diffusion behaviors
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Small ◽  
2021 ◽  
pp. 2106498
Author(s):  
Jing‐Wen Yuan ◽  
Yu‐Ning Zhang ◽  
Yu‐Ru Liu ◽  
Wei Li ◽  
Shuo‐Xing Dou ◽  
...  

Calphad ◽  
2021 ◽  
Vol 74 ◽  
pp. 102286
Author(s):  
Juan Chen ◽  
Yue Wang ◽  
Jinkun Xiao ◽  
Xiao-gang Lu ◽  
Lijun Zhang

2021 ◽  
Vol 11 ◽  
Author(s):  
Yuan Li ◽  
Michelle M. Kim ◽  
Daniel R. Wahl ◽  
Theodore S. Lawrence ◽  
Hemant Parmar ◽  
...  

Simple SummaryGlioblastoma (GBM) is the most common and aggressive primary brain tumor. Diffusion kurtosis imaging (DKI) has characterized non-Gaussian diffusion behaviors in brain normal tissue and gliomas, but there are very limited efforts in investigating treatment responses of kurtosis in GBM. This study aimed to investigate whether any parameter derived from the DKI is a significant predictor of overall survival (OS). We found that the large mean, 80 and 90 percentile kurtosis values in the contrast enhanced gross tumor volume (Gd-GTV) on post-Gd T1-weighted images pre-RT were significantly associated with reduced OS. In the multivariate Cox model, the mean kurtosis Gd-GTV pre-RT after considering effects of age, extent of surgery, and methylation were significant predictors of OS. In addition, the 80 and 90 percentile kurtosis values in Gd-GTV post RT were significantly associated with progression free survival (PFS). The DKI model demonstrates the potential to predict outcomes in the patients with GBM.PurposeNon-Gaussian diffusion behaviors in gliomas have been characterized by diffusion kurtosis imaging (DKI). But there are very limited efforts in investigating the kurtosis in glioblastoma (GBM) and its prognostic and predictive values. This study aimed to investigate whether any of the diffusion kurtosis parameters derived from DKI is a significant predictor of overall survival.Methods and MaterialsThirty-three patients with GBM had pre-radiation therapy (RT) and mid-RT diffusion weighted (DW) images. Kurtosis and diffusion coefficient (DC) values in the contrast enhanced gross tumor volume (Gd-GTV) on post-Gd T1 weighted images pre-RT and mid-RT were calculated. Univariate and multivariate Cox models were used to evaluate the DKI parameters and clinical factors for prediction of OS and PFS.ResultsThe large mean kurtosis values in the Gd-GTV pre-RT were significantly associated with reduced OS (p = 0.02), but the values at mid-RT were not (p > 0.8). In the multivariate Cox model, the mean kurtosis in the Gd-GTV pre-RT (p = 0.009) was still a significant predictor of OS after adjusting effects of age, O6-Methylguanine-DNA Methyl transferase (MGMT) methylation and extent of resection. In Gd-GTV post-RT, 80 and 90 percentile kurtosis values were significant predictors (p ≤ 0.05) for progression free survival (PFS).ConclusionThe DKI model demonstrates the potential to predict OS and PFS in the patients with GBM. Further development and histopathological validation of the DKI model will warrant its role in clinical management of GBM.


2021 ◽  
Vol 148 ◽  
pp. 111009
Author(s):  
Zhaoyang Wang ◽  
Ping Lin ◽  
Erhui Wang

Carbon ◽  
2021 ◽  
Author(s):  
Kun Su ◽  
Lu Pan ◽  
Yihui Zhang ◽  
Chong Wang ◽  
Yijun Yang ◽  
...  
Keyword(s):  

Membranes ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 392
Author(s):  
Yasuhisa Hasegawa ◽  
Chie Abe

Computational chemistry is a promising technique for the prediction of material properties. Adsorption and diffusion behaviors in zeolite micropores are important for zeolite membranes. In this study, we investigated novel non-bonding interaction parameters of all-silica zeolites for the prediction of the adsorption and diffusion behaviors by focusing on the Si atom of zeolite frameworks. Our parameters (σ = 0.421 nm, ε = 0.954 kJ mol−1, and q = +1.10 e) were close to theoretically derived values, and the adsorption isotherms of CO2 and CH4 on several zeolites could be predicted with high accuracy. Furthermore, the parameters gave the suitable self-diffusivities of CO2 and CH4 within MFI-type zeolite micropores through molecular dynamics simulation. Those suggest that our derived parameters are useful for selecting zeolite structure as the membrane material.


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