Inhibition of Aurora B by CCT137690 sensitizes colorectal cells to radiotherapy

Colorectal cancer is the third most commonly diagnosed cancer worldwide. Although surgery remains the best treatment for this disease, adjuvant chemotherapy and radiotherapy are also very important in clinical practice. However, the notorious refractory lack of responses to radiochemotherapy greatly limits the application of radiochemotherapy in the context of colorectal cancer. There is a growing interest in the role that Aurora B may play in colorectal cancer cell survival as well as other cancer subtypes. In the current study, we sought to ascertain whether blocking of Aurora B signaling machinery by a small molecule inhibitor, CCT137690, could synergize radiation-induced colorectal cancer cell death. Results showed that CCT137690 increases the sensitivity of SW620 cells to radiation. Mechanistic studies revealed that Aurora B-Survivin pathway may be involved in this synergistic effect. Taken together, our results for the first time show that Aurora B inhibition and radiation exert a synergistic effect, resulting in enhanced colorectal cancer cell death. This synergistic effect is clinically relevant as lower doses of radiation could be used for cancer treatment, and could provide significant clinical benefits in terms of colorectal cancer management, while reducing unwanted side-effects

CDBA: a novel multi-branch feature fusion model for EEG-based emotion recognition

EEG-based emotion recognition through artificial intelligence is one of the major areas of biomedical and machine learning, which plays a key role in understanding brain activity and developing decision-making systems. However, the traditional EEG-based emotion recognition is a single feature input mode, which cannot obtain multiple feature information, and cannot meet the requirements of intelligent and high real-time brain computer interface. And because the EEG signal is nonlinear, the traditional methods of time domain or frequency domain are not suitable. In this paper, a CNN-DSC-Bi-LSTM-Attention (CDBA) model based on EEG signals for automatic emotion recognition is presented, which contains three feature-extracted channels. The normalized EEG signals are used as an input, the feature of which is extracted by multi-branching and then concatenated, and each channel feature weight is assigned through the attention mechanism layer. Finally, Softmax was used to classify EEG signals. To evaluate the performance of the proposed CDBA model, experiments were performed on SEED and DREAMER datasets, separately. The validation experimental results show that the proposed CDBA model is effective in classifying EEG emotions. For triple-category (positive, neutral and negative) and four-category (happiness, sadness, fear and neutrality), the classification accuracies were respectively 99.44% and 99.99% on SEED datasets. For five classification (Valence 1—Valence 5) on DREAMER datasets, the accuracy is 84.49%. To further verify and evaluate the model accuracy and credibility, the multi-classification experiments based on ten-fold cross-validation were conducted, the elevation indexes of which are all higher than other models. The results show that the multi-branch feature fusion deep learning model based on attention mechanism has strong fitting and generalization ability and can solve nonlinear modeling problems, so it is an effective emotion recognition method. Therefore, it is helpful to the diagnosis and treatment of nervous system diseases, and it is expected to be applied to emotion-based brain computer interface systems

A Multi-Channel Feature Fusion CNN-Bi-LSTM Epilepsy EEG Classification and Prediction Model Based on Attention Mechanism

Epilepsy is the unstable state caused by excessive discharge of brain cells. In more than 30 percent of epilepsy cases, seizures cannot be controlled with medication or surgery. Refractory epilepsy seriously affects the health of patients and brings great economic burden to families. Therefore, this requires an effective seizure classification and prediction method to reduce risk in epilepsy patients. Researchers proposed machine learning or deep learning methods to predict seizures. However, automatic screening of electrode channels and improvement of predictive accuracy remain a challenge. A multi-channel feature fusion model CNN-Bi-LSTM. This method only requires simple preprocessing. CNN is responsible for extracting spatial features, Bi-LSTM is responsible for extracting temporal features, and finally, two channel weights are allocated through the attention mechanism to filter out the results of the more weighted electrode channel output classification. The performance of the model is tested on the CHB-MIT dataset, and the output is divided into three categories, including normal, pre-seizure and mid-seizure. The ten-fold cross-validation average accuracy is 94.83%, the precision is 94.84%, the recall is 94.84%, the F1-score is 94.83%, and the MCC is 92.26% across CHB-MIT EEG. The ten-fold cross-validation average accuracy of UCI data set is 77.62%, the precision is 77.66%, the recall is 77.62%, the F1-score is 77.60%, and the MCC is 72.03%. The results showed that this method is superior to existing methods and can predict the EEG signals of epilepsy in advance. This work will be extended to design a removable epilepsy predictive device for real-time use

