MEAs-Filter: a novel filter framework utilizing evolutionary algorithms for cardiovascular diseases diagnosis.

Cardiovascular disease management often involves adjusting medication dosage based on changes in electrocardiogram (ECG) signals' waveform and rhythm. However, the diagnostic utility of ECG signals is often hindered by various types of noise interference. In this work, we propose a novel filter based on a multi-engine evolution framework named MEAs-Filter to address this issue. Our approach eliminates the need for predefined dimensions and allows adaptation to diverse ECG morphologies. By leveraging state-of-the-art optimization algorithms as evolution engine and incorporating prior information inputs from classical filters, MEAs-Filter achieves superior performance while minimizing order. We evaluate the effectiveness of MEAs-Filter on a real ECG database and compare it against commonly used filters such as the Butterworth, Chebyshev filters, and evolution algorithm-based (EA-based) filters. The experimental results indicate that MEAs-Filter outperforms other filters by achieving a reduction of approximately 30% to 60% in terms of the loss function compared to the other algorithms. In denoising experiments conducted on ECG waveforms across various scenarios, MEAs-Filter demonstrates an improvement of approximately 20% in signal-to-noise (SNR) ratio and a 9% improvement in correlation. Moreover, it does not exhibit higher losses of the R-wave compared to other filters. These findings highlight the potential of MEAs-Filter as a valuable tool for high-fidelity extraction of ECG signals, enabling accurate diagnosis in the field of cardiovascular diseases.

Machine discovery of partial differential equations from spatiotemporal data: A sparse Bayesian learning framework.

This study presents a general framework, namely, Sparse Spatiotemporal System Discovery (S3d), for discovering dynamical models given by Partial Differential Equations (PDEs) from spatiotemporal data. S3d is built on the recent development of sparse Bayesian learning, which enforces sparsity in the estimated PDEs. This approach enables a balance between model complexity and fitting error with theoretical guarantees. The proposed framework integrates Bayesian inference and a sparse priori distribution with the sparse regression method. It also introduces a principled iterative re-weighted algorithm to select dominant features in PDEs and solve for the sparse coefficients. We have demonstrated the discovery of the complex Ginzburg-Landau equation from a traveling-wave convection experiment, as well as several other PDEs, including the important cases of Navier-Stokes and sine-Gordon equations, from simulated data.

UAV Swarm Mission Planning in Dynamic Environment Using Consensus-Based Bundle Algorithm.

To solve the real-time complex mission-planning problem for Multiple heterogeneous Unmanned Aerial Vehicles (UAVs) in the dynamic environments, this paper addresses a new approach by effectively adapting the Consensus-Based Bundle Algorithms (CBBA) under the constraints of task timing, limited UAV resources, diverse types of tasks, dynamic addition of tasks, and real-time requirements. We introduce the dynamic task generation mechanism, which satisfied the task timing constraints. The tasks that require the cooperation of multiple UAVs are simplified into multiple sub-tasks to perform by a single UAV independently. We also introduce the asynchronous task allocation mechanism. This mechanism reduces the computational complexity of the algorithm and the communication time between UAVs. The partial task redistribution mechanism has been adopted for achieving the dynamic task allocation. The real-time performance of the algorithm is assured on the premise of optimal results. The feasibility and real-time performance of the algorithm are validated by conducting dynamic simulation experiments.

Classification of Cardiovascular Disease via A New SoftMax Model.

Cardiovascular disease clinical diagnosis is an essentially problem of pattern recognition. In the traditional intelligent diagnosis, the evaluation of classification algorithm is based on the final accuracy of the disease diagnosis. In this paper, a new classification method called Softmax regression model is proposed and it uses the known state data of two-layer neural network structure of the Softmax regression model for training and learning, and then calculate the probability of reclassification data belonging to each category. These categories are corresponding to the maximum probability and the classification result of the data to be classified. It provides a new method for classification of disease with higher speed and higher accuracy. Experiment is designed to compare with the K-nearest neighbours and BP neural networks, and also verify the classification accuracy of Softmax regression model. ECG data from MIT-BIH open database is considered for the experiment. The correct classification rate of the diagnosis reaches 94.44% which outperforms than K- nearest neighbor method (77.78%) and BP neural network (72.27%) in regards to the detection of the Cardiovascular disease.

