Cross-domain prediction approach of human lower limb voluntary movement intention for exoskeleton robot based on EEG signals.

Background and Objective: Exoskeleton robot control should ideally be based on human voluntary movement intention. The readiness potential (RP) component of the motion-related cortical potential is observed before movement in the electroencephalogram and can be used for intention prediction. However, its single-trial features are weak and highly variable, and existing methods cannot achieve high cross-temporal and cross-subject accuracies in practical online applications. Therefore, this work aimed to combine a deep convolutional neural network (CNN) framework with a transfer learning (TL) strategy to predict the lower limb voluntary movement intention, thereby improving the accuracy while enhancing the model generalization capability; this would also provide sufficient processing time for the response of the exoskeleton robotic system and help realize robot control based on the intention of the human body. Methods: The signal characteristics of the RP for lower limb movement were analyzed, and a parameter TL strategy based on CNN was proposed to predict the intention of voluntary lower limb movements. We recruited 10 subjects for offline and online experiments. Multivariate empirical-mode decomposition was used to remove the artifacts, and the moment of onset of voluntary movement was labeled using lower limb electromyography signals during network training. Results: The RP features can be observed from multiple data overlays before the onset of voluntary lower limb movements, and these features have long latency periods. The offline experimental results showed that the average movement intention prediction accuracy was 95.23% ± 1.25% for the right leg and 91.21% ± 1.48% for the left leg, which showed good cross-temporal and cross-subject generalization while greatly reducing the training time. Online movement intention prediction can predict results about 483.9 ± 11.9 ms before movement onset with an average accuracy of 82.75%. Conclusion: The proposed method has a higher prediction accuracy with a lower training time, has good generalization performance for cross-temporal and cross-subject aspects, and is well-prioritized in terms of the temporal responses; these features are expected to lay the foundation for further investigations on exoskeleton robot control.

A novel brain-controlled prosthetic hand method integrating AR-SSVEP augmentation, asynchronous control, and machine vision assistance.

Background and objective: The brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP) is expected to help disabled patients achieve alternative prosthetic hand assistance. However, the existing study still has some shortcomings in interaction aspects such as stimulus paradigm and control logic. The purpose of this study is to innovate the visual stimulus paradigm and asynchronous decoding/control strategy by integrating augmented reality technology, and propose an asynchronous pattern recognition algorithm, thereby improving the interaction logic and practical application capabilities of the prosthetic hand with the BCI system. Methods: An asynchronous visual stimulus paradigm based on an augmented reality (AR) interface was proposed in this paper, in which there were 8 control modes, including Grasp, Put down, Pinch, Point, Fist, Palm push, Hold pen, and Initial. According to the attentional orienting characteristics of the paradigm, a novel asynchronous pattern recognition algorithm that combines center extended canonical correlation analysis and support vector machine (Center-ECCA-SVM) was proposed. Then, this study proposed an intelligent BCI system switch based on a deep learning object detection algorithm (YOLOv4) to improve the level of user interaction. Finally, two experiments were designed to test the performance of the brain-controlled prosthetic hand system and its practical performance in real scenarios. Results: Under the AR paradigm of this study, compared with the liquid crystal display (LCD) paradigm, the average SSVEP spectrum amplitude of multiple subjects increased by 17.41%, and the signal-noise ratio (SNR) increased by 3.52%. The average stimulus pattern recognition accuracy was 96.71 ± 3.91%, which was 2.62% higher than the LCD paradigm. Under the data analysis time of 2s, the Center-ECCA-SVM classifier obtained 94.66 ± 3.87% and 97.40 ± 2.78% asynchronous pattern recognition accuracy under the Normal metric and the Tolerant metric, respectively. And the YOLOv4-tiny model achieves a speed of 25.29fps and a 96.4% confidence in the prosthetic hand in real-time detection. Finally, the brain-controlled prosthetic hand helped the subjects to complete 4 kinds of daily life tasks in the real scene, and the time-consuming were all within an acceptable range, which verified the effectiveness and practicability of the system. Conclusion: This research is based on improving the user interaction level of the prosthetic hand with the BCI system, and has made improvements in the SSVEP paradigm, asynchronous pattern recognition, interaction, and control logic. Furthermore, it also provides support for BCI areas for alternative prosthetic control, and movement disorder rehabilitation programs.