DSCNN-LSTMs: A Lightweight and Efficient Model for Epilepsy Recognition

Epilepsy is the second most common disease of the nervous system. Because of its high disability rate and the long course of the disease, it is a worldwide medical problem and social public health problem. Therefore, the timely detection and treatment of epilepsy are very important. Currently, medical professionals use their own diagnostic experience to identify seizures by visual inspection of the electroencephalogram (EEG). Not only does it require a lot of time and effort, but the process is also very cumbersome. Machine learning-based methods have recently been proposed for epilepsy detection, which can help clinicians make rapid and correct diagnoses. However, these methods often require extracting the features of EEG signals before using the data. In addition, the selection of features often requires domain knowledge, and feature types also have a significant impact on the performance of the classifier. In this paper, a one-dimensional depthwise separable convolutional neural network and long short-term memory networks (1D DSCNN-LSTMs) model is proposed to identify epileptic seizures by autonomously extracting the features of raw EEG. On the UCI dataset, the performance of the proposed 1D DSCNN-LSTMs model is verified by cross-validation and time complexity comparison. Compared with other previous models, the experimental results show that the highest recognition rates of binary and quintuple classification are 99.57% and 81.30%, respectively. It can be concluded that the 1D DSCNN-LSTMs model proposed in this paper is an effective method to identify seizures based on EEG signals

Trickster Geographies in Shakespeare\u27s Comedy of Errors

Because Shakespeare uses a wide spectrum of tricksters—from the merely mischievous Autolycus in Winter’s Tale to the irredeemably wicked Iago in Othello—Comedy of Errors is often overlooked when evaluating Shakespeare’s use of trickery. Indeed, the main characters of this comedy are confused victims of circumstance rather than deliberate deceivers, but that is precisely what makes the idea of the trickery in Comedy of Errors so provocative. Rather than a an archetypal trickster, there are instead three trickster geographies, the Mediterranean, Ephesus, and the marketplace, that affect the people within their borders, compelling them to take part in the deceit, however unwittingly. What results from the diffuse and disembodied trickster geographies is a series of inversions that threaten to destroy identities, relationships, and social order

IL-11 expression was increased after IR.

<p>Mice were subjected to 90min of partial liver ischemia, followed by 0h, 1h, 3h and 6h reperfusion. Kinetics of IL-11 gene expression was analyzed in ischemic liver by RT-PCR. Expression of IL-11 was normalized with that of HPRT. Data are expressed as mean±SD (n = 6/group). *P<0.05, **P<0.001 vs sham group.</p

IL-11 activated STAT3 in hepatocytes in vivo and in vitro.

<p>(A) IL-11 was administered in mice for the indicated time. The lysates from liver were immunoblotted with anti-p-STAT3 and anti-STAT3 antibody. Representative data are shown (lower). Quantative analyses of p-STAT3 are shown (upper). Data are exprresed as mean±SD (n = 6/group). **P<0.001. (B) Hepatocytes were treated by IL-11 for the indicated time. The lysates from hepatocytes were immunoblotted with anti-p-STAT3 and anti-STAT3 antibody. Representative data are shown (lower). Quantative analyses of p-STAT3 are shown (upper). Data are expressed as mean±SD (n = 6/group), **P<0.001.</p

IL-11 treatment decreased hepatocellular apoptosis induced by IR.

<p>(A)Liver apoptosis was examined by TUNEL staining: Sham group, IR group and IL-11+IR group. (B) Apoptotic cells were quantified in six high-power fields (400x), and expressed as percentages of apoptotic cells among total cells. (C) Western blot-assisted detection of Bcl-2, Bax, Caspase-3 and β-actin. (D) Caspase-3 activity. Data are expressed as mean±SD (n = 6/group), **P<0.001.</p

IL-11 treatment attenuated liver injury induced by IR.

<p>Mice were administered with recombinant human IL-IL-11 (500μg/kg, ip) or medium (PBS) 1 hour prior to ischemia, followed by 6h reperfusion. (A) sALT. (B) Histopathalogic analysis of livers harvested 6 hours after reperfusion. Sham group: Normal hepatic architecture; IR group: severe hepatic lobule distortion, sinusoidal congestion, apparent edema, vacuolization and massive necrosis; IL-11+IR group: mild vacuolization, punctate necrosis and edeman. (C) The severity of liver IRI by Suzuki’s histological grading. Data are expressed as mean±SD (n = 6/group), **P<0.001.</p