Densities of L-serine-n-propanol-water ternary solutions have been measured at 298.15K by an oscillating-tube densimeter. Apparent molar volumes, limiting partial molar volumes, transfer partial molar volumes and hydration numbers for L-serine have been calculated. The transfer volumes from water to n-propanol-water mixtures and hydration numbers have been discussed in terms of the structural hydration interaction model. The results show that a dominant interaction between -OH group of n-propanol and the zwitterionic group of serine gives positive contribution to the transfer volume. The transfer volumes of serine from water to n-propanol-water mixed solvents are positive, and increase with increasing n-propanol concentration, while the hydration numbers decrease with increasing n-propanol concentration.
Recent technological advances in the field of microfluidic chips have provided great opportunities to boost the development of exosome-based cancer diagnosis. The emerging microfluidic chips with intrinsic multi-functions can be highly competent for the establishment of next-generation analysis platform for exosome. Herein, efforts are made to give in-depth insights into the recent advances of engineering microfluidic chips on the representative examples of the current exosome-related research, ranging from exosome separation, enrichment, controllable release to downstream analysis. We discussed the challenges and solutions for the exosome analysis in the cancer diagnosis, especially the exosome heterogeneity, single exosome analysis, combination analysis of the exosomal protein and nucleic acid and the immuno-therapy related monitoring. The development trends and promising research directions of the exosome study in the future were also discussed. This review will shed light on facilitating design of novel microfluidic chips for the exosome mediated cancer diagnosis.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Additional file 3 Supplementary figures. Figure S1 Plot of the precision under the second hypothesis. Figure S2 Plot of the precision under the third hypothesis. Figure S3 Plot of the accuracy under the second hypothesis. Figure S4 Plot of the accuracy under the third hypothesis. Figure S5 Plot of the FPR under the second hypothesis. Figure S6 Plot of the FPR under the third hypothesis. Figure S7 Plot of the MCC under the second hypothesis. Figure S8 Plot of the MCC under the third hypothesis. Figure S9 Plot of the sensitivity under the second hypothesis. Figure S10 Plot of the sensitivity under the third hypothesis. Figure S11 Plot of the ROC curve and the AUC value under the second hypothesis. Figure S12 Plot of the ROC under the third hypothesis. The DSLD2 method is developed in this paper. Figure S13 Precision-recall plot under the second hypothesis. Figure S14 Precision-recall plot under the third hypothesis. Figure S15 Bias plot of 6 meta-analysis methods when τ2 is set to 1.0 and SMD is chosen as the effect size measure. Figure S16 RMSE plot of 6 meta-analysis methods when τ2 is set to 1.0 and SMD is chosen as the effect size measure. Figure S17 Bias plot of 6 meta-analysis methods when τ2 is set to 1.0 and MD is chosen as the effect size measure. Figure S18 RMSE plot of 6 meta-analysis methods when τ2 is set to 1.0 and MD is chosen as the effect size measure. Figure S19 Mean of I2 plot of 6 meta-analysis methods when τ2 is set to 1.0 and SMD is chosen as the effect size measure. Figure S20 Mean of I2 plot of 6 meta-analysis methods when τ2 is set to 1.0 and MD is chosen as the effect size measure.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Valve replacement is the main therapy for valvular heart disease, in which a diseased valve is replaced by mechanical heart valve (MHV) or bioprosthetic heart valve (BHV). Since the 2000s, BHV surpassed MHV as the leading option of prosthetic valve substitute because of its excellent hemocompatible and hemodynamic properties. However, BHV is apt to structural valve degeneration (SVD), resulting in limited durability. Calcification is the most frequent presentation and the core pathophysiological process of SVD. Understanding the basic mechanisms of BHV calcification is an essential prerequisite to address the limited-durability issues. In this narrative review, we provide a comprehensive summary about the mechanisms of BHV calcification on 1) composition and site of calcifications; 2) material-associated mechanisms; 3) host-associated mechanisms, including immune response and foreign body reaction, oxidative stress, metabolic disorder, and thrombosis. Strategies that target these mechanisms may be explored for novel drug therapy to prevent or delay BHV calcification.
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