Image1_A four-lncRNA risk signature for prognostic prediction of osteosarcoma.JPEG

Aim: Osteosarcoma is the most common primary malignant tumor of bone. However, our understanding of the prognostic indicators and the genetic mechanisms of the disease progression are still incomplete. The aim of this study was to identify a long noncoding RNA (lncRNA) risk signature for osteosarcoma survival prediction.Methods: RNA sequencing data and relevant clinical information of osteosarcoma patients were downloaded from the database of Therapeutically Applicable Research to Generate Effective Treatments (TARGET). We analyzed the differentially expressed lncRNAs between deceased and living patients by univariate and multivariate Cox regression analysis to identify a risk signature. We calculated a prognostic risk score for each sample according to this prognosis signature, and divided patients into high-risk and low-risk groups according to the median value of the risk score (0.975). Kaplan–Meier analysis and receiver operating characteristic (ROC) curve statistics were used to evaluate the performance of the signature. Next, we analyzed the signature’s potential function through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene-set enrichment analysis (GSEA). Lastly, qRT-PCR was used to validate the expression levels of the four lncRNAs in clinical samples.Results: Twenty-six differentially expressed lncRNAs were identified between deceased and living patients. Four of these lncRNAs (CTB-4E7.1, RP11-553A10.1, RP11-24N18.1, and PVRL3-AS1) were identified as independent prognostic factors, and a risk signature of these four lncRNAs for osteosarcoma survival prediction was constructed. Kaplan–Meier analysis showed that the five-year survival time in high-risk and low-risk groups was 33.1% and 82.5%, and the area under the curve (AUC) of the ROC was 0.784, which demonstrated that the prognostic signature was reliable and had the potential to predict the survival of patients with osteosarcoma. The expression level of the four lncRNAs in osteosarcoma tissues and cells was determined by qRT-PCR. Functional enrichment analysis suggested that the signature might be related to osteosarcoma through regulation of the MAPK signaling pathway, the PI3K-Akt signaling pathway, and the extracellular matrix and also provided new insights into the study of osteosarcoma, including the role of papillomavirus infection, olfactory receptor activity, and olfactory transduction in osteosarcoma.Conclusion: We constructed a novel lncRNA risk signature that served as an independent biomarker for predicting the prognosis of osteosarcoma patients.</p

DataSheet1_A four-lncRNA risk signature for prognostic prediction of osteosarcoma.ZIP

Aim: Osteosarcoma is the most common primary malignant tumor of bone. However, our understanding of the prognostic indicators and the genetic mechanisms of the disease progression are still incomplete. The aim of this study was to identify a long noncoding RNA (lncRNA) risk signature for osteosarcoma survival prediction.Methods: RNA sequencing data and relevant clinical information of osteosarcoma patients were downloaded from the database of Therapeutically Applicable Research to Generate Effective Treatments (TARGET). We analyzed the differentially expressed lncRNAs between deceased and living patients by univariate and multivariate Cox regression analysis to identify a risk signature. We calculated a prognostic risk score for each sample according to this prognosis signature, and divided patients into high-risk and low-risk groups according to the median value of the risk score (0.975). Kaplan–Meier analysis and receiver operating characteristic (ROC) curve statistics were used to evaluate the performance of the signature. Next, we analyzed the signature’s potential function through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene-set enrichment analysis (GSEA). Lastly, qRT-PCR was used to validate the expression levels of the four lncRNAs in clinical samples.Results: Twenty-six differentially expressed lncRNAs were identified between deceased and living patients. Four of these lncRNAs (CTB-4E7.1, RP11-553A10.1, RP11-24N18.1, and PVRL3-AS1) were identified as independent prognostic factors, and a risk signature of these four lncRNAs for osteosarcoma survival prediction was constructed. Kaplan–Meier analysis showed that the five-year survival time in high-risk and low-risk groups was 33.1% and 82.5%, and the area under the curve (AUC) of the ROC was 0.784, which demonstrated that the prognostic signature was reliable and had the potential to predict the survival of patients with osteosarcoma. The expression level of the four lncRNAs in osteosarcoma tissues and cells was determined by qRT-PCR. Functional enrichment analysis suggested that the signature might be related to osteosarcoma through regulation of the MAPK signaling pathway, the PI3K-Akt signaling pathway, and the extracellular matrix and also provided new insights into the study of osteosarcoma, including the role of papillomavirus infection, olfactory receptor activity, and olfactory transduction in osteosarcoma.Conclusion: We constructed a novel lncRNA risk signature that served as an independent biomarker for predicting the prognosis of osteosarcoma patients.</p

