Background Plasma aldosterone-to-renin ratio (ARR) is popularly used for screening primary aldosteronism (PA). Some medications, including diuretics, are known to have an effect on ARR and cause false-negative and false-positive results in PA screening. Currently, there are no studies on the effects of sodium–glucose cotransporter-2 (SGLT2) inhibitors, which are known to have diuretic effects, on ARR. We aimed to investigate the effects of SGLT2 inhibitors on ARR. Methods We employed a retrospective design; the study was conducted from April 2016 to December 2018 and carried out in three hospitals. Forty patients with diabetes and hypertension were administered SGLT2 inhibitors. ARR was evaluated before 2 to 6 months after the administration of SGLT2 inhibitors to determine their effects on ARR. Results No significant changes in the levels of ARR (90.9 ± 51.6 vs. 81.4 ± 62.9) were found. Body mass index, diastolic blood pressure, heart rate, fasting plasma glucose, and hemoglobin A1c were significantly decreased by SGLT2 inhibitors. Serum creatinine was significantly increased. Conclusion SGLT2 inhibitor administration yielded minimal effects on ARR and did not increase false-negative results in PA screening in patients with diabetes and hypertension more than 2 months after administration.

No abstract

No abstract

No abstract

SUMMARY We investigated the influence of basic human behaviors on the plant bioelectric potential. We analyzed four basic human behaviors, namely, touching a plant, opening a door, approaching a plant, and turning on a light. The specific responses in the bioelectric potential were found to be influenced by each behavior. The bioelectric potential has a pulsatile response due to behavior. Therefore, we attempted to learn and recognize human behaviors by extracting characteristics from the planet bioelectric potential. The method of recognizing basic human behaviors that we propose uses a low level of the cepstrum in the plant bioelectric potential. Some data were not recognized correctly because of individual differences in human behaviors. However, the F‐measure had an average of value 0.76, which shows that the proposed method may be an effective way of recognizing basic human behaviors by using the plant bioelectric potential.

No abstract

Renal pathology is essential for diagnosing and assessing the severity and prognosis of kidney diseases. Deep learning-based approaches have developed rapidly and have been applied in renal pathology. However, methods for the automated classification of normal and abnormal renal tubules remain scarce. Using a deep learning-based method, we aimed to classify normal and abnormal renal tubules, thereby assisting renal pathologists in the evaluation of renal biopsy specimens. Consequently, we developed a U-Net-based segmentation model using randomly selected regions obtained from 21 renal biopsy specimens. Further, we verified its performance in multiclass segmentation by calculating the Dice coefficients (DCs). We used 15 cases of tubulointerstitial nephritis to assess its applicability in aiding routine diagnoses conducted by renal pathologists and calculated the agreement ratio between diagnoses conducted by two renal pathologists and the time taken for evaluation. We also determined whether such diagnoses were improved when the output of segmentation was considered. The glomeruli and interstitium had the highest DCs, whereas the normal and abnormal renal tubules had intermediate DCs. Following the detailed evaluation of the tubulointerstitial compartments, the proximal, distal, atrophied, and degenerated tubules had intermediate DCs, whereas the arteries and inflamed tubules had low DCs. The annotation and output areas involving normal and abnormal tubules were strongly correlated in each class. The pathological concordance for the glomerular count, t, ct, and ci scores of the Banff classification of renal allograft pathology remained high with or without the segmented images. However, in terms of time consumption, the quantitative assessment of tubulitis, tubular atrophy, degenerated tubules, and the interstitium was improved significantly when renal pathologists considered the segmentation output. Deep learning algorithms can assist renal pathologists in the classification of normal and abnormal tubules in renal biopsy specimens, thereby facilitating the enhancement of renal pathology and ensuring appropriate clinical decisions.

The development of technology is growing rapidly; one of the most popular among the scientist is robotics technology. Recently, the robot was created to resemble the function of the human brain. Robots can make decisions without being helped by humans, known as AI (Artificial Intelligent). Now, this technology is being developed so that it can be used in wheeled vehicles, where these vehicles can run without any obstacles. Furthermore, of research, Nvidia introduced an autonomous vehicle named Nvidia Dave-2, which became popular. It showed an accuracy rate of 90%. The CNN (Convolutional Neural Network) method is used in the track recognition process with input in the form of a trajectory that has been taken from several angles. The data is trained using Jupiter's notebook, and then the training results can be used to automate the movement of the robot on the track where the data has been retrieved. The results obtained are then used by the robot to determine the path it will take. Many images that are taken as data, precise the results will be, but the time to train the image data will also be longer. From the data that has been obtained, the highest train loss on the first epoch is 1.829455, and the highest test loss on the third epoch is 30.90127. This indicates better steering control, which means better stability.