Customizable Organic Charge-Transfer Cocrystals for the Dual-Mode Optoelectronics in the NIR (II) Window.

Organic molecules have been regarded as ideal candidates for near-infrared (NIR) optoelectronic active materials due to their customizability and ease of large-scale production. However, constrained by the intricate molecular design and severe energy gap law, the realization of optoelectronic devices in the second near-infrared (NIR (II)) region with required narrow band gaps presents more challenges. Herein, we have originally proposed a cocrystal strategy that utilizes intermolecular charge-transfer interaction to drive the redshift of absorption and emission spectra of a series BFXTQ (X = 0, 1, 2, 4) cocrystals, resulting in the spectra located at NIR (II) window and reducing the optical bandgap to ∼0.98 eV. Significantly, these BFXTQ-based optoelectronic devices can exhibit dual-mode optoelectronic characteristics. An investigation of a series of BFXTQ-based photodetectors exhibits detectivity (D*) surpassing 1013 Jones at 375 to 1064 nm with a maximum of 1.76 × 1014 Jones at 1064 nm. Moreover, the radiative transition of CT excitons within the cocrystals triggers NIR emission over 1000 nm with a photoluminescence quantum yield (PLQY) of ∼4.6% as well as optical waveguide behavior with a low optical-loss coefficient of 0.0097 dB/µm at 950 nm. These results promote the advancement of an emerging cocrystal approach in micro/nanoscale NIR multifunctional optoelectronics.

Programmable in-situ co-assembly of organic multi-block nanowires for cascade optical waveguides.

Organic heterostructures (OHs) with multi-segments exhibit special optoelectronic properties compared with monomeric structures. Nevertheless, the synthesis of multi-block heterostructures remains challenging due to compatibility issues between segment parts, which restricts their application in optical waveguides and integrated optics. Herein, we demonstrate programmable in-situ co-assembly engineering, combining multi-step spontaneous self-assembly processes to promote the synthesis of multi-block heterostructures with a rational arrangement of three or more segments. The rational design of segments enables exciton manipulation and ensures optical waveguides and proper output among the multi-segment OHs. This work enables the controllable growth of segments within multi-block OHs, providing a pathway to construct complex OHs for the rational development of future optical applications.

Oriented Self-Assembly of Hierarchical Branch Organic Crystals for Asymmetric Photonics.

Organic hierarchical branch micro/nanostructures constituted by single crystals with inherent multichannel characteristics exhibit superior potential in regulating photon transmission for photonic circuits. However, organic branch micro/nanostructures with precise branch positions are extremely difficult to achieve due to the randomness of the nucleation process. Herein, by taking advantage of the dislocation stress field-impurity interaction that solute molecules deposit preferentially along the dislocation line, twinning deformation was introduced into microcrystals to induce oriented nucleation sites, and ultimately organic branch microstructures with controllable branch sites were fabricated. The growth mechanism of these controllable single crystals with an angle of 140° between trunk and branch is attributed to the low lattice mismatching ratio (η) of 4.8%. These as-prepared hierarchical branch single crystals with asymmetrical optical waveguide characteristics have been demonstrated as an optical logic gate with multiple input/out channels, which provides a route to command the nucleation sites and offers potential applications in the organic optoelectronics at the micro/nanoscale.

CMR Manifestations, Influencing Factors and Molecular Mechanism of Myocarditis Induced by COVID-19 Mrna Vaccine.

Although immunization with the 2019 coronavirus disease (COVID-19) mRNA vaccine is considered to be an effective measure to reduce the number of serious cases or deaths associated with COVID-19, rare cases of cardiac complications have been reported in the literature, encompassing acute myocardial injury, arrhythmia, vasculitis, endothelial dysfunction, thrombotic myocardial infarction and myocarditis. Interestingly, patients diagnosed with myocarditis after receiving the COVID-19 mRNA vaccine exhibit abnormal cardiac magnetic resonance (CMR) findings, suggesting CMR can be a valuable non-invasive diagnostic tool. In populations immunized with the COVID-19 mRNA vaccine, the risk in teenagers and young men is significantly higher. Myocardial injury in male patients is mainly myocarditis, while in female patients, myocarditis and pericardial effusion are predominantly found. Generally, the symptoms of myocarditis are relatively mild and complete recovery can be achieved. Moreover, the incidence rate associated with the second dose is significantly higher than with the first or third dose. This article brings together the latest evidence on CMR characteristics, influencing factors and pathogenesis of myocarditis caused by the COVID-19 mRNA vaccine. At the same time, we make recommendations for populations requiring immunization with the COVID-19 mRNA vaccine.

