Background The Y-AIDA study was designed to investigate the renal- and home blood pressure (BP)-modulating effects of add-on dapagliflozin treatment in Japanese individuals with type 2 diabetes mellitus (T2DM) and albuminuria. Methods We conducted a prospective, multicenter, single-arm study. Eighty-six patients with T2DM, HbA1c 7.0–10.0%, estimated glomerular filtration rate (eGFR) ≥ 45 mL/min/1.73 m 2 , and urine albumin-to-creatinine ratio (UACR) ≥ 30 mg/g creatinine (gCr) were enrolled, and 85 of these patients were administered add-on dapagliflozin for 24 weeks. The primary and key secondary endpoints were change from baseline in the natural logarithm of UACR over 24 weeks and change in home BP profile at week 24. Results Baseline median UACR was 181.5 mg/gCr (interquartile range 47.85, 638.0). Baseline morning, evening, and nocturnal home systolic/diastolic BP was 137.6/82.7 mmHg, 136.1/79.3 mmHg, and 125.4/74.1 mmHg, respectively. After 24 weeks, the logarithm of UACR decreased by 0.37 ± 0.73 ( P < 0.001). In addition, changes in morning, evening, and nocturnal home BP from baseline were as follows: morning systolic/diastolic BP − 8.32 ± 11.42/− 4.18 ± 5.91 mmHg (both P < 0.001), evening systolic/diastolic BP − 9.57 ± 12.08/− 4.48 ± 6.45 mmHg (both P < 0.001), and nocturnal systolic/diastolic BP − 2.38 ± 7.82/− 1.17 ± 5.39 mmHg ( P = 0.0079 for systolic BP, P = 0.0415 for diastolic BP). Furthermore, the reduction in UACR after 24 weeks significantly correlated with an improvement in home BP profile, but not with changes in other variables, including office BP. Multivariate linear regression analysis also revealed that the change in morning home systolic BP was a significant contributor to the change in log-UACR. Conclusions In Japanese patients with T2DM and diabetic nephropathy, dapagliflozin significantly improved albuminuria levels and the home BP profile. Improved morning home systolic BP was associated with albuminuria reduction. Trial registration The study is registered at the UMIN Clinical Trials Registry (UMIN000018930; http://www.umin.ac.jp/ctr/index-j.htm ). The study was conducted from July 1, 2015 to August 1, 2018. Electronic supplementary material The online version of this article (10.1186/s12933-019-0912-3) contains supplementary material, which is available to authorized users.

[Purpose] This study aimed to clarify the effects of therapeutic ultrasound on range of motion and stretch pain and the relationships between the effects. [Subjects] The subjects were 15 healthy males. [Methods] Subjects performed all three interventions: (1) ultrasound (US group), (2) without powered ultrasound (placebo group), and (3) rest (control group). Ultrasound was applied at 3 MHz with an intensity of 1.0 W/cm2 and a 100% duty cycle for 10 minutes. The evaluation indices were active and passive range of motion (ROM), stretch pain (visual analog scale; VAS), and skin surface temperature (SST). The experimental protocol lasted a total of 40 minutes; this was comprised of 10 minutes before the intervention, 10 minutes during the intervention (US, placebo, and control), and 20 minutes after the intervention. [Results] ROM and SST were significantly higher in the US group than in the placebo and control groups for the 20 minutes after ultrasound, though there was no change in stretch pain. [Conclusion] The effects of ultrasound on ROM and SST were maintained for 20 minutes after the intervention. The SST increased with ultrasound and decreased afterwards. Additionally, the SST tended to return to baseline levels within 20 minutes after ultrasound exposure. Therefore, these effects were caused by a combination of thermal and mechanical effects of the ultrasound.

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SUMMARYThere have been many trials in which the waveform recognition method, which is intended to replace expert observation by computer processing, has been used to extract the features of biological signals during sleep, and to automatically score sleep stages based on feature parameters. This paper proposes a waveform recognition method which extracts the feature parameters based on the characteristics of the biological signal during sleep, and a method of automatic sleep stage scoring by decision-tree learning, which is currently considered to be one of the most successful machine learning methods in practice. As the first step in the method, the features corresponding to the state of the EEG, the EOG, and the EMG during sleep are compared to the features of characteristic waves such as the α-wave, δ-wave, sleep spindle, K-complex, and REM, and the feature parameters needed in order to judge the sleep stage are extracted. Using canonical discriminant analysis and discretization method RWS based on the random walk, the feature parameters are converted to a small number of discrete variables. Based on training instances, obtained by the bootstrap method, a set of multiple small decision trees (a committee) is formed, and the sleep stage is scored by majority decision in the classification results. The method is applied to the PSG chart digital data provided by the Japan Sleep Society, and the performance of the system is evaluated experimentally. It is verified that the proposed method is promising as a method of automatic sleep stage scoring with high accuracy, requiring little expenditure of time in learning and classification.

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