Deep learning-derived spatial organization features on histology images predicts prognosis in colorectal liver metastasis patients after hepatectomy.

Histopathological images of colorectal liver metastases (CRLM) contain rich morphometric information that may predict patients' outcomes. However, to our knowledge, no study has reported any practical deep learning framework based on the histology images of CRLM, and their direct association with prognosis remains largely unknown. In this study, we developed a deep learning-based framework for fully automated tissue classification and quantification of clinically relevant spatial organization features (SOFs) in H&E-stained images of CRLM. The SOFs based risk-scoring system demonstrated a strong and robust prognostic value that is independent of the current clinical risk score (CRS) system in independent clinical cohorts. Our framework enables fully automated tissue classification of H&E images of CRLM, which could significantly reduce assessment subjectivity and the workload of pathologists. The risk-scoring system provides a time- and cost-efficient tool to assist clinical decision-making for patients with CRLM, which could potentially be implemented in clinical practice.

A Channel-Projection Mixed-Scale Convolutional Neural Network for Motor Imagery EEG Decoding.

Motor imagery electroencephalography (EEG) decoding is an essential part of brain-computer interfaces (BCIs) which help motor-disabled patients to communicate with the outside world by external devices. Recently, deep learning algorithms using decomposed spectrums of EEG as inputs may omit important spatial dependencies and different temporal scale information, thus generated the poor decoding performance. In this paper, we propose an end-to-end EEG decoding framework, which employs raw multi-channel EEG as inputs, to boost decoding accuracy by the channel-projection mixed-scale convolutional neural network (CP-MixedNet) aided by amplitude-perturbation data augmentation. Specifically, the first block in CP-MixedNet is designed to learn primary spatial and temporal representations from EEG signals. The mixed-scale convolutional block is then used to capture mixed-scale temporal information, which effectively reduces the number of training parameters when expanding reception fields of the network. Finally, based on the features extracted in previous blocks, the classification block is constructed to classify EEG tasks. The experiments are implemented on two public EEG datasets (BCI competition IV 2a and High gamma dataset) to validate the effectiveness of the proposed approach compared to the state-of-the-art methods. The competitive results demonstrate that our proposed method is a promising solution to improve the decoding performance of motor imagery BCIs.

An Investigation Into Vortioxetine Salts: Crystal Structure, Thermal Stability, and Solubilization.

Three 1:1 salts containing vortioxetine (VOT), an orally antidepressant drug, and 3 aryl monoacids have been designed and successfully prepared by liquid-assisted grinding based on the ΔpKa rule. The C-O bond lengths (∼1.25 Å) in the COOH groups show that the proton transfer has occurred from aryl monoacid to piperazine N1 atom of vortioxetine in the crystal structures. Three salts feature cyclic [2 + 2] structural units through R(4)4 (12) N-H···O hydrogen bonding interactions which result in the remarkable thermal stabilities, and VOT-p-aminobenzoic acid shows 2-dimensional framework by linking cyclic [2 + 2] units through additional hydrogen bonding interactions. The equilibrium solubility of VOT in VOT-p-aminobenzoic acid salt can be largely improved up to 0.50 mg/mL (about 450% above the free base) at 25°C in water, which also accelerates the intrinsic dissolution rate.

[A meta-analysis on efficacy of intra-aortic balloon pump therapy in patients with acute myocardial infarction].

OBJECTIVE: To analyze the efficacy and safety of intra-aortic balloon pump (IABP) therapy in patients with acute myocardial infarction(AMI) based on the meta-analysis. METHODS: Eligible published randomized controlled clinical research (RCT) were retrieved in the Pubmed, EMBase, Cochrane, China biological medical literature, Wanfang, VIP and CNKI database from 1980 to April 2, 2012. The analysis was performed with the software of RevMan 5.1. RESULTS: Thirteen RCTs with 1958 patients (AMI with IABP therapy, n = 970,without IABP therapy, n = 988) were included. The 30-day mortality between the two groups was similar (RR = 0.77, 95%CI 0.58-1.03, P = 0.08), but the 30-day mortality in the cardiac shock subgroup was significantly lower in IABP group than in without IABP group (RR = 0.65, 95%CI 0.44-0.97, P = 0.04). The 6-month mortality was significantly lower in IABP group than in without IABP group (RR = 0.72, 95%CI 0.55-0.94, P = 0.02). The incidence of major bleeding was significantly higher in IABP group than in without IABP group (RR = 1.43, 95%CI 1.16-1.75, P < 0.01). CONCLUSION: IABP therapy is effective to reduce earlier mortality post AMI, particularly for patients with cardiac shock.