Lasting enhancements in neural efficiency by multi-session transcranial direct current stimulation during working memory training.

The neural basis for long-term behavioral improvements resulting from multi-session transcranial direct current stimulation (tDCS) combined with working memory training (WMT) remains unclear. In this study, we used task-related electroencephalography (EEG) measures to investigate the lasting neurophysiological effects of anodal high-definition (HD)-tDCS applied over the left dorsolateral prefrontal cortex (dlPFC) during a challenging WMT. Thirty-four healthy young adults were randomized to sham or active tDCS groups and underwent ten 30-minute training sessions over ten consecutive days, preceded by a pre-test and followed by post-tests performed one day and three weeks after the last session, respectively, by performing high-load WM tasks along with EEG recording. Multi-session HD-tDCS significantly enhanced the behavioral benefits of WMT. Compared to the sham group, the active group showed facilitated increases in theta, alpha, beta, and gamma task-related oscillations at the end of training and significantly increased P300 response 3 weeks post-training. Our findings suggest that applying anodal tDCS over the left dlPFC during multi-session WMT can enhance the behavioral benefits of WMT and facilitate sustained improvements in WM-related neural efficiency.

Automated recognition of epilepsy from EEG signals using a combining space-time algorithm of CNN-LSTM.

Intelligent recognition methods for classifying non-stationary and non-invasive epileptic diagnoses are essential tools in neurological research. Electroencephalogram (EEG) signals exhibit better temporal characteristics in the detection of epilepsy compared to radiation medical images like computed tomography (CT) and magnetic resonance imaging (MRI), as they provide real-time insights into the disease' condition. While classical machine learning methods have been used for epilepsy EEG classification, they still often require manual parameter adjustments. Previous studies primarily focused on binary epilepsy recognition (epilepsy vs. healthy subjects) rather than as ternary status recognition (continuous epilepsy vs. intermittent epilepsy vs. healthy subjects). In this study, we propose a novel deep learning method that combines a convolution neural network (CNN) with a long short-term memory (LSTM) network for multi-class classification including both binary and ternary tasks, using a publicly available benchmark database on epilepsy EEGs. The hybrid CNN-LSTM automatically acquires knowledge without the need for extra pre-processing or manual intervention. Besides, the joint network method benefits from memory function and stronger feature extraction ability. Our proposed hybrid CNN-LSTM achieves state-of-the-art performance in ternary classification, outperforming classical machine learning and the latest deep learning models. For the three-class classification, in the method achieves an accuracy, specificity, sensitivity, and ROC of 98%, 97.4, 98.3% and 96.8%, respectively. In binary classification, the method achieves better results, with ACC of 100%, 100%, and 99.8%, respectively. Our dual stream spatiotemporal hybrid network demonstrates superior performance compared to other methods. Notably, it eliminates the need for manual operations, making it more efficient for doctors to diagnose during the clinical process and alleviating the workload of neurologists.

Cardiomyocytes induced from hiPSCs by well-defined compounds have therapeutic potential in heart failure by secreting PDGF-BB.

Recent studies have suggested that transplant of hiPS-CMs is a promising approach for treating heart failure. However, the optimally clinical benefits have been hampered by the immature nature of the hiPS-CMs, and the hiPS-CMs-secreted proteins contributing to the repair of cardiomyocytes remain largely unidentified. Here, we established a saponin+ compound optimally induced system to generate hiPS-CMs with stable functional attributes in vitro and transplanted in heart failure mice. Our study showed enhanced therapeutic effects of optimally induced hiPS-CMs by attenuating cardiac remodeling and dysfunction, these beneficial effects were concomitant with reduced cardiomyocytes death and increased angiogenesis. Moreover, the optimally induced hiPS-CMs could gathering to the injured heart and secret an abundant PDGF-BB. The reparative effect of the optimally induced hiPS-CMs in the hypoxia-injured HCMs was mimicked by PDGF-BB but inhibited by PDGF-BB neutralizing antibody, which was accompanied by the changed expression of p-PI3K and p-Akt proteins. It is highly possible that the PI3K/Akt pathway is regulated by the PDGF-BB secreted from the compound induced hiPS-CMs to achieve a longer lasting myocardial repair effect compared with the standard induced hiPS-CMs. Taken together, our data strongly implicate that the compound induced hiPS-CMs promote the recovery of injured hearts via paracrine action. In this process, the paracrine factor PDGF-BB derived from the compound induced hiPS-CMs reduces isoproterenol-induced adverse cardiac remodeling, which is associated with improved cardiac function, and these effects are mediated by the PI3K/Akt pathway, suggesting that the optimally induced hiPS-CMs may serve as a new promising cell therapy for clinical applications.

