Image_3_Identification of Epigenetic-Dysregulated lncRNAs Signature in Osteosarcoma by Multi-Omics Data Analysis.tif

BackgroundAlterations of epigenetic modification patterns are potential markers of cancer. The current study characterized six histone modifications in osteosarcoma and identified epigenetically dysregulated long non-coding RNAs (epi-lncRNAs).MethodsMulti-omics data were obtained from osteosarcoma cell line SJSA1 and a normal cell line. Differentially expressed lncRNAs (DElncRNAs) between osteosarcoma and normal skeletal muscle were analyzed using Limma. MACS2 was applied to identify the “peaks” modified by each histone in the cell. Promoters or enhancers of DElncRNA were overlapped with differential histone-modified regions (DHMR) to screen epi-lncRNAs. Univariate and multivariate Cox regression analysis were performed to detect the genes closely related to the prognosis of osteosarcoma and to construct risk models.ResultsA total of 17 symbolic epi-lncRNA in osteosarcoma were screened, and 13 of them were differentially expressed between osteosarcoma and normal samples. Eight epi-lncRNAs were retained by Univariate Cox regression analysis. Four of these epi-lncRNAs were used to construct an epi-lncRNA signature. The risk score of each osteosarcoma sample in the high- or low-risk group was estimated according to the epi-lncRNA signature. The overall survival (OS) of the low-risk group was significantly better than that of the high-risk group. The area under the receiver operating characteristic (ROC) curve of the model was 0.79 and 0.82 for 1-, 3-, and 5-year OS, respectively.ConclusionOur results revealed the histone modification pattern in osteosarcoma and developed 4-epi-lncRNA signature to predict the prognosis of osteosarcoma, laying a foundation for the identification of highly specific epigenetic biomarkers for osteosarcoma.</p

Image_2_Identification of Epigenetic-Dysregulated lncRNAs Signature in Osteosarcoma by Multi-Omics Data Analysis.tif

BackgroundAlterations of epigenetic modification patterns are potential markers of cancer. The current study characterized six histone modifications in osteosarcoma and identified epigenetically dysregulated long non-coding RNAs (epi-lncRNAs).MethodsMulti-omics data were obtained from osteosarcoma cell line SJSA1 and a normal cell line. Differentially expressed lncRNAs (DElncRNAs) between osteosarcoma and normal skeletal muscle were analyzed using Limma. MACS2 was applied to identify the “peaks” modified by each histone in the cell. Promoters or enhancers of DElncRNA were overlapped with differential histone-modified regions (DHMR) to screen epi-lncRNAs. Univariate and multivariate Cox regression analysis were performed to detect the genes closely related to the prognosis of osteosarcoma and to construct risk models.ResultsA total of 17 symbolic epi-lncRNA in osteosarcoma were screened, and 13 of them were differentially expressed between osteosarcoma and normal samples. Eight epi-lncRNAs were retained by Univariate Cox regression analysis. Four of these epi-lncRNAs were used to construct an epi-lncRNA signature. The risk score of each osteosarcoma sample in the high- or low-risk group was estimated according to the epi-lncRNA signature. The overall survival (OS) of the low-risk group was significantly better than that of the high-risk group. The area under the receiver operating characteristic (ROC) curve of the model was 0.79 and 0.82 for 1-, 3-, and 5-year OS, respectively.ConclusionOur results revealed the histone modification pattern in osteosarcoma and developed 4-epi-lncRNA signature to predict the prognosis of osteosarcoma, laying a foundation for the identification of highly specific epigenetic biomarkers for osteosarcoma.</p

Data_Sheet_1_How Learners’ Corrective Feedback Beliefs Modulate Their Oral Accuracy: A Comparative Study on High- and Low-Accuracy Learners of Chinese as a Second Language.pdf

This paper explores the differences in high-accuracy and low-accuracy learners’ beliefs about corrective feedback when learning Chinese as a second language (henceforth, CSL). In this study, we collected data through a questionnaire survey and an oral test with 76 CSL learners in a Chinese university. The analysis revealed that both high- and low-accuracy CSL learners shared the same beliefs in whether and how the learner errors should be corrected but differed in their beliefs about when is the best time to correct, which error should be corrected, and who the corrector should be. Specifically, the discrepancy between high- and low-accuracy groups’ beliefs about corrective feedback was found to be related to the participants’ oral accuracy. Our results confirm that learners’ CF beliefs can modulate their language accuracy. The corrective feedback beliefs held by high-accuracy groups have implications for improving low-accuracy groups’ oral accuracy. Through comparison with findings on corrective feedback beliefs of English as a foreign/second language (henceforth, EFL/ESL) learners, this study suggested that language pedagogies developed from the research of EFL/ESL learners’ CF beliefs should be able to shed light on this area and have significance for CSL learners. Implications for correcting learner errors in teaching CSL are also provided in the paper.</p