Probing mechanical adaptation of neurite outgrowth on a hydrogel material using atomic force microscopy.

In this study, we describe the design and initial results of probing mechanical adaptation of neurite growth of lightly fixed neurons on a hydrogel substrate by using atomic force microscopy (AFM). It has been shown previously that cells are responsive to the physical conditions of their micro-environment, and that certain cells can adjust their own stiffness as part of the adaptation to the substrate. AFM, a powerful tool to probe micro- and nano-scale structures, has been utilized in assessing topography, morphology, and structural change of neuronal cells. We used AFM with a robust force analysis approach in this study to probe the mechanical properties of both neurites and the substrate at close proximity. We first confirmed the robustness and consistency of the approach specific to soft materials by comparing measurements made on the same reference material using different methods. Subsequently, it was found that the primary spinal cord neurons that were lightly fixed exhibited different stiffnesses between the cell body and neurites. Furthermore, in comparison to the rigidity of the substrate, the stiffness of the neurites was lower, whereas that of the neuronal cell body was higher.

Effect of dynamic stiffness of the substrates on neurite outgrowth by using a DNA-crosslinked hydrogel.

Central nervous system tissues, like other tissue types, undergo constant remodeling, which potentially leads to changes in their mechanical stiffness. Moreover, mechanical compliance of central nervous system tissues can also be modified under external load such as that experienced in traumatic brain or spinal cord injury, and during pathological processes. Thus, the neuronal responses to the dynamic stiffness of the microenvironment are of significance. In this study, we induced decrease in stiffness by using a DNA-crosslinked hydrogel, and subjected rat spinal cord neurons to such dynamic stiffness. The neurons respond to the dynamic cues as evidenced by the primary neurite structure, and the response from each neurite property (e.g., axonal length and primary dendrite number) is consistent with the behavior on static gels of same substrate rigidity, with one exception of mean primary dendrite length. The results on cell population distribution confirm the neuronal responses to the dynamic stiffness. Quantification on the focal adhesion kinase expression in the neuronal cell body on dynamic gels suggests that neurons also modify adhesion in coping with the dynamic stiffnesses. The results reported here extend the neuronal mechanosensing capability to dynamic stiffness of extracellular matrix, and give rise to a novel way of engineering neurite outgrowth in time dimension.

The relationship between fibroblast growth and the dynamic stiffnesses of a DNA crosslinked hydrogel.

The microenvironment of cells is dynamic and undergoes remodeling with time. This is evident in development, aging, pathological processes, and at tissue-biomaterial interfaces. But in contrast, the majority of the biomimetic materials have static properties. Here, we show that a previously developed DNA crosslinked hydrogel circumvents the need of environmental factors and undergoes controlled stiffness change via DNA delivery, a feasible approach to initiate property changes in vivo, different from previous attempts. Two types of fibroblasts, L929 and GFP, were subject to the alterations in substrate rigidity presented in the hydrogels. Our results show that exogenous DNA does not cause appreciable cell shape change. Cells do respond to mechanical alterations as demonstrated in the cell projection area and polarity (e.g., Soft vs. Soft-->Medium), and the responses vary depending on magnitude (e.g., Soft-->Medium vs. Soft-->Stiff) and range of stiffness changes (e.g., Soft-->Medium vs. Medium-->Stiff). The two types of fibroblasts share specific responses in common (e.g., Soft-->Medium), while differ in others (e.g., Medium-->Stiff). For each cell type, the projection area and polarity respond differently. This approach provides insight into pathology (e.g., cancer) and tissue functioning, and assists in designing biomaterials with controlled dynamic stiffness by choosing the range and magnitude of stiffness change.

Neurite outgrowth on a DNA crosslinked hydrogel with tunable stiffnesses.