Peripheral Lymphocytes in Primary Liver Cancers: Elevated NK and CD8+ T Cells and Dysregulated Selenium Metabolism.

Peripheral blood lymphocytes (PBLs), which play a pivotal role in orchestrating the immune system, garner minimal attention in hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). The impact of primary liver cancers on PBLs remains unexplored. In this study, flow cytometry facilitated the quantification of cell populations, while transcriptome of PBLs was executed utilizing 10× single-cell sequencing technology. Additionally, pertinent cases were curated from the GEO database. Subsequent bioinformatics and statistical analyses were conducted utilizing R (4.2.1) software. Elevated counts of NK cells and CD8+ T cells were observed in both ICC and HCC when compared to benign liver disease (BLD). In the multivariate Cox model, NK cells and CD8+ T cells emerged as independent risk factors for recurrence-free survival. Single-cell sequencing of PBLs uncovered the downregulation of TGFß signaling in tumor-derived CD8+ T cells. Pathway enrichment analysis, based on differential expression profiling, highlighted aberrations in selenium metabolism. Proteomic analysis of preoperative and postoperative peripheral blood samples from patients undergoing tumor resection revealed a significant upregulation of SELENBP1 and a significant downregulation of SEPP1. Primary liver cancer has a definite impact on PBLs, manifested by alterations in cellular quantities and selenoprotein metabolism.

An effective purification of double-effect distillation for bio-based pentamethylene diisocyanate.

Bio-based pentamethylene diisocyanate (PDI) is a new type of sustainable isocyanate, which has important applications in coatings, foams, and adhesives. Technical-economic analysis of the PDI distillation process can promote the industrialization of PDI. The thermal analysis of PDI facilitates the smooth running of the simulation process. A new PDI heat capacity prediction method was established. The distillation processes of a crude PDI solution by conventional distillation and double-effect distillation were studied. Countercurrent double-effect distillation showed the best energy-saving effects in all double-effect distillation. However, combined with total annual charge (TAC), parallel double-effect distillation was the optimal method for PDI purification. Parallel double-effect distillation can significantly reduce the TAC of production PDI, which is 33.39% lower than that of the conventional distillation. The study demonstrates a clear economic incentive for reducing the cost of PDI purification by parallel double-effect distillation.

Study on the diversity of mental states and neuroplasticity of the brain during human-machine interaction.

Introduction: With the increasing demand for human-machine collaboration systems, more and more attention has been paid to the influence of human factors on the performance and security of the entire system. Especially in high-risk, high-precision, and difficult special tasks (such as space station maintenance tasks, anti-terrorist EOD tasks, surgical robot teleoperation tasks, etc.), there are higher requirements for the operator's perception and cognitive level. However, as the human brain is a complex and open giant system, the perception ability and cognitive level of the human are dynamically variable, so that it will seriously affect the performance and security of the whole system. Methods: The method proposed in this paper innovatively explained this phenomenon from two dimensions of brain space and time and attributed the dynamic changes of perception, cognitive level, and operational skills to the mental state diversity and the brain neuroplasticity. In terms of the mental state diversity, the mental states evoked paradigm and the functional brain network analysis method during work were proposed. In terms of neuroplasticity, the cognitive training intervention paradigm and the functional brain network analysis method were proposed. Twenty-six subjects participated in the mental state evoked experiment and the cognitive training intervention experiment. Results: The results showed that (1) the mental state of the subjects during work had the characteristics of dynamic change, and due to the influence of stimulus conditions and task patterns, the mental state showed diversity. There were significant differences between functional brain networks in different mental states, the information processing efficiency and the mechanism of brain area response had changed significantly. (2) The small-world attributes of the functional brain network of the subjects before and after the cognitive training experiment were significantly different. The brain had adjusted the distribution of information flow and resources, reducing costs and increasing efficiency as a whole. It was demonstrated that the global topology of the cortical connectivity network was reconfigured and neuroplasticity was altered through cognitive training intervention. Discussion: In summary, this paper revealed that mental state and neuroplasticity could change the information processing efficiency and the response mechanism of brain area, thus causing the change of perception, cognitive level and operational skills, which provided a theoretical basis for studying the relationship between neural information processing and behavior.

