Clinical trials of N-acetylcysteine in psychiatry and neurology: A systematic review

N-acetylcysteine (NAC) is recognized for its role in acetaminophen overdose and as a mucolytic. Over the past decade, there has been growing evidence for the use of NAC in treating psychiatric and neurological disorders, considering its role in attenuating pathophysiological processes associated with these disorders, including oxidative stress, apoptosis, mitochondrial dysfunction, neuroinflammation and glutamate and dopamine dysregulation. In this systematic review we find favorable evidence for the use of NAC in several psychiatric and neurological disorders, particularly autism, Alzheimer's disease, cocaine and cannabis addiction, bipolar disorder, depression, trichotillomania, nail biting, skin picking, obsessive-compulsive disorder, schizophrenia, drug-induced neuropathy and progressive myoclonic epilepsy. Disorders such as anxiety, attention deficit hyperactivity disorder and mild traumatic brain injury have preliminary evidence and require larger confirmatory studies while current evidence does not support the use of NAC in gambling, methamphetamine and nicotine addictions and amyotrophic lateral sclerosis. Overall, NAC treatment appears to be safe and tolerable. Further well designed, larger controlled trials are needed for specific psychiatric and neurological disorders where the evidence is favorable.

A clustering-based multi-layer distributed ensemble for neurological diagnostics in cloud services

This paper investigates the problem of minimizing data transfer between different data centers of the cloud during the neurological diagnostics of cardiac autonomic neuropathy (CAN). This problem has never been considered in the literature before. All classifiers considered for the diagnostics of CAN previously assume complete access to all data, which would lead to enormous burden of data transfer during training if such classifiers were deployed in the cloud. We introduce a new model of clustering-based multi-layer distributed ensembles (CBMLDE). It is designed to eliminate the need to transfer data between different data centers for training of the classifiers. We conducted experiments utilizing a dataset derived from an extensive DiScRi database. Our comprehensive tests have determined the best combinations of options for setting up CBMLDE classifiers. The results demonstrate that CBMLDE classifiers not only completely eliminate the need in patient data transfer, but also have significantly outperformed all base classifiers and simpler counterpart models in all cloud frameworks.

Tone entropy analysis of foetal heart rate variability

Development of the foetal autonomic nervous system can be indirectly understood by looking at the changes in beat to beat variability in foetal heart rates. This study presents Tone-Entropy (T-E) analysis of foetal heart rate variability (HRV) at multiple lags (1–8) to understand the influence of gestational ages (early and late) on the development of the foetal autonomic nervous system (ANS). The analysis was based on foetal electrocardiograms (FECGs) of 46 healthy foetuses of 20–32 weeks (early group) and 22 foetuses of 35–41 weeks (late group). Tone represents sympatho-vagal balance and entropy the total autonomic activities. Results show that tone increases and entropy decreases at all lags for the late foetus group. On the other hand, tone decreases and entropy increases at lags 1–4 in the early foetus group. Increasing tone in late foetuses might represent significant maturation of sympathetic nervous systems because foetuses approaching to delivery period need increased sympathetic activity. T-E could be quantitative clinical index to determine the early foetuses from late ones on the basis of maturation of autonomic nervous system.

Multi-lag HRV analysis discriminates disease progression of post-infarct people with no diabetes versus diabetes

Diabetes mellitus is associated with multi-organ system dysfunction including the cardiovascular and autonomic nervous system. Although it is well documented that post-infarct patients are at higher risk of sudden cardiac death, diabetes adds an additional risk associated with autonomic neuropathy. However it is not known how the presence of diabetes in post-infarct patients affects cardiac rhythm. The majority of HRV algorithms for determining cardiac inter-beat interval changes describe only beat-to-beat variation determined over the whole heart rate recording and therefore do not consider the ability of a heart beat to influence a train of succeeding beats nor whether or how the temporal dynamics of the inter-beat intervals changes. This study used Poincaré Plot derived features and incorporated increased lag intervals to compare post-infarct patients with no history of prior infarct with or without diabetes and found that for the nondiabetic post-infarct patients only increased lag of short term correlation (SD1) predicted mortality, whereas in the diabetic post-infarct group only long-term correlations (SD2) significantly predicted mortality at a follow-up period of eight years. Temporal dynamics measured as a complex correlation measure (CCM) was also a significant predictor of mortality only in the diabetic post-infarct cohort. This study highlights the different pathophysiological progression and risk profile associated with presence of diabetes in a post-infarct patient population at eight year follow-up.