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Type 2 diabetes (T2D) affects a large part of the adult population and impairs its quality of life. Because of this, natural compounds with antioxidant, anti-inflammatory and hypoglycemic properties have been used as adjuvants. Among these compounds, resveratrol (RV) stands out, a polyphenol that has been studied in several clinical trials, the results of which are controversial. We conducted a randomized clinical trial on 97 older adults with T2D to evaluate the effect of RV on oxidative stress markers and sirtuin 1, using doses of 1000 mg/day (EG1000, n = 37) and 500 mg/day (EG500, n = 32) compared with a placebo (PG, n = 28). Biochemical markers, oxidative stress and sirtuin 1 levels were measured at baseline and after six months. We observed a statistically significant increase (p < 0.05) in total antioxidant capacity, antioxidant gap, the percentage of subjects without oxidant stress and sirtuin 1 levels in EG1000. In the PG, we observed a significant increase (p < 0.05) in lipoperoxides, isoprostanes and C-reactive protein levels. An increase in the oxidative stress score and in the percentage of subjects with mild and moderate oxidative stress was observed too. Our findings suggest that 1000 mg/day of RV exerts a more efficient antioxidant effect than 500 mg/day. [ABSTRACT FROM AUTHOR]

The identification of the emotional states corresponding to the four quadrants of the valence/arousal space has been widely analyzed in the scientific literature by means of multiple techniques. Nevertheless, most of these methods were based on the assessment of each brain region separately, without considering the possible interactions among different areas. In order to study these interconnections, this study computes for the first time the functional connectivity metric called cross-sample entropy for the analysis of the brain synchronization in four groups of emotions from electroencephalographic signals. Outcomes reported a strong synchronization in the interconnections among central, parietal and occipital areas, while the interactions between left frontal and temporal structures with the rest of brain regions presented the lowest coordination. These differences were statistically significant for the four groups of emotions. All emotions were simultaneously classified with a 95.43% of accuracy, overcoming the results reported in previous studies. Moreover, the differences between high and low levels of valence and arousal, taking into account the state of the counterpart dimension, also provided notable findings about the degree of synchronization in the brain within different emotional conditions and the possible implications of these outcomes from a psychophysiological point of view. [ABSTRACT FROM AUTHOR]

Background: Several clinical trials have suggested that resveratrol has hypoglycemic properties; however, there are other studies in which such an effect has not been observed. Methods: We carried out a systematic search in several databases; seventeen studies were selected for the systematic review and fifteen were included in the meta-analysis. Results: Resveratrol decreases glucose levels in subjects aged 45–59 years at doses <250 mg/day (−8.64 mg/dL, p < 0.00001), 250–500 mg/day (−22.24 mg/dL, p = 0.0003), and 500–1000 mg/day (−28.40 mg/dL, p = 0.0008), while in subjects older than 60 years, it only decreases with doses of 250–500 mg/day. Likewise, HbA1c improved in subjects aged 45–59 years with doses of 250–500 mg (−0.60%, p < 0.00001), but not in subjects older than 60 years. Insulin levels improved in subjects aged 45–59 years with doses < 250 mg/day (−0.80 mIU/L, p = 0.0003) and doses of 250–500 mg/day (−5.0 mIU/L, p = 0.0003), although in subjects older than 60 years, they only improved with doses of 250–500 mg/day (−1.79 mIU/L, p = 0.01). On the other hand, HOMA-IR only improved in subjects older than 60 years with doses of 250–500 mg/day (−0.40, p = 0.01). Conclusions: Resveratrol has a statistically significant dose–response effect on glucose concentrations, HbA1c, and insulin levels; however, there is not enough scientific evidence to propose a therapeutic dose. [ABSTRACT FROM AUTHOR]

Traditionally, the brain has been studied as an ensemble of independent structures with determined functions. However, it has been demonstrated that the brain operates as a network in which all regions are interconnected. Apart from physical links, the brain presents functional associations between non-physically connected regions that work synchronized in a common mental process. For this reason, the study of functional connectivity is essential to reveal new insights about brain's behavior. In this work, a nonlinear functional connectivity metric called cross-sample entropy is applied for the first time to emotions recognition. Concretely, it has been computed for the detection of distress because of being one of the most influencing emotions in developed societies with several negative implications for health. Results reveal a strong coordinated activity between channels in central, parietal and occipital areas in each brain hemisphere separately, and also in the inter-hemispheric interactions among the same regions. Moreover, an augmented amount of similar dynamics under distress conditions in all brain regions with respect to a calm state reveals an increase in self-coordination of brain activity in distressful situations. [ABSTRACT FROM AUTHOR]

