Sodium–glucose cotransporter 2 (SGLT2) inhibitors reduce the risk of major adverse cardiovascular (CV) events and hospitalization for heart failure (HF) in patients with type 2 diabetes (T2D). Using CV MRI (CMR) and 31P-MRS in a longitudinal cohort study, we aimed to investigate the effects of the selective SGLT2 inhibitor empagliflozin on myocardial energetics and cellular volume, function, and perfusion. Eighteen patients with T2D underwent CMR and 31P-MRS scans before and after 12 weeks’ empagliflozin treatment. Plasma N-terminal prohormone B-type natriuretic peptide (NT-proBNP) levels were measured. Ten volunteers with normal glycemic control underwent an identical scan protocol at a single visit. Empagliflozin treatment was associated with significant improvements in phosphocreatine-to-ATP ratio (1.52 to 1.76, P = 0.009). This was accompanied by a 7% absolute increase in the mean left ventricular ejection fraction (P = 0.001), 3% absolute increase in the mean global longitudinal strain (P = 0.01), 8 mL/m2 absolute reduction in the mean myocardial cell volume (P = 0.04), and 61% relative reduction in the mean NT-proBNP (P = 0.05) from baseline measurements. No significant change in myocardial blood flow or diastolic strain was detected. Empagliflozin thus ameliorates the “cardiac energy-deficient” state, regresses adverse myocardial cellular remodeling, and improves cardiac function, offering therapeutic opportunities to prevent or modulate HF in T2D.
Sodium–glucose-cotransporter-2 (SGLT2) inhibitors reduce the risk of major adverse CV events and hospitalization for heart failure in type 2 diabetes (T2D) patients. Utilising cardiovascular magnetic resonance imaging (CMR) and 31phosphorus magnetic resonance spectroscopy(<sup>31</sup>P-MRS) in a longitudinal cohort study, we aimed to investigate the effects of the selective SGLT2i empagliflozin on myocardial energetics, cellular volume, function and perfusion. Eighteen T2D patients underwent CMR and <sup>31</sup>P-MRS scans before and after twelve-week empagliflozin treatment. Plasma N-terminal pro hormone B-type natriuretic peptide (NT-proBNP) levels were measured. Ten volunteers with normal glycaemic control underwent an identical scan protocol on a single visit.<i> </i>Empagliflozin treatment was associated with significant improvements in PCr/ATP ratio (1.52 to 1.76, p=0.009). This was accompanied by a 7% absolute increase in the mean LVEF (p=0.001), 3% absolute increase in the mean global longitudinal strain (p=0.01), 8 ml/m2 absolute reduction in the mean myocardial cell volume (p=0.04) and 61% relative reduction in the mean NT-proBNP (p=0.05) from baseline measurements. No significant change in myocardial blood flow or diastolic strain was detected.<b> </b>Empagliflozin thus ameliorates the ‘cardiac energy-deficient’ state, regresses adverse myocardial cellular remodelling, and improves cardiac function, offering therapeutic opportunities to prevent or modulate heart failure in T2D.
End-to-end learning for autonomous driving uses a convolutional neural network (CNN) to predict the steering angle from a raw image input. Most of the solutions available for end-to-end autonomous driving are computationally too expensive, which increases the inference of autonomous driving in real time. Therefore, in this paper, CNN architecture has been trained which is lightweight and achieves comparable results to Nvidia’s PilotNet. The data used to train and evaluate the network is collected from the Car Learning to Act (CARLA) simulator. To evaluate the proposed architecture, the MSE (mean squared error) is used as the performance metric. Results of the experiment shows that the proposed model is 4x lighter than Nvidia’s PilotNet in term of parameters but still attains comparable results to PilotNet. The proposed model has achieved 5.1 × 10 − 4 MSE on testing data while PilotNet MSE was 4.7 × 10 − 4 .
Weed management is becoming increasingly important for sustainable crop production. Weeds cause an average yield loss of 11.5% billion in Pakistan, which is more than PKR 65 billion per year. A real-time laser weeding robot can increase the crop’s yield by efficiently removing weeds. Therefore, it helps decrease the environmental risks associated with traditional weed management approaches. However, to work efficiently and accurately, the weeding robot must have a robust weed detection mechanism to avoid physical damage to the targeted crops. This work focuses on developing a lightweight weed detection mechanism to assist laser weeding robots. The weed images were collected from six different agriculture farms in Pakistan. The dataset consisted of 9000 images of three crops: okra, bitter gourd, sponge gourd, and four weed species (horseweed, herb paris, grasses, and small weeds). We chose a single-shot object detection model, YOLO5. The selected model achieved a mAP of 0.88@IOU 0.5, indicating that the model predicted a large number of true positive (TP) with much less prediction of false positive (FP) and false negative (FN). While SSD-ResNet50 achieved a mAP of 0.53@IOU 0.5, the model predicted fewer TP with significant outcomes as FP or FN. The superior performance of the YOLOv5 model made it suitable for detecting and classifying weeds and crops within fields. Furthermore, the model was ported to an Nvidia Xavier AGX standalone device to make it a high-performance and low-power computation detection system. The model achieved an FPS rate of 27. Therefore, it is highly compatible with the laser weeding robot, which takes approximately 22.04 h at a velocity of 0.25 feet per second to remove weeds from a one-acre plot.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.