Compound motion decoding based on sEMG consisting of gestures, wrist angles, and strength.

This study aimed to highlight the demand for upper limb compound motion decoding to provide a more diversified and flexible operation for the electromyographic hand. In total, 60 compound motions were selected, which were combined with four gestures, five wrist angles, and three strength levels. Both deep learning methods and machine learning classifiers were compared to analyze the decoding performance. For deep learning, three structures and two ways of label encoding were assessed for their training processes and accuracies; for machine learning, 24 classifiers, seven features, and a combination of classifier chains were analyzed. Results show that for this relatively small sample multi-target surface electromyography (sEMG) classification, feature combination (mean absolute value, root mean square, variance, 4th-autoregressive coefficient, wavelength, zero crossings, and slope signal change) with Support Vector Machine (quadric kernel) outstood because of its high accuracy, short training process, less computation cost, and stability (p < 0.05). The decoding result achieved an average test accuracy of 98.42 ± 1.71% with 150 ms sEMG. The average accuracy for separate gestures, wrist angles, and strength levels were 99.35 ± 0.67%, 99.34 ± 0.88%, and 99.04 ± 1.16%. Among all 60 motions, 58 showed a test accuracy greater than 95%, and one part was equal to 100%.

Electroencephalogram and surface electromyogram fusion-based precise detection of lower limb voluntary movement using convolution neural network-long short-term memory model.

The electroencephalogram (EEG) and surface electromyogram (sEMG) fusion has been widely used in the detection of human movement intention for human-robot interaction, but the internal relationship of EEG and sEMG signals is not clear, so their fusion still has some shortcomings. A precise fusion method of EEG and sEMG using the CNN-LSTM model was investigated to detect lower limb voluntary movement in this study. At first, the EEG and sEMG signal processing of each stage was analyzed so that the response time difference between EEG and sEMG can be estimated to detect lower limb voluntary movement, and it can be calculated by the symbolic transfer entropy. Second, the data fusion and feature of EEG and sEMG were both used for obtaining a data matrix of the model, and a hybrid CNN-LSTM model was established for the EEG and sEMG-based decoding model of lower limb voluntary movement so that the estimated value of time difference was about 24 ∼ 26 ms, and the calculated value was between 25 and 45 ms. Finally, the offline experimental results showed that the accuracy of data fusion was significantly higher than feature fusion-based accuracy in 5-fold cross-validation, and the average accuracy of EEG and sEMG data fusion was more than 95%; the improved average accuracy for eliminating the response time difference between EEG and sEMG was about 0.7 ± 0.26% in data fusion. In the meantime, the online average accuracy of data fusion-based CNN-LSTM was more than 87% in all subjects. These results demonstrated that the time difference had an influence on the EEG and sEMG fusion to detect lower limb voluntary movement, and the proposed CNN-LSTM model can achieve high performance. This work provides a stable and reliable basis for human-robot interaction of the lower limb exoskeleton.

Feasibility study of personalized speed adaptation method based on mental state for teleoperated robots.