Schizophrenia is a chronic psychiatric disorder that is highly debilitating. One of the most frequent symptoms is the presence of auditory hallucinations (AH), which could be related to alterations in brain electrical activity measurable with electroencephalography (EEG). Although many previous works have recorded EEG signals of schizophrenia patients with medical EEG devices, the study of AH has never been developed by means of portable EEG measuring instruments. Therefore, the aim of this study is to detect AH in schizophrenia patients with a wireless EEG device. For that purpose, the spectral power from EEG recordings of periods with and without AH has been evaluated in a group of nine schizophrenia patients. Results reported that the main activation during hallucinations was found in right frontal locations, whereas the left hemisphere presented a stronger activation in hallucination-free periods. Furthermore, a generalized decrease of spectral power in hallucination with respect to hallucination-free episodes has been observed. Hence, this work demonstrates the possibility of detecting AH episodes with a wearable EEG device. In addition, the results obtained were compatible with the default model network, reporting a greater activation during no hallucination periods compared to hallucination moments. • Auditory verbal hallucinations in schizophrenia can be detected with wearable EEGs. • Activation during hallucinations is higher in right frontal locations of the brain. • During no hallucinations, the left brain hemisphere is more active in SZ patients. • Hallucination episodes were related to a generalized decrease of spectral power. [ABSTRACT FROM AUTHOR]

Negative stress, also named distress, is nowadays one of the most studied emotional states due to its high impact on advanced societies. Its automatic identification from physiological recordings can be extremely useful to prevent concomitant physical health problems as well as other mental disorders. However, this task is still an unresolved challenge. Recently, quadratic sample entropy (QSE) applied to the electroencephalogram signal has proven to be the most promising single metric to discern between emotional states of calm and negative stress. This index estimates repetitive patterns in a time series without considering time data order within each one, thus ignoring some relevant dynamics. Hence, in this work conditional entropy (CEn) and its correction (CCEn) are studied to complement and improve QSE ability in detection of distress. Both CEn and CCEn symbolize original time series to consider ordinal patterns and, thus, quantify accurately data predictability. Results obtained from 279 samples (146 associated with calm and 133 to distress) provide a similar discriminant power, about 65%, both for conditional entropies and QSE. More interestingly, these metrics also reveal the presence of complementary brain dynamics under a emotional state of distress. Thus, CCEn and QSE suggest a synchronization between opposite frontal and parietal brain regions from both hemispheres, such that when the level of negative stress increases, a more irregular activity is found in left frontal and right parietal areas. Simultaneously, more predictable activity is noticed in right frontal and left parietal regions. These findings agree with previous neurophysiological studies and allow to improve the identification of distress. Precisely, a very simple discriminant model based on CCEn and QSE is able to discern more than 80% of samples, thus overcoming slightly the results reported by previous works, where dozens or hundreds of variables have to be combined with advanced classifiers. [ABSTRACT FROM AUTHOR]

Automatic identification of negative stress is an unresolved challenge that has received great attention in the last few years. Many studies have analyzed electroencephalographic (EEG) recordings to gain new insights about how the brain reacts to both short- and long-term stressful stimuli. Although most of them have only considered linear methods, the heterogeneity and complexity of the brain has recently motivated an increasing use of nonlinear metrics. Nonetheless, brain dynamics reflected in EEG recordings often exhibit a multiscale nature and no study dealing with this aspect has been developed yet. Hence, in this work two nonlinear indices for quantifying regularity and predictability of time series from several time scales are studied for the first time to discern between visually elicited emotional states of calmness and negative stress. The obtained results have revealed the maximum discriminant ability of 86.35% for the second time scale, thus suggesting that brain dynamics triggered by negative stress can be more clearly assessed after removal of some fast temporal oscillations. Moreover, both metrics have also been able to report complementary information for some brain areas. [ABSTRACT FROM AUTHOR]

