sj-docx-2-tva-10.1177_15248380231175918 – Supplemental material for Reducing the Methodological Heterogeneity (“Noise”) in the Literature Predicting In-Prison Interpersonal Harm in Male Populations

Supplemental material, sj-docx-2-tva-10.1177_15248380231175918 for Reducing the Methodological Heterogeneity (“Noise”) in the Literature Predicting In-Prison Interpersonal Harm in Male Populations by Nancy Wolff, Eva Aizpurua and Dan Peng in Trauma, Violence, & Abuse</p

sj-docx-1-tva-10.1177_15248380231175918 – Supplemental material for Reducing the Methodological Heterogeneity (“Noise”) in the Literature Predicting In-Prison Interpersonal Harm in Male Populations

Supplemental material, sj-docx-1-tva-10.1177_15248380231175918 for Reducing the Methodological Heterogeneity (“Noise”) in the Literature Predicting In-Prison Interpersonal Harm in Male Populations by Nancy Wolff, Eva Aizpurua and Dan Peng in Trauma, Violence, & Abuse</p

Novel Starlike Amphiphilic Graft Copolymers with Hydrophilic Poly(acrylic acid) Backbone and Hydrophobic Poly(methyl methacrylate) Side Chains

Novel Starlike Amphiphilic Graft Copolymers with Hydrophilic Poly(acrylic acid) Backbone and Hydrophobic Poly(methyl methacrylate) Side Chain

Image_2_Integrated Multi-Omics Analysis Identified PTPRG and CHL1 as Key Regulators of Immunophenotypes in Clear Cell Renal Cell Carcinoma(ccRCC).jpeg

Despite the increasing importance and status of immune checkpoint blockade (ICB), little is known about the underlying molecular mechanisms determining the target clear cell renal cell carcinoma (ccRCC) population. In this study, we screened out 6 immune cells strongly correlated with expression levels of PD-L1 and IFN-γ based on the ccRCC samples extracted from GSE and TCGA data sets. By performing unsupervised clustering and lasso regression analysis, we grouped the ccRCC into 4 clusters and selected the two most distinct sub-clusters for further investigation—cluster A1 and B1. Next, we compared the two clusters in terms of mRNA, somatic mutations, copy number variations, DNA methylation, miRNA, lncRNA and constructed the differentially expressed genes (DEGs) hub by combing together the previous results at levels of DNA methylation, miRNA, and lncRNA. PTPRG and CHL1 were identified as key nodes in the regulation hub of immunophenotypes in ccRCC patients. Finally, we established the prognosis model by using Lasso-Cox regression and Kaplan–Meier analysis, recognizing WNT2, C17orf66, and PAEP as independent significant risk factors while IRF4 as an independent protective factor.</p

Image_1_Integrated Multi-Omics Analysis Identified PTPRG and CHL1 as Key Regulators of Immunophenotypes in Clear Cell Renal Cell Carcinoma(ccRCC).jpeg

Despite the increasing importance and status of immune checkpoint blockade (ICB), little is known about the underlying molecular mechanisms determining the target clear cell renal cell carcinoma (ccRCC) population. In this study, we screened out 6 immune cells strongly correlated with expression levels of PD-L1 and IFN-γ based on the ccRCC samples extracted from GSE and TCGA data sets. By performing unsupervised clustering and lasso regression analysis, we grouped the ccRCC into 4 clusters and selected the two most distinct sub-clusters for further investigation—cluster A1 and B1. Next, we compared the two clusters in terms of mRNA, somatic mutations, copy number variations, DNA methylation, miRNA, lncRNA and constructed the differentially expressed genes (DEGs) hub by combing together the previous results at levels of DNA methylation, miRNA, and lncRNA. PTPRG and CHL1 were identified as key nodes in the regulation hub of immunophenotypes in ccRCC patients. Finally, we established the prognosis model by using Lasso-Cox regression and Kaplan–Meier analysis, recognizing WNT2, C17orf66, and PAEP as independent significant risk factors while IRF4 as an independent protective factor.</p

Effects of anti-ganglioside GD2 14G2a monoclonal antibody (mAb) alone or in combination with ET A receptor (ETAR) antagonist on osteosarcoma (OS) cell invasiveness.

<p>In vitro cell invasion assays were performed with a Fluorimetric Cell Invasion Assay kit (Chemicon; Millipore) in Saos-2 (<i>A</i>), MG-63 (<i>B</i>) and SJSA-1 (<i>C</i>) OS cells treated with control IgG (PK136 mAb, 50 µg/mL), 14G2a mAb (50 µg/mL), selective ETAR antagonist BQ123 (5 µM), and 14G2a (50 µg/mL)+BQ123 (5 µM) for 48 hours. Cells with knockdown of ETAR (ETAR-shRNA) with or without 14G2a mAb treatment were also tested. Cells treated with selective phosphatidylinositide 3-kinase (PI3K) inhibitor BKM120 (50 µM) was used as a positive control. Cell invasion was determined by fluorescence and shown as fold changes to that of the untreated control cells (designated as 1). Each experiment was repeated for three times in duplicates. Data values were expressed as Mean+SD. ^a<i>p</i><0.05 vs. control or control IgG; ^b<i>p</i><0.05 vs. BQ123; ^c<i>p</i><0.05 vs. ETAR-shRNA; ^d<i>p</i><0.05 vs. 14G2a; ^e<i>p</i><0.05 vs. 14G2a+BQ123; ^f<i>p</i><0.05 vs. 14G2a+ETAR-shRNA.</p

Effects of anti-ganglioside GD2 14G2a monoclonal antibody (mAb) alone or in combination with ET A receptor (ETAR) antagonist on matrix metalloproteinase-2 (MMP-2) activity in osteosarcoma (OS) cells.

