Novel prognostic gene signature for pancreatic ductal adenocarcinoma based on hypoxia

Abstract Background Currently, there is lack of marker to accurately assess the prognosis of patients diagnosed with pancreatic ductal adenocarcinoma (PDAC). This study aims to establish a hypoxia-related risk scoring model that can effectively predict the prognosis and chemotherapy outcomes of PDAC patients. Methods Using unsupervised consensus clustering algorithms, we comprehensively analyzed The Cancer Genome Atlas (TCGA) data to identify two distinct hypoxia clusters and used the weighted gene co-expression network analysis (WGCNA) to examine gene sets significantly associated with these hypoxia clusters. Then univariate Cox regression, the least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression were used to construct a signature and its efficacy was evaluated using the International Cancer Genome Consortium (ICGC) PDAC cohort. Further, the correlation between the risk scores obtained from the signature and carious clinical, pathological, immunophenotype, and immunoinfiltration factors as well as the differences in immunotherapy potential and response to common chemotherapy drugs between high-risk and low-risk groups were evaluated. Results From a total of 8 significantly related modules and 4423 genes, 5 hypoxia-related signature genes were identified to construct a risk model. Further analysis revealed that the overall survival rate (OS) of patients in the low-risk group was significantly higher than the high-risk group. Univariate and multivariate Cox regression analysis showed that the risk scoring signature was an independent factor for prognosis prediction. Analysis of immunocyte infiltration and immunophenotype showed that the immune score and the anticancer immune response in the high-risk were significantly lower than that in the low-risk group. Conclusion The constructed hypoxia-associated prognostic signature demonstrated could be used as a potential risk classifier for PDAC

Analysis and Applications of GlobeLand30: A Review

Abstract: GlobeLand30, donated to the United Nations by China in September 2014, is the first wall-to-wall 30 m global land cover (GLC) data product. GlobeLand30 is widely used by scientists and users around the world. This paper provides a review of the analysis and applications of GlobeLand30 based on its data-downloading statistics and published studies. An average accuracy of 80% for full classes or one single class is achieved by third-party researchers from more than 10 countries through sample-based validation or comparison with existing data. GlobeLand30 has users from more than 120 countries on five continents, and from all five Social Benefit Areas. The significance of GlobeLand30 is demonstrated by a number of published papers dealing with land-cover status and change analysis, cause-and-consequence analysis, and the environmental parameterization of Earth system models. Accordingly, scientific data sharing in the field of geosciences and Earth observation is promoted, and fine-resolution GLC mapping and applications worldwide are stimulated. The future development of GlobeLand30, including comprehensive validation, continuous updating, and monitoring of sustainable development goals, is also discussed

First developments towards TiO2 nanoimprint for photonic sensor fabrication

National audienceno abstrac

Influence of Temperature, Solvents, and Excipients on Crystal Transformation of Agomelatine

Agomelatine is a new and novel non-SSRI potential treatment option for major depressive disorders, discovered and developed by Servier Laboratories with first marketing approval in 2009. In this work, the solution-mediated crystal form I to II transformation process was studied. The influence factors, such as system temperature, crystallization solvents, and some pharmaceutical excipients, were investigated. DSC, XRPD, SEM, react IR, and in situ FBRM were used to monitor the transformation process. Compared with crystal form I of agomelatine, form II was the thermodynamic stable one, therefore, the transformation was a spontaneous process. By increasing the system temperature and/or the volume ratio of IPA in the solvent mixture, the transformation process would be accelerated. Lactose hydrate and HPMC would slightly inhibit the transformation, while PVP K30 would accelerate transformation process by increasing the solubility

Targeted modification of polyamide nanofiltration membrane for efficient separation of monosaccharides and monovalent salt

Separation of monosaccharides and monovalent salts is important in the biorefinery and food industry. Nanofiltration (NF) technology is promising for this purpose but its poor selectivity still needs to be addressed. In this work, the separation of monosaccharides and monovalent salts by NF is improved by regulating the surface charge and pore size distribution of polyamide NF membranes. The carboxyl groups on the polyamide membrane are selectively activated by N-(3-Dimethylaminopropyl)-N?-ethyl carbodiimide (EDC) and N-hydroxy succinimide (NHS), and subsequently molecules with amino groups are grafted on the membrane to reduce the effective pore size and electronegativity. The conditions and grafted molecules are optimized, and polyethyleneimine (600 Da) is selected as the best for enhancing the separation of glucose/fructose and KCl. Such targeted modification is found to reduce the effective mean pore size while maintaining the porosity of the NF270 membrane, due to pore segmentation. This results in a remarkable improvement in glucose/fructose rejection (from 67.96% to 84.14%) and separation factor (from 2.20 to 6.78), with only a 4.70% permeability loss. The modified membrane also maintains separation performance in crossflow filtration and after alkaline cleaning (pH 12), which outperforms the pristine NF270 and those modified by mussel-inspired coating and simple physical adsorption

TiO2 nanoimprint for photonic sensor application

National audienceno abstrac