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Background: Liver is the most common metastatic site of colorectal cancer (CRC) and liver metastasis (LM) determines subsequent treatment as well as prognosis of patients, especially in T1 patients. T1 CRC patients with LM are recommended to adopt surgery and systematic treatments rather than endoscopic therapy alone. Nevertheless, there is still no effective model to predict the risk of LM in T1 CRC patients. Hence, we aim to construct an accurate predictive model and an easy-to-use tool clinically. Methods: We integrated two independent CRC cohorts from Surveillance Epidemiology and End Results database (SEER, training dataset) and Xijing hospital (testing dataset). Artificial intelligence (AI) and machine learning (ML) methods were adopted to establish the predictive model. Results: A total of 16,785 and 326 T1 CRC patients from SEER database and Xijing hospital were incorporated respectively into the study. Every single ML model demonstrated great predictive capability, with an area under the curve (AUC) close to 0.95 and a stacking bagging model displaying the best performance (AUC = 0.9631). Expectedly, the stacking model exhibited a favorable discriminative ability and precisely screened out all eight LM cases from 326 T1 patients in the outer validation cohort. In the subgroup analysis, the stacking model also demonstrated a splendid predictive ability for patients with tumor size ranging from one to50mm (AUC = 0.956). Conclusion: We successfully established an innovative and convenient AI model for predicting LM in T1 CRC patients, which was further verified in the external dataset. Ultimately, we designed a novel and easy-to-use decision tree, which only incorporated four fundamental parameters and could be successfully applied in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Gastric cancer (GC) is a globally prevalent cancer, ranking fifth for incidence and fourth for mortality worldwide. The N6-methyladenosine (m6A) related long noncoding RNAs (lncRNAs) were widely investigated in recent studies. Nevertheless, the underlying prognostic implication and tumor immune mechanism of m6A-related lncRNA in GC remain unknown. Methods: We systematically assessed the m6A modification expression of 407 GC clinical samples based on 23 m6A regulators and comprehensively associated these genes with lncRNAs. Then, we constructed a m6A-related lncRNA prognostic signature (m6A-LPS) to evaluate both status and prognosis of the disease. Immune-related mechanisms were explored via dissecting tumor-infiltrating cells as well as applying tumor immune dysfunction and the exclusion algorithm. Furthermore, we validated the latent regulative mechanism of m6A-related lncRNA in GC cell lines. Results: The m6A-LPS containing nine hub lncRNAs was built, which possessed a superior capability to predict the outcomes of GC patients. Meanwhile, we found an intimate correlation between the m6A-LPS and tumor infiltrating cells, and that the low-risk group had a higher expression of immune checkpoints and responsed more to immunotherapy than the high-risk group. Clinically, these crucial lncRNAs expression levels were verified in ten pairs of GC samples. In in vitro experiments, the abilities of migration and proliferation were significantly enhanced via downregulating the lncRNA AC026691.1. Both migrative and proliferative capabilities of tumor cells were significantly enhanced via downregulating the lncRNA AC026691.1. in vitro. Conclusions: Collectively, the m6A-LPS could provide a novel prediction insight into the prognosis of GC patients and serve as an independent clinical factor for GC. These m6A-related lncRNAs might remodel the tumor microenvironment and affect the anti-cancer ability of immune checkpoint blockers. Importantly, lncRNA AC026691.1 could inhibit both migration and proliferation of GC by means of FTO regulation. [ABSTRACT FROM AUTHOR]

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]