Mechanical cues arising from extracellular matrices greatly affect cellular properties, and hence, are of significance in designing biomaterials. In this study, a DNA crosslinked hydrogel was employed to examine cellular responses of spinal cord neurons to substrate compliances. Using DNA as crosslinkers in polymeric hydrogel formation has given rise to a new class of hydrogels with a number of attractive properties (e.g., reversible gelation and controlled crosslinking). Here, it was demonstrated that by varying length of crosslinker, monomer concentration, and level of crosslinking, DNA gel stiffnesses span from approximately 100 Pa to 30 kPa. Assessment of neurite outgrowth on functionalized DNA gels showed that although primary dendrite length is not significantly affected, spinal cord neurons extend more primary dendrites and shorter axons on stiffer gels. Additionally, a greater proportion of neurons have more primary dendrites and shorter axons on stiffer gels. There is a pronounced reduction in focal adhesion kinase (FAK) when neurons are exposed to stiffer substrates, suggesting its involvement in neuronal mechanosensing and neuritogenesis in response to stiffness. These results demonstrate the importance of mechanical aspects of the cell-ECM interactions, and provide guidance for the design of mechanical properties of bio-scaffolds for neural tissue engineering applications.

Enoxaparin versus unfractionated heparin with fibrinolysis for ST-elevation myocardial infarction.

BACKGROUND: Unfractionated heparin is often used as adjunctive therapy with fibrinolysis in patients with ST-elevation myocardial infarction. We compared a low-molecular-weight heparin, enoxaparin, with unfractionated heparin for this purpose. METHODS: We randomly assigned 20,506 patients with ST-elevation myocardial infarction who were scheduled to undergo fibrinolysis to receive enoxaparin throughout the index hospitalization or weight-based unfractionated heparin for at least 48 hours. The primary efficacy end point was death or nonfatal recurrent myocardial infarction through 30 days. RESULTS: The primary end point occurred in 12.0 percent of patients in the unfractionated heparin group and 9.9 percent of those in the enoxaparin group (17 percent reduction in relative risk, P<0.001). Nonfatal reinfarction occurred in 4.5 percent of the patients receiving unfractionated heparin and 3.0 percent of those receiving enoxaparin (33 percent reduction in relative risk, P<0.001); 7.5 percent of patients given unfractionated heparin died, as did 6.9 percent of those given enoxaparin (P=0.11). The composite of death, nonfatal reinfarction, or urgent revascularization occurred in 14.5 percent of patients given unfractionated heparin and 11.7 percent of those given enoxaparin (P<0.001); major bleeding occurred in 1.4 percent and 2.1 percent, respectively (P<0.001). The composite of death, nonfatal reinfarction, or nonfatal intracranial hemorrhage (a measure of net clinical benefit) occurred in 12.2 percent of patients given unfractionated heparin and 10.1 percent of those given enoxaparin (P<0.001). CONCLUSIONS: In patients receiving fibrinolysis for ST-elevation myocardial infarction, treatment with enoxaparin throughout the index hospitalization is superior to treatment with unfractionated heparin for 48 hours but is associated with an increase in major bleeding episodes. These findings should be interpreted in the context of net clinical benefit. (ClinicalTrials.gov number, NCT00077792.).

Coarse-grained model simulations of mixed-lipid systems: composition and line tension of a stabilized bilayer edge.

Bilayer disks and ribbons composed of a mixture of short- and long-tail phospholipids have been studied by molecular dynamics with a coarse-grained model. The effects of system composition on the edge structure, composition, and line tension were analyzed. Increases in the fraction of short-tail lipids tend to decrease the line tension (i.e., stabilize the edge) but not eliminate it. The short-tail lipid is generally enriched at the curved rim forming the bilayer edge, with an excess of 3 to 4 molecules per nanometer (relative to the bulk), but complete segregation was not observed. In all mixtures, a region depleted in the short-tail component occurs just before the edge, corresponding to a bulge in the bilayer thickness. The bulge and depletion are more prominent as the bilayer composition shifts toward a majority of short-tail lipids. In one case, a net excess of long-tail lipids at the edge was demonstrated, suggesting that certain circumstances give rise to a "segregation inversion" in which the long-tail lipid behaves as an edge stabilizer.

