Population characteristics and diagnosis rate of chronic kidney disease by eGFR and proteinuria in Japanese clinical practice: an observational database study.

Chronic kidney disease (CKD) guidelines recommend early identification and intervention to delay the progression of CKD. The Kidney Disease: Improving Global Outcomes (KDIGO) heatmap is widely used for risk evaluation in CKD management; however, real-world evidence on clinical characteristics based on the KDIGO heatmap remains limited worldwide including Japan. In order to understand the management of CKD including its diagnostic rates in a Japanese clinical setting on the basis of KDIGO heatmap, we utilized a medical record database that contains estimated glomerular filtration rate (eGFR) and urine protein data. Adult individuals (≥ 18 years) with two eGFR results of < 90 mL/min/1.73 m2, 90-360 days apart, were included. Approximately half of patients (452,996/788,059) had proteinuria test results and 6.9% (54,073) had quantitative results. CKD diagnosis rate in patients without proteinuria data was 5.9%, with a lower rate (2.9%) in stage G2; the corresponding rates with quantitative test results were 43.5% and 31.3%, respectively. The most frequent comorbidities were hypertension, diabetes, and cardiovascular disease, and their prevalence increased as the eGFR and proteinuria stages progressed. This study revealed a low rate of proteinuria assessment, especially using quantitative methods, and diagnosis in individuals with suspected CKD. With emerging treatment options to prevent CKD progression and complication onset, there is a need for early evaluation and diagnosis of CKD.

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.

Phase separation in lipid bilayer membranes induced by intermixing at a boundary of two phases with different components.

We have demonstrated that dynamic phase separation is induced by coalescence of two self-spreading supported lipid bilayers (SLBs) with different components. Coalescence between a phosphocholine/sphingolipid SLB and a phosphocholine/cholesterol one forms raft-like liquid ordered (Lo) domains, which can be observed by fluorescence microscopy at the boundary of two phases. This phase separation process indicates that lipid molecules, such as sphingolipid and cholesterol, are intermixed. When saturated phospholipid is used instead of sphingolipid, small Lo domains are formed. Cholesterol is harder to incorporate with domains of saturated phospholipid than that of sphingolipid. This technique is very useful for observation of lipid-lipid interactions.