The electroencephalogram (EEG) is the most common tool used to study mental disorders. In the last years, the use of this recording for recognition of negative stress has been receiving growing attention. However, precise identification of this emotional state is still an interesting unsolved challenge. Nowadays, stress presents a high prevalence in developed countries and, moreover, its chronic condition often leads to concomitant physical and mental health problems. Recently, a measure of time series irregularity, such as quadratic sample entropy (QSEn), has been suggested as a promising single index for discerning between emotions of calm and stress. Unfortunately, this index only considers repetitiveness of similar patterns and, hence, it is unable to quantify successfully dynamics associated with the data temporal structure. With the aim of extending QSEn ability for identification of stress from the EEG signal, permutation entropy (PEn) and its modification to be amplitude-aware (AAPEn) have been analyzed in the present work. These metrics assess repetitiveness of ordinal patterns, thus considering causal information within each one of them and obtaining improved estimates of predictability. Results have shown that PEn and AAPEn present a discriminant power between emotional states of calm and stress similar to QSEn, i.e., around 65%. Additionally, they have also revealed complementary dynamics to those quantified by QSEn, thus suggesting a synchronized behavior between frontal and parietal counterparts from both hemispheres of the brain. More precisely, increased stress levels have resulted in activation of the left frontal and right parietal regions and, simultaneously, in relaxing of the right frontal and left parietal areas. Taking advantage of this brain behavior, a discriminant model only based on AAPEn and QSEn computed from the EEG channels P3 and P4 has reached a diagnostic accuracy greater than 80%, which improves slightly the current state of the art. Moreover, because this classification system is notably easier than others previously proposed, it could be used for continuous monitoring of negative stress, as well as for its regulation towards more positive moods in controlled environments. [ABSTRACT FROM AUTHOR]

Recognition of emotions is still an unresolved challenge, which could be helpful to improve current human-machine interfaces. Recently, nonlinear analysis of some physiological signals has shown to play a more relevant role in this context than their traditional linear exploration. Thus, the present work introduces for the first time the application of three recent entropy-based metrics: sample entropy (SE), quadratic SE (QSE) and distribution entropy (DE) to discern between emotional states of calm and negative stress (also called distress). In the last few years, distress has received growing attention because it is a common negative factor in the modern lifestyle of people from developed countries and, moreover, it may lead to serious mental and physical health problems. Precisely, 279 segments of 32-channel electroencephalographic (EEG) recordings from 32 subjects elicited to be calm or negatively stressed have been analyzed. Results provide that QSE is the first single metric presented to date with the ability to identify negative stress. Indeed, this metric has reported a discriminant ability of around 70%, which is only slightly lower than the one obtained by some previous works. Nonetheless, discriminant models from dozens or even hundreds of features have been previously obtained by using advanced classifiers to yield diagnostic accuracies about 80%. Moreover, in agreement with previous neuroanatomy findings, QSE has also revealed notable differences for all the brain regions in the neural activation triggered by the two considered emotions. Consequently, given these results, as well as easy interpretation of QSE, this work opens a new standpoint in the detection of emotional distress, which may gain new insights about the brain's behavior under this negative emotion. [ABSTRACT FROM AUTHOR]

• Dispersion entropy is able to detect calm and negative stress from brain signals. • Right frontal and left parietal are the most relevant areas for distress detection. • Dispersion patterns are more suitable for distress than for calm recognition. • Combining symbolic and regularity entropies overcame the results in previous works. Negative stress, or distress, represents a serious problem in advanced societies given its adverse consequences for health. Many studies have focused on the detection of distress from physiological signals such as the electroencephalogram (EEG). To this respect, the combination of regularity-based quadratic sample entropy (QSampEn) and symbolic amplitude-aware permutation entropy (AAPE) has reported valuable outcomes in distress recognition. In the present work, the recently introduced symbolic metric called dispersion entropy (DispEn) is applied for the first time to the same problem. Statistically significant results reported by the single metric have demonstrated its capability for calm and distress detection. Furthermore, relevant differences have been found between the combination of QSampEn with either AAPE or DispEn, finding that the assessment of ordinal and dispersion patterns leads to distinct and complementary outcomes. Finally, the combination of the three entropy metrics has considerably overcome the results ever reported by other indices in similar studies. [ABSTRACT FROM AUTHOR]

In recent years, electroencephalographic (EEG) signals have been intensively used in the area of emotion recognition, partcularly in distress identification due to its negative impact on physical and mental health. Traditionally, brain activity has been studied from a frequency perspective by computing the power spectral density of the EEG recordings and extracting features from different frequency sub-bands. However, these features are often individually extracted from single EEG channels, such that each brain region is separately evaluated, even when it has been corroborated that mental processes are based on the coordination of different brain areas working simultaneously. To take advantage of the brain's behaviour as a synchronized network, in the present work, 2-D and 3-D spectral images constructed from common 32 channel EEG signals are evaluated for the first time to discern between emotional states of calm and distress using a well-known deep-learning algorithm, such as AlexNet. The obtained results revealed a significant improvement in the classification performance regarding previous works, reaching an accuracy about 84%. Moreover, no significant differences between the results provided by the diverse approaches considered to reconstruct 2-D and 3-D spectral maps from the original location of the EEG channels over the scalp were noticed, thus suggesting that these kinds of images preserve original spatial brain information. [ABSTRACT FROM AUTHOR]

