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Severe defects in human IFNγ immunity predispose individuals to both Bacillus Calmette-Guérin disease and tuberculosis, whereas milder defects predispose only to tuberculosis1. Here we report two adults with recurrent pulmonary tuberculosis who are homozygous for a private loss-of-function TNF variant. Neither has any other clinical phenotype and both mount normal clinical and biological inflammatory responses. Their leukocytes, including monocytes and monocyte-derived macrophages (MDMs) do not produce TNF, even after stimulation with IFNγ. Blood leukocyte subset development is normal in these patients. However, an impairment in the respiratory burst was observed in granulocyte-macrophage colony-stimulating factor (GM-CSF)-matured MDMs and alveolar macrophage-like (AML) cells2 from both patients with TNF deficiency, TNF- or TNFR1-deficient induced pluripotent stem (iPS)-cell-derived GM-CSF-matured macrophages, and healthy control MDMs and AML cells differentiated with TNF blockers in vitro, and in lung macrophages treated with TNF blockers ex vivo. The stimulation of TNF-deficient iPS-cell-derived macrophages with TNF rescued the respiratory burst. These findings contrast with those for patients with inherited complete deficiency of the respiratory burst across all phagocytes, who are prone to multiple infections, including both Bacillus Calmette-Guérin disease and tuberculosis3. Human TNF is required for respiratory-burst-dependent immunity to Mycobacterium tuberculosis in macrophages but is surprisingly redundant otherwise, including for inflammation and immunity to weakly virulent mycobacteria and many other infectious agents.
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Macrófagos , Tuberculosis Pulmonar , Factores de Necrosis Tumoral , Adulto , Femenino , Humanos , Masculino , Factor Estimulante de Colonias de Granulocitos y Macrófagos/metabolismo , Homocigoto , Células Madre Pluripotentes Inducidas/metabolismo , Células Madre Pluripotentes Inducidas/inmunología , Células Madre Pluripotentes Inducidas/citología , Inflamación/inmunología , Interferón gamma/inmunología , Mutación con Pérdida de Función , Pulmón/citología , Pulmón/efectos de los fármacos , Macrófagos/citología , Macrófagos/efectos de los fármacos , Macrófagos/inmunología , Macrófagos/metabolismo , Macrófagos/patología , Macrófagos Alveolares/citología , Macrófagos Alveolares/efectos de los fármacos , Macrófagos Alveolares/inmunología , Macrófagos Alveolares/microbiología , Macrófagos Alveolares/patología , Mycobacterium tuberculosis/inmunología , Fenotipo , Especies Reactivas de Oxígeno/metabolismo , Receptores Tipo I de Factores de Necrosis Tumoral/deficiencia , Receptores Tipo I de Factores de Necrosis Tumoral/genética , Receptores Tipo I de Factores de Necrosis Tumoral/metabolismo , Estallido Respiratorio , Tuberculosis Pulmonar/inmunología , Tuberculosis Pulmonar/microbiología , Tuberculosis Pulmonar/genética , Inhibidores del Factor de Necrosis Tumoral/farmacología , Factores de Necrosis Tumoral/deficiencia , Factores de Necrosis Tumoral/genética , Adolescente , Adulto JovenRESUMEN
Currently, renewable energies, including wind energy, have been experiencing significant growth. Wind energy is transformed into electric energy through the use of wind turbines (WTs), which are located outdoors, making them susceptible to harsh weather conditions. These conditions can cause different types of damage to WTs, degrading their lifetime and efficiency, and, consequently, raising their operating costs. Therefore, condition monitoring and the detection of early damages are crucial. One of the failures that can occur in WTs is the occurrence of cracks in their blades. These cracks can lead to the further deterioration of the blade if they are not detected in time, resulting in increased repair costs. To effectively schedule maintenance, it is necessary not only to detect the presence of a crack, but also to assess its level of severity. This work studies the vibration signals caused by cracks in a WT blade, for which four conditions (healthy, light, intermediate, and severe cracks) are analyzed under three wind velocities. In general, as the proposed method is based on machine learning, the vibration signal analysis consists of three stages. Firstly, for feature extraction, statistical and harmonic indices are obtained; then, the one-way analysis of variance (ANOVA) is used for the feature selection stage; and, finally, the k-nearest neighbors algorithm is used for automatic classification. Neural networks, decision trees, and support vector machines are also used for comparison purposes. Promising results are obtained with an accuracy higher than 99.5%.