[Effects of density on growth and gene transcription characteristics of Rehmannia glutinosa].

The present study analyzed the effects of planting density on the development, quality, and gene transcription characte-ristics of Rehmannia glutinosa using 85-5 and J9 as materials with three planting densities of 5 000, 25 000, and 50 000 plants/Mu(1 Mu≈667 m~2). The agronomic characteristics of leaves and tuberous roots, the content of catalpol and acteoside, and the changes of gene expression were determined. The results showed that the leaf size, the diameter of tuberous root, leaf biomass, tuberous root number, and tuberous root biomass per plant at low density were significantly higher than those of medium and high densities. The content of catalpol and acteoside in leaves was higher at high density. The content of catalpol in tuberous roots was higher at low density, and the change trend was similar to that in leaves, while the content of acteoside in tuberous roots was higher at high density. Transcriptome analysis found that about 1/2 of the expansin genes could change regularly in response to density treatment, which was rela-ted to the development of tuberous roots. The change trend of the gene expression of multiple catalytic enzymes involved in the biosynthesis of catalpol and acteoside was consistent with that of their content, which was presumedly involved in the accumulation and regulation of density-responsive medicinal components. Based on the analysis of the development, medicinal components, and gene expression characteristics of R. glutinosa at different densities, this study is expected to provide an important basis for regulating the quality and yield of medicinal materials of R. glutinosa by managing the planting density.

Strong Surface-Bound Sulfur in Carbon Nanotube Bridged Hierarchical Mo2 C-Based MXene Nanosheets for Lithium-Sulfur Batteries.

In this work, hydroxyl-functionalized Mo2 C-based MXene nanosheets are synthesized by facilely removing the Sn layer of Mo2 SnC. The hydroxyl-functionalized surface of Mo2 C suppresses the shuttle effect of lithium polysulfides (LiPSs) through strong interaction between Mo atoms on the MXenes surface and LiPSs. Carbon nanotubes (CNTs) are further introduced into Mo2 C phase to enlarge the specific surface area of the composite, improve its electronic conductivity, and alleviate the volume change during discharging/charging. The strong surface-bound sulfur in the hierarchical Mo2 C-CNTs host can lead to a superior electrochemical performance in lithium-sulfur batteries. A large reversible capacity of ≈925 mAh g-1 is observed after 250 cycles at a current density of 0.1 C (1 C = 1675 mAh g-1 ) with good rate capability. Notably, the electrodes with high loading amounts of sulfur can also deliver good electrochemical performances, i.e., initial reversible capacities of ≈1314 mAh g-1 (2.4 mAh cm-2 ), ≈1068 mAh g-1 (3.7 mAh cm-2 ), and ≈959 mAh g-1 (5.3 mAh cm-2 ) at various areal loading amounts of sulfur (1.8, 3.5, and 5.6 mg cm-2 ) are also observed, respectively.

Odorless Isocyanide Chemistry: One-Pot Synthesis of Heterocycles via the Passerini and Postmodification Tandem Reaction Based on the in Situ Capture of Isocyanides.

This paper reports the tandem reaction strategy of the Passerini/Staudinger/aza-Wittig reaction based on the in situ capture of isocyanides. According to this strategy, isocyanides are synthesized in situ and immediately work as the substrate for the Passerini reaction and postmodification tandem reaction in one pot. In addition, two types of new compounds, 5-oxo-3,5-dihydrobenzo[ e][1,4]oxazepines and 6-oxo-5,6-dihydro-2 H-1,4-oxazines, were synthesized using the tandem reaction strategy that includes five-step transformations in one pot.