The assessment of physiological signals such as the electroencephalography (EEG) has become a key point in the research area of emotion detection. This study compares the performance of two EEG devices, a low-cost brain–computer interface (BCI) (Emotiv EPOC+) and a high-end EEG (BrainVision), for the detection of four emotional conditions over 20 participants. For that purpose, signals were acquired with both devices under the same experimental procedure, and a comparison was made under three different scenarios, according to the number of channels selected and the sampling frequency of the signals analyzed. A total of 16 statistical, spectral and entropy features were extracted from the EEG recordings. A statistical analysis revealed a major number of statistically significant features for the high-end EEG than the BCI device under the three comparative scenarios. In addition, different machine learning algorithms were used for evaluating the classification performance of the features extracted from high-end EEG and low-cost BCI in each scenario. Artificial neural networks reported the best performance for both devices with an F 1 -score of 75.08% for BCI and 98.78% for EEG. Although the professional EEG outcomes were higher than the low-cost BCI ones, both devices demonstrated a notable performance for the classification of the four emotional conditions. [ABSTRACT FROM AUTHOR]

Background Alpha lipoic acid (ALA) is a compound with antioxidant and anti-inflammatory properties. Recent studies have suggested that taking ALA supplements could be an effective way of treating diabetes and its complications, although its use in diabetic older adults is still controversial. Aim To determine the effect of intake of ALA on oxidative stress and chronic inflammation markers in diabetic older adults. Design A randomized, double-blind controlled trial was carried out. We evaluated 108 older adults diabetics without hepatic or renal damage. They were divided into 3 groups: i) treatment group (TG), n= 42 subjects, intake 600 mg/day of ALA, ii) placebo group (PG), n= 38 subjects, intake 600 mg/day of placebo and iii) control group (CG), n= 28 subjects, without intake of ALA or placebo. We measured before and after of six months of treatment, the plasmatic levels of isoprostane by ELISA, SOD and GPx by spectrophotometry, and TNF-α, IL-1β, IL-6 by flow cytometry. Differences within and between groups were compared by repeated measures ANOVA test. Outcomes: After 6 months the serum isoprostane levels the TG and PG had a decreased statistically significance in comparison to CG, however it was not observed significantly differences between both (TG, basal 100±5 vs. post 57±3; PG, basal 106±7 vs. post 77±5; CG, basal 94±9 vs. six months 107±10 pg/mL, p<0,05). Likewise the GPx activity was increased in TG and PG groups regard to CG, but it was not observed significantly differences between both (TG, basal 8409±507 vs. post 9694±458; PG, basal 8273±570 vs. post 8691±377; CG, basal 9531±749 vs. six months 6223±630 UI/L, p<0,05). Similar results were observed in the levels of TNF-α, IL6 and IL1β. Conclusion Our findings suggest that the intake of 600mg/day of ALA has an effect antioxidant and anti-inflammatory similar to placebo. [ABSTRACT FROM AUTHOR]

Background: Personal convictions in referral to pain cause misbeliefs in health professionals, which can influence patients who suffer from non-specific chronic low back pain. Likewise, health professionals' beliefs affect their advice and attitudes towards patients' treatment, becoming a possible cause of greater disability. The development of educational interventions based on the best scientific evidence in neurophysiology of pain could be a way to provide information and advice to primary care health professionals to change their cognition towards chronic non-specific low back pain. The use of Information and Communication Technologies allows the development of web sites, which might be one of the effective resources to modify misbeliefs and attitudes, in relation to the origin and meaning of non-specific chronic low back pain, of primary care professionals and that may modify their attitudes in patients' treatment. Methods: The aim of this project is to identify misbeliefs and attitudes of primary care physicians and nurses about chronic non-specific low back pain to develop a web-based educational tool using different educational formats and gamification techniques. This study has a mixed-method sequential exploratory design. The participants are medical and nursing staff working in primary care centers in the city of Lleida, Spain. For the qualitative phase of this study, the authors will use personal semi-structured interviews. For the quantitative phase the authors will use an experimental study design. Subjects will be randomly allocated using a simple random sample technique. The intervention group will have access to the web site where they will find information related to non-specific chronic low back pain, based on the information obtained in the qualitative phase. The control group will have access to a video explaining the clinical practice guidelines on low back pain. Discussion: This study has been designed to explore and modify the beliefs and attitudes about chronic low back pain of physicians and nurses working in primary care settings, using a web-based educational tool with different educational formats and gamification techniques. The aim of the educational intervention is to change their knowledge about the origin and meaning of pain, with the result of reducing their misbeliefs and attitudes of fear avoidance. Trial registration: ClinicalTrials.gov Identifier: NCT02962817. Date of registration: 11/09/2016. [ABSTRACT FROM AUTHOR]