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Renal Drug-Metabolizing Enzymes in Experimental Animals and Humans
Published in Robin S. Goldstein, Mechanisms of Injury in Renal Disease and Toxicity, 2020
Human liver has been the initial source for characterizing the human soluble enzymes. It is possible to separate up to 15 forms of the alpha class using chromatofocusing and FPLC (Vander Jagt et al., 1985; Ostlund-Farrants et al., 1987). Human mu-class isoenzymes are present in liver, but they are of low abundance relative to the alpha family. Recently, using cDNA clones, a member of the mu class has been characterized (Taylor et al., 1991). The only pi-class enzyme so far found in humans shows very close homology with subunit 7 cDNA (Kano et al., 1987; Cowell et al., 1988). Recently a new family named theta has been isolated and characterized from human liver. It resembles the rat 5-5 family in that it has no activity with l-chloro-2,4-dinitrobenzene as a substrate, but relatively high activity with 1,2-epoxy-3(p-nitrophenoxy) propane (Table 3) and does not bind to GSH-or S-hexyl GSH affinity columns (Meyer et al., 1991).
Current and Emerging Clinical Applications of the auditory Steady-State Response
Published in Stavros Hatzopoulos, Andrea Ciorba, Mark Krumm, Advances in Audiology and Hearing Science, 2020
Adults with dyslexia tend to have lower 40 Hz ASSR amplitude values in comparison with normal control subjects. De Vos et al. (2017) compared ASSR findings for modulation rates of 4, 10, 20, and 40 Hz in normal reading and dyslexic adolescents. The objective was to measure theta and alpha activity, which reflects the processing of syllabic information and beta, and low-gamma oscillation that reveals information about phonemic rate processing. Immature hemispheric specialization for processing phoneme rate modulation is an underlying reading problem associated with dyslexia. Results of the study indicated that auditory neural synchronization of alpha and beta oscillation is atypical in dyslexia. This finding revealed deviant neural processing of both syllabic and phonemic rate information through bottom-up neural encoding as well as top-down inhibitory processes associated with selective attention. Moreover, significantly enhanced neural synchronization of beta oscillations suggesting that oversynchronization of beta range oscillation may be a compensatory mechanism to improve the processing of phonemic rate information. There was no evidence of deficits in theta or low-gamma synchronization. In addition, no significant hemispheric asymmetry of cortical responses was noted in subjects with dyslexia in comparison with a control group (De Vos et al., 2017).
Priors in R-INLA
Published in Virgilio Gómez-Rubio, Bayesian Inference with INLA, 2020
Parameter hyper is a named list so that each element in the list defines the prior for a different hyperparameter. The names used in the list can be the names of the parameters or those used for the internal representation. These can be checked in the documentation or using function inla.models(), as described in Section 2.3.2. The next example focuses on the iid latent effect, which only has the precision hyperparameter, and shows the name in the internal representation ("theta"), the name of the parameter ("log precision") and a short name ("prec"): ## [1] "theta" ## [1] "log precision" ## attr(,"inla.read.only") ## [1] FALSE ## [1] "prec" ## attr(,"inla.read.only") ## [1] FALSE
The Ability of Forensic Psychiatric Patients with Substance Use Disorder to Learn Neurofeedback
Published in International Journal of Forensic Mental Health, 2019
Sandra Fielenbach, Franc C.L. Donkers, Marinus Spreen, Stefan Bogaerts
Another possible explanation for the low number of responders might be the fact that patients were allowed to continue using prescription medication during the course of the study. It can be considered unethical to ask patients to stop taking medication for the sake of an intervention for which efficacy is not yet established. However, to date, the effects of medication on the trainability of EEG-frequency bands are unclear. It is possible that the effects of medication might ‘overrule’ training effects of neurofeedback. Previous research has shown that stimulant medication can produce a normalization of relative power in the theta band frequency in the resting-state EEG of patients with ADHD (Clarke, Barry, Bond, McCarthy, & Selikowitz, 2002). It is possible that patients with this type of medication might not be able to further normalize theta frequency through neurofeedback. Nonetheless, even if stimulant medication prevents patients from further decreasing their theta—frequency power, additional research is needed to investigate why more than half of the patients in the current study increased their theta frequency when the neurofeedback protocol was aimed at decreasing it.
Investigation of attention deficit hyperactivity disorder (ADHD) sub-types in children via EEG frequency domain analysis
Published in International Journal of Neuroscience, 2018
Ramazan Aldemir, Esra Demirci, Huseyin Per, Mehmet Canpolat, Sevgi Özmen, Mahmut Tokmakçı
Delta band values in FP2-F4, Cz-Pz, C4-P4 channels and alpha band values in FP1-F7, T3-P4 channels had statistically significant values when comparing the ADHD-I subgroup of ADHD and the control group in terms of mean power values, also alpha band in T3-T5, and C3-P3 channels were statistically significant values. There was no statistical significance found in the theta band. When the frequency values that had maximum amplitude of the band were taken into the account, a significant difference exists between the channels in the delta band in FP2-F4, C3-P3 channels. The theta band in FP1-F7, T6-O2 channels and the beta band in FP1-F3 channel had no significance value. The association of these values with EEG electrodes were indicated in Figure 2, and their statistical significance values were indicated in Table 3.
Electroencephalographic changes using virtual reality program: technical note
Published in Neurological Research, 2018
Síria Monyelle Silva de Oliveira, Candice Simões Pimenta de Medeiros, Thaiana Barbosa Ferreira Pacheco, Nathalia Priscilla Oliveira Silva Bessa, Fernanda Gabrielle Mendonça Silva, Nathália Stéphany Araújo Tavares, Isabelle Ananda Oliveira Rego, Tania Fernandes Campos, Fabrícia Azevedo da Costa Cavalcanti
Each electrical signal is registered synchronously in terms of frequency and is known as brain rhythms: delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), and gamma (Above 30 Hz) [2]. The delta rhythm is related to a coma state, deep sleep, newborns, and some brain dysfunction. Theta rhythm is generally observed in states of deep meditation, relaxation, and automated activities. Alpha rhythm is associated with mild states of alertness, meditation and alertness with closed eyes. The beta rhythm is observed in alert states, mental effort, decision-making and external information processing. Finally, gamma rhythm is associated with information processing, voluntary movements, and attention control [1,2].