Image_1_Identification of Epigenetic-Dysregulated lncRNAs Signature in Osteosarcoma by Multi-Omics Data Analysis.pdf

BackgroundAlterations of epigenetic modification patterns are potential markers of cancer. The current study characterized six histone modifications in osteosarcoma and identified epigenetically dysregulated long non-coding RNAs (epi-lncRNAs).MethodsMulti-omics data were obtained from osteosarcoma cell line SJSA1 and a normal cell line. Differentially expressed lncRNAs (DElncRNAs) between osteosarcoma and normal skeletal muscle were analyzed using Limma. MACS2 was applied to identify the “peaks” modified by each histone in the cell. Promoters or enhancers of DElncRNA were overlapped with differential histone-modified regions (DHMR) to screen epi-lncRNAs. Univariate and multivariate Cox regression analysis were performed to detect the genes closely related to the prognosis of osteosarcoma and to construct risk models.ResultsA total of 17 symbolic epi-lncRNA in osteosarcoma were screened, and 13 of them were differentially expressed between osteosarcoma and normal samples. Eight epi-lncRNAs were retained by Univariate Cox regression analysis. Four of these epi-lncRNAs were used to construct an epi-lncRNA signature. The risk score of each osteosarcoma sample in the high- or low-risk group was estimated according to the epi-lncRNA signature. The overall survival (OS) of the low-risk group was significantly better than that of the high-risk group. The area under the receiver operating characteristic (ROC) curve of the model was 0.79 and 0.82 for 1-, 3-, and 5-year OS, respectively.ConclusionOur results revealed the histone modification pattern in osteosarcoma and developed 4-epi-lncRNA signature to predict the prognosis of osteosarcoma, laying a foundation for the identification of highly specific epigenetic biomarkers for osteosarcoma.</p

Supplementary_Table_1_A Network Pharmacology Study: Reveal the Mechanisms of Palovarotene Against Heterotopic Ossification.csv

Heterotopic ossification (HO) occurs when bone forms within non-ossifying tissues, such as in muscle. Palovarotene, an activator of retinoic acid receptor γ (RAR-γ), has been shown to inhibit the formation of ectopic bone in HO model mice, but its specific mechanism of action remains unclear. This study will explore the target and molecular mechanism of Palovarotene's action on HO by network pharmacology study. We collected the relevant targets of Palovarotene and HO from the database, obtained the potential targets of Palovarotene acting on HO through Venn analysis, and constructed the protein-protein interaction (PPI) network. Then, Gene Ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment Analysis and Module-based Network Analysis were performed for potential targets, and in addition, PPI Network Topology Analysis and Gene-Phenotype Correlation Analysis were performed. The results suggested that MAPK1, MDM2, and other targets as well as P53 signaling pathway and PI3K–Akt signaling pathway may be closely related to Palovarotene treatment of HO. We carried out verification experiments to confirm our finding, alkaline phosphatase and alizarin red staining in vitro and Micro-CT as well as hematoxylin-eosin staining in vivo were performed to verify treatment for HO of Palovarotene, reverse transcription polymerase chain reaction was also used to explore the transcription changes of MAPK1, MDM2, and osteogenic genes. This study systematically elucidated the possible mechanism of Palovarotene in the treatment of HO through network pharmacology study, revealing a new direction for the further application of Palovarotene in the treatment of HO.</p

Supplementary_Table_2_A Network Pharmacology Study: Reveal the Mechanisms of Palovarotene Against Heterotopic Ossification.csv

Heterotopic ossification (HO) occurs when bone forms within non-ossifying tissues, such as in muscle. Palovarotene, an activator of retinoic acid receptor γ (RAR-γ), has been shown to inhibit the formation of ectopic bone in HO model mice, but its specific mechanism of action remains unclear. This study will explore the target and molecular mechanism of Palovarotene's action on HO by network pharmacology study. We collected the relevant targets of Palovarotene and HO from the database, obtained the potential targets of Palovarotene acting on HO through Venn analysis, and constructed the protein-protein interaction (PPI) network. Then, Gene Ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment Analysis and Module-based Network Analysis were performed for potential targets, and in addition, PPI Network Topology Analysis and Gene-Phenotype Correlation Analysis were performed. The results suggested that MAPK1, MDM2, and other targets as well as P53 signaling pathway and PI3K–Akt signaling pathway may be closely related to Palovarotene treatment of HO. We carried out verification experiments to confirm our finding, alkaline phosphatase and alizarin red staining in vitro and Micro-CT as well as hematoxylin-eosin staining in vivo were performed to verify treatment for HO of Palovarotene, reverse transcription polymerase chain reaction was also used to explore the transcription changes of MAPK1, MDM2, and osteogenic genes. This study systematically elucidated the possible mechanism of Palovarotene in the treatment of HO through network pharmacology study, revealing a new direction for the further application of Palovarotene in the treatment of HO.</p

