Reconstitution of Human <i>Ether-a-go-go</i>-Related Gene Channels in Microfabricated Silicon Chips

This paper reports on the reconstitution of human <i>ether-a-go-go-</i>related gene (hERG) channels in artificial bilayer lipid membranes (BLMs) formed in micropores fabricated in silicon chips. The hERG channels were isolated from Chinese hamster ovary cell lines expressing the channels and incorporated into the BLMs formed by a process in which the two lipid monolayers were folded into the micropores. The characteristic features of hERG channels reported by the patch-clamp method, including single-channel conductance, voltage dependence, sensitivity to typical drugs and dependence on the potassium concentration, were investigated in the BLM reconstitution system. The BLM with hERG channels incorporated exhibited a lifetime of ∼65 h and a tolerance to repetitive solution exchanges. Such stable BLMs containing biological channels have the potential for use in a variety of applications, including high-throughput drug screening for various ion-channel proteins

Host Cell Prediction of Exosomes Using Morphological Features on Solid Surfaces Analyzed by Machine Learning

Exosomes are extracellular nanovesicles released from any cells and found in any body fluid. Because exosomes exhibit information of their host cells (secreting cells), their analysis is expected to be a powerful tool for early diagnosis of cancers. To predict the host cells, we extracted multidimensional feature data about size, shape, and deformation of exosomes immobilized on solid surfaces by atomic force microscopy (AFM). The key idea is combination of support vector machine (SVM) learning for individual exosome particles and their interpretation by principal component analysis (PCA). We observed exosomes derived from three different cancer cells on SiO₂/Si, 3-aminopropyltriethoxysilane-modified-SiO₂/Si, and TiO₂ substrates by AFM. Then, 14-dimensional feature vectors were extracted from AFM particle data, and classifiers were trained in 14-dimensional space. The prediction accuracy for host cells of test AFM particles was examined by the cross-validation test. As a result, we obtained prediction of exosome host cells with the best accuracy of 85.2% for two-class SVM learning and 82.6% for three-class one. By PCA of the particle classifiers, we concluded that the main factors for prediction accuracy and its strong dependence on substrates are incremental decrease in the PCA-defined aspect ratio of the particles with their volume

Histogram of circulating 25OHD levels (A) and 1, 25OHD levels (B) in patients with type 2 diabetes and with CKD stage 1∼5.

<p>Blood sampling was performed at entry; thus disease duration differed among patients. Serum 25OHD and 1,25OHD were measured by radioimmunoassay.</p

Patients' characteristics of the study population and association with eGFR stage ^*¹.

*<p>1: Stage 1 chronic kidney disease (CKD), estimated glomerular filtration rate (eGFR) ≥90 ml/min/1.73 m²; Stage 2 CKD, eGFR ≥60 to <90 ml/min/1.73 m²; Stage 3 CKD, eGFR ≥30 to <60 ml/min/1.73 m²; Stage 4 CKD, eGFR ≥15 to <30 ml/min/1.73 m²; and Stage 5 CKD, eGFR <15 ml/min/1.73 m². *2: IQR: interquartile range. *3: P-value was evaluated with single ordered logistic regression model for eGFR stages. *4: P-value was calculated with chi-square test. *5: ACEI, angiotensin converting enzyme inhibitor; ARB, angiotensin II receptor blocker *6: The number of patients who could be measured was small and deleted from the analysis.</p

Multiple ordered logistic regression model to see the interaction between Fok<i>I</i> genotypes and 25OHD/1,25OHD levels on eGFR stage adjusted with 7 possible confounders^*.

*<p>All variables in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051171#pone-0051171-t002" target="_blank">table 2</a> as well as confounders disease duration; use of ACEI/ARB; use of statin; serum Ca, P, and iPTH levels; and calendar month were simultaneously computed with ordered logistic regression model.</p

Two-way scatter graph for eGFR vs. 1,25OHD, stratified by patients with Fok<i>I</i> TT and with Fok<i>I</i> CC or CT.

<p>Fitting curves were drawn by calculating the prediction for eGFR from a linear regression of eGFR on 1,25OHD either in patients with Fok<i>I</i> TT and with Fok<i>I</i> CC or CT using STATA ver. 12.0.</p

Serum 25OHD and 1,25OHD levels from January to December in patients with type 2 diabetes and with CKD stage 1∼5.

<p>Associations are shown using the total study population (A), a subpopulation with CKD stages 3∼5 (B), and a subpopulation with CKD stages 1∼2 (C). The central box extends from the 25^th to the 75^th percentile. All dots outside this range are outliers, which are not typical of the rest of the data.</p

Two-way scatter graph for eGFR vs. 25OHD (A) or eGFR vs. 1,25OHD (B) in patients with type 2 diabetes and with CKD stage 1∼5.

<p>Two-way scatter graph for eGFR vs. 25OHD (A) or eGFR vs. 1,25OHD (B) in patients with type 2 diabetes and with CKD stage 1∼5.</p