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Robotic systems are a fundamental part of modern industrial development. In this regard, they are required for long periods, in repetitive processes that must comply with strict tolerance ranges. Hence, the positional accuracy of the robots is critical, since degradation of this can represent a considerable loss of resources. In recent years, prognosis and health management (PHM) methodologies, based on machine and deep learning, have been applied to robots, in order to diagnose and detect faults and identify the degradation of robot positional accuracy, using external measurement systems, such as lasers and cameras; however, their implementation is complex in industrial environments. In this respect, this paper proposes a method based on discrete wavelet transform, nonlinear indices, principal component analysis, and artificial neural networks, in order to detect a positional deviation in robot joints, by analyzing the currents of the actuators. The results show that the proposed methodology allows classification of the robot positional degradation with an accuracy of 100%, using its current signals. The early detection of robot positional degradation, allows the implementation of PHM strategies on time, and prevents losses in manufacturing processes.
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Intramolecular charge transfer (ICT) effects are responsible for the photoluminescent properties of coumarins. Hence, optical properties with different applications can be obtained by ICT modulation. Herein, four 3-acetyl-2H-chromen-2-ones (1a-d) and their corresponding fluorescent hybrids 3- (phenylhydrazone)-chromen-2-ones (2a-d) were synthesized in 74-65% yields. The UV-Vis data were in the 295-428 nm range. The emission depends on the substituent in position C-7 bearing electron-donating groups. Compounds 1b-d showed good optical properties due to the D-π-A structural arrangement. In compounds 2a-d, there is a quenching effect of fluorescence in solution. However, in the solid, an increase is shown due to an aggregation-induced emission (AIE) effect given by the rotational restraints and stacking in the crystal. Computational calculations of the HOMO-LUMO orbitals indicate high absorbance and emission values of the molecules, and gap values represent the bathochromic effect and the electronic efficiency of the compounds. Compounds 1a-d and 2a-d are good candidates for optical applications, such as OLEDs, organic solar cells, or fluorescence markers.
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Cumarinas , Electrones , Cumarinas/química , Teoría Funcional de la Densidad , Espectrometría de FluorescenciaRESUMEN
Sudden Cardiac Death (SCD) is an unexpected sudden death due to a loss of heart function and represents more than 50% of the deaths from cardiovascular diseases. Since cardiovascular problems change the features in the electrical signal of the heart, if significant changes are found with respect to a reference signal (healthy), then it is possible to indicate in advance a possible SCD occurrence. This work proposes SCD identification using Electrocardiogram (ECG) signals and a sparse representation technique. Moreover, the use of fixed feature ranking is avoided by considering a dictionary as a flexible set of features where each sparse representation could be seen as a dynamic feature extraction process. In this way, the involved features may differ within the dictionary's margin of similarity, which is better-suited to the large number of variations that an ECG signal contains. The experiments were carried out using the ECG signals from the MIT/BIH-SCDH and the MIT/BIH-NSR databases. The results show that it is possible to achieve a detection 30 min before the SCD event occurs, reaching an an accuracy of 95.3% under the common scheme, and 80.5% under the proposed multi-class scheme, thus being suitable for detecting a SCD episode in advance.
