC (p < 0.01; Cohen's d = 0.97). A Repeated Measures ANOVA revealed no significant between-group differences in performance over time (p > 0.05). CONCLUSIONS: The results of this longitudinal, family study suggest that impaired processing speed may represent a suitable cognitive endophenotype for BD. Further research on the field is required to confirm these preliminary findings.

Cognitive impairment and consumption of mental healthcare resources in outpatients with bipolar disorder.

Cognitive dysfunction is a major predictor of functional outcomes, and loss of occupational functioning is usually linked with a higher cost of illness. However, the association between cognitive impairment and consumption of health resources has not been studied in bipolar disorder to date. This study aims to examine this relationship. This is an observational, retrospective study of a representative sample of euthymic outpatients between 18 and 55 years, fulfilling DSM 5 criteria for bipolar disorder and recruited at a catchment area in Spain. Cognitive performance was screened with the Spanish version of the Screen for Cognitive Impairment in Psychiatry (SCIP-S), and several variables of health resources consumption during the previous year were registered. A total of 72 patients were assessed. Cognitive impairment according to the SCIP-S was significantly associated with the number of scheduled clinical appointments (p < 0.005) and hospital admissions (p < 0.04) but not with other health resources consumption variables. These results need to be interpreted with caution given that neither a control group nor a comprehensive, objective neuropsychological battery were used. However, despite these limitations, this study shows that in euthymic outpatients with bipolar disorder, those with suspected cognitive impairment had consumed a higher number of health resources over the previous year. These preliminary results may foster similar studies on the relationship between mental healthcare resource use and cognitive dysfunction in bipolar disorder and other psychiatric disorders.

Grip Strength, Neurocognition, and Social Functioning in People WithType-2 Diabetes Mellitus, Major Depressive Disorder, Bipolar Disorder, and Schizophrenia.

BACKGROUND: Frailty is a common syndrome among older adults and patients with several comorbidities. Grip strength (GS) is a representative parameter of frailty because it is a valid indicator of current and long-term physical conditions in the general population and patients with severe mental illnesses (SMIs). Physical and cognitive capacities of people with SMIs are usually impaired; however, their relationship with frailty or social functioning have not been studied to date. The current study aimed to determine if GS is a valid predictor of changes in cognitive performance and social functioning in patients with type-2 diabetes mellitus and SMIs. METHODS: Assessments of social functioning, cognitive performance, and GS (measured with an electronic dynamometer) were conducted in 30 outpatients with type 2 diabetes mellitus, 35 with major depressive disorder, 42 with bipolar disorder, 30 with schizophrenia, and 28 healthy controls, twice during 1-year, follow-up period. Descriptive analyses were conducted using a one-way analysis of variance for continuous variables and the chi-squared test for categorical variables. Differences between groups for the motor, cognitive, and social variables at T1 and T2 were assessed using a one-way analysis of covariance, with sex and age as co-variates (p < 0.01). To test the predictive capacity of GS at baseline to explain the variance in cognitive performance and social functioning at T2, a linear regression analysis was performed (p < 0.05). RESULTS: Predictive relationships were found among GS when implicated with clinical, cognitive, and social variables. These relationships explained changes in cognitive performance after one year of follow-up; the variability percentage was 67.7%, in patients with type-2 diabetes mellitus and 89.1% in patients with schizophrenia. Baseline GS along with other variables, also predicted changes in social functioning in major depressive disorder, bipolar disorder, and schizophrenia, with variability percentages of 67.3, 36, and 59%, respectively. CONCLUSION: GS combined with other variables significantly predicted changes in cognitive performance and social functioning in people with SMIs or type-2 diabetes mellitus. Interventions aimed to improve the overall physical conditions of patients who have poor GS could be a therapeutic option that confers positive effects on cognitive performance and social functioning.

Visual memory dysfunction as a neurocognitive endophenotype in bipolar disorder patients and their unaffected relatives. Evidence from a 5-year follow-up Valencia study.

BACKGROUND: Scarce research has focused on Visual Memory (VM) deficits as a possible neurocognitive endophenotype of bipolar disorder (BD). The main aim of this longitudinal, family study with healthy controls was to explore whether VM dysfunction represents a neurocognitive endophenotype of BD. METHODS: Assessment of VM by Rey-Osterrieth Complex Figure Test (ROCF) was carried out on a sample of 317 subjects, including 140 patients with BD, 60 unaffected first-degree relatives (BD-Rel), and 117 genetically-unrelated healthy controls (HC), on three occasions over a 5-year period (T1, T2, and T3). BD-Rel group scores were analyzed only at T1 and T2. RESULTS: Performance of BD patients was significantly worse than the HC group (p < 0.01). Performance of BD-Rel was also significantly different from HC scores at T1 (p < 0.01) and T2 (p = 0.05), and showed an intermediate profile between the BD and HC groups. Only among BD patients, there were significant differences according to sex, with females performing worse than males (p = 0.03). Regarding other variables, education represented significant differences only in average scores of BD-Rel group (p = 0.01). LIMITATIONS: Important attrition in BD-Rel group over time was detected, which precluded analysis at T3. CONCLUSIONS: BD patients show significant deficits in VM that remain stable over time, even after controlling sociodemographic and clinical variables. Unaffected relatives also show stable deficits in VM. Accordingly, the deficit in VM could be considered a potential endophenotype of BD, which in turn may be useful as a predictor of the evolution of the disease. Further studies are needed to confirm these findings.

