Enhanced Catalytic Hydrogenation Activity and Selectivity of Pt‑M_<i>x</i>O_<i>y</i>/Al₂O₃ (M = Ni, Fe, Co) Heteroaggregate Catalysts by <i>in Situ</i> Transformation of PtM Alloy Nanoparticles

PtM (M = Ni, Fe, Co) alloy nanoparticles were synthesized by a liquid phase reduction method employing butyllithium as a reducing agent. The alumina-supported PtM materials were then used as precursors to obtain the Pt-M_<i>x</i>O_<i>y</i>/Al₂O₃ catalysts through calcination. The influence of synthesis conditions of PtM alloy nanoparticles and the catalytic performance of the Pt-M_<i>x</i>O_<i>y</i>/Al₂O₃ catalysts for <i>p</i>-chloronitrobenzene hydrogenation reaction were investigated. The relevant characterizations such as XRD, XPS, and TEM were conducted for PtM alloy nanoparticles and Pt-M_<i>x</i>O_<i>y</i>/Al₂O₃ catalysts, and the result showed that the PtM nanoparticles are uniform alloy. Moreover, compared to PtM alloy nanoparticles, the Pt particle size of Pt-M_<i>x</i>O_<i>y</i>/Al₂O₃ using PtM alloy nanoparticle precursors did not increase by calcination, indicating good thermal stability. The catalytic activities of Pt-M_<i>x</i>O_<i>y</i>/Al₂O₃ for <i>p</i>-chloronitrobenzene hydrogenation reaction were significantly higher than that of control Pt/Al₂O₃ catalysts due to its strong Pt-M_<i>x</i>O_<i>y</i> interaction

Image_6_A prognostic model and immune regulation analysis of uterine corpus endometrial carcinoma based on cellular senescence.tif

BackgroundThis study aimed to explore the clinical significance of cellular senescence in uterine corpus endometrial carcinoma (UCEC).MethodsCluster analysis was performed on GEO data and TCGA data based on cellular senescence related genes, and then performed subtype analysis on differentially expressed genes between subtypes. The prognostic model was constructed using Lasso regression. Survival analysis, microenvironment analysis, immune analysis, mutation analysis, and drug susceptibility analysis were performed to evaluate the practical relevance. Ultimately, a clinical nomogram was constructed and cellular senescence-related genes expression was investigated by qRT-PCR.ResultsWe ultimately identified two subtypes. The prognostic model divides patients into high-risk and low-risk groups. There were notable discrepancies in prognosis, tumor microenvironment, immunity, and mutation between the two subtypes and groups. There was a notable connection between drug-sensitive and risk scores. The nomogram has good calibration with AUC values between 0.75-0.8. In addition, cellular senescence-related genes expression was investigated qRT-PCR.ConclusionOur model and nomogram may effectively forecast patient prognosis and serve as a reference for patient management.</p

Image_1_A prognostic model and immune regulation analysis of uterine corpus endometrial carcinoma based on cellular senescence.tif

BackgroundThis study aimed to explore the clinical significance of cellular senescence in uterine corpus endometrial carcinoma (UCEC).MethodsCluster analysis was performed on GEO data and TCGA data based on cellular senescence related genes, and then performed subtype analysis on differentially expressed genes between subtypes. The prognostic model was constructed using Lasso regression. Survival analysis, microenvironment analysis, immune analysis, mutation analysis, and drug susceptibility analysis were performed to evaluate the practical relevance. Ultimately, a clinical nomogram was constructed and cellular senescence-related genes expression was investigated by qRT-PCR.ResultsWe ultimately identified two subtypes. The prognostic model divides patients into high-risk and low-risk groups. There were notable discrepancies in prognosis, tumor microenvironment, immunity, and mutation between the two subtypes and groups. There was a notable connection between drug-sensitive and risk scores. The nomogram has good calibration with AUC values between 0.75-0.8. In addition, cellular senescence-related genes expression was investigated qRT-PCR.ConclusionOur model and nomogram may effectively forecast patient prognosis and serve as a reference for patient management.</p

DataSheet_1_A prognostic model and immune regulation analysis of uterine corpus endometrial carcinoma based on cellular senescence.docx

BackgroundThis study aimed to explore the clinical significance of cellular senescence in uterine corpus endometrial carcinoma (UCEC).MethodsCluster analysis was performed on GEO data and TCGA data based on cellular senescence related genes, and then performed subtype analysis on differentially expressed genes between subtypes. The prognostic model was constructed using Lasso regression. Survival analysis, microenvironment analysis, immune analysis, mutation analysis, and drug susceptibility analysis were performed to evaluate the practical relevance. Ultimately, a clinical nomogram was constructed and cellular senescence-related genes expression was investigated by qRT-PCR.ResultsWe ultimately identified two subtypes. The prognostic model divides patients into high-risk and low-risk groups. There were notable discrepancies in prognosis, tumor microenvironment, immunity, and mutation between the two subtypes and groups. There was a notable connection between drug-sensitive and risk scores. The nomogram has good calibration with AUC values between 0.75-0.8. In addition, cellular senescence-related genes expression was investigated qRT-PCR.ConclusionOur model and nomogram may effectively forecast patient prognosis and serve as a reference for patient management.</p