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Electrocardiografía , Procesamiento de Señales Asistido por Computador , Bases de Datos Factuales , Muerte Súbita Cardíaca , Corazón , HumanosRESUMEN
The economic and personal consequences that a car accident generates for society have been increasing in recent years. One of the causes that can generate a car accident is the stress level the driver has; consequently, the detection of stress events is a highly desirable task. In this article, the efficacy that statistical time features (STFs), such as root mean square, mean, variance, and standard deviation, among others, can reach in detecting stress events using electromyographical signals in drivers is investigated, since they can measure subtle changes that a signal can have. The obtained results show that the variance and standard deviation coupled with a support vector machine classifier with a cubic kernel are effective for detecting stress events where an AUC of 0.97 is reached. In this sense, since SVM has different kernels that can be trained, they are used to find out which one has the best efficacy using the STFs as feature inputs and a training strategy; thus, information about model explain ability can be determined. The explainability of the machine learning algorithm allows generating a deeper comprehension about the model efficacy and what model should be selected depending on the features used to its development.
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Automóviles , Máquina de Vectores de Soporte , Algoritmos , Electromiografía , Aprendizaje AutomáticoRESUMEN
One of the most critical devices in an electrical system is the transformer. It is continuously under different electrical and mechanical stresses that can produce failures in its components and other electrical network devices. The short-circuited turns (SCTs) are a common winding failure. This type of fault has been widely studied in literature employing the vibration signals produced in the transformer. Although promising results have been obtained, it is not a trivial task if different severity levels and a common high-level noise are considered. This paper presents a methodology based on statistical time features (STFs) and support vector machines (SVM) to diagnose a transformer under several SCTs conditions. As STFs, 19 indicators from the transformer vibration signals are computed; then, the most discriminant features are selected using the Fisher score analysis, and the linear discriminant analysis is used for dimension reduction. Finally, a support vector machine classifier is employed to carry out the diagnosis in an automatic way. Once the methodology has been developed, it is implemented on a field-programmable gate array (FPGA) to provide a system-on-a-chip solution. A modified transformer capable of emulating different SCTs severities is employed to validate and test the methodology and its FPGA implementation. Results demonstrate the effectiveness of the proposal for diagnosing the transformer condition as an accuracy of 96.82% is obtained.
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In this paper, the natural frequencies (NFs) identification by finite element method (FEM) is applied to a two degrees-of-freedom (2-DOF) planar robot, and its validation through a novel experimental methodology, the Multiple Signal Classification (MUSIC) algorithm, is presented. The experimental platforms are two different 2-DOF planar robots with different materials for the links and different types of actuators. The FEM is carried out using ANSYS™ software for the experiments, with vibration signal analysis by MUSIC algorithm. The advantages of the MUSIC algorithm against the commonly used fast Fourier transform (FFT) method are also presented for a synthetic signal contaminated by three different noise levels. The analytical and experimental results show that the proposed methodology identifies the NFs of a high-resolution robot even when they are very closed and when the signal is embedded in high-level noise. Furthermore, the results show that the proposed methodology can obtain a high-frequency resolution with a short sample data set. Identifying the NFs of robots is useful for avoiding such frequencies in the path planning and in the selection of controller gains that establish the bandwidth.
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A new EEG-based methodology is presented for differential diagnosis of the Alzheimer's disease (AD), Mild Cognitive Impairment (MCI), and healthy subjects employing the discrete wavelet transform (DWT), dispersion entropy index (DEI), a recently-proposed nonlinear measurement, and a fuzzy logic-based classification algorithm. The effectiveness and usefulness of the proposed methodology are evaluated by employing a database of measured EEG data acquired from 135 subjects, 45 MCI, 45 AD and 45 healthy subjects. The proposed methodology differentiates MCI and AD patients from HC subjects with an accuracy of 82.6-86.9%, sensitivity of 91 %, and specificity of 87 %.
