Supported Diagnosis of Attention Deficit and Hyperactivity Disorder from EEG Based on Interpretable Kernels for Hidden Markov Models.

As a neurodevelopmental pathology, Attention Deficit Hyperactivity Disorder (ADHD) mainly arises during childhood. Persistent patterns of generalized inattention, impulsivity, or hyperactivity characterize ADHD that may persist into adulthood. The conventional diagnosis relies on clinical observational processes yielding high rates of overdiagnosis due to varying interpretations among specialists or missing information. Although several studies have designed objective behavioral features to overcome such an issue, they lack significance. Despite electroencephalography (EEG) analyses extracting alternative biomarkers using signal processing techniques, the nonlinearity and nonstationarity of EEG signals restrain performance and generalization of hand-crafted features. This work proposes a methodology to support ADHD diagnosis by characterizing EEG signals from hidden Markov models (HMM), classifying subjects based on similarity measures for probability functions, and spatially interpreting the results using graphic embeddings of stochastic dynamic models. The methodology learns a single HMM for EEG signal from each patient, so favoring the inter-subject variability. Then, the Probability Product Kernel, specifically developed for assessing the similarity between HMMs, fed a support vector machine that classifies subjects according to their stochastic dynamics. Lastly, the kernel variant of Principal Component Analysis provided a means to visualize the EEG transitions in a two-dimensional space, evidencing dynamic differences between ADHD and Healthy Control children. From the electrophysiological perspective, we recorded EEG under the Stop Signal Task modified with reward levels, which considers cognitive features of interest as insufficient motivational circuits recruitment. The methodology compares the supported diagnosis in two EEG channel setups (whole channel set and channels of interest in frontocentral area) and four frequency bands (Theta, Alpha, Beta rhythms, and a wideband). Results evidence an accuracy rate of 97.0% in the Beta band and in the channels where previous works found error-related negativity events. Such accuracy rate strongly supports the dual pathway hypothesis and motivational deficit concerning the pathophysiology of ADHD. It also demonstrates the utility of joining inhibitory and motivational paradigms with dynamic EEG analysis into a noninvasive and affordable diagnostic tool for ADHD patients.

Growth and viability of Streptococcus mutans in sucrose with different concentrations of Stevia rebaudiana Bertoni.

OBJECTIVE: To evaluate total absorbance, planktonic growth, biofilm formation, viability, metabolic activity, and pH of Streptococcus mutans UA159 cultures when different dilutions of Stevia rebaudiana Bertoni were applied and to determine the minimum inhibitory concentration (MIC) and the minimum biofilm inhibitory concentration (MBIC) of Stevia on S. mutans. MATERIALS AND METHODS: The effects of different dilutions of Stevia (0-400 mg/ml) on S. mutans total growth, planktonic growth, biofilm formation, viability, metabolic activity, and pH during a 72-h growth period were evaluated in this in vitro study. A stock solution was prepared by mixing 10 ml of tryptic soy broth (TSB) supplemented with 1% sucrose (TSBS) and 4 g of Stevia. RESULTS: S. mutans total growth and biofilm formation decreased with reduced concentrations of Stevia. Furthermore, the MIC was 25 mg/ml and the MBIC was 6.25 mg/ml. Complete eradication of S. mutans was not observed with any of the Stevia concentrations. Planktonic growth of S. mutans was not repressed by high concentrations of Stevia and most of the Stevia concentrations generated an increased pH. CONCLUSION: Because Stevia reduces biofilm and acid production, Stevia can be considered a non-cariogenic sweetener. CLINICAL RELEVANCE: This study confirms the anticariogenic effect of Stevia, like it has been previously reported, but more studies on the most effective concentration are needed, and in the present study, the minimum inhibitory concentration (MIC) and the minimum biofilm inhibitory concentration (MBIC) was determined in the presence of sucrose. Additionally, this is the first study to evaluate the effect of Stevia on S. mutans metabolic activity.