Mechanical Behaviour of Lump-Grout Backfill Material for Underground Spaces

Large-size lump (e.g., coal reject or waste rocks) is one of the most popular backfill materials for underground mines attributed to its cost-effective and easy-to-obtain. The massive void between lumps, however, does generally affect the overall performance in eliminating the ground subsidence. This paper presents a conceptual backfill material, termed as lump-grout (LG) material, the attractive feature of which is that the voids between lumps are totally filled up with high-water content cementitious grout (CG) material. To explore the mechanical behaviour of LG material, a total of 36 LG specimens were prepared and tested under the uniaxial compression and the tensile loading. Other 20 CG specimens were tested in parallel for comparison. Key configurations investigated in the present research covered the particle size of lumps and the water-to-powder ratio of CG material. Test results indicated that the stress–strain behaviour of LG specimen is predominately affected by the strength of CG material. The failure of LG material generally occurred along the interface between lumps and the cementitious grout material rather than the lumps themselves. Compared to CG material, the axial deformation ability of LG material is slightly superior, when the utilization of CG material is significantly reduced. In addition to the backfill material, the proposed LG material can be also used as the infill material for the artificial standing supports for underground spaces

Table_4_Construction and validation of a robust prognostic model based on immune features in sepsis.csv

PurposeSepsis, with life-threatening organ failure, is caused by the uncontrolled host response to infection. Immune response plays an important role in the pathophysiology of sepsis. Immune-related genes (IRGs) are promising novel biomarkers that have been used to construct the diagnostic and prognostic model. However, an IRG prognostic model used to predict the 28-day mortality in sepsis was still limited. Therefore, the study aimed to develop a prognostic model based on IRGs to identify patients with high risk and predict the 28-day mortality in sepsis. Then, we further explore the circulating immune cell and immunosuppression state in sepsis.Materials and methodsThe differentially expressed genes (DEGs), differentially expressed immune-related genes (DEIRGs), and differentially expressed transcription factors (DETFs) were obtained from the GEO, ImmPort, and Cistrome databases. Then, the TFs-DEIRGs regulatory network and prognostic prediction model were constructed by Cox regression analysis and Pearson correlation analysis. The external datasets also validated the reliability of the prognostic model. Based on the prognostic DEIRGs, we developed a nomogram and conducted an independent prognosis analysis to explore the relationship between DEIRGs in the prognostic model and clinical features in sepsis. Besides, we further evaluate the circulating immune cells state in sepsis.ResultsA total of seven datasets were included in our study. Among them, GSE65682 was identified as a discovery cohort. The results of GSEA showed that there is a significant correlation between sepsis and immune response. Then, based on a P value ConclusionsOur study provides a robust prognostic model based on 22 DEIRGs which can predict 28-day mortality and immunosuppression status in sepsis. The higher risk score was positively associated with 28-day mortality and the development of immunosuppression. IRGs are a promising biomarker that might facilitate personalized treatments for sepsis.</p

Table_9_Construction and validation of a robust prognostic model based on immune features in sepsis.docx

PurposeSepsis, with life-threatening organ failure, is caused by the uncontrolled host response to infection. Immune response plays an important role in the pathophysiology of sepsis. Immune-related genes (IRGs) are promising novel biomarkers that have been used to construct the diagnostic and prognostic model. However, an IRG prognostic model used to predict the 28-day mortality in sepsis was still limited. Therefore, the study aimed to develop a prognostic model based on IRGs to identify patients with high risk and predict the 28-day mortality in sepsis. Then, we further explore the circulating immune cell and immunosuppression state in sepsis.Materials and methodsThe differentially expressed genes (DEGs), differentially expressed immune-related genes (DEIRGs), and differentially expressed transcription factors (DETFs) were obtained from the GEO, ImmPort, and Cistrome databases. Then, the TFs-DEIRGs regulatory network and prognostic prediction model were constructed by Cox regression analysis and Pearson correlation analysis. The external datasets also validated the reliability of the prognostic model. Based on the prognostic DEIRGs, we developed a nomogram and conducted an independent prognosis analysis to explore the relationship between DEIRGs in the prognostic model and clinical features in sepsis. Besides, we further evaluate the circulating immune cells state in sepsis.ResultsA total of seven datasets were included in our study. Among them, GSE65682 was identified as a discovery cohort. The results of GSEA showed that there is a significant correlation between sepsis and immune response. Then, based on a P value ConclusionsOur study provides a robust prognostic model based on 22 DEIRGs which can predict 28-day mortality and immunosuppression status in sepsis. The higher risk score was positively associated with 28-day mortality and the development of immunosuppression. IRGs are a promising biomarker that might facilitate personalized treatments for sepsis.</p

