Multimedia schools : a practical journal of technology for education, including multimedia, CD-ROM, online, internet & hardware in K-12

Loading of cells with cholesterol by adding cholesterol in combination with BSA is reproducible. HAECs were cultured in regular endothelial cell culture medium (basal medium) or incubated in serum free medium in the presence of 40 μg/ml cholesterol and 1% fatty acid free BSA for 36 h, and cellular cholesterol content was assayed. Values are means of triplicate assays (±SD)

Table_1_Construction and verification of an endoplasmic reticulum stress-related prognostic model for endometrial cancer based on WGCNA and machine learning algorithms.xlsx

BackgroundEndoplasmic reticulum (ER) stress arises from the accumulation of misfolded or unfolded proteins within the cell and is intricately linked to the initiation and progression of various tumors and their therapeutic strategies. However, the precise role of ER stress in uterine corpus endometrial cancer (UCEC) remains unclear.MethodsData on patients with UCEC and control subjects were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Using differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA), we identified pivotal differentially expressed ER stress-related genes (DEERGs). Further validation of the significance of these genes in UCEC was achieved through consensus clustering and bioinformatic analyses. Using Cox regression analysis and several machine learning algorithms (least absolute shrinkage and selection operator [LASSO], eXtreme Gradient Boosting [XGBoost], support vector machine recursive feature elimination [SVM-RFE], and Random Forest), hub DEERGs associated with patient prognosis were effectively identified. Based on the four identified hub genes, a prognostic model and nomogram were constructed. Additionally, a drug sensitivity analysis and in vitro validation experiments were performed.ResultsA total of 94 DEERGs were identified in patients with UCEC and healthy controls. Consensus clustering analysis revealed significant differences in prognosis, typical immune checkpoints, and tumor microenvironments between the subtypes. Using Cox regression analysis and machine learning, four hub DEERGs, MYBL2, RADX, RUSC2, and CYP46A1, were identified to construct a prognostic model. The reliability of the model was validated using receiver operating characteristic (ROC) curves. Decision curve analysis (DCA) demonstrated the superior predictive ability of the nomogram in terms of 3- and 5-year survival, compared with that of other clinical indicators. Drug sensitivity analysis revealed increased sensitivity to dactinomycin, docetaxel, selumetinib, and trametinib in the low-risk group. The expressions of RADX, RUSC2, and CYP46A1 were downregulated, whereas that of MYBL2 was upregulated in UCEC tissues, as demonstrated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and immunofluorescence assays.ConclusionThis study developed a stable and accurate prognostic model based on multiple bioinformatics analyses, which can be used to assess the prognosis of UCEC. This model may contribute to future research on the risk stratification of patients with UCEC and the formulation of novel treatment strategies.</p

DataSheet_1_Construction and verification of an endoplasmic reticulum stress-related prognostic model for endometrial cancer based on WGCNA and machine learning algorithms.csv

BackgroundEndoplasmic reticulum (ER) stress arises from the accumulation of misfolded or unfolded proteins within the cell and is intricately linked to the initiation and progression of various tumors and their therapeutic strategies. However, the precise role of ER stress in uterine corpus endometrial cancer (UCEC) remains unclear.MethodsData on patients with UCEC and control subjects were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Using differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA), we identified pivotal differentially expressed ER stress-related genes (DEERGs). Further validation of the significance of these genes in UCEC was achieved through consensus clustering and bioinformatic analyses. Using Cox regression analysis and several machine learning algorithms (least absolute shrinkage and selection operator [LASSO], eXtreme Gradient Boosting [XGBoost], support vector machine recursive feature elimination [SVM-RFE], and Random Forest), hub DEERGs associated with patient prognosis were effectively identified. Based on the four identified hub genes, a prognostic model and nomogram were constructed. Additionally, a drug sensitivity analysis and in vitro validation experiments were performed.ResultsA total of 94 DEERGs were identified in patients with UCEC and healthy controls. Consensus clustering analysis revealed significant differences in prognosis, typical immune checkpoints, and tumor microenvironments between the subtypes. Using Cox regression analysis and machine learning, four hub DEERGs, MYBL2, RADX, RUSC2, and CYP46A1, were identified to construct a prognostic model. The reliability of the model was validated using receiver operating characteristic (ROC) curves. Decision curve analysis (DCA) demonstrated the superior predictive ability of the nomogram in terms of 3- and 5-year survival, compared with that of other clinical indicators. Drug sensitivity analysis revealed increased sensitivity to dactinomycin, docetaxel, selumetinib, and trametinib in the low-risk group. The expressions of RADX, RUSC2, and CYP46A1 were downregulated, whereas that of MYBL2 was upregulated in UCEC tissues, as demonstrated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and immunofluorescence assays.ConclusionThis study developed a stable and accurate prognostic model based on multiple bioinformatics analyses, which can be used to assess the prognosis of UCEC. This model may contribute to future research on the risk stratification of patients with UCEC and the formulation of novel treatment strategies.</p