The teleoperated robotic system can support humans to complete tasks in high-risk, high-precision and difficult special environments. Because this kind of special working environment is easy to cause stress, high mental workload, fatigue and other mental states of the operator, which will reduce the quality of operation and even cause safety accidents, so the mental state of the people in this system has received extensive attention. However, the existence of individual differences and mental state diversity is often ignored, so that most of the existing adjustment strategy is out of a match between mental state and adaptive decision, which cannot effectively improve operational quality and safety. Therefore, a personalized speed adaptation (PSA) method based on policy gradient reinforcement learning was proposed in this paper. It can use electroencephalogram and electro-oculogram to accurately perceive the operator's mental state, and adjust the speed of the robot individually according to the mental state of different operators, in order to perform teleoperation tasks efficiently and safely. The experimental results showed that the PSA method learns the mapping between the mental state and the robot's speed regulation action by means of rewards and punishments, and can adjust the speed of the robot individually according to the mental state of different operators, thereby improving the operating quality of the system. And the feasibility and superiority of this method were proved. It is worth noting that the PSA method was validated on 6 real subjects rather than a simulation model. To the best of our knowledge, the PSA method is the first implementation of online reinforcement learning control of teleoperated robots involving human subjects.

An asynchronous artifact-enhanced electroencephalogram based control paradigm assisted by slight facial expression.

In this study, an asynchronous artifact-enhanced electroencephalogram (EEG)-based control paradigm assisted by slight-facial expressions (sFE-paradigm) was developed. The brain connectivity analysis was conducted to reveal the dynamic directional interactions among brain regions under sFE-paradigm. The component analysis was applied to estimate the dominant components of sFE-EEG and guide the signal processing. Enhanced by the artifact within the detected electroencephalogram (EEG), the sFE-paradigm focused on the mainstream defect as the insufficiency of real-time capability, asynchronous logic, and robustness. The core algorithm contained four steps, including "obvious non-sFE-EEGs exclusion," "interface 'ON' detection," "sFE-EEGs real-time decoding," and "validity judgment." It provided the asynchronous function, decoded eight instructions from the latest 100 ms signal, and greatly reduced the frequent misoperation. In the offline assessment, the sFE-paradigm achieved 96.46% ± 1.07 accuracy for interface "ON" detection and 92.68% ± 1.21 for sFE-EEGs real-time decoding, with the theoretical output timespan less than 200 ms. This sFE-paradigm was applied to two online manipulations for evaluating stability and agility. In "object-moving with a robotic arm," the averaged intersection-over-union was 60.03 ± 11.53%. In "water-pouring with a prosthetic hand," the average water volume was 202.5 ± 7.0 ml. During online, the sFE-paradigm performed no significant difference (P = 0.6521 and P = 0.7931) with commercial control methods (i.e., FlexPendant and Joystick), indicating a similar level of controllability and agility. This study demonstrated the capability of sFE-paradigm, enabling a novel solution to the non-invasive EEG-based control in real-world challenges.

Realizing the Application of EEG Modeling in BCI Classification: Based on a Conditional GAN Converter.

Electroencephalogram (EEG) modeling in brain-computer interface (BCI) provides a theoretical foundation for its development. However, limited by the lack of guidelines in model parameter selection and the inability to obtain personal tissue information in practice, EEG modeling in BCI is mainly focused on the theoretical qualitative level which shows a gap between the theory and its application. Based on such problems, this work combined the surface EEG simulation with a converter based on the generative adversarial network (GAN), to establish the connection from simulated EEG to its application in BCI classification. For the scalp EEGs modeling, a mathematical model was built according to the physics of surface EEG, which consisted of the parallel 3-population neural mass model, the equivalent dipole, and the forward computation. For application, a converter based on the conditional GAN was designed, to transfer the simulated theoretical-only EEG to its practical version, in the lack of individual bio-information. To verify the feasibility, based on the latest microexpression-assisted BCI paradigm proposed by our group, the converted simulated EEGs were used in the training of BCI classifiers. The results indicated that, compared with training with insufficient real data, by adding the simulated EEGs, the overall performance showed a significant improvement (P = 0.04 < 0.05), and the test performance can be improved by 2.17% ± 4.23, in which the largest increase was up to 12.60% ± 1.81. Through this work, the link from theoretical EEG simulation to BCI classification has been initially established, providing an enhanced novel solution for the application of EEG modeling in BCI.

Homology Characteristics of EEG and EMG for Lower Limb Voluntary Movement Intention.