[Molecular cloning and expression analysis of iridoid synthase genes from Rehmannia glutinosa].

Iridoid synthase( IS),the key enzyme in the natural biosynthesis of vegetal iridoids,catalyzes the irreversible cyclization of 10-oxogeranial to epi-iridodial. In this study,we screened the Rehmannia glutinosa transcriptome data by BLASTn with Catharanthus roseus CrIS cDNA,and found four c DNA fragments with length of 1 527,1 743,1 425,1 718 bp,named RgIS1,RgIS2,RgIS3 and RgIS4,respectively. Bioinformatics analysis revealed that the four iridoid synthase genes encoding proteins with 389-392 amino acid residues,protein molecular weights were between 44. 30-44. 74 k Da,and theoretical isoelectric points were between 5. 30 and 5. 87. Subcellular localization predictions showed that the four iridoid synthase were distributed in the cytoplasm. Structure analysis revealed that R. glutinosa iridoid synthases contain six conserved short-chain dehydrogenase/reductase( SDR) motifs,and their 3 D models were composed typical dinucleotide-binding " Rossmann" folds covered by helical C-terminal extensions. Using the amino acid sequences of four R. glutinosa iridoid synthases,phylogenetic analysis was performed,the result indicated that RgIS3,CrIS and Olea europaea OeIS were grouped together,the other R. glutinosa iridoid synthases and fifteen proteins in other plants had close relationship. Real-time fluorescent quantitative PCR revealed that RgIS1 and RgIS3 highly expressed in unfold leaves,however,RgIS2 and RgIS4 highly expressed in stems and tuberous roots,respectively. RgIS3 showed higher expression levels in non-radial striations( nRS) of the two cultivars,and RgIS1 and RgIS2 had higher expression levels in nRS of QH,while RgIS4 had less expression levels in nRS of QH1. RgIS1,RgIS2 and RgIS3 were up-regulated by Me JA treatment,although the time and degree of response differed. Our findings are helpful to reveal molecular function of R. glutinosa iridoid synthases and provide a clue for studing the molecular mechanism of iridoid biosynthesis.

Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network.

BACKGROUND: Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers. METHODS: Alternatively, this paper applies a deep recurrent neural network (RNN) with a sliding window cropping strategy (SWCS) to signal classification of MI-BCIs. The spatial-frequency features are first extracted by the filter bank common spatial pattern (FB-CSP) algorithm, and such features are cropped by the SWCS into time slices. By extracting spatial-frequency-sequential relationships, the cropped time slices are then fed into RNN for classification. In order to overcome the memory distractions, the commonly used gated recurrent unit (GRU) and long-short term memory (LSTM) unit are applied to the RNN architecture, and experimental results are used to determine which unit is more suitable for processing EEG signals. RESULTS: Experimental results on common BCI benchmark datasets show that the spatial-frequency-sequential relationships outperform all other competing spatial-frequency methods. In particular, the proposed GRU-RNN architecture achieves the lowest misclassification rates on all BCI benchmark datasets. CONCLUSION: By introducing spatial-frequency-sequential relationships with cropping time slice samples, the proposed method gives a novel way to construct and model high accuracy and robustness MI-BCIs based on limited trials of EEG signals.

Wavelet transform and deep learning-based obstructive sleep apnea detection from single-lead ECG signals.

Sleep apnea is a common sleep disorder. Traditional testing and diagnosis heavily rely on the expertise of physicians, as well as analysis and statistical interpretation of extensive sleep testing data, resulting in time-consuming and labor-intensive processes. To address the problems of complex feature extraction, data imbalance, and low model capacity, we proposed an automatic sleep apnea classification model (CA-EfficientNet) based on the wavelet transform, a lightweight neural network, and a coordinated attention mechanism. The signal is converted into a time-frequency image by wavelet transform and put into the proposed model for classification. The effects of input time window, wavelet transform type and data balancing on the classification performance are considered, and a cost-sensitive algorithm is introduced to more accurately distinguish between normal and abnormal breathing events. PhysioNet apnea ECG database was used for training and evaluation. The 3-min Frequency B-Spline wavelets transform of ECG signal was carried out, and Dice Loss was used to train the classification model of sleep breathing. The classification accuracy was 93.44%, sensitivity was 88.9%, specificity was 96.2% and most indexes were better than other related work.