Microwave-Assisted Synthesis of Various ZnO Hierarchical Nanostructures: Effects of Heating Parameters of Microwave Oven

Several novel hierarchical ZnO nanostructures have been successfully prepared in mixed solvents of ethylene glycol (EG)−water via a facile microwave-assisted method. By only change of the heating parameters of the microwave oven, ZnO nanostructures with straw-bundle-like, wide chrysanthemum-like, and oat-arista-like morphologies and microspheres were obtained. The products were characterized by means of X-ray powder diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and room-temperature photoluminescence spectrometry (PLS). The possible mechanisms for the growth of these hierarchical ZnO nanostructures were tentatively proposed

Image_3_The causal relationship between air pollution, obesity, and COVID-19 risk: a large-scale genetic correlation study.pdf

ObjectiveObservational evidence reported that air pollution is a significant risk element for numerous health problems, such as obesity and coronavirus disease 2019 (COVID-19), but their causal relationship is currently unknown. Our objective was to probe the causal relationship between air pollution, obesity, and COVID-19 and to explore whether obesity mediates this association.MethodsWe obtained instrumental variables strongly correlated to air pollutants [PM2.5, nitrogen dioxide (NO2) and nitrogen oxides (NOx)], 9 obesity-related traits (abdominal subcutaneous adipose tissue volume, waist-to-hip ratio, body mass index, hip circumference, waist circumference, obesity class 1-3, visceral adipose tissue volume), and COVID-19 phenotypes (susceptibility, hospitalization, severity) from public genome-wide association studies. We used clinical and genetic data from different public biological databases and performed analysis by two-sample and two-step Mendelian randomization.ResultsPM2.5 genetically correlated with 5 obesity-related traits, which obesity class 1 was most affected (beta = 0.38, 95% CI = 0.11 - 0.65, p = 6.31E-3). NO2 genetically correlated with 3 obesity-related traits, which obesity class 1 was also most affected (beta = 0.33, 95% CI = 0.055 - 0.61, p = 1.90E-2). NOx genetically correlated with 7 obesity-related traits, which obesity class 3 was most affected (beta = 1.16, 95% CI = 0.42-1.90, p = 2.10E-3). Almost all the obesity-related traits genetically increased the risks for COVID-19 phenotypes. Among them, body mass index, waist circumference, hip circumference, waist-to-hip ratio, and obesity class 1 and 2 mediated the effects of air pollutants on COVID-19 risks (p ConclusionOur study suggested that exposure to heavy air pollutants causally increased risks for obesity. Besides, obesity causally increased the risks for COVID-19 phenotypes. Attention needs to be paid to weight status for the population who suffer from heavy air pollution, as they are more likely to be susceptible and vulnerable to COVID-19.</p

Correlation between IIEF-5 scores and UPOINT positive number.

<p>Mean IIEF-5 scores were compared with the positive number of urinary (U), psychosocial (P), organ specific (O), infection (I), neurological/systemic (N), and tenderness (T) UPOINT domains. Significant difference was seen in the IIEF-5 score between groups (all P<0.001), and the number of positive UPOINT domains was significantly inversely correlated with the IIEF-5 scores (Spearman r = 0.631, P = 0.007).</p

Interactive effect between temperature and fine particulate matter on chronic disease hospital admissions in the urban area of Tianjin, China

This study focuses on effects of fine particulate matter (PM2.5) on chronic disease under different levels of temperature. We obtained type 2 diabetes, cerebral stroke and coronary heart disease hospital admissions (HAs) from five hospitals in urban Tianjin as well as the concentrations of PM2.5, nitrogen dioxide (NO2) and sulphur dioxide (SO2). We used distributed lag nonlinear models to explore nonlinear and lag effects of PM2.5. In single-pollutant models, PM2.5 was positively associated with type 2 diabetes, cerebral stroke and coronary heart disease HAs, with strongest effects at lag1, lag0 and lag06, respectively. The corresponding relative risk rates (RR%) were 1.836%, 2.083% and 6.428%. In co-pollutant models, the correlation between PM2.5 and HAs on high-temperature days was generally stronger than that on low-temperature days. This study indicated that PM2.5 can increase HA rates for these chronic diseases, and effects of PM2.5 on high-temperature days were stronger than that on low-temperature days.</p