Image_1_Construction and validation of a robust prognostic model based on immune features in sepsis.tif

PurposeSepsis, with life-threatening organ failure, is caused by the uncontrolled host response to infection. Immune response plays an important role in the pathophysiology of sepsis. Immune-related genes (IRGs) are promising novel biomarkers that have been used to construct the diagnostic and prognostic model. However, an IRG prognostic model used to predict the 28-day mortality in sepsis was still limited. Therefore, the study aimed to develop a prognostic model based on IRGs to identify patients with high risk and predict the 28-day mortality in sepsis. Then, we further explore the circulating immune cell and immunosuppression state in sepsis.Materials and methodsThe differentially expressed genes (DEGs), differentially expressed immune-related genes (DEIRGs), and differentially expressed transcription factors (DETFs) were obtained from the GEO, ImmPort, and Cistrome databases. Then, the TFs-DEIRGs regulatory network and prognostic prediction model were constructed by Cox regression analysis and Pearson correlation analysis. The external datasets also validated the reliability of the prognostic model. Based on the prognostic DEIRGs, we developed a nomogram and conducted an independent prognosis analysis to explore the relationship between DEIRGs in the prognostic model and clinical features in sepsis. Besides, we further evaluate the circulating immune cells state in sepsis.ResultsA total of seven datasets were included in our study. Among them, GSE65682 was identified as a discovery cohort. The results of GSEA showed that there is a significant correlation between sepsis and immune response. Then, based on a P value ConclusionsOur study provides a robust prognostic model based on 22 DEIRGs which can predict 28-day mortality and immunosuppression status in sepsis. The higher risk score was positively associated with 28-day mortality and the development of immunosuppression. IRGs are a promising biomarker that might facilitate personalized treatments for sepsis.</p

Table_1_Construction and validation of a robust prognostic model based on immune features in sepsis.csv

PurposeSepsis, with life-threatening organ failure, is caused by the uncontrolled host response to infection. Immune response plays an important role in the pathophysiology of sepsis. Immune-related genes (IRGs) are promising novel biomarkers that have been used to construct the diagnostic and prognostic model. However, an IRG prognostic model used to predict the 28-day mortality in sepsis was still limited. Therefore, the study aimed to develop a prognostic model based on IRGs to identify patients with high risk and predict the 28-day mortality in sepsis. Then, we further explore the circulating immune cell and immunosuppression state in sepsis.Materials and methodsThe differentially expressed genes (DEGs), differentially expressed immune-related genes (DEIRGs), and differentially expressed transcription factors (DETFs) were obtained from the GEO, ImmPort, and Cistrome databases. Then, the TFs-DEIRGs regulatory network and prognostic prediction model were constructed by Cox regression analysis and Pearson correlation analysis. The external datasets also validated the reliability of the prognostic model. Based on the prognostic DEIRGs, we developed a nomogram and conducted an independent prognosis analysis to explore the relationship between DEIRGs in the prognostic model and clinical features in sepsis. Besides, we further evaluate the circulating immune cells state in sepsis.ResultsA total of seven datasets were included in our study. Among them, GSE65682 was identified as a discovery cohort. The results of GSEA showed that there is a significant correlation between sepsis and immune response. Then, based on a P value ConclusionsOur study provides a robust prognostic model based on 22 DEIRGs which can predict 28-day mortality and immunosuppression status in sepsis. The higher risk score was positively associated with 28-day mortality and the development of immunosuppression. IRGs are a promising biomarker that might facilitate personalized treatments for sepsis.</p

Table_6_Construction and validation of a robust prognostic model based on immune features in sepsis.docx

