Assessment of left ventricular function in patients with type 2 diabetes mellitus by non-invasive myocardial work

BackgroundDiabetes mellitus (DM) is a chronic disease that poses a serious risk of cardiovascular diseases. Therefore, early detection of impaired cardiac function with non-invasive myocardial imaging is critical for improving the prognosis of patients with DM.PurposeThis study aimed to assess the left ventricular (LV) function in patients with type 2 diabetes mellitus (T2DM) by non-invasive myocardial work technique.Materials and methodsIn all, 67 patients with T2DM and 28 healthy controls were included and divided into a DM group and a control group. Two-dimensional dynamic images of apical three-chamber view, apical two-chamber view, and apical four-chamber view were collected from all subjects, consisting of at least three cardiac cycles. LV myocardial strain parameters, including global longitudinal strain (GLS) and peak strain dispersion (PSD), as well as myocardial work parameters, including global constructive work (GCW), global wasted work (GWW), global work index (GWI), and global work efficiency (GWE), were obtained and analyzed.ResultsA total of 15 subjects were randomly selected to assess intra-observer and inter-observer consistency of myocardial work parameters and strain parameters, which showed excellent results (intra-class correlation coefficients: 0.856 - 0.983, P<0.001). Compared with the control group, the DM group showed significantly higher PSD (37.59 ± 17.18 ms vs. 27.72 ± 13.52 ms, P<0.05) and GWW (63.98 ± 43.63 mmHg% vs. 39.28 ± 25.67 mmHg%, P<0.05), and lower GWE (96.38 ± 2.02% vs. 97.72 ± 0.98%, P<0.001). Furthermore, the PSD was positively correlated with GWW (r = 0.565, P<0.001) and negatively correlated with GWE (r = -0.569, P<0.001).ConclusionUncoordinated LV myocardial strain, higher GWW, and lower GWE in patients with T2DM may serve as indicators for the early assessment of cardiac impairment in T2DM

Predicting 1-, 3-, 5-, and 8-year all-cause mortality in a community-dwelling older adult cohort: relevance for predictive, preventive, and personalized medicine

Background: Population aging is a global public health issue involving increased prevalence of age-related diseases, and concomitant burden on medical resources and the economy. Ninety-two diseases have been identified as age-related, accounting for 51.3% of the global adult disease burden. The economic cost per capita for older people over 60 years is 10 times that of the younger population. From the aspects of predictive, preventive, and personalized medicine (PPPM), developing a risk-prediction model can help identify individuals at high risk for all-cause mortality and provide an opportunity for targeted prevention through personalized intervention at an early stage. However, there is still a lack of predictive models to help community-dwelling older adults do well in healthcare. Objectives: This study aims to develop an accurate 1-, 3-, 5-, and 8-year all-cause mortality risk-prediction model by using clinical multidimensional variables, and investigate risk factors for 1-, 3-, 5-, and 8-year all-cause mortality in community-dwelling older adults to guide primary prevention. Methods: This is a two-center cohort study. Inclusion criteria: (1) community-dwelling adult, (2) resided in the districts of Chaonan or Haojiang for more than 6 months in the past 12 months, and (3) completed a health examination. Exclusion criteria: (1) age less than 60 years, (2) more than 30 incomplete variables, (3) no signed informed consent. The primary outcome of the study was all-cause mortality obtained from face-to-face interviews, telephone interviews, and the medical death database from 2012 to 2021. Finally, we enrolled 5085 community-dwelling adults, 60 years and older, who underwent routine health screening in the Chaonan and Haojiang districts, southern China, from 2012 to 2021. Of them, 3091 participants from Chaonan were recruited as the primary training and internal validation study cohort, while 1994 participants from Haojiang were recruited as the external validation cohort. A total of 95 clinical multidimensional variables, including demographics, lifestyle behaviors, symptoms, medical history, family history, physical examination, laboratory tests, and electrocardiogram (ECG) data were collected to identify candidate risk factors and characteristics. Risk factors were identified using least absolute shrinkage and selection operator (LASSO) models and multivariable Cox proportional hazards regression analysis. A nomogram predictive model for 1-, 3-, 5- and 8-year all-cause mortality was constructed. The accuracy and calibration of the nomogram prediction model were assessed using the concordance index (C-index), integrated Brier score (IBS), receiver operating characteristic (ROC), and calibration curves. The clinical validity of the model was assessed using decision curve analysis (DCA). Results: Nine independent risk factors for 1-, 3-, 5-, and 8-year all-cause mortality were identified, including increased age, male, alcohol status, higher daily liquor consumption, history of cancer, elevated fasting glucose, lower hemoglobin, higher heart rate, and the occurrence of heart block. The acquisition of risk factor criteria is low cost, easily obtained, convenient for clinical application, and provides new insights and targets for the development of personalized prevention and interventions for high-risk individuals. The areas under the curve (AUC) of the nomogram model were 0.767, 0.776, and 0.806, and the C-indexes were 0.765, 0.775, and 0.797, in the training, internal validation, and external validation sets, respectively. The IBS was less than 0.25, which indicates good calibration. Calibration and decision curves showed that the predicted probabilities were in good agreement with the actual probabilities and had good clinical predictive value for PPPM. Conclusion: The personalized risk prediction model can identify individuals at high risk of all-cause mortality, help offer primary care to prevent all-cause mortality, and provide personalized medical treatment for these high-risk individuals from the PPPM perspective. Strict control of daily liquor consumption, lowering fasting glucose, raising hemoglobin, controlling heart rate, and treatment of heart block could be beneficial for improving survival in elderly populations

