Lipid-bound ApoE3 self-assemble into elliptical disc-shaped particles

A Novel Prediction Model for Brain Glioma Image Segmentation Based on the Theory of Bose-Einstein Condensate

Background

The input image of a blurry glioma image segmentation is, usually, very unclear. It is difficult to obtain the accurate contour line of image segmentation. The main challenge facing the researchers is to correctly determine the area where the points on the contour line belong to the glioma image. This article highlights the mechanism of formation of glioma and provides an image segmentation prediction model to assist in the accurate division of glioma contour points. The proposed prediction model of segmentation associated with the process of the formation of glioma is innovative and challenging. Bose-Einstein Condensate (BEC) is a microscopic quantum phenomenon in which atoms condense to the ground state of energy as the temperature approaches absolute zero. In this article, we propose a BEC kernel function and a novel prediction model based on the BEC kernel to detect the relationship between the process of the BEC and the formation of a brain glioma. Furthermore, the theoretical derivation and proof of the prediction model are given from micro to macro through quantum mechanics, wave, oscillation of glioma, and statistical distribution of laws. The prediction model is a distinct segmentation model that is guided by BEC theory for blurry glioma image segmentation.

Results

Our approach is based on five tests. The first three tests aimed at confirming the measuring range of T and μ in the BEC kernel. The results are extended from −10 to 10, approximating the standard range to T ≤ 0, and μ from 0 to 6.7. Tests 4 and 5 are comparison tests. The comparison in Test 4 was based on various established cluster methods. The results show that our prediction model in image evaluation parameters of P, R, and F is the best amongst all the existent ten forms except for only one reference with the mean value of F that is between 0.88 and 0.93, while our approach returns between 0.85 and 0.99. Test 5 aimed to further compare our results, especially with CNN (Convolutional Neural Networks) methods, by challenging Brain Tumor Segmentation (BraTS) and clinic patient datasets. Our results were also better than all reference tests. In addition, the proposed prediction model with the BEC kernel is feasible and has a comparative validity in glioma image segmentation.

Conclusions

Theoretical derivation and experimental verification show that the prediction model based on the BEC kernel can solve the problem of accurate segmentation of blurry glioma images. It demonstrates that the BEC kernel is a more feasible, valid, and accurate approach than a lot of the recent year segmentation methods. It is also an advanced and innovative model of prediction deducing from micro BEC theory to macro glioma image segmentation.

TXNIP interaction with GLUT1 depends on PI(4,5)P2

GLUT1 is a major glucose facilitator expressed ubiquitously among tissues. Upregulation of its expression plays an important role in the development of many types of cancer and metabolic diseases. Thioredoxin-interacting protein (TXNIP) is an α-arrestin that acts as an adaptor for GLUT1 in clathrin-mediated endocytosis. It regulates cellular glucose uptake in response to both intracellular and extracellular signals via its control on GLUT1-4. In order to understand the interaction between GLUT1 and TXNIP, we generated GLUT1 lipid nanodiscs and carried out isothermal titration calorimetry and single-particle electron microscopy experiments. We found that GLUT1 lipid nanodiscs and TXNIP interact in a 1:1 ratio and that this interaction requires phosphatidylinositol 4,5-bisphosphate (PI(4,5)P₂ or PIP₂).

Screening of the binding affinity of serum proteins to lipid nanoparticles in a cell free environment

Lipid nanoparticles (LNPs) are promising drug and gene carriers. Upon intravenous administration, LNPs' experience different degree of cellular uptake depending on their formulation. Currently, in vitro and in vivo studies are the gold standard for assessing the fate of nano carriers once administered, but they are time consuming and expensive. In this work, we propose a time and cost-effective method to screen a wide range of LNP formulations and select the most promising candidates for in vitro and in vivo studies. Two different approaches were explored to investigate the binding affinity between LNPs and serum proteins using sensor functionalisation with either protein specific antibody or PEG specific antibody. The first approach allowed to identify the presence of a specific protein in the protein corona of lipid particles (reconstituted and native high-density lipoproteins (rHDL and HDL), and low-density lipoproteins LDL); while the second one provided a versatile platform for the immobilisation of pegylated-particles in order to follow the interaction with serum proteins and hence predict the composition of LNP protein corona. Sensing was done using Quartz Crystal Microbalance with Dissipation (QCM-D) but the approach is extendable to other surface sensing techniques such as Surface Plasmon Resonance (SPR) or ellipsometry.