Actor-Critic With Synthesis Loss for Solving Approximation Biases.

Approximation biases of value functions are considered a key problem in reinforcement learning (RL). In particular, existing RL algorithms are hindered by overestimation and underestimation biases, i.e., value mismatching between RL's actual returns and action-value approximations limits the performance of RL algorithms. In this article, we first develop a new synthesis loss function for RL's action-value estimation integrating a regularization term and a modified "clipped double Q-learning" structure for solving overestimation and underestimation biases. To minimize the differences between action-value estimations and actual returns in RL, we develop a new discrepancy function to determine the type and magnitude of approximation biases. Then, two coefficients embedded in the synthesis loss are automatically tuned by minimizing the discrepancy function during training to minimize approximation biases. We further design a new actor-critic (AC) algorithm, named AC with synthesis loss (ACSL), by integrating the synthesis loss function and an error-controlled mechanism. Experimental results on continuous control tasks illustrate that the proposed ACSL algorithm outperforms other cutting-edge RL methods in many tasks and that the proposed synthesis loss function is easily implemented into other algorithms and significantly reduces approximation biases while improving performance. The proposed method can successfully handle many complex continuous control tasks and can greatly outperform other state-of-the-art algorithms on most tasks.

Uncovering the Over-Smoothing Challenge in Image Super-Resolution: Entropy-Based Quantification and Contrastive Optimization.

PSNR-oriented models are a critical class of super-resolution models with applications across various fields. However, these models tend to generate over-smoothed images, a problem that has been analyzed previously from the perspectives of models or loss functions, but without taking into account the impact of data properties. In this paper, we present a novel phenomenon that we term the center-oriented optimization (COO) problem, where a model's output converges towards the center point of similar high-resolution images, rather than towards the ground truth. We demonstrate that the strength of this problem is related to the uncertainty of data, which we quantify using entropy. We prove that as the entropy of high-resolution images increases, their center point will move further away from the clean image distribution, and the model will generate over-smoothed images. Implicitly optimizing the COO problem, perceptual-driven approaches such as perceptual loss, model structure optimization, or GAN-based methods can be viewed. We propose an explicit solution to the COO problem, called Detail Enhanced Contrastive Loss (DECLoss). DECLoss utilizes the clustering property of contrastive learning to directly reduce the variance of the potential high-resolution distribution and thereby decrease the entropy. We evaluate DECLoss on multiple super-resolution benchmarks and demonstrate that it improves the perceptual quality of PSNR-oriented models. Moreover, when applied to GAN-based methods, such as RaGAN, DECLoss helps to achieve state-of-the-art performance, such as 0.093 LPIPS with 24.51 PSNR on 4× downsampled Urban100, validating the effectiveness and generalization of our approach.

Multi-scale representation of surface-enhanced Raman spectroscopy data for deep learning-based liver cancer detection.

The early detection of liver cancer greatly improves survival rates and allows for less invasive treatment options. As a non-invasive optical detection technique, Surface-Enhanced Raman Spectroscopy (SERS) has shown significant potential in early cancer detection, providing multiple advantages over conventional methods. The majority of existing cancer detection methods utilize multivariate statistical analysis to categorize SERS data. However, these methods are plagued by issues such as information loss during dimensionality reduction and inadequate ability to handle nonlinear relationships within the data. To overcome these problems, we first use wavelet transform with its multi-scale analysis capability to extract multi-scale features from SERS data while minimizing information loss compared to traditional methods. Moreover, deep learning is employed for classification, leveraging its strong nonlinear processing capability to enhance accuracy. In addition, the chosen neural network incorporates a data augmentation method, thereby enriching our training dataset and mitigating the risk of overfitting. Moreover, we acknowledge the significance of selecting the appropriate wavelet basis functions in SERS data processing, prompting us to choose six specific ones for comparison. We employ SERS data from serum samples obtained from both liver cancer patients and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding performance, surpassing the majority of multivariate statistical analysis and traditional machine learning classification methods, with an accuracy of 99.38 %, a sensitivity of 99.8 %, and a specificity of 97.0 %. These results indicate that the combination of SERS, wavelet transform, and deep learning has the potential to function as a non-invasive tool for the rapid detection of liver cancer.