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Algoritmos , Enfermedad de Alzheimer/diagnóstico , Disfunción Cognitiva/diagnóstico , Demencia/clasificación , Electroencefalografía/métodos , Anciano , Anciano de 80 o más Años , Entropía , Femenino , Lógica Difusa , Humanos , Masculino , Sensibilidad y Especificidad , Procesamiento de Señales Asistido por ComputadorRESUMEN
Although induction motors (IMs) are robust and reliable electrical machines, they can suffer different faults due to usual operating conditions such as abrupt changes in the mechanical load, voltage, and current power quality problems, as well as due to extended operating conditions. In the literature, different faults have been investigated; however, the broken rotor bar has become one of the most studied faults since the IM can operate with apparent normality but the consequences can be catastrophic if the fault is not detected in low-severity stages. In this work, a methodology based on convolutional neural networks (CNNs) for automatic detection of broken rotor bars by considering different severity levels is proposed. To exploit the capabilities of CNNs to carry out automatic image classification, the short-time Fourier transform-based time-frequency plane and the motor current signature analysis (MCSA) approach for current signals in the transient state are first used. In the experimentation, four IM conditions were considered: half-broken rotor bar, one broken rotor bar, two broken rotor bars, and a healthy rotor. The results demonstrate the effectiveness of the proposal, achieving 100% of accuracy in the diagnosis task for all the study cases.
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In this work, the degradation of sulfamethazine (SMT), sulfadiazine (SMD), and sulfamethoxazole (SMX) by using UV light, UV/H2O2, and UV/S2O8-2 was analyzed. Direct photolysis was studied by varying the lamp power and the solution pH. DFT calculations were carried out to corroborate the efficiency of the degradation as a function of the solution pH. The variation of the apparent rate constant, kap, was determined in the indirect photolysis by employing an experimental Box-Behnken-type response surface design. The results evidenced that SMX can be efficiently degraded by applying UV radiation independent of the operating conditions. Nevertheless, the quantum yields for SMT and SMD were close to zero, indicating a low energy efficiency for their photochemical transformation. The effect of the solution pH showed that the photodegradation of sulfonamides depends both on the amount of radiation absorbed as the electronic density. Calculations based on density functional theory and supported by the quantum theory of atoms in molecules allowed to describe fragmentation patterns in the systems under study, proving the lability of S14-C2, N17-C18, and N22-O22 bonds, for SMT, SMD, and SMX, respectively. From response surface methodology, four statistically reliable equations were obtained to determine the kap value as a function of the system operating conditions. Finally, SO4â¢- radicals proved to have a higher reactivity to degrade SMT and SMD compared with HO⢠radicals regardless of the operating conditions of the system.
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Sulfametoxazol , Contaminantes Químicos del Agua , Teoría Funcional de la Densidad , Peróxido de Hidrógeno , Cinética , Oxidación-Reducción , Fotólisis , Sulfadiazina , Sulfametazina , Rayos UltravioletaRESUMEN
Heart diseases are among the most common death causes in the population. Particularly, sudden cardiac death (SCD) is the cause of 10% of the deaths around the world. For this reason, it is necessary to develop new methodologies that can predict this event in the earliest possible stage. This work presents a novel methodology to predict when a person can develop an SCD episode before it occurs. It is based on the adroit combination of the empirical mode decomposition, nonlinear measurements, such as the Higuchi fractal and permutation entropy, and a neural network. The obtained results show that the proposed methodology is capable of detecting an SCD episode 25 min before it appears with a 94% accuracy. The main benefits of the proposal are: (1) an improved detection time of 25% compared with previously published works, (2) moderate computational complexity since only two features are used, and (3) it uses the raw ECG without any preprocessing stage, unlike recent previous works.
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Muerte Súbita Cardíaca/patología , Electrocardiografía/métodos , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Análisis de Varianza , Entropía , Femenino , Humanos , Masculino , Persona de Mediana Edad , Redes Neurales de la Computación , Adulto JovenRESUMEN
BACKGROUND: EEG signals obtained from Mild Cognitive Impairment (MCI) and the Alzheimer's disease (AD) patients are visually indistinguishable. NEW METHOD: A new methodology is presented for differential diagnosis of MCI and the AD through adroit integration of a new signal processing technique, the integrated multiple signal classification and empirical wavelet transform (MUSIC-EWT), different nonlinear features such as fractality dimension (FD) from the chaos theory, and a classification algorithm, the enhanced probabilistic neural network model of Ahmadlou and Adeli using the EEG signals. RESULTS: Three different FD measures are investigated: Box dimension (BD), Higuchi's FD (HFD), and Katz's FD (KFD) along with another measure of the self-similarities of the signals known as the Hurst exponent (HE). The accuracy of the proposed method was verified using the monitored EEG signals from 37 MCI and 37 AD patients. COMPARISON WITH EXISTING METHODS: The proposed method is compared with other methodologies presented in the literature recently. CONCLUSIONS: It was demonstrated that the proposed method, MUSIC-EWT algorithm combined with nonlinear features BD and HE, and the EPNN classifier can be employed for differential diagnosis of MCI and AD patients with an accuracy of 90.3%.
