Image_1_Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancer.tif

BackgroundAccumulative studies have demonstrated the close relationship between tumor immunity and pyroptosis, apoptosis, and necroptosis. However, the role of PANoptosis in gastric cancer (GC) is yet to be fully understood.MethodsThis research attempted to identify the expression patterns of PANoptosis regulators and the immune landscape in GC by integrating the GSE54129 and GSE65801 datasets. We analyzed GC specimens and established molecular clusters associated with PANoptosis-related genes (PRGs) and corresponding immune characteristics. The differentially expressed genes were determined with the WGCNA method. Afterward, we employed four machine learning algorithms (Random Forest, Support Vector Machine, Generalized linear Model, and eXtreme Gradient Boosting) to select the optimal model, which was validated using nomogram, calibration curve, decision curve analysis (DCA), and two validation cohorts. Additionally, this study discussed the relationship between infiltrating immune cells and variables in the selected model.ResultsThis study identified dysregulated PRGs and differential immune activities between GC and normal samples, and further identified two PANoptosis-related molecular clusters in GC. These clusters demonstrated remarkable immunological heterogeneity, with Cluster1 exhibiting abundant immune infiltration. The Support Vector Machine signature was found to have the best discriminative ability, and a 5-gene-based SVM signature was established. This model showed excellent performance in the external validation cohorts, and the nomogram, calibration curve, and DCA indicated its reliability in predicting GC patterns. Further analysis confirmed that the 5 selected variables were remarkably related to infiltrating immune cells and immune-related pathways.ConclusionTaken together, this work demonstrates that the PANoptosis pattern has the potential as a stratification tool for patient risk assessment and a reflection of the immune microenvironment in GC.</p

DataSheet_1_Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancer.zip

BackgroundAccumulative studies have demonstrated the close relationship between tumor immunity and pyroptosis, apoptosis, and necroptosis. However, the role of PANoptosis in gastric cancer (GC) is yet to be fully understood.MethodsThis research attempted to identify the expression patterns of PANoptosis regulators and the immune landscape in GC by integrating the GSE54129 and GSE65801 datasets. We analyzed GC specimens and established molecular clusters associated with PANoptosis-related genes (PRGs) and corresponding immune characteristics. The differentially expressed genes were determined with the WGCNA method. Afterward, we employed four machine learning algorithms (Random Forest, Support Vector Machine, Generalized linear Model, and eXtreme Gradient Boosting) to select the optimal model, which was validated using nomogram, calibration curve, decision curve analysis (DCA), and two validation cohorts. Additionally, this study discussed the relationship between infiltrating immune cells and variables in the selected model.ResultsThis study identified dysregulated PRGs and differential immune activities between GC and normal samples, and further identified two PANoptosis-related molecular clusters in GC. These clusters demonstrated remarkable immunological heterogeneity, with Cluster1 exhibiting abundant immune infiltration. The Support Vector Machine signature was found to have the best discriminative ability, and a 5-gene-based SVM signature was established. This model showed excellent performance in the external validation cohorts, and the nomogram, calibration curve, and DCA indicated its reliability in predicting GC patterns. Further analysis confirmed that the 5 selected variables were remarkably related to infiltrating immune cells and immune-related pathways.ConclusionTaken together, this work demonstrates that the PANoptosis pattern has the potential as a stratification tool for patient risk assessment and a reflection of the immune microenvironment in GC.</p

Image_2_Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancer.tif

BackgroundAccumulative studies have demonstrated the close relationship between tumor immunity and pyroptosis, apoptosis, and necroptosis. However, the role of PANoptosis in gastric cancer (GC) is yet to be fully understood.MethodsThis research attempted to identify the expression patterns of PANoptosis regulators and the immune landscape in GC by integrating the GSE54129 and GSE65801 datasets. We analyzed GC specimens and established molecular clusters associated with PANoptosis-related genes (PRGs) and corresponding immune characteristics. The differentially expressed genes were determined with the WGCNA method. Afterward, we employed four machine learning algorithms (Random Forest, Support Vector Machine, Generalized linear Model, and eXtreme Gradient Boosting) to select the optimal model, which was validated using nomogram, calibration curve, decision curve analysis (DCA), and two validation cohorts. Additionally, this study discussed the relationship between infiltrating immune cells and variables in the selected model.ResultsThis study identified dysregulated PRGs and differential immune activities between GC and normal samples, and further identified two PANoptosis-related molecular clusters in GC. These clusters demonstrated remarkable immunological heterogeneity, with Cluster1 exhibiting abundant immune infiltration. The Support Vector Machine signature was found to have the best discriminative ability, and a 5-gene-based SVM signature was established. This model showed excellent performance in the external validation cohorts, and the nomogram, calibration curve, and DCA indicated its reliability in predicting GC patterns. Further analysis confirmed that the 5 selected variables were remarkably related to infiltrating immune cells and immune-related pathways.ConclusionTaken together, this work demonstrates that the PANoptosis pattern has the potential as a stratification tool for patient risk assessment and a reflection of the immune microenvironment in GC.</p

Enhancing the Alpha-To-Gamma Phase Transition of Poly(vinylidene fluoride) via Dehydrofluorination Modification

Due to the high activating energy, it is very difficult to initiate the α-to-γ phase transition of poly(vinylidene fluoride) (PVDF), resulting in an extremely slow transition rate. Here, introducing a small number of double bonds into the PVDF molecular chains through dehydrofluorination is demonstrated to markedly decrease the activating energy and enhance the phase transition efficiency. It is found that the introduced double bonds during the dehydrofluorination reaction accelerate the α-to-γ phase transition, which is reflected by the shortened induction period and increased transition rate. The α-to-γ phase transition in PVDF modified with double bonds occurs mostly from the nuclei of α-spherulites rather than from the scarce boundaries initiated by γ-spherulites as in unmodified PVDF. Comparative analysis reveals that the energy storage performance of γ-PVDF films prepared through the phase transition surpasses that of α-PVDF ones. Compared to α-PVDF, the energy storage density of the modified γ-PVDF exhibits a remarkable enhancement of 181%, while the energy storage efficiency experiences a notable improvement of 124%. Consequently, a molecular modification strategy for the α-to-γ phase transition is introduced, enabling efficient production of γ-PVDF with enhanced energy storage properties and positioning it as an ideal material for driving technological advancements in electronic devices, electric vehicles, and renewable energy sectors

Additional file 1: Figure S1. of Epidemiology of invasive group B streptococcal disease in infants from urban area of South China, 2011–2014

Partial sequence diagram for seven house-keeping genes. adhP gene(A), pheS gene(B), atr gene(C), glnA gene(D), sdhA gene(E), glcK gene(F), tkt gene(G). (DOCX 849 kb