Assessment of prognosis and responsiveness to immunotherapy in colorectal cancer patients based on the level of immune cell infiltration

ObjectiveTo build a new prognostic risk assessment model based on immune cell co-expression networks for predicting overall survival and evaluating the efficacy of immunotherapy for colon cancer patients.MethodsThe Cancer Genome Atlas (TCGA) database was used to obtain mRNA expression profiling data, clinical information, and somatic mutation data from colorectal cancer patients. The degree of tumor immune cell infiltration of the samples was analyzed using the CIBERSORT algorithm. Co-expression of immune-related genes was analyzed using weighted correlation network analysis (WGCNA) and gene modules were identified. Prognosis-related genes were screened and models were constructed using LASSO-Cox analysis. The models were validated by survival analysis. The prognostic potential of the models was quantitatively assessed using Cox regression analysis and the development of column line plots. Immunotherapy sensitivity analysis was performed using CIBERSORT and TIMER algorithms. Gene biofunction analysis was performed using Gene set enrichment analysis (GSEA) and Gene set variation analysis (GSVA). And the chemotherapeutic response to different drugs was assessed.ResultsWe established a novel prognostic model utilizing the WGCNA method, which demonstrated robust predictive accuracy for patient survival. The high-risk subgroup in our model exhibited elevated immune cell infiltration coupled with a higher tumor mutation burden, but the difference in response to immunotherapy was not significant compared to the low-risk group. Furthermore, we identified distinct chemotherapy responses to 39 drugs between these risk subgroups.ConclusionThis study revealed a significant correlation between high levels of immune infiltration and unfavorable prognosis in patients with colon cancer. Furthermore, an accurate prognostic risk prediction model based on the co-expression of relevant genes by immune cells was developed, enabling precise prediction of survival of colon cancer patients. These findings offer valuable insights for accurate prognostication and comprehensive management of individuals diagnosed with colon cancer

Uncertainty Analysis and Design of Air Suspension Systems for City Buses Based on Neural Network Model and True Probability Density

The accuracy of uncertainty analysis in suspension systems is closely tied to the precision of the probability distribution of sprung mass. Consequently, traditional assumptions regarding the probability distribution fail to guarantee the accuracy of uncertainty analyses results. To achieve more precise uncertainty analysis outcomes, this paper proposes a data-driven approach for analyzing the uncertainties in bus air suspension systems. Firstly, a bus vehicle dynamics model is established to investigate the influence of sprung mass on suspension system performance. Subsequently, a deep neural network model is trained using road test data, for the accurate identification of the sprung mass. The historical mass of the bus is then computed using vehicle network data to obtain the true probability density of the sprung mass. Lastly, the real probability distribution of the sprung mass is utilized to perform uncertainty analysis on the bus suspension system, and the results are compared with those obtained by assuming a probability distribution. Comparative analysis reveals substantial disparities in uncertainty response, with a maximum relative error of 9% observed for wheel dynamic loads, thus emphasizing the significance of precise probability distribution information concerning the sprung mass

Equity Evaluation of Street-Level Greenery Based on Green View Index from Street View Images: A Case Study of Hangzhou, China

Equity in urban greenery is essential to improving residents’ well-being and contributing to environmental justice. Research on equity in street-scale urban greenery remains limited, but this study addresses it by employing the green view index (GVI), a widely recognized indicator for assessing green space quality from a pedestrian perspective, using semantic segmentation methods and Baidu Street View (BSV) images to quantify street-level greenery. Through spatial clustering and hot spot analysis, the visibility and spatial distribution of street greenery in Hangzhou’s central urban area were examined. Furthermore, the Lorenz curve, Gini coefficient, and location entropy were applied to evaluate disparities in green visibility across urban spaces. The results show that the average GVI at the sample point level, road level, and district level in the study area are 0.167, 0.142, and 0.177, respectively. Meanwhile, the spatial heterogeneity of the GVI is highly pronounced, with distinct clustering characteristics. The Gini coefficient of street greenery visibility is 0.384, indicating a moderate level of inequality in the distribution of greenery resources. Notably, a higher GVI does not necessarily correspond to better internal greenery equity, highlighting disparities in the distribution of urban greenery. This study offers a more precise and refined quantification of urban greenery equity, providing critical insights for addressing spatial disparities and informing urban planning strategies aimed at promoting equitable green infrastructure

How Ethical Leadership Prompts Employees’ Voice Behavior? The Roles of Employees’ Affective Commitment and Moral Disengagement

Previous literature has demonstrated that ethical leadership could predict employees’ voice behavior. However, it’s not clear how to heighten these positive effects of ethical leadership on employees’ voice behavior. Building on the AET and moral disengagement studies, we developed an integrated model. A three-wave field study (N = 232) investigated the relationship between ethical leadership and voice behavior by focusing on the mediating role of employees’ affective commitment and the moderating role of employees’ moral disengagement. Our matched data analysis results indicated that: (1) employees’ affective commitment partly mediated the relationship between ethical leadership and employees’ voice behavior. In addition, employees’ moral disengagement moderated (2) the effect of ethical leadership on employees’ affective commitment and (3) the effect of employees’ affective commitment on voice behavior, similarly, (4) the indirect effect of ethical leadership on employees’ voice behavior via employees’ affective commitment. Theoretical and practical implications of these results are discussed.</jats:p