Imaging assisted evaluation of antitumor efficacy of a new histone deacetylase inhibitor in the castration-resistant prostate cancer.

PURPOSE: Castration-resistant prostate cancer (CRPC) is the most common cause of death in men. The effectiveness of HDAC inhibitors has been demonstrated by preclinical models, but not in clinical studies, probably due to the ineffectively accumulation of HDACI in prostate cancer cells. The purpose of this work was to evaluate effects of a novel HDACI (CN133) on CRPC xenograft model and 22Rv1 cells, and develops methods, PET/CT imaging, to detect the therapeutic effects of CN133 on this cancer. METHODS: We designed and performed study to compare the effects of CN133 with SAHA on the 22Rv1 xenograft model and 22Rv1 cells. Using PET/CT imaging with [11C] Martinostat and [18F] FDG, we imaged mice bearing 22Rv1 xenografts before and after 21-day treatment with placebo and CN133 (1 mg/kg), and uptake on pre-treatment and post-treatment imaging was measured. The anti-tumor mechanisms of CN133 were investigated by qPCR, western blot, and ChIP-qPCR. RESULTS: Our data showed that the CN133 treatment led to a 50% reduction of tumor volume compared to the placebo that was more efficacious than SAHA treatment in this preclinical model. [11C] Martinostat PET imaging could identify early lesions of prostate cancer and can also be used to monitor the therapeutic effect of CN133 in CRPC. Using pharmacological approaches, we demonstrated that effects of CN133 showed almost 100-fold efficacy than SAHA treatment in the experiment of cell proliferation, invasion, and migration. The anti-tumor mechanisms of CN133 were due to the inhibition of AR signaling pathway activity by decreased HDAC 2 and 3 protein expressions. CONCLUSION: Taken together, these studies provide not only a novel epigenetic approach for prostate cancer therapy but also offering a potential tool, [11C] Martinostat PET/CT imaging, to detect the early phase of prostate cancer and monitor therapeutic effect of CN133. These results will likely lead to human trials in the future.

Synthesis of 11C-labeled DNA polymerase-ß inhibitor 5-methoxyflavone and PET/CT imaging thereof.

