BACKGROUND Gastric cancer(GC)is a highly aggressive malignancy with a heterogeneous nature,which makes prognosis prediction and treatment determination difficult.Inflammation is now recognized as one of the hallmarks ...BACKGROUND Gastric cancer(GC)is a highly aggressive malignancy with a heterogeneous nature,which makes prognosis prediction and treatment determination difficult.Inflammation is now recognized as one of the hallmarks of cancer and plays an important role in the aetiology and continued growth of tumours.Inflammation also affects the prognosis of GC patients.Recent reports suggest that a number of inflammatory-related biomarkers are useful for predicting tumour prognosis.However,the importance of inflammatory-related biomarkers in predicting the prognosis of GC patients is still unclear.AIM To investigate inflammatory-related biomarkers in predicting the prognosis of GC patients.was constructed using the least absolute shrinkage and selection operator Cox regression model based on the GEO database.GC patients from the GSE26253 cohort were used for validation.Univariate and multivariate Cox analyses were used to determine the independent prognostic factors,and a prognostic nomogram was established.The calibration curve and the area under the curve based on receiver operating characteristic analysis were utilized to evaluate the predictive value of the nomogram.The decision curve analysis results were plotted to quantify and assess the clinical value of the nomogram.Gene set enrichment analysis was performed to explore the potential regulatory pathways involved.The relationship between tumour immune infiltration status and risk score was analysed via Tumour Immune Estimation Resource and CIBERSORT.Finally,we analysed the association between risk score and patient sensitivity to commonly used chemotherapy and targeted therapy agents.RESULTS A prognostic model consisting of three inflammatory-related genes(MRPS17,GUF1,and PDK4)was constructed.Independent prognostic analysis revealed that the risk score was a separate prognostic factor in GC patients.According to the risk score,GC patients were stratified into high-and low-risk groups,and patients in the high-risk group had significantly worse prognoses according to age,sex,TNM stage and Lauren type.Consensus clustering identified three subtypes of inflammation that could predict GC prognosis more accurately than traditional grading and staging.Finally,the study revealed that patients in the low-risk group were more sensitive to certain drugs than were those in the high-risk group,indicating a link between inflammation-related genes and drug sensitivity.CONCLUSION In conclusion,we established a novel three-gene prognostic signature that may be useful for predicting the prognosis and personalizing treatment decisions of GC patients.展开更多
In this article,we address the task of experimental evaluation of autonomous map-based navigation of a Boston Dynamics Spot quadruped robot in confined environments equipped with the developed autonomy package that in...In this article,we address the task of experimental evaluation of autonomous map-based navigation of a Boston Dynamics Spot quadruped robot in confined environments equipped with the developed autonomy package that incorporates a 3D lidar,an IMU,and an onboard computer.For that,we propose an integrated software and hardware system that is considered as an enabler for robot localization and risk-aware path planning,based on the known map.The system design itself is modular and incorporates the perception capabilities required for autonomous navigation.The Spot robot first is utilized to build the offline map of the environment by utilizing the Google Cartographer simultaneous localization and mapping(SLAM)package.During the next step,the online environmental information from the autonomy package sensors and the offline map are provided to the onboard computer to localize the robot on the known map by utilizing means provided by Cartographer.Finally,the occupancy information is provided to the online grid-based path planner that generates risk-aware paths.The extensive experimental evaluation of the proposed system is performed in corridors and SubT environments.展开更多
文摘BACKGROUND Gastric cancer(GC)is a highly aggressive malignancy with a heterogeneous nature,which makes prognosis prediction and treatment determination difficult.Inflammation is now recognized as one of the hallmarks of cancer and plays an important role in the aetiology and continued growth of tumours.Inflammation also affects the prognosis of GC patients.Recent reports suggest that a number of inflammatory-related biomarkers are useful for predicting tumour prognosis.However,the importance of inflammatory-related biomarkers in predicting the prognosis of GC patients is still unclear.AIM To investigate inflammatory-related biomarkers in predicting the prognosis of GC patients.was constructed using the least absolute shrinkage and selection operator Cox regression model based on the GEO database.GC patients from the GSE26253 cohort were used for validation.Univariate and multivariate Cox analyses were used to determine the independent prognostic factors,and a prognostic nomogram was established.The calibration curve and the area under the curve based on receiver operating characteristic analysis were utilized to evaluate the predictive value of the nomogram.The decision curve analysis results were plotted to quantify and assess the clinical value of the nomogram.Gene set enrichment analysis was performed to explore the potential regulatory pathways involved.The relationship between tumour immune infiltration status and risk score was analysed via Tumour Immune Estimation Resource and CIBERSORT.Finally,we analysed the association between risk score and patient sensitivity to commonly used chemotherapy and targeted therapy agents.RESULTS A prognostic model consisting of three inflammatory-related genes(MRPS17,GUF1,and PDK4)was constructed.Independent prognostic analysis revealed that the risk score was a separate prognostic factor in GC patients.According to the risk score,GC patients were stratified into high-and low-risk groups,and patients in the high-risk group had significantly worse prognoses according to age,sex,TNM stage and Lauren type.Consensus clustering identified three subtypes of inflammation that could predict GC prognosis more accurately than traditional grading and staging.Finally,the study revealed that patients in the low-risk group were more sensitive to certain drugs than were those in the high-risk group,indicating a link between inflammation-related genes and drug sensitivity.CONCLUSION In conclusion,we established a novel three-gene prognostic signature that may be useful for predicting the prognosis and personalizing treatment decisions of GC patients.
基金the European Unions Horizon 2020 Research and Innovation Programme under the Grant Agreement No.869379 illuMINEation.
文摘In this article,we address the task of experimental evaluation of autonomous map-based navigation of a Boston Dynamics Spot quadruped robot in confined environments equipped with the developed autonomy package that incorporates a 3D lidar,an IMU,and an onboard computer.For that,we propose an integrated software and hardware system that is considered as an enabler for robot localization and risk-aware path planning,based on the known map.The system design itself is modular and incorporates the perception capabilities required for autonomous navigation.The Spot robot first is utilized to build the offline map of the environment by utilizing the Google Cartographer simultaneous localization and mapping(SLAM)package.During the next step,the online environmental information from the autonomy package sensors and the offline map are provided to the onboard computer to localize the robot on the known map by utilizing means provided by Cartographer.Finally,the occupancy information is provided to the online grid-based path planner that generates risk-aware paths.The extensive experimental evaluation of the proposed system is performed in corridors and SubT environments.
