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This paper presents an adaptive robust interaction force control algorithm for an underactuated walking lower limb hydraulic exoskeleton. In order to deal with the problem of passive joints (less of control inputs), holonomic constraints from the wearer are considered in system dynamics, which help transform the underactuated exoskeleton dynamics into fully actuated dynamics in Cartesian space. Based on the established dynamic model, adaptive robust interaction force controllers are presented both for the phase of double leg support and single leg support to effectively deal with multiple walking phases, negative effect of high-order nonlinearities of hydraulic systems as well as various model uncertainties and external disturbances. An additional torque allocation method is proposed to deal with the over-actuated characteristic in double leg support phase. Comparative simulations are carried out. The results demonstrate that the proposed force controller can achieve precision and strong robust interaction force control performance to load change and variation of the human trajectory. [ABSTRACT FROM AUTHOR]

Lower limb exoskeleton which augments the human performance is a wearable human–machine integrated system used to assist people carrying heavy loads. Recently, underactuated lower limb exoskeleton systems with some passive joints become more and more attractive due to the advantages of smaller weight, lower system energy consumption and lower cost. However, because of the less of control inputs, the existed control methods of fully actuated exoskeletons cannot be extended to underactuated systems, which makes the robust controller design of underactuated lower limb exoskeletons becomes more challenged. This article focuses on the high-performance human–machine interaction force control design of underactuated lower limb exoskeletons with passive ankle joint. In order to solve the reduction of control inputs, the holonomic constraint from the wearer is considered, which help transform the dynamics of 3-degree-of-freedom underactuated exoskeleton in joint space into a 2-degree-of-freedom fully actuated system in Cartesian space. A two-level interaction force controller using adaptive robust control algorithm is proposed to effectively address the negative effect of various model uncertainties and external disturbances. In order to facilitate the control parameter selection, a gain tuning method is also presented. Comparative simulations are carried out, which indicate that the proposed two-level interaction force controller achieves smaller interaction force and better robust performance to various modeling errors and disturbances. [ABSTRACT FROM AUTHOR]

Hydraulic lower limb exoskeletons are wearable robotic systems, which can help people carry heavy loads. Recently, underactuated exoskeletons with some passive joints have been developed in large numbers for the purpose of decreasing the weight and energy consumption of the system. There are many control algorithms for a multi-joint fully actuated exoskeleton, which cannot be applied for underactuated systems due to the reduction in the number of control inputs. Besides, since the hydraulic actuator is not a desired force output source, there exist high order nonlinearities in hydraulic exoskeletons, which makes the controller design more challenging than motor driven exoskeleton systems. This paper proposed a precision interaction force controller for a 3DOF underactuated hydraulic stance leg exoskeleton. First, the control effect of the wearer is considered and the posture of the exoskeleton back is assumed as a desired trajectory under the control of the wearer. Under this assumption, the system dynamics are changed from a 3DOF underactuated system in joint space to a 2DOF fully actuated system in Cartesian space. Then, a three-level interaction force controller is designed in which the high-level controller conducts human motion intent inference, the middle level controller tracks human motion and the low-level controller achieves output force tracking of hydraulic cylinders. The MIMO adaptive robust control algorithm is applied in the controller design to effectively address the high order nonlinearities of the hydraulic system, multi-joint couplings and various model uncertainties. A gain tuning method is also provided to facilitate the controller gains selection for engineers. Comparative simulations are conducted, which demonstrate that the principal human-machine interaction force components can be minimized and good robust performance to load change and modeling errors can be achieved. [ABSTRACT FROM AUTHOR]

Background: Current clinical guidelines recommend the removal of at least 12 lymph nodes (LNs) in resectable colorectal cancer (CRC). With advancements in lymphadenectomy technologies, the number of retrieved lymph nodes (rLNs) has markedly increased. This study aimed to investigate the lowest number of rLNs in node-negative patients., Materials and Methods: A total of 1103 N0 and 208 N1a stage patients were enrolled in our cohort, while 8503 N0 and 1276 N1a patients from the Surveillance, Epidemiology, and End Results CRC database were included. Propensity score matching and multivariate Cox regression analyses were performed to mitigate the influence of selection bias and control for potential confounding variables., Results: The median number of rLNs in N0 patients increased from 13.5 (interquartile range [IQR]: 9-18) in 2013 to 17 (IQR: 15-20) in 2019. The restrictive cubic spline illustrated a nonlinear relationship between rLNs and prognosis (nonlinearity, P =0.009), with a threshold ( N =16) influencing clinical outcomes. Patients at either N0 or N1a stage with sufficient rLNs (≥16) demonstrated superior prognoses to those with a limited rLNs (<16). After adjusting for clinical confounders, similar prognoses were observed in N0 limited and N1a adequate populations. Furthermore, Kaplan-Meier curves revealed that N0 limited patients who received chemotherapy exhibited better outcomes than those who did not., Conclusions: Among patients with node-negative CRC, it is crucial to remove 16 or more LNs effectively. Fewer than 16 rLNs should be regarded as an independent risk factor, implying the need for adjuvant chemotherapy., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

