Saffron aqueous extract prevents metabolic syndrome in patients with schizophrenia on olanzapine treatment: a randomized triple blind placebo controlled study.

OBJECTIVE: The aim of this study was to assess whether saffron aqueous extract (SAE) or its active constituent, crocin, prevents olanzapine-induced metabolic syndrome (MetS) and insulin resistance in patients with schizophrenia. METHODS: 66 patients diagnosed with schizophrenia who were on olanzapine treatment (5-20 mg daily) were randomly allocated to receive a capsule of SAE (n=22; 30 mg daily), crocin (n=22; 30 mg daily) or placebo (n=22) in a 12-week triple-blind trial. Patients were screened not to have MetS at baseline and further assessment was done at weeks 6 and 12. Measurement of fasting blood glucose (FBS) and serum lipids were repeated at weeks 2, 6 and 12. Fasting blood levels of insulin and HbA1c were also measured at baseline and week 12. HOMA-IR and HOMA-ß were determined to evaluate insulin resistance. RESULTS: 61 patients completed the trial and no serious adverse effects were reported. Time-treatment interaction showed a significant difference in FBS in both SAE and crocin groups compared to placebo (p=0.004). In addition, SAE could effectively prevent reaching the criteria of metabolic syndrome (0 patients) compared to crocin (9.1%) and placebo (27.3%) as early as week 6. CONCLUSION: SAE could prevent metabolic syndrome compared to crocin and placebo. Furthermore, both SAE and crocin prevented increases in blood glucose during the study.

Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking Neural Networks.

Depression severity can be classified into distinct phases based on the Beck depression inventory (BDI) test scores, a subjective questionnaire. However, quantitative assessment of depression may be attained through the examination and categorization of electroencephalography (EEG) signals. Spiking neural networks (SNNs), as the third generation of neural networks, incorporate biologically realistic algorithms, making them ideal for mimicking internal brain activities while processing EEG signals. This study introduces a novel framework that for the first time, combines an SNN architecture and a long short-term memory (LSTM) structure to model the brain's underlying structures during different stages of depression and effectively classify individual depression levels using raw EEG signals. By employing a brain-inspired SNN model, our research provides fresh perspectives and advances knowledge of the neurological mechanisms underlying different levels of depression. The methodology employed in this study includes the utilization of the synaptic time dependent plasticity (STDP) learning rule within a 3-dimensional brain-template structured SNN model. Furthermore, it encompasses the tasks of classifying and predicting individual outcomes, visually representing the structural alterations in the brain linked to the anticipated outcomes, and offering interpretations of the findings. Notably, our method achieves exceptional accuracy in classification, with average rates of 98% and 96% for eyes-closed and eyes-open states, respectively. These results significantly outperform state-of-the-art deep learning methods.

Continuous Scoring of Depression From EEG Signals via a Hybrid of Convolutional Neural Networks.

Depression score is traditionally determined by taking the Beck depression inventory (BDI) test, which is a qualitative questionnaire. Quantitative scoring of depression has also been achieved by analyzing and classifying pre-recorded electroencephalography (EEG) signals. Here, we go one step further and apply raw EEG signals to a proposed hybrid convolutional and temporal-convolutional neural network (CNN-TCN) to continuously estimate the BDI score. In this research, the EEG signals of 119 individuals are captured by 64 scalp electrodes through successive eyes-closed and eyes-open intervals. Moreover, all the subjects take the BDI test and their scores are determined. The proposed CNN-TCN provides mean squared error (MSE) of 5.64±1.6 and mean absolute error (MAE) of 1.73±0.27 for eyes-open state and also provides MSE of 9.53±2.94 and MAE of 2.32±0.35 for the eyes-closed state, which significantly surpasses state-of-the-art deep network methods. In another approach, conventional EEG features are elicited from the EEG signals in successive frames and apply them to the proposed CNN-TCN in conjunction with known statistical regression methods. Our method provides MSE of 10.81±5.14 and MAE of 2.41±0.59 that statistically outperform the statistical regression methods. Moreover, the results with raw EEG are significantly better than those with EEG features.