The co-creation process of a platform for healthcare engineering design and innovation (HEDI)

The HEDI platform stands for the Healthcare Engineering Design and Innovation; it is a proposed platform aimd to promote co-creation of medical products with cooperation of different stakeholders with specialized expertise. Co-creation activities can be conducted either offline or online. In this study, the offline co-creation mechanisms were tested through three projects to gain insights into what worked and what did not work. Online co-creation platforms such as Quirky, Kaizao were analyzed to summerize their attributes. Based on the investigation of offline co-creation methanisms and online co-creation platforms, insights were drawn for the development of the co-creation platform of HEDI. The Value-Methodology-Execution (VME) Pyramid was used to illustrate the structure and significance of co-creation for healthcare engineering design and innovation

Palladium-Catalyzed Decarbonylative Alkynylation of Amides

A palladium-catalyzed decarbonylative alkynylation of amides via C–N bond activation is developed. Compared with the reported Ni/Cu catalyzed reaction, which only proceeded well with silylacetylenes, this transformation was also applicable to both aromatic and aliphatic terminal alkynes, including those bearing functional groups, and thus provided a general and straightforward access to diverse internal alkynes

Metabolic Engineering of Escherichia coli for Increased Bioproduction of <i>N</i>‑Acetylneuraminic Acid

N-Acetylneuraminic acid (NeuAc) is widely used in the food and pharmaceutical industries. Therefore, it is important to develop an efficient and eco-friendly method for NeuAc production. Here, we achieved de novo biosynthesis of NeuAc in an engineered plasmid-free Escherichia coli strain, which efficiently synthesizes NeuAc using glycerol as the sole carbon source, via clustered regularly interspaced palindromic repeat (CRISPR)/CRISPR-associated protein 9-based genome editing. NeuAc key precursor, N-acetylmannosamine (ManNAc; 0.40 g/L), was produced by expressing UDP-N-acetylglucosamine-2-epimerase and glucosamine-6-phosphate synthase (GlmS) mutants and blocking the NeuAc catabolic pathway in E. coli BL21 (DE3). The expression levels of GlmM and GlmU-GlmSA metabolic modules were optimized, significantly increasing the ManNAc titer to 8.95 g/L. Next, the expression levels of NeuAc synthase from different microorganisms were optimized, leading to the production of 6.27 g/L of NeuAc. Blocking the competing pathway of NeuAc biosynthesis increased the NeuAc titer to 9.65 g/L. In fed-batch culture in a 3 L fermenter, NeuAc titer reached 23.46 g/L with productivity of 0.69 g/L/h, which is the highest level achieved by microbial synthesis using glycerol as the sole carbon source in E. coli. The strategies used in our study can aid in the efficient bioproduction of NeuAc and its derivatives

Data_Sheet_1_An application of BWM for risk control in reverse logistics of medical waste.docx

The pollution posed by medical waste complicate the procedures of medical waste logistics (MWL), and the increasingly frequent occurrence of public health emergencies has magnified the risks posed by it. In this study, the authors established an index of the factors influencing the risks posed by MWL along five dimensions: the logistics business, emergency capacity, equipment, personnel, and management. The best-worst case method was used to identify the critical risk-related factors and rank them by importance. Following this, we assessed the risk posed by MWL in four major cities in China as an example and propose the corresponding measures of risk control. The results showed that the linking of business processes was the most important factor influencing the risk posed by MWL. The other critical risk-related factors included the location of the storage site, the capacity for emergency transportation, measures to manage emergencies, and the safety of packaging. Of the cities considered, Beijing was found to be a high-risk city, and its MWL needed to be improved as soon as possible in light of the relevant critical risks. Shanghai, Guangzhou, and Shenzhen were evaluated as general-risk cities, which meant that the risks of MWL were not a priority in these areas, and the other goals of urban development should be comprehensively considered during the long-term planning for MWL in these municipalities.</p

Additional file 2 of Transcriptome-wide 5-methylcytosine modification profiling of long non-coding RNAs in A549 cells infected with H1N1 influenza A virus

Supplementary Material

Additional file 4 of Transcriptome-wide 5-methylcytosine modification profiling of long non-coding RNAs in A549 cells infected with H1N1 influenza A virus

Supplementary Material

Additional file 1 of Transcriptome-wide 5-methylcytosine modification profiling of long non-coding RNAs in A549 cells infected with H1N1 influenza A virus

Supplementary Material

Additional file 3 of Transcriptome-wide 5-methylcytosine modification profiling of long non-coding RNAs in A549 cells infected with H1N1 influenza A virus

Supplementary Material