PurposeSepsis, with life-threatening organ failure, is caused by the uncontrolled host response to infection. Immune response plays an important role in the pathophysiology of sepsis. Immune-related genes (IRGs) are promising novel biomarkers that have been used to construct the diagnostic and prognostic model. However, an IRG prognostic model used to predict the 28-day mortality in sepsis was still limited. Therefore, the study aimed to develop a prognostic model based on IRGs to identify patients with high risk and predict the 28-day mortality in sepsis. Then, we further explore the circulating immune cell and immunosuppression state in sepsis.Materials and methodsThe differentially expressed genes (DEGs), differentially expressed immune-related genes (DEIRGs), and differentially expressed transcription factors (DETFs) were obtained from the GEO, ImmPort, and Cistrome databases. Then, the TFs-DEIRGs regulatory network and prognostic prediction model were constructed by Cox regression analysis and Pearson correlation analysis. The external datasets also validated the reliability of the prognostic model. Based on the prognostic DEIRGs, we developed a nomogram and conducted an independent prognosis analysis to explore the relationship between DEIRGs in the prognostic model and clinical features in sepsis. Besides, we further evaluate the circulating immune cells state in sepsis.ResultsA total of seven datasets were included in our study. Among them, GSE65682 was identified as a discovery cohort. The results of GSEA showed that there is a significant correlation between sepsis and immune response. Then, based on a P value ConclusionsOur study provides a robust prognostic model based on 22 DEIRGs which can predict 28-day mortality and immunosuppression status in sepsis. The higher risk score was positively associated with 28-day mortality and the development of immunosuppression. IRGs are a promising biomarker that might facilitate personalized treatments for sepsis.</p

Table_8_Construction and validation of a robust prognostic model based on immune features in sepsis.docx

PurposeSepsis, with life-threatening organ failure, is caused by the uncontrolled host response to infection. Immune response plays an important role in the pathophysiology of sepsis. Immune-related genes (IRGs) are promising novel biomarkers that have been used to construct the diagnostic and prognostic model. However, an IRG prognostic model used to predict the 28-day mortality in sepsis was still limited. Therefore, the study aimed to develop a prognostic model based on IRGs to identify patients with high risk and predict the 28-day mortality in sepsis. Then, we further explore the circulating immune cell and immunosuppression state in sepsis.Materials and methodsThe differentially expressed genes (DEGs), differentially expressed immune-related genes (DEIRGs), and differentially expressed transcription factors (DETFs) were obtained from the GEO, ImmPort, and Cistrome databases. Then, the TFs-DEIRGs regulatory network and prognostic prediction model were constructed by Cox regression analysis and Pearson correlation analysis. The external datasets also validated the reliability of the prognostic model. Based on the prognostic DEIRGs, we developed a nomogram and conducted an independent prognosis analysis to explore the relationship between DEIRGs in the prognostic model and clinical features in sepsis. Besides, we further evaluate the circulating immune cells state in sepsis.ResultsA total of seven datasets were included in our study. Among them, GSE65682 was identified as a discovery cohort. The results of GSEA showed that there is a significant correlation between sepsis and immune response. Then, based on a P value ConclusionsOur study provides a robust prognostic model based on 22 DEIRGs which can predict 28-day mortality and immunosuppression status in sepsis. The higher risk score was positively associated with 28-day mortality and the development of immunosuppression. IRGs are a promising biomarker that might facilitate personalized treatments for sepsis.</p

Table_5_Construction and validation of a robust prognostic model based on immune features in sepsis.docx

PurposeSepsis, with life-threatening organ failure, is caused by the uncontrolled host response to infection. Immune response plays an important role in the pathophysiology of sepsis. Immune-related genes (IRGs) are promising novel biomarkers that have been used to construct the diagnostic and prognostic model. However, an IRG prognostic model used to predict the 28-day mortality in sepsis was still limited. Therefore, the study aimed to develop a prognostic model based on IRGs to identify patients with high risk and predict the 28-day mortality in sepsis. Then, we further explore the circulating immune cell and immunosuppression state in sepsis.Materials and methodsThe differentially expressed genes (DEGs), differentially expressed immune-related genes (DEIRGs), and differentially expressed transcription factors (DETFs) were obtained from the GEO, ImmPort, and Cistrome databases. Then, the TFs-DEIRGs regulatory network and prognostic prediction model were constructed by Cox regression analysis and Pearson correlation analysis. The external datasets also validated the reliability of the prognostic model. Based on the prognostic DEIRGs, we developed a nomogram and conducted an independent prognosis analysis to explore the relationship between DEIRGs in the prognostic model and clinical features in sepsis. Besides, we further evaluate the circulating immune cells state in sepsis.ResultsA total of seven datasets were included in our study. Among them, GSE65682 was identified as a discovery cohort. The results of GSEA showed that there is a significant correlation between sepsis and immune response. Then, based on a P value ConclusionsOur study provides a robust prognostic model based on 22 DEIRGs which can predict 28-day mortality and immunosuppression status in sepsis. The higher risk score was positively associated with 28-day mortality and the development of immunosuppression. IRGs are a promising biomarker that might facilitate personalized treatments for sepsis.</p