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Enfermedad de Alzheimer/diagnóstico , Disfunción Cognitiva/diagnóstico , Electroencefalografía , Procesamiento de Señales Asistido por Computador , Anciano , Algoritmos , Enfermedad de Alzheimer/fisiopatología , Disfunción Cognitiva/fisiopatología , Diagnóstico Diferencial , Femenino , Humanos , Masculino , Dinámicas no Lineales , Reconocimiento de Normas Patrones Automatizadas/métodos , Sensibilidad y EspecificidadRESUMEN
Sudden cardiac death (SCD) is one of the main causes of death among people. A new methodology is presented for predicting the SCD based on ECG signals employing the wavelet packet transform (WPT), a signal processing technique, homogeneity index (HI), a nonlinear measurement for time series signals, and the Enhanced Probabilistic Neural Network classification algorithm. The effectiveness and usefulness of the proposed method is evaluated using a database of measured ECG data acquired from 20 SCD and 18 normal patients. The proposed methodology presents the following significant advantages: (1) compared with previous works, the proposed methodology achieves a higher accuracy using a single nonlinear feature, HI, thus requiring low computational resource for predicting an SCD onset in real-time, unlike other methodologies proposed in the literature where a large number of nonlinear features are used to predict an SCD event; (2) it is capable of predicting the risk of developing an SCD event up to 20 min prior to the onset with a high accuracy of 95.8%, superseding the prior 12 min prediction time reported recently, and (3) it uses the ECG signal directly without the need for transforming the signal to a heart rate variability signal, thus saving time in the processing.
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Muerte Súbita Cardíaca , Electrocardiografía , Procesamiento de Señales Asistido por Computador , Análisis de Ondículas , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Arritmias Cardíacas , Humanos , Israel , Persona de Mediana Edad , Adulto JovenRESUMEN
Mild cognitive impairment (MCI) is a cognitive disorder characterized by memory impairment, greater than expected by age. A new methodology is presented to identify MCI patients during a working memory task using MEG signals. The methodology consists of four steps: In step 1, the complete ensemble empirical mode decomposition (CEEMD) is used to decompose the MEG signal into a set of adaptive sub-bands according to its contained frequency information. In step 2, a nonlinear dynamics measure based on permutation entropy (PE) analysis is employed to analyze the sub-bands and detect features to be used for MCI detection. In step 3, an analysis of variation (ANOVA) is used for feature selection. In step 4, the enhanced probabilistic neural network (EPNN) classifier is applied to the selected features to distinguish between MCI and healthy patients. The usefulness and effectiveness of the proposed methodology are validated using the sensed MEG data obtained experimentally from 18 MCI and 19 control patients.
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Disfunción Cognitiva/diagnóstico , Procesamiento Automatizado de Datos/métodos , Potenciales Evocados/fisiología , Magnetoencefalografía/métodos , Análisis de Varianza , Electroencefalografía , Entropía , Femenino , Humanos , Masculino , Pruebas Neuropsicológicas , Reproducibilidad de los Resultados , Análisis de OndículasRESUMEN
This paper presents a new EEMD-MUSIC- (ensemble empirical mode decomposition-multiple signal classification-) based methodology to identify modal frequencies in structures ranging from free and ambient vibration signals produced by artificial and natural excitations and also considering several factors as nonstationary effects, close modal frequencies, and noisy environments, which are common situations where several techniques reported in literature fail. The EEMD and MUSIC methods are used to decompose the vibration signal into a set of IMFs (intrinsic mode functions) and to identify the natural frequencies of a structure, respectively. The effectiveness of the proposed methodology has been validated and tested with synthetic signals and under real operating conditions. The experiments are focused on extracting the natural frequencies of a truss-type scaled structure and of a bridge used for both highway traffic and pedestrians. Results show the proposed methodology as a suitable solution for natural frequencies identification of structures from free and ambient vibration signals.
