Label-free evaluation and monitoring of living cell conditions or functions by means of chemical and/or physical sensors in a real-time manner are increasingly desired in the field of basic research of cells and clinical diagnosis. In order to perform multi-parametric analysis of living cells on a chip, we here developed a surface plasmon resonance (SPR) imaging (SPRI)-impedance sensor that can detect both refractive index (RI) and impedance changes on a sensor chip with comb-shaped electrodes. We then investigated the potential of the sensor for label-free and real-time analysis of living cell reactions in response to stimuli. We cultured rat basophilic leukemia (RBL)-2H3 cells on the sensor chip, which was a glass slide coated with comb-shaped electrodes, and detected activation of RBL-2H3 cells, such as degranulation and morphological changes, in response to a dinitro-phenol-conjugated human serum albumin (DNP-HSA) antigen. Moreover, impedance analysis revealed that the changes of impedance derived from RBL-2H3 cell activation appeared in the range of 1 kHz–1 MHz. Furthermore, we monitored living cell-derived RI and impedance changes simultaneously on a sensor chip using the SPRI-impedance sensor. Thus, we developed a new technique to monitor both impedance and RI derived from living cells by using a comb-shaped electrode sensor chip. This technique may enable us to clarify complex living cell functions which affect the RI and impedance and apply this to medical applications, such as accurate clinical diagnosis of type I allergy.

Parallel facing electrodes (PFE) for adherent cell monitoring by electrochemical impedance spectroscopy (EIS) was developed, and its characteristics were investigated by both computer simulation and experiment. The PFE consists of two facing gold electrode strips separated by 40 m, and the area of its intersection is 500 m  500 m. Computer simulation of EIS with adherent cells showed distinct difference in solution resistance for different cell coverage, which was confirmed by experimental results using latex beads suspension. A well-defined relationship between solution resistance and cell coverage in our PFE is promising for quantitative evaluation of cell density, morphology and fatality.

Objective:Type 2 diabetes, along with hypertension, is a major cause of worsening renal function. It would be very meaningful if we could detect future deterioration of renal function.Design and method:Predictive models were created using machine learning techniques. We used the medical information database of 2533 patients who attended the outpatient clinic of our hospital between January 2003 and March 2015. The point at which the estimated glomerular filtration rate(eGFR) first falls below 60 mL/min/1.73 m2 is used as the reference point. The input period was defined as the period from the reference point to one year before the reference point. Time series during the input period for eGFR, hematocrit, and urinary protein were extracted from blood and urine tests as features for predicting worsening renal function. The primary endpoint was a 50% reduction in eGFR during the evaluation period of three years since reference point from the mean value during the input period. The optimal combination was selected to maximize prediction performance using up to 4 features considering the number of endpoints reached. The prediction results were evaluated on AUC with 10 repeated 2-fold cross validation.Results:Among 2533 patients, 1409 patients had the reference point. Of those, 377 patients for whom a record existed for the input and evaluation periods but did not reach the primary endpoint, and 36 patients reached the primary endpoint. The mean eGFR during the input period was 67.9 ± 8.8 mL/min/1.73 m2, the mean age was 64.9 ± 10.2, 41.6% was women. The mean AUC was 0.82 ± 0.033 when the maximum and minimum values of urinary protein, minimum of eGFR and the difference between maximum and minimum values of eGFR during the input period were used as features.Conclusions:In this study, we developed a prediction model for worsening renal function using time series measurements in patients with relatively preserved renal function(eGFR> 60 mL/min/1.73 m2), and the results showed that it is very important to consider the variable components of urine protein and eGFR, which are laboratory values reflecting renal impairment. Recently, SGLT2 inhibitors have been used to treat kidney disease, but this study analyzed data before the release of SGLT2 inhibitors. We would like to examine whether our model can be applied to patients taking SGLT2 inhibitors.