In the field of lower limb exoskeletons, besides its electromechanical system design and control, attention has been paid to realizing the linkage of exoskeleton robots to humans via electroencephalography (EEG) and electromyography (EMG). However, even the state of the art performance of lower limb voluntary movement intention decoding still faces many obstacles. In the following work, focusing on the perspective of the inner mechanism, a homology characteristic of EEG and EMG for lower limb voluntary movement intention was conducted. A mathematical model of EEG and EMG was built based on its mechanism, which consists of a neural mass model (NMM), neuromuscular junction model, EMG generation model, decoding model, and musculoskeletal biomechanical model. The mechanism analysis and simulation results demonstrated that EEG and EMG signals were both excited by the same movement intention with a response time difference. To assess the efficiency of the proposed model, a synchronous acquisition system for EEG and EMG was constructed to analyze the homology and response time difference from EEG and EMG signals in the limb movement intention. An effective method of wavelet coherence was used to analyze the internal correlation between EEG and EMG signals in the same limb movement intention. To further prove the effectiveness of the hypothesis in this paper, six subjects were involved in the experiments. The experimental results demonstrated that there was a strong EEG-EMG coherence at 1 Hz around movement onset, and the phase of EEG was leading the EMG. Both the simulation and experimental results revealed that EEG and EMG are homologous, and the response time of the EEG signals are earlier than EMG signals during the limb movement intention. This work can provide a theoretical basis for the feasibility of EEG-based pre-perception and fusion perception of EEG and EMG in human movement detection.

Novel approach for electromyography-controlled prostheses based on facial action.

Individuals with severe tetraplegia frequently require to control their complex assistive devices using body movement with the remaining activity above the neck. Electromyography (EMG) signals from the contractions of facial muscles enable people to produce multiple command signals by conveying information about attempted movements. In this study, a novel EMG-controlled system based on facial actions was developed. The mechanism of different facial actions was processed using an EMG control model. Four asymmetric and symmetry actions were defined to control a two-degree-of-freedom (2-DOF) prosthesis. Both indoor and outdoor experiments were conducted to validate the feasibility of EMG-controlled prostheses based on facial action. The experimental results indicated that the new paradigm presented in this paper yields high performance and efficient control for prosthesis applications. Graphical abstract Individuals with severe tetraplegia frequently require to control their complex assistive devices using body movement with the remaining activity above the neck. Electromyography (EMG) signals from the contractions of facial muscles enable people to produce multiple command signals by conveying information about attempted movements. In this study, a novel EMG-controlled system based on facial actions was developed. The mechanism of different facial actions was processed using an EMG control model. Four asymmetric and symmetry actions were defined to control a two-degree-of-freedom (2-DOF) prosthesis. Both indoor and outdoor experiments were conducted to validate the feasibility of EMG-controlled prostheses based on facial action. The experimental results indicated that the new paradigm presented in this paper yields high performance and efficient control for prosthesis applications.

microRNA-17 functions as an oncogene by downregulating Smad3 expression in hepatocellular carcinoma.

The sekelsky mothers against dpp3 (Smad3) functions as a transcriptional modulator activated by transforming growth factor-ß (TGF-ß). Accumulated evidences indicated that Smad3 played the important roles in carcinogenesis and progression of hepatocellular carcinoma (HCC). Up to now, the regulatory mechanism of Smad3 in HCC still remains unclear. It has been known that some particular microRNAs (miRNAs) involve in carcinogenesis through the regulation of gene expressions with targeting mRNAs. In our study, the unknown candidates of miRNAs that target Smad3 mRNA were searched by using a newly established in vivo approach, the miRNA in vivo precipitation (miRIP). Using a loss-of-function assay, we demonstrated that miR-17 directly targeted Smad3 in HCC cells and inhibition on miR-17 increased Smad3 expression. Furthermore, we found that downregulation on Smad3 expression was consistent with high level of miR-17 in HCC tissues of patients when compared with around normal liver tissues. The manipulated miR-17 silence in HCC cells suppressed their growth of both in vitro and in vivo. Such suppression on cell growth could be recovered through downregulating Smad3. In addition, miR-17 affected cell proliferation through arresting cell cycle in G1 phase. The negative correlation between levels of miR-17 and protein levels of Smad3 was supported by the results of analysis with HCC tissue chip. In summary, for the first time, we confirmed that miR-17 directly targeted Smad3 mRNA and downregulated Smad3 protein expression in HCC. Our results indicated that the increased expression of miR-17 promoted carcinogenesis of HCC through down-regulations of Smad3, suggesting miR-17 might serve as the potential diagnostic and therapeutic targets for clinical HCC.

