Diabetic neuropathy is one of the most frequent peripheral neuropathies associated with hyperalgesia and hyperesthesia. Besides alteration in the levels of neurotransmitter, alteration in the neuronal nitric oxide synthase (nNOS) is a key factor in the pathogenesis of diabetic neuropathy. The present study was aimed at evaluating the role of PDE-5 inhibitor on nociception in streptozotocin-induced diabetes in animal models of nociception (writhing assay in mice and paw hyperalgesia test in rats). Diabetic animals showed a significant decrease in pain threshold as compared to non-diabetic animals in both tests, indicating diabetes induced hyperalgesia in mice and rats. The PDE-5 inhibitor, sildenafil, significantly increased the pain threshold in both diabetic and non-diabetic animals. However, L-NAME, a non-specific NOS inhibitor and methylene blue (MB), a guanylate cyclase inhibitor blocked the antinociceptive effect. The per se administration of L-NAME or MB augmented the hyperalgesic response in diabetic animals with little or no effect in non-diabetic animals, indicating the alteration of NO-cGMP pathway in diabetes. The results in the present study demonstrate that the decreased nNOS-cGMP system may play a crucial role in the pathogenesis of diabetic neuropathy.
Schizophrenia is a fatal mental disorder, which affects millions of people globally by the disturbance in their thinking, feeling and behaviour. In the age of the internet of things assisted with cloud computing and machine learning techniques, the computer-aided diagnosis of schizophrenia is essentially required to provide its patients with an opportunity to own a better quality of life. In this context, the present paper proposes a spectral features based convolutional neural network (CNN) model for accurate identification of schizophrenic patients using spectral analysis of multichannel EEG signals in real-time. This model processes acquired EEG signals with filtering, segmentation and conversion into frequency domain. Then, given frequency domain segments are divided into six distinct spectral bands like delta, theta-1, theta-2, alpha, beta and gamma. The spectral features including mean spectral amplitude, spectral power and Hjorth descriptors (Activity, Mobility and Complexity) are extracted from each band. These features are independently fed to the proposed spectral features-based CNN and long short-term memory network (LSTM) models for classification. This work also makes use of raw time-domain and frequency-domain EEG segments for classification using temporal CNN and spectral CNN models of same architectures respectively. The overall analysis of simulation results of all models exhibits that the proposed spectral features based CNN model is an efficient technique for accurate and prompt identification of schizophrenic patients among healthy individuals with average classification accuracies of 94.08% and 98.56% for two different datasets with optimally small classification time.
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