INTRODUCTION: "Cell-cycle hypothesis" is emerging in recent years to suggest that aberrant cell cycle re-entry of differentiated neurons leads to a remarkable genetic disequilibrium which is likely to be the primary cause of neuronal apoptosis. DNA polymerase-ß is involved in neuronal DNA replication during cell cycle re-entry, thus constituting a promising target for Alzheimer's disease treatment. Recently, 5-methoxyflavone was identified as a candidate molecule endowed with good biological activity and selectivity on the DNA pol-ß in multiple in vitro AD models. In vivo assays, especially the brain uptake of 5-methoxyflavone, is need to be evaluated for further development for AD treatment. We report herein the synthesis of 11C-labeled 5-methoxyflavone, and the evaluation of in vivo properties of 5-[11C]methoxyflavone in rodents. METHODS: The strategy for synthesis of 5-[11C]methoxyflavone was developed by treating precursor 5-hydroxyflavone with [11C]CH3I and KOH in anhydrous DMF. 5-[11C]Methoxyflavone was purified, then evaluated in mice by using PET/CT imaging. RESULTS: The 5-[11C]methoxyflavone was synthesized conveniently in an average decay corrected yield of 22% (n = 3) with a radiochemical purity >99%. The average molar radioactivity of 5-[11C]methoxyflavone was 383 GBq/µmol. The average concentration was 0.107 µg/mL. PET/CT imaging in mice showed 5-[11C]methoxyflavone rapidly passed through the blood-brain barrier with 8.36 ±â€¯0.61%ID/g at 2 min post injection, and the radioactivity accumulation in brain was still noticeable with 2.48 ±â€¯0.59%ID/g at 28 min post injection. The clearance rate was 3.37 (brain2 min/brain28 min ratio). The blood and muscle uptakes were low. The lung displayed high initial uptake and subsequent rapid clearance, while the liver and kidney displayed a relatively slow clearance. Real-time imaging showed that 5-[11C]methoxyflavone accumulated immediately in the heart, then transferred to the liver and intestine, and was not observed in lower digestive tract. CONCLUSIONS: 5-[11C]Methoxyflavone was synthesized conveniently in one step. The results of PET/CT imaging in C57BL/6 mice suggested 5-[11C]methoxyflavone possesses appropriate pharmacokinetic properties and favorable brain uptake, thus being proved to be suitable for further development for AD treatment.

Detection Analysis of Epileptic EEG Using a Novel Random Forest Model Combined With Grid Search Optimization.

In the automatic detection of epileptic seizures, the monitoring of critically ill patients with time varying EEG signals is an essential procedure in intensive care units. There is an increasing interest in using EEG analysis to detect seizure, and in this study we aim to get a better understanding of how to visualize the information in the EEG time-frequency feature, and design and train a novel random forest algorithm for EEG decoding, especially for multiple-levels of illness. Here, we propose an automatic detection framework for epileptic seizure based on multiple time-frequency analysis approaches; it involves a novel random forest model combined with grid search optimization. The short-time Fourier transformation visualizes seizure features after normalization. The dimensionality of features is reduced through principal component analysis before feeding them into the classification model. The training parameters are optimized using grid search optimization to improve detection performance and diagnostic accuracy by in the recognition of three different levels epileptic of conditions (healthy subjects, seizure-free intervals, seizure activity). Our proposed model was used to classify 500 samples of raw EEG data, and multiple cross-validations were adopted to boost the modeling accuracy. Experimental results were evaluated by an accuracy, a confusion matrix, a receiver operating characteristic curve, and an area under the curve. The evaluations indicated that our model achieved the more effective classification than some previous typical methods. Such a scheme for computer-assisted clinical diagnosis of seizures has a potential guiding significance, which not only relieves the suffering of patient with epilepsy to improve quality of life, but also helps neurologists reduce their workload.

Automated Recognition of Epileptic EEG States Using a Combination of Symlet Wavelet Processing, Gradient Boosting Machine, and Grid Search Optimizer.

Automatic recognition methods for non-stationary electroencephalogram (EEG) data collected from EEG sensors play an essential role in neurological detection. The integrated approaches proposed in this study consist of Symlet wavelet processing, a gradient boosting machine, and a grid search optimizer for a three-class classification scheme for normal subjects, intermittent epilepsy, and continuous epilepsy. Fourth-order Symlet wavelets are adopted to decompose the EEG data into five frequencies sub-bands, such as gamma, beta, alpha, theta, and delta, whose statistical features were computed and used as classification features. The grid search optimizer is used to automatically find the optimal parameters for training the classifier. The classification accuracy of the gradient boosting machine was compared with that of a conventional support vector machine and a random forest classifier constructed according to previous descriptions. Multiple performance indices were used to evaluate the proposed classification scheme, which provided better classification accuracy and detection effectiveness than has been recently reported in other studies on three-class classification of EEG data.