Ventricle Surface Reconstruction from Cardiac MR Slices Using Deep Learning

Reconstructing 3D ventricular surfaces from 2D cardiac MR data is challenging due to the sparsity of the input data and the presence of interslice misalignment. It is usually formulated as a 3D mesh fitting problem often incorporating shape priors and smoothness regularization, which might affect accuracy when handling pathological cases. We propose to formulate the 3D reconstruction as a volumetric mapping problem followed by isosurfacing from dense volumetric data. Taking advantage of deep learning algorithms, which learn to predict each voxel label without explicitly defining the shapes, our method is capable of generating anatomically meaningful surfaces with great flexibility. The sparse 3D volumetric input can process contours with any orientations and thus can utilize information from multiple short- and long-axis views. In addition, our method can provide correction of motion artifacts. We have validated our method using a statistical shape model on reconstructing 3D shapes from both spatially consistent and misaligned input data

Numerical simulation of the multiple core localized low shear toroidal Alfvenic eigenmodes

In modern tokamak experiments, scenarios with weak central magnetic shear has been proposed. It is necessary to study the Alfvenic mode activities in such scenarios. Theoretical researches have predicted the multiplicity of core-localized toroidally induced Alfvenic eigenmodes for ε/s > 1, where ε is the inverse aspect ratio and s is magnetic shear. We numerically investigate the existence of multiplicity of core-localized TAEs and mode characteristics using NOVA code in the present work. We firstly verify the existence of the multiplicity for zero beta plasma and the even mode at the forbidden zone. For finite beta plasma, the mode parities become more distinguishable, and the frequencies of odd modes are close to the upper tip of the continuum, while the frequencies of even modes are close to the lower tip of the continuum. Their frequencies are well separated by the forbidden zone. With the increasing value of ε/s, more modes with multiple radial nodes will appear, which is in agreement with theoretical prediction. The discrepancy between theoretical prediction and our numerical simulation is also discussed in the main text

Cost-Effective Edge Server Placement in Wireless Metropolitan Area Networks

Remote clouds are gradually unable to achieve ultra-low latency to meet the requirements of mobile users because of the intolerable long distance between remote clouds and mobile users and the network congestion caused by the tremendous number of users. Mobile edge computing, a new paradigm, has been proposed to mitigate aforementioned effects. Existing studies mostly assume the edge servers have been deployed properly and they just pay attention to how to minimize the delay between edge servers and mobile users. In this paper, considering the practical environment, we investigate how to deploy edge servers effectively and economically in wireless metropolitan area networks. Thus, we address the problem of minimizing the number of edge servers while ensuring some QoS requirements. Aiming at more consistence with a generalized condition, we extend the definition of the dominating set, and transform the addressed problem into the minimum dominating set problem in graph theory. In addition, two conditions are considered for the capacities of edge servers: one is that the capacities of edge servers can be configured on demand, and the other is that all the edge servers have the same capacities. For the on-demand condition, a greedy based algorithm is proposed to find the solution, and the key idea is to iteratively choose nodes that can connect as many other nodes as possible under the delay, degree and cluster size constraints. Furthermore, a simulated annealing based approach is given for global optimization. For the second condition, a greedy based algorithm is also proposed to satisfy the capacity constraint of edge servers and minimize the number of edge servers simultaneously. The simulation results show that the proposed algorithms are feasible

Task-Offloading Strategy Based on Performance Prediction in Vehicular Edge Computing