Background: Advanced hepatocellular carcinoma (HCC) is characterized as symptomatic tumors [performance status (PS) score of 1-2], vascular invasion and extrahepatic spread, but patients with PS1 alone may be eliminated from this stage. Although liver resection is used for liver-confined HCC, its role in patients with PS1 alone remains controversial. Therefore, we aimed to explore its application in such patients and identify potential candidates., Methods: Eligible liver-confined HCC patients undergoing liver resection were retrospectively screened in 15 Chinese tertiary hospitals, with limited tumor burden, liver function and PS scores. Cox-regression survival analysis was used to investigate the prognostic factors and develop a risk-scoring system, according to which patients were substratified using fitting curves and the predictive values of PS were explored in each stratification., Results: From January 2010 to October 2021, 1535 consecutive patients were selected. In the whole cohort, PS, AFP, tumor size and albumin were correlated with survival (adjusted P<0.05), based on which risk scores of every patient were calculated and ranged from 0 to 18. Fitting curve analysis demonstrated that the prognostic abilities of PS varied with risk scores and that the patients should be divided into three risk stratifications. Importantly, in the low-risk stratification, PS lost its prognostic value, and patients with PS1 alone achieved a satisfactory 5-year survival rate of 78.0%, which was comparable with that PS0 patients (84.6%)., Conclusion: Selected patients with PS1 alone and an ideal baseline condition may benefit from liver resection and may migrate forward to BCLC stage A., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Bai, Wang, Zhao, Han, Zhao, Chen, Zhu, Han, Bai, Lou, Zhang, Yang, Zuo, Fan, Chen, Jia, Wu, Ren, Zhu, Ma, Xu, Tang, Du, Zhao, Li, Qi, Han, Chen and Liu.)

Background: Hepatoma arterial-embolization prognostic (HAP) series scores have been proposed for prognostic prediction in patients with unresectable hepatocellular carcinoma (uHCC) undergoing transarterial chemoembolization (TACE). However, their prognostic value in TACE plus sorafenib (TACE-S) remains unknown. Here, we aim to evaluate their prognostic performance in such conditions and identify the best model for this combination therapy., Methods: Between January 2012 and December 2018, consecutive patients with uHCC receiving TACE-S were recruited from 15 tertiary hospitals in China. Cox regression analyses were used to investigate the prognostic values of baseline factors and every scoring system. Their prognostic performance and discriminatory performance were evaluated and confirmed in subgroup analyses., Results: A total of 404 patients were enrolled. In the whole cohort, the median follow-up period was 44.2 (interquartile range (IQR), 33.2-60.7) months, the median overall survival (OS) time was 13.2 months, and 336 (83.2%) patients died at the end of the follow-up period. According to multivariate analyses, HAP series scores were independent prognostic indicators of OS. In addition, the C-index, Akaike information criterion (AIC) values, and time-dependent area under the receiver operating characteristic (ROC) curve (AUC) indicated that modified HAP (mHAP)-III had the best predictive performance. Furthermore, the results remained consistent in most subsets of patients., Conclusion: HAP series scores exhibited good predictive ability in uHCC patients accepting TACE-S, and the mHAP-III score was found to be superior to the other HAP series scores in predicting OS. Future prospective high-quality studies should be conducted to confirm our results and help with treatment decision-making., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Zhu, Wang, Zhao, Han, Zhao, Chen, Zhu, Han, Bai, Lou, Zhang, Yang, Zuo, Fan, Chen, Jia, Wu, Ren, Bai, Ma, Xu, Tang, Han, Zhao, Qi, Li, Du, Chen and Liu.)

Necroptosis is a programmed form of necrotic cell death in regulating cancer ontogenesis, progression, and tumor microenvironment (TME) and could drive tumor-infiltrating cells to release pro-inflammatory cytokines, incurring strong immune responses. Nowadays, there are few identified biomarkers applied in clinical immunotherapy, and it is increasingly recognized that high levels of tumor necroptosis could enhance the response to immunotherapy. However, comprehensive characterization of necroptosis associated with TME and immunotherapy in Hepatocellular carcinoma (HCC) remains unexplored. Here, we computationally characterized necroptosis landscape in HCC samples from TCGA and ICGA cohorts and stratified them into two necroptosis clusters (A or B) with significantly different characteristics in clinical prognosis, immune cell function, and TME-landscapes. Additionally, to further evaluate the necroptosis levels of each sample, we established a novel necroptosis-related gene score (NRGscore). We further investigated the TME, tumor mutational burden (TMB), clinical response to immunotherapy, and chemotherapeutic drug sensitivity of HCC subgroups stratified by the necroptosis landscapes. The NRGscore is robust and highly predictive of HCC clinical outcomes. Further analysis indicated that the high NRGscore group resembles the immune-inflamed phenotype while the low score group is analogous to the immune-exclusion or metabolism phenotype. Additionally, the high NRGscore group is more sensitive to immune checkpoint blockade-based immunotherapy, which was further validated using an external HCC cohort, metastatic melanoma cohort, and advanced urothelial cancer cohort. Besides, the NRGscore was demonstrated as a potential biomarker for chemotherapy, wherein the high NRGscore patients with more tumor stem cell composition could be more sensitive to Cisplatin, Doxorubicin, Paclitaxel-based chemotherapy, and Sorafenib therapy. Collectively, a comprehensive characterization of the necroptosis in HCC suggested its implications for predicting immune infiltration and response to immunotherapy of HCC, providing promising strategies for treatment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhu, Han, Zhao, Zhu, Ma, Xu, Bai, Tang, Xu and Liu.)