<p>MMP-2 activities were determined with a SensoLyte 520 MMP-2 Assay Kit (AnaSpec) in Saos-2 (<i>A</i>), MG-63 (<i>B</i>) and SJSA-1 (<i>C</i>) OS cells treated with control IgG (PK136 mAb, 50 µg/mL), 14G2a mAb (50 µg/mL), selective ETAR antagonist BQ123 (5 µM), and 14G2a (50 µg/mL)+BQ123 (5 µM) for 48 hours. Cells with knockdown of ETAR (ETAR-shRNA) with or without 14G2a mAb treatment were also tested. Cells treated with selective phosphatidylinositide 3-kinase (PI3K) inhibitor BKM120 (50 µM) was used as a positive control. The MMP-2 activity was shown as fold changes to that of the untreated control cells (designated as 1). Each experiment was repeated for three times in duplicates. Data values were expressed as Mean+SD. ^a<i>p</i><0.05 vs. control or control IgG; ^b<i>p</i><0.05 vs. BQ123; ^c<i>p</i><0.05 vs. ETAR-shRNA; ^d<i>p</i><0.05 vs. 14G2a; ^e<i>p</i><0.05 vs. 14G2a+BQ123; ^f<i>p</i><0.05 vs. 14G2a+ETAR-shRNA.</p

Effects of anti-ganglioside GD2 14G2a monoclonal antibody (mAb) alone or in combination with ET A receptor (ETAR) antagonist on phosphorylated Akt (P-Akt) level in osteosarcoma (OS) cells.

<p>Levels of total Akt and P-Akt at serine 473 (ser473) were determined by Western blot analyses in Saos-2 (<i>A</i>), MG-63 (<i>B</i>) and SJSA-1 (<i>C</i>) cells treated with control IgG (50 µg/mL, lane 2), selective ETAR antagonist BQ123 (5 µM, lane 3), stably-transduced ETAR-shRNA (lane 4), 14G2a mAb (50 µg/mL, lane 5), 14G2a+BQ123 (lane 6), 14G2a+ETAR-shRNA (lane 7), and selective phosphatidylinositide 3-kinase (PI3K) inhibitor BKM120 (50 µM, lane 8). The untreated control was in <i>lane 1</i>. Density of the P-Akt (ser473) blot was normalized against that of total Akt to obtain a relative blot density, which was expressed as fold changes to the relative P-Akt (ser473) blot density of the untreated control cells (designated as 1). Three independent experiments were performed for each Western blot analysis. Data values were expressed as Mean+SD. ^a<i>p</i><0.05 vs. control or control IgG; ^b<i>p</i><0.05 vs. BQ123; ^c<i>p</i><0.05 vs. ETAR-shRNA; ^d<i>p</i><0.05 vs. 14G2a; ^e<i>p</i><0.05 vs. 14G2a+BQ123; ^f<i>p</i><0.05 vs. 14G2a+ETAR-shRNA.</p

Effects of anti-ganglioside GD2 14G2a monoclonal antibody (mAb) alone or in combination with ET A receptor (ETAR) antagonist on osteosarcoma (OS) cell viability.

<p>Methlythiazoletetrazolium (MTT) cell viability assays were performed in Saos-2 (<i>A</i>), MG-63 (<i>B</i>) and SJSA-1 (<i>C</i>) OS cells treated with control IgG (PK136 mAb, 50 µg/mL), 14G2a mAb (50 µg/mL), selective ETAR antagonist BQ123 (5 µM), and 14G2a (50 µg/mL)+BQ123 (5 µM) for 24 or 48 hours. Cells with knockdown of ETAR (ETAR-shRNA) with or without 14G2a mAb treatment were also tested. Cells treated with selective phosphatidylinositide 3-kinase (PI3K) inhibitor BKM120 (50 µM) was used as a positive control. Viability of the control cells was designated as 100%. The inhibition rate of cell viability was calculated and shown as a percentage of the control cell viability. Each experiment was repeated for three times in triplicates. Data values were expressed as Mean+SD.</p

Emulsion Polymerization Strategy for Heterogenization of Olefin Polymerization Catalysts

Heterogenization of homogeneous metal catalysts on solid supports has been widely studied for product morphology control in ethylene polymerization. In this contribution, an emulsion polymerization strategy was introduced to achieve heterogenization of various imino-based nickel olefin polymerization catalysts. A series of polymeric microspheres were prepared through emulsion copolymerization of vinyl-functionalized imino ligands with comonomers such as styrene or methyl methacrylate. The corresponding heterogeneous nickel catalysts demonstrated superior properties in ethylene polymerization and copolymerization with methyl 10-undecylenate. Their catalytic properties can be controlled by tuning the comonomer type, composition, and particle size. Most importantly, this strategy can achieve product morphology control and avoid reactor fouling while generating polyethylene products with minimum inorganic contamination. In addition, the introduction of polymer microspheres can improve the mechanical properties and surface properties of polyolefin products