In vehicular edge computing, network performance and computing resources dynamically change, and vehicles should find the optimal strategy for offloading their tasks to servers to achieve a rapid computing service. In this paper, we address the multi-layered vehicle edge-computing framework, where each vehicle can choose one of three strategies for task offloading. For the best offloading performance, we propose a prediction-based task-offloading scheme for the vehicles, in which a deep-learning model is designed to predict the task-offloading result (success/failure) and service delay, and then the predicted strategy with successful task offloading and minimum service delay is chosen as the final offloading strategy. In the proposed model, an automatic feature-generation model based on CNN is proposed to capture the intersection of features to generate new features, avoiding the performance instability caused by manually designed features. The simulation results demonstrate that each part of the proposed model has an important impact on the prediction accuracy, and the proposed scheme has the higher Area Under Curve (AUC) than other methods. Compared with SVM- and MLP-based methods, the proposed scheme has the average failure rate decreased by 21.2% and 6.3%, respectively. It can be seen that our prediction-based scheme can effectively deal with dynamic changes in network performance and computing resources

Risk factors for the delayed viral clearance in COVID-19 patients

Comorbidities are important for the disease outcome of COVID-19, however, which underlying diseases that contribute the most to aggravate the conditions of COVID-19 patients are still unclear. Viral clearance is the most important laboratory test for defining the recovery of COVID-19 infections. To better understand which underlying diseases that are risk factors for delaying the viral clearance, we retrospectively analyzed 161 COVID-19 clinical cases in the Zhongnan Hospital of Wuhan University, Wuhan, China between January 5 and March 13, 2020. The demographic, clinical and laboratory data, as well as patient treatment records were collected. Univariable and multivariable analysis were performed to explore the association between delayed viral clearance and other factors by using logistic regression. Survival analyses by Kaplan-Meier and Cox regression modeling were employed to identify factors negatively influencing the viral clearance negatively. We found that hypertension and intravenous immunoglobulin adversely affected the time of viral RNA shedding. Hypertension was the most important risk factor to delay the SARS-CoV-2 virus clearance, however, the use of Angiotensin-Converting Enzyme Inhibitors(ACEI)/Angiotensin Receptor Blockers(ARB) did not shorten the time for virus clearance in these hypertensive patients' virus clearance. We conclude that patients having hypertension and intravenous immunoglobulin may delay the viral clearance in COVID-19 patients

Cytotoxic Terpenoids from the Roots of Dracocephalum taliense

A chemical investigation of methanol extract from the roots of Dracocephalum taliense led to the isolation of a new aromatic abietane diterpenoid, 12-methoxy-18-hydroxy-sugiol (1), and one highly-oxygenated ursane triterpenoid, 2α,3α-dihydroxy-11α,12α-epoxy-urs-28,13β-olide (2), together with 15 known natural products (3–17). Among these, compounds 1–13 and 15–17 were detected for the first time in the genus of Dracocephalum. The structures of all of these isolates were determined by extensively spectroscopic analyses. In the anti-inflammatory assay, compounds 1 and 2 had no obvious inhibitory activity on the release of cytokine IL-2 in lipopolysaccharide-induced RAW 264.7 macrophages. However, compound 2 exhibited significant cytotoxic activity against cell lines HepG2 (IC50 = 6.58 ± 0.14 μM) and NCI-H1975 (IC50 = 7.17 ± 0.26 μM)

Seed Pre-Soaking with Melatonin Improves Wheat Yield by Delaying Leaf Senescence and Promoting Root Development

The effects of exogenous application of melatonin (MEL) on promoting plant growth and alleviating environmental stresses are already known, but the potential value in crop production is still poorly understood. In this study, the effects of seed pre-soaking with MEL on winter wheat (Triticum aestivum L.) growth and yield were investigated in a continuous two-year pot experiment and another year of field experimentation. Results showed that seed pre-soaking with different concentrations of MEL (10, 100 and 500 μM) for 24 h increased grain yields per plant from 29% to 80% in pot experiment and increased grain yield per area from 4–19% in field experiment, compared with the controls. Further analysis showed that the beneficial effects of MEL on improving wheat grain yield can be ascribed to: (1) increased spike number by enhancing tiller number; (2) enhanced carbon assimilation capacity by maintaining large leaf area, high photosynthetic rate and delaying leaf senescence; (3) promoted growth in root system. The result of this study suggests that MEL could be considered as an effective plant growth regulator for improving grain production in winter wheat