Background: Alternative splicing (AS) is a critical mechanism of post-transcriptional regulation and has been widely reported to be associated with the tumor progression and tumor microenvironment (TME) formation. However, the role of AS in lung adenocarcinoma (LUAD) has not been clearly elucidated. This study presents a comprehensive analysis exploring the impact of AS on prognosis and TME in LUAD., Methods: The gene expression transcriptome profiles and survival data were obtained from The Cancer Genome Atlas (TCGA) database, and the splicing profiles were obtained from the TCGA SpliceSeq database. Base on prognostic AS events, a prognostic signature was constructed using Least Absolute Shrinkage and Selection Operator (LASSO) regression followed by multivariate Cox regression analysis. Survival outcomes was analyzed using the Kaplan-Meier method and the predictive performance of the signature was evaluated using receiver operating characteristic (ROC) curve analysis. Furthermore, the landscape of the TME was assessed by ESTIMATE, Microenvironment Cell Population (MCP)-counter, and single-sample Gene-Set Enrichment Analysis (ssGSEA) algorithms., Results: A total of 127 prognostic AS events with P value <0.001 from 89 genes in LUAD were confirmed. A prognostic signature was constructed based on 20 prognostic AS events. Kaplan-Meier survival analysis demonstrated that higher risk scores were associated with poorer overall survival (OS). The area under the ROC curve of risk scores predicting the 1-, 3-, and 5-year survival probability were 0.791, 0.847, and 0.832, respectively. Furthermore, significant relationship was observed between the prognostic signature and the landscape of the TME. High-risk patients had lower stromal/immune scores, higher tumor purity, and significantly decreased abundance of majority immune cells, and immune-related signatures (P<0.05). Finally, a potential regulatory mechanism of the AS events is displayed in a regulatory network., Conclusions: This research highlights the prognostic value of AS events for patients with LUAD and provide new insight into the regulation of the TME by AS. Notably, AS may affect the patient's prognosis by altering the TME. Our findings provide important guidance for the development of novel biomarkers and therapeutic targets in patients with LUAD., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-22-1531/coif). The authors have no conflicts of interest to declare., (2022 Annals of Translational Medicine. All rights reserved.)

Background: Epilepsy is a complex chronic disease of the nervous system which influences the health of approximately 70 million patients worldwide. In the past few decades, despite the development of novel antiepileptic drugs, around one-third of patients with epilepsy have developed drug-resistant epilepsy. We performed a bioinformatic analysis to explore the underlying diagnostic markers and mechanisms of drug-resistant epilepsy. Methods: Weighted correlation network analysis (WGCNA) was applied to genes in epilepsy samples downloaded from the Gene Expression Omnibus database to determine key modules. The least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to screen the genes resistant to carbamazepine, phenytoin, and valproate, and sensitivity of the three-class classification SVM model was verified through the receiver operator characteristic (ROC) curve. A protein-protein interaction (PPI) network was utilized to analyze the protein interaction relationship. Finally, ingenuity pathway analysis (IPA) was adopted to conduct disease and function pathway and network analysis. Results: Through WGCNA, 72 genes stood out from the key modules related to drug resistance and were identified as candidate resistance genes. Intersection analysis of the results of the LASSO and SVM-RFE algorithms selected 11, 4, and 5 drug-resistant genes for carbamazepine, phenytoin, and valproate, respectively. Subsequent union analysis obtained 17 hub resistance genes to construct a three-class classification SVM model. ROC showed that the model could accurately predict patient resistance. Expression of 17 hub resistance genes in healthy subjects and patients was significantly different. The PPI showed that there are six resistance genes ( CD247 , CTSW , IL2RB , MATK , NKG7 , and PRF1 ) that may play a central role in the resistance of epilepsy patients. Finally, IPA revealed that resistance genes ( PRKCH and S1PR5 ) were involved in "CREB signaling in Neurons." Conclusion: We obtained a three-class SVM model that can accurately predict the drug resistance of patients with epilepsy, which provides a new theoretical basis for research and treatment in the field of drug-resistant epilepsy. Moreover, resistance genes PRKCH and S1PR5 may cooperate with other resistance genes to exhibit resistance effects by regulation of the cAMP-response element-binding protein (CREB) signaling pathway., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Han, Wu, Zhu, Kou, Li and Deng.)