Haspin knockdown can inhibit progression and development of pancreatic cancer in vitro and vivo.

BACKGROUND: Pancreatic cancer is one of the most aggressive and lethal malignancies and it is the eighth most common cause of death from cancer worldwide. The purpose of this study was to investigate the role of GSG2 (HASPIN) in the development and progression of pancreatic cancer. MATERIAL AND METHODS: GSG2 expression was detected by immunohistochemistry in tumor tissue samples, and by qRT-PCR and Western blot assay in human pancreatic cancer cell lines. Cell proliferation was evaluated by MTT assay. Giemsa staining was used for analyzing colony formation. Cell cycle and cell apoptosis were determined using Fluorescence activated Cells Sorting. Wound healing assay and transwell assay were applied for examining cell migration. The molecular mechanism was investigated by human apoptosis antibody array. Tumor-bearing animal model was constructed to verify the effects of GSG2 on pancreatic cancer in vivo. RESULTS: GSG2 expression was upregulated in pancreatic cancer tissues and human pancreatic cancer cell lines: PANC-1 and SW1990. Higher expression of GSG2 in tumor samples was associated with poorer prognosis. GSG2 knockdown suppressed cell proliferation, colony formation, metastasis and promoted cell apoptosis, which was also verified in vivo. In addition, GSG2 knockdown blocked the cell cycle in G2. It was also found that downregulation of GSG2 inhibited Bcl-2, Bcl-w, cIAP, HSP60 and Livin expression as well as promoted IGFBP-6 expression. CONCLUSION: This study revealed that GSG2 upregulation was associated with pancreatic cancer progression. GSG2 knockdown inhibited cell proliferation, colony formation and migration, blocked cell cycle at G2 phase, and induced cell apoptosis. Therefore, GSG2 might serve as a potential therapeutic target for pancreatic cancer therapy and a market for prognosis.

An approach for brain-controlled prostheses based on Scene Graph Steady-State Visual Evoked Potentials.

Brain control technology can restore communication between the brain and a prosthesis, and choosing a Brain-Computer Interface (BCI) paradigm to evoke electroencephalogram (EEG) signals is an essential step for developing this technology. In this paper, the Scene Graph paradigm used for controlling prostheses was proposed; this paradigm is based on Steady-State Visual Evoked Potentials (SSVEPs) regarding the Scene Graph of a subject's intention. A mathematic model was built to predict SSVEPs evoked by the proposed paradigm and a sinusoidal stimulation method was used to present the Scene Graph stimulus to elicit SSVEPs from subjects. Then, a 2-degree of freedom (2-DOF) brain-controlled prosthesis system was constructed to validate the performance of the Scene Graph-SSVEP (SG-SSVEP)-based BCI. The classification of SG-SSVEPs was detected via the Canonical Correlation Analysis (CCA) approach. To assess the efficiency of proposed BCI system, the performances of traditional SSVEP-BCI system were compared. Experimental results from six subjects suggested that the proposed system effectively enhanced the SSVEP responses, decreased the degradation of SSVEP strength and reduced the visual fatigue in comparison with the traditional SSVEP-BCI system. The average signal to noise ratio (SNR) of SG-SSVEP was 6.31 ±â€¯2.64 dB, versus 3.38 ±â€¯0.78 dB of traditional-SSVEP. In addition, the proposed system achieved good performances in prosthesis control. The average accuracy was 94.58% ±â€¯7.05%, and the corresponding high information transfer rate (IRT) was 19.55 ±â€¯3.07 bit/min. The experimental results revealed that the SG-SSVEP based BCI system achieves the good performance and improved the stability relative to the conventional approach.