Study on the Thermal Degradation Kinetics of Biodegradable Poly(propylene carbonate) during Melt Processing by Population Balance Model and Rheology

The degradation behavior of poly­(propylene carbonate) (PPC) was investigated during melt processing to infer the mechanism and kinetics of thermal degradation. First, the degradation experiments were carried out in a miniature conical twin-screw extruder at different temperatures, rotating speeds, and processing times. Gel permeation chromatography (GPC) was applied to analyze the molecular weight and molecular weight distributions (MWDs) of melt processed PPC samples. The degradation process at various processing conditions was described by the population balance equations (PBEs) with random chain scission and chain end scission. By comparing the prediction of PBE model with the experimental evolution of molecular weight, it is proposed that random chain scission and chain end scission occur simultaneously. At temperature higher than 160 °C, random chain scission dominates with the activation energy about 120 kJ/mol. Second, a method combining the PBE model and rheology was suggested to determine the kinetics of degradation directly from the torque of mixer during melt processing without further measurements on molecular weight. Such method was applied to melt mixing of PPC in a batch mixer, from which a higher kinetic parameter of thermal degradation and similar activation energy were successfully determined as compared to those obtained from extrusion experiments

Fetal DNA hypermethylation in tight junction pathway is associated with neural tube defects: A genome-wide DNA methylation analysis

<p>Neural tube defects (NTDs) are a spectrum of severe congenital malformations of fusion failure of the neural tube during early embryogenesis. Evidence on aberrant DNA methylation in NTD development remains scarce, especially when exposure to environmental pollutant is taken into consideration. DNA methylation profiling was quantified using the Infinium HumanMethylation450 array in neural tissues from 10 NTD cases and 8 non-malformed controls (stage 1). Subsequent validation was performed using a Sequenom MassARRAY system in neural tissues from 20 NTD cases and 20 non-malformed controls (stage 2). Correlation analysis of differentially methylated CpG sites in fetal neural tissues and polycyclic aromatic hydrocarbons concentrations in fetal neural tissues and maternal serum was conducted. Differentially methylated CpG sites of neural tissues were further validated in fetal mice with NTDs induced by benzo(a)pyrene given to pregnant mice. Differentially hypermethylated CpG sites in neural tissues from 17 genes and 6 pathways were identified in stage 1. Subsequently, differentially hypermethylated CpG sites in neural tissues from 6 genes (<i>BDKRB2, CTNNA1, CYFIP2, MMP7, MYH2</i>, and <i>TIAM2</i>) were confirmed in stage 2. Correlation analysis showed that methylated CpG sites in <i>CTNNA1</i> and <i>MYH2</i> from NTD cases were positively correlated to polycyclic aromatic hydrocarbon level in fetal neural tissues and maternal serum. The correlation was confirmed in NTD-affected fetal mice that were exposed to benzo(a)pyrene in utero. In conclusion, hypermethylation of the <i>CTNNA1</i> and <i>MYH2</i> genes in tight junction pathway is associated with the risk for NTDs, and the DNA methylation aberration may be caused by exposure to benzo(a)pyrene.</p

Positively Charged Combinatory Drug Delivery Systems against Multi-Drug-Resistant Breast Cancer: Beyond the Drug Combination

The formation and development of cancer is usually accompanied by angiogenesis and is related to multiple pathways. The inhibition of one pathway by monotherapy might result in the occurrence of drug resistance, tumor relapse, or metastasis. Thus, a combinatory therapeutic system that targets several independent pathways simultaneously is preferred for the treatment. To this end, we prepared combinatory drug delivery systems consisting of cytotoxic drug SN38, pro-apoptotic KLAK peptide, and survivin siRNA with high drug loading capacity and reductive responsiveness for the treatment of multi-drug-resistant (MDR) cancer. With the help of positive charge and the synergistic effect of different drug, the combinatory systems inhibited the growth of doxorubicin-resistant breast cancer cells (MCF-7/ADR) efficiently. Interestingly, the systems without siRNA showed more superior <i>in vivo</i> anticancer efficacy than those with siRNA which exhibited enhanced <i>in vitro</i> cytotoxicity and pro-apoptotic ability. This phenomenon could be attributed to the preferential tumor accumulation, strong tumor penetration, and excellent tumor vasculature targeting ability of the combinatory micelles of SN38 and KLAK. As a result, a combinatory multitarget therapeutic system with positive charge induced tumor accumulation and vasculature targeting which can simultaneously inhibit the growth of both tumor cell and tumor vasculature was established. This work also enlightened us to the fact that the design of combinatory drug delivery systems is not just a matter of simple drug combination. Besides the cytotoxicity and pro-apoptotic ability, tumor accumulation, tumor penetration, or vascular targeting may also influence the eventual antitumor effect of the combinatory system