Image_4_A prognostic model and immune regulation analysis of uterine corpus endometrial carcinoma based on cellular senescence.tif

BackgroundThis study aimed to explore the clinical significance of cellular senescence in uterine corpus endometrial carcinoma (UCEC).MethodsCluster analysis was performed on GEO data and TCGA data based on cellular senescence related genes, and then performed subtype analysis on differentially expressed genes between subtypes. The prognostic model was constructed using Lasso regression. Survival analysis, microenvironment analysis, immune analysis, mutation analysis, and drug susceptibility analysis were performed to evaluate the practical relevance. Ultimately, a clinical nomogram was constructed and cellular senescence-related genes expression was investigated by qRT-PCR.ResultsWe ultimately identified two subtypes. The prognostic model divides patients into high-risk and low-risk groups. There were notable discrepancies in prognosis, tumor microenvironment, immunity, and mutation between the two subtypes and groups. There was a notable connection between drug-sensitive and risk scores. The nomogram has good calibration with AUC values between 0.75-0.8. In addition, cellular senescence-related genes expression was investigated qRT-PCR.ConclusionOur model and nomogram may effectively forecast patient prognosis and serve as a reference for patient management.</p

Image_2_A prognostic model and immune regulation analysis of uterine corpus endometrial carcinoma based on cellular senescence.tif

BackgroundThis study aimed to explore the clinical significance of cellular senescence in uterine corpus endometrial carcinoma (UCEC).MethodsCluster analysis was performed on GEO data and TCGA data based on cellular senescence related genes, and then performed subtype analysis on differentially expressed genes between subtypes. The prognostic model was constructed using Lasso regression. Survival analysis, microenvironment analysis, immune analysis, mutation analysis, and drug susceptibility analysis were performed to evaluate the practical relevance. Ultimately, a clinical nomogram was constructed and cellular senescence-related genes expression was investigated by qRT-PCR.ResultsWe ultimately identified two subtypes. The prognostic model divides patients into high-risk and low-risk groups. There were notable discrepancies in prognosis, tumor microenvironment, immunity, and mutation between the two subtypes and groups. There was a notable connection between drug-sensitive and risk scores. The nomogram has good calibration with AUC values between 0.75-0.8. In addition, cellular senescence-related genes expression was investigated qRT-PCR.ConclusionOur model and nomogram may effectively forecast patient prognosis and serve as a reference for patient management.</p

Image_5_A prognostic model and immune regulation analysis of uterine corpus endometrial carcinoma based on cellular senescence.tif

BackgroundThis study aimed to explore the clinical significance of cellular senescence in uterine corpus endometrial carcinoma (UCEC).MethodsCluster analysis was performed on GEO data and TCGA data based on cellular senescence related genes, and then performed subtype analysis on differentially expressed genes between subtypes. The prognostic model was constructed using Lasso regression. Survival analysis, microenvironment analysis, immune analysis, mutation analysis, and drug susceptibility analysis were performed to evaluate the practical relevance. Ultimately, a clinical nomogram was constructed and cellular senescence-related genes expression was investigated by qRT-PCR.ResultsWe ultimately identified two subtypes. The prognostic model divides patients into high-risk and low-risk groups. There were notable discrepancies in prognosis, tumor microenvironment, immunity, and mutation between the two subtypes and groups. There was a notable connection between drug-sensitive and risk scores. The nomogram has good calibration with AUC values between 0.75-0.8. In addition, cellular senescence-related genes expression was investigated qRT-PCR.ConclusionOur model and nomogram may effectively forecast patient prognosis and serve as a reference for patient management.</p

Image_3_A prognostic model and immune regulation analysis of uterine corpus endometrial carcinoma based on cellular senescence.tif

BackgroundThis study aimed to explore the clinical significance of cellular senescence in uterine corpus endometrial carcinoma (UCEC).MethodsCluster analysis was performed on GEO data and TCGA data based on cellular senescence related genes, and then performed subtype analysis on differentially expressed genes between subtypes. The prognostic model was constructed using Lasso regression. Survival analysis, microenvironment analysis, immune analysis, mutation analysis, and drug susceptibility analysis were performed to evaluate the practical relevance. Ultimately, a clinical nomogram was constructed and cellular senescence-related genes expression was investigated by qRT-PCR.ResultsWe ultimately identified two subtypes. The prognostic model divides patients into high-risk and low-risk groups. There were notable discrepancies in prognosis, tumor microenvironment, immunity, and mutation between the two subtypes and groups. There was a notable connection between drug-sensitive and risk scores. The nomogram has good calibration with AUC values between 0.75-0.8. In addition, cellular senescence-related genes expression was investigated qRT-PCR.ConclusionOur model and nomogram may effectively forecast patient prognosis and serve as a reference for patient management.</p

Additional file 3: of Unravelling personalized dysfunctional gene network of complex diseases based on differential network model

Table S2. R_T12D_MCL.csv: the differential modules mined in diabetes dataset

Additional file 2: of Unravelling personalized dysfunctional gene network of complex diseases based on differential network model

Table S1. R_pca_MCL.csv: the differential modules mined in prostate cancer dataset