Fibrinogen and C-reactive protein score is a prognostic index for patients with hepatocellular carcinoma undergoing curative resection: a prognostic nomogram study.

Background: While curative resection is the established strategy for Hepatocellular carcinoma (HCC) patients, the prognosis still remains poor, and the efficiency of existing prediction models is unsatisfactory. Therefore, we aimed to develop a credible and easy-to-use prognostic index for patients with HCC undergoing curative therapy. Methods: A total of 768 patients with HCC, who underwent curative resection from December 2010 to June 2012 in Zhongshan Hospital, were divided into a training cohort with 616 patients and a validating cohort of 152 patients at a ratio of 4 to 1 by random allocation. Then, a retrospective cohort study was conducted to identify effective prognostic indexes. Results: FC-score, which incorporates fibrinogen and C-reactive protein, was established. In the multivariate analysis for OS and RFS, FC-score has shown to be a significant independent prognostic index in both the training cohort and validation cohort. Furthermore, the C-index of the FC-score for OS and RFS were 0.698 and 0.594 respectively, which were superior to other inflammation systems such as IBI, IBS, and GPS. Then, we developed a novel nomogram, which integrated FC-score into the conventional BCLC staging system. This new nomogram gave rise to a new C-index of 0.746 (95%CI: 0.743-0.749) for OS, and 0.654 (95%CI: 0.652-0.656) for RFS. The calibration curve and decision curve analysis indicated that our nomogram was highly consistent between predicted and actual observations. Conclusions: FC-score represents a novel, convenient, reliable, and accurate prognostic predictor for both OS and RFS in HCC patients undergoing curative therapy.

A novel and validated prognostic nomogram based on liver fibrosis and tumor burden for patients with hepatocellular carcinoma after curative resection.

BACKGROUND AND OBJECTIVES: Most conventional staging systems were formulated concerning the tumor burden rather than the severity of liver fibrosis, which plays a central role in tumor promotion. The aim of this study was to formulate a prognostic nomogram comprehensively considering these two aspects for HCC after hepatectomy. METHODS: The prognostic significances of the four indicators namely laminin, hyaluronic acid, human procollagen type-III, and collagen type-IV that reflect liver fibrosis were explored in two independent cohorts. A nomogram was established based on the results of multivariate analysis. The predictive accuracy of the nomogram was measured by concordance index (C-index) and calibration. The decision curve analysis (DCA) was used to evaluate the clinical benefit of the nomogram. RESULTS: Preoperative serum laminin level is an independent prognostic factor for overall survival in HCC patients after resection. The C-indices of the nomogram in the training and validation cohorts were 0.779 and 0.719, respectively. The calibration showed optimal agreement between the prediction by nomogram and actual observation. Moreover, the C-indices and DCA revealed that the nomogram provided better clinical benefit compared with the BCLC stage, CLIP score, and AJCC 7th edition. CONCLUSIONS: The prognostic nomogram constructed on laminin represents a superior predictive model.

An Approach for Brain-Controlled Prostheses Based on a Facial Expression Paradigm.

One of the most exciting areas of rehabilitation research is brain-controlled prostheses, which translate electroencephalography (EEG) signals into control commands that operate prostheses. However, the existing brain-control methods have an obstacle between the selection of brain computer interface (BCI) and its performance. In this paper, a novel BCI system based on a facial expression paradigm is proposed to control prostheses that uses the characteristics of theta and alpha rhythms of the prefrontal and motor cortices. A portable brain-controlled prosthesis system was constructed to validate the feasibility of the facial-expression-based BCI (FE-BCI) system. Four types of facial expressions were used in this study. An effective filtering algorithm based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) and sample entropy (SampEn) was used to remove electromyography (EMG) artifacts. A wavelet transform (WT) was applied to calculate the feature set, and a back propagation neural network (BPNN) was employed as a classifier. To prove the effectiveness of the FE-BCI system for prosthesis control, 18 subjects were involved in both offline and online experiments. The grand average accuracy over 18 subjects was 81.31 ± 5.82% during the online experiment. The experimental results indicated that the proposed FE-BCI system achieved good performance and can be efficiently applied for prosthesis control.