Duration of euthymia and predominant polarity in bipolar disorder.

BACKGROUND: The concept of Predominant Polarity (PP) provides relevant information for clinical practice and has been widely described as course specifier for Bipolar Disorder (BD), however it has not been incorporated in DSM-5 yet. A descriptive study was conducted to identify clinical patterns associated with PP in outpatients attending a Mental Health Unit. METHODS: Clinical and socio-demographic characteristics were assessed from a sample of 118 euthymic outpatients fulfilling DSM 5 criteria for BDI or II recruited at a catchment area. According to their PP, patients were divided into three subgroups: depressive (DPP; 39.0%), manic (MPP; 32.2%) or indeterminate (IPP; 28.8%). Subgroups of PP were compared regarding a comprehensive set of demographic and clinical features. RESULTS: PP subgroups significantly differed in duration of euthymia, measured in months since the last episode (p < 0.04), with MMP patients showing longer periods (42.4 months) than those with DPP and IPP (18.6 and 18.1 months, respectively). Moreover, history of seasonal pattern was significantly higher in the DPP group compared with the PPM group (p < 0.001). There were no significant correlations between PP and type of last episode, length of illness, number of previous admissions, history of psychotic symptoms, or number of suicide attempts. LIMITATIONS: Cross sectional design, relatively modest sample size. CONCLUSIONS: Our study showed similar results to previous literature regarding distribution of predominant polarity. The association found between PP and duration of euthymia represents a novel finding which awaits confirmation and adds further support to the usefulness of PP in clinical practice.

Optimization of anemia treatment in hemodialysis patients via reinforcement learning.

OBJECTIVE: Anemia is a frequent comorbidity in hemodialysis patients that can be successfully treated by administering erythropoiesis-stimulating agents (ESAs). ESAs dosing is currently based on clinical protocols that often do not account for the high inter- and intra-individual variability in the patient's response. As a result, the hemoglobin level of some patients oscillates around the target range, which is associated with multiple risks and side-effects. This work proposes a methodology based on reinforcement learning (RL) to optimize ESA therapy. METHODS: RL is a data-driven approach for solving sequential decision-making problems that are formulated as Markov decision processes (MDPs). Computing optimal drug administration strategies for chronic diseases is a sequential decision-making problem in which the goal is to find the best sequence of drug doses. MDPs are particularly suitable for modeling these problems due to their ability to capture the uncertainty associated with the outcome of the treatment and the stochastic nature of the underlying process. The RL algorithm employed in the proposed methodology is fitted Q iteration, which stands out for its ability to make an efficient use of data. RESULTS: The experiments reported here are based on a computational model that describes the effect of ESAs on the hemoglobin level. The performance of the proposed method is evaluated and compared with the well-known Q-learning algorithm and with a standard protocol. Simulation results show that the performance of Q-learning is substantially lower than FQI and the protocol. When comparing FQI and the protocol, FQI achieves an increment of 27.6% in the proportion of patients that are within the targeted range of hemoglobin during the period of treatment. In addition, the quantity of drug needed is reduced by 5.13%, which indicates a more efficient use of ESAs. CONCLUSION: Although prospective validation is required, promising results demonstrate the potential of RL to become an alternative to current protocols.

Application of machine learning techniques to analyse the effects of physical exercise in ventricular fibrillation.

This work presents the application of machine learning techniques to analyse the influence of physical exercise in the physiological properties of the heart, during ventricular fibrillation. To this end, different kinds of classifiers (linear and neural models) are used to classify between trained and sedentary rabbit hearts. The use of those classifiers in combination with a wrapper feature selection algorithm allows to extract knowledge about the most relevant features in the problem. The obtained results show that neural models outperform linear classifiers (better performance indices and a better dimensionality reduction). The most relevant features to describe the benefits of physical exercise are those related to myocardial heterogeneity, mean activation rate and activation complexity.

Design of a configurable multispectral imaging system based on an AOTF.

In this paper, we present a configurable multispectral imaging system based on an acousto-optic tunable filter (AOTF). Typically, AOTFs are used to filter a single wavelength at a time, but thanks to the use of a versatile sweeping frequency generator implemented with a direct digital synthesizer, the imager may capture a configurable spectral range. Experimental results show a good spectral and imaging response of the system for spectral bandwidth up to a 50 nm.

BELM: Bayesian extreme learning machine.

The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This brief proposes a bayesian approach to ELM, which presents some advantages over other approaches: it allows the introduction of a priori knowledge; obtains the confidence intervals (CIs) without the need of applying methods that are computationally intensive, e.g., bootstrap; and presents high generalization capabilities. Bayesian ELM is benchmarked against classical ELM in several artificial and real datasets that are widely used for the evaluation of machine learning algorithms. Achieved results show that the proposed approach produces a competitive accuracy with some additional advantages, namely, automatic production of CIs, reduction of probability of model overfitting, and use of a priori knowledge.