Molecular dynamics simulations of the lipid bilayer edge.

Phospholipid bilayers have been intensively studied by molecular dynamics (MD) simulation in recent years. The properties of bilayer edges are important in determining the structure and stability of pores formed in vesicles and biomembranes. In this work, we use molecular dynamics simulation to investigate the structure, dynamics, and line tension of the edges of bilayer ribbons composed of pure dimyristoylphosphatidylcholine (DMPC) or palmitoyl-oleoylphosphatidylethanolamine (POPE). As expected, we observe a significant reorganization of lipids at and near the edges. The treatment of electrostatic effects is shown to have a qualitative impact on the structure and stability of the edge, and significant differences are observed in the dynamics and structure of edges formed by DMPC and palmitoyl-oleoylphosphatidylethanolamine. From the pressure anisotropy in the simulation box, we calculate a line tension of approximately 10-30 pN for the DMPC edge, in qualitative agreement with experimental estimates for similar lipids.

Efficacy and safety of LY315920Na/S-5920, a selective inhibitor of 14-kDa group IIA secretory phospholipase A2, in patients with suspected sepsis and organ failure.

OBJECTIVE: Concentrations of group IIA secretory phospholipase A, an inflammatory response mediator, are increased in the plasma of patients with sepsis and septic shock, and the extent of elevation is correlated with mortality. LY315920Na/S-5920 is a selective inhibitor of group IIA secretory phospholipase A that has been shown to inhibit serum group IIA secretory phospholipase A enzyme activity in patients with severe sepsis. The primary objectives of this study were to determine whether there was a dose-response relationship between two doses of LY315920Na/S-5920 compared with placebo in the reduction of 28-day all-cause mortality in patients with severe sepsis and to determine whether LY315920Na/S-5920 had an acceptable safety profile.(2) (2) (2) DESIGN: Multicenter, double-blind, placebo-controlled trial of two doses of LY315920Na/S-5920 in a parallel design. PATIENTS: A total of 586 patients with severe sepsis at 72 institutions in the United States. INTERVENTIONS Patients enrolled within 72 hrs from onset of first sepsis-induced organ failure were randomized (1:1:1) to low-dose LY315920Na/S-5920 (target plasma concentration of 200 ng/mL, n = 196), high-dose LY315920Na/S-5920 (800 ng/mL, n = 194), or placebo (n = 196). Study medication was administered as a constant-rate intravenous infusion for 168 hrs. MEASUREMENTS AND MAIN RESULTS: The study was stopped prematurely because it was unlikely that a statistically significant difference in mortality between LY315920Na/S-5920 and placebo would be found. There was no effect of LY315920Na/S-5920 on the primary end point of 28-day all-cause mortality across the entire study population. The 28-day all-cause mortality was distributed as follows: placebo group, 33.2% (65/196 patients); low-dose LY315920Na/S-5920, 37.2% (73/196); and high-dose LY315920Na/S-5920, 36.1% (70/194); p = .525. However, in a prospectively planned analysis, there was a favorable overall dose-response effect on 28-day all-cause mortality in patients administered LY315920Na/S-5920 within 18 hrs of onset of the first sepsis-induced organ failure. Among these patients, 28-day all-cause mortality was distributed as follows: placebo group, 43.5% (20/46 patients); low-dose LY315920Na/S-5920, 31.4% (16/51); and high-dose LY315920Na/S-5920, 20.8% (10/48); p = .018. CONCLUSIONS: Administration of LY315920Na/S-5920 had an acceptable safety profile in patients with severe sepsis. There was no overall survival benefit associated with the use of LY315920Na/S-5920 in this study. However, prospectively planned secondary analyses suggested that treatment with LY315920Na/S-5920 was associated with an improvement in survival in patients treated within 18 hrs of the first sepsis-induced organ failure.