Prognostic Nomograms Stratify Survival of Patients with Hepatocellular Carcinoma Without Portal Vein Tumor Thrombosis After Curative Resection.

BACKGROUND: The prognosis of patients with hepatocellular carcinoma (HCC) without portal vein tumor thrombosis (PVTT) after curative resection is at variance. We identified the risk factors of poor postoperative prognosis and consequently developed prognostic nomograms generating individual risk of death and recurrence for this subgroup of patients with HCC. METHODS: The risk factors were identified and nomograms were developed based on a retrospective study of 734 patients in the primary cohort who underwent curative resection for HCC from 2010 to 2012. The predictive accuracy and discriminative ability of the nomograms were determined by concordance index (C-index) and calibration curve and compared with traditional staging systems of HCC. The results were validated in an independent cohort of 349 patients operated at the same institution in 2007. RESULTS: All of the independent factors for survival in multivariate analysis in the primary cohort were selected into the nomograms. The calibration curve for probability of survival showed good agreement between prediction by nomograms and actual observation. The C-indices of the nomograms for predicting overall survival and recurrence-free survival were 0.755 (95% confidence interval [CI], 0.752-0.758) and 0.665 (95% CI, 0.662-0.668), respectively, which were statistically higher than the C-indices of other HCC prognostic models. The results were further confirmed in the validation cohort. CONCLUSION: The proposed nomograms resulted in more accurate prognostic prediction for patients with HCC without PVTT after curative resection. The Oncologist 2017;22:561-569 IMPLICATIONS FOR PRACTICE: Hepatocellular carcinoma (HCC) poses a great therapeutic challenge due to the poor prognosis in patients underwent surgical resection. The portal vein tumor thrombosis (PVTT) as a robust risk factor for survival has been routinely integrated to staging systems. Nonetheless, the prognosis stratification for patients without PVTT was neglected to some extent. Herein, independent risk factors of OS and RFS in HCC patients without PVTT were reconfirmed. A predictive nomogram was constructed on these risk factors and was demonstrated to be a more accurate predictive model in HCC patients without PVTT, compared with the traditional staging systems.

Serum gamma-glutamyl transferase levels affect the prognosis of patients with intrahepatic cholangiocarcinoma who receive postoperative adjuvant transcatheter arterial chemoembolization: A propensity score matching study.

BACKGROUND: The postoperative prognosis of patients with intrahepatic cholangiocarcinoma (ICC) is far from satisfactory. Whether postoperative adjuvant transcatheter arterial chemoembolization (TACE) is effective for patients with ICC after R0 liver resection remains controversial. MATERIALS AND METHODS: We retrospectively reviewed the data of 272 patients with ICC who received a radical hepatectomy at our center between 2000 and 2011. After the propensity score of postoperative TACE was calculated, 75 patients who received TACE were matched at a 1:2 ratios with 150 patients who did not receive TACE. Univariate and multivariate Cox analyses were performed to identify the independent predictors of overall survival. RESULTS: The postoperative protective effect of adjuvant TACE was significantly influenced by serum gamma-glutamyl transferase (GGT) levels (P for interaction = 0.026). Postoperative TACE was not a significant predictor of overall survival (hazard ratio = 0.89, P = 0.704) in patients with GGT levels ≤ 54 U/L. Postoperative TACE was a significant predictor of overall survival in patients with GGT levels >54 U/L (hazard ratio = 0.44, P = 0.001). Regarding short-term outcomes, a total of 74 patients (32.9%) had varying degrees of complications, and the majority of these complications were grade I (18.7%) or II (10.2%). CONCLUSION: The safety of postoperative TACE was validated, and the results suggest that only patients with elevated serum GGT levels will benefit from this treatment following curative liver resection for ICC.