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Ruido , Procesamiento de Señales Asistido por Computador , Algoritmos , VibraciónRESUMEN
Objetivo. Describir los resultados clínicos de los primeros pacientes con Enfermedad de Parkinson avanzada y complicaciones motoras tratados por estimulación bilateral crónica del núcleo subtalámico en el Hospital Nacional Edgardo Rebagliati Martins. Material y Métodos. Se revisaron las historias de los pacientes registrados en la base de datos de la Unidad de Trastornos del Movimiento del Departamento de Neurología del Hospital Nacional Edgardo Rebagliati Martins. Se consideraron los datos demográficos, los puntajes de la parte III del Unified Parkinson´s Disease Rating Scale (UPDRS) antes y después de la estimulación, las dosis de levodopa usadas y los eventos adversos reportados. Resultados. Diez pacientes fueron identificados. La edad media fue 54,5 ± 10 años, con un tiempo de enfermedad de 13,2 ± 7,9 años. En la condición medicación off, la estimulación redujo en un 45,5% el puntaje en la parte III del UPDRS. La dosis media de levodopa fue reducida en una tercera parte. Dos pacientes tuvieron infecciones del dispositivo. Conclusiones. La estimulación bilateral del núcleo subtalámico produce mejoría importante en los síntomas motores de los pacientes con Enfermedad de Parkinson avanzada mientras ellos están sin los efectos de la medicación.
Objective. Describe the clinical outcomes of the first patients with advanced ParkinsonÆs disease and motor complications treated by chronic bilateral stimulation of the subthalamic nucleus at the Edgardo Rebagliati Martins Hospital. Material and Methods. The Movement Disorders Unit database and patient medical records were reviewed. Demographic data regarding the timing of the procedure, Unified ParkinsonÆs Disease Rating Scale (UPDRS) part III scores before and after stimulation, levodopa doses and adverse events were considered. Results.Ten patients were identified with a mean age of 54.5± 10years, and a mean duration of disease of 13,2 ± 7,9 years. Off medication mean UPDRS part III score was decreased by 45.54%. The mean dose of levodopa was reduced by one third. Two patients had infections of the device. Conclusion. Bilateral stimulation of the subthalamic nucleus in our country produces improvement in motor function of patients with advanced ParkinsonÆs disease as they are without the effects of medication.
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Humanos , Enfermedad de Parkinson/terapia , Estimulación Encefálica Profunda , Núcleo Subtalámico , Informes de CasosRESUMEN
Celiac disease (CD) is an autoimmune-mediated enteropathy triggered by dietary gluten in genetically prone individuals. The current treatment for CD is a strict lifelong gluten-free diet. However, in some CD patients following a strict gluten-free diet, the symptoms do not remit. These cases may be refractory CD or due to gluten contamination; however, the lack of response could be related to other dietary ingredients, such as maize, which is one of the most common alternatives to wheat used in the gluten-free diet. In some CD patients, as a rare event, peptides from maize prolamins could induce a celiac-like immune response by similar or alternative pathogenic mechanisms to those used by wheat gluten peptides. This is supported by several shared features between wheat and maize prolamins and by some experimental results. Given that gluten peptides induce an immune response of the intestinal mucosa both in vivo and in vitro, peptides from maize prolamins could also be tested to determine whether they also induce a cellular immune response. Hypothetically, maize prolamins could be harmful for a very limited subgroup of CD patients, especially those that are non-responsive, and if it is confirmed, they should follow, in addition to a gluten-free, a maize-free diet.