Self-Organizing Type-2 Fuzzy Double Loop Recurrent Neural Network for Uncertain Nonlinear System Control.

Nonlinear systems, such as robotic systems, play an increasingly important role in our modern daily life and have become more dominant in many industries; however, robotic control still faces various challenges due to diverse and unstructured work environments. This article proposes a double-loop recurrent neural network (DLRNN) with the support of a Type-2 fuzzy system and a self-organizing mechanism for improved performance in nonlinear dynamic robot control. The proposed network has a double-loop recurrent structure, which enables better dynamic mapping. In addition, the network combines a Type-2 fuzzy system with a double-loop recurrent structure to improve the ability to deal with uncertain environments. To achieve an efficient system response, a self-organizing mechanism is proposed to adaptively adjust the number of layers in a DLRNN. This work integrates the proposed network into a conventional sliding mode control (SMC) system to theoretically and empirically prove its stability. The proposed system is applied to a three-joint robot manipulator, leading to a comparative study that considers several existing control approaches. The experimental results confirm the superiority of the proposed system and its effectiveness and robustness in response to various external system disturbances.

Lateral epitaxial growth of two-dimensional organic heterostructures.

Two-dimensional organic lateral heterostructures (2D OLHs) are attractive for the fabrication of functional materials. However, it is difficult to control the nucleation, growth and orientation of two distinct components. Here we report the combination of two methods-liquid-phase growth and vapour-phase growth-to synthesize 2D OLHs from perylene and a perylenecarboxaldehyde derivative, with a lateral size of ~20 µm and a tunable thickness ranging from 20 to 400 nm. The screw dislocation growth behaviour of the 2D crystals shows the spiral arrangement of atoms within the crystal lattice, which avoids volume expansion and contraction of OLH, thereby minimizing lateral connection defects. Selective control of the nucleation and sequential growth of 2D crystals leads to structural inversion of the 2D OLHs by the vapour-phase growth method. The resulting OLHs show good light-transport capabilities and tunable spatial exciton conversion, useful for photonic applications. This synthetic strategy can be extended to other families of organic polycyclic aromatic hydrocarbons, as demonstrated with other pyrene and perylene derivatives.

High adsorption to methylene blue based on Fe3O4-N-banana-peel biomass charcoal.

This research focused on utilizing banana peel as the primary material for producing mesoporous biomass charcoal through one-step potassium hydroxide activation. Subsequently, the biomass charcoal underwent high-temperature calcination with varying impregnation ratios of KOH : BC for different durations in tubular furnaces set at different temperatures. The resultant biomass charcoal was then subjected to hydrothermal treatment with FeCl3·6H2O to produce biochar/iron oxide composites. The adsorption capabilities of these composites towards methylene blue (MB) were examined under various conditions, including pH (ranging from 3 to 12), temperature variations, and initial MB concentrations (ranging from 50 to 400 mg L-1). The adsorption behavior aligned with the Langmuir model and demonstrated quasi-secondary kinetics. After five adsorption cycles, the capacity decreased from 618.64 mg g-1 to 497.18 mg g-1, indicating considerable stability. Notably, Fe3O4-N-BC exhibited exceptional MB adsorption performance.

Preparation of Aminated Sodium Lignosulfonate and Efficient Adsorption of Methyl Blue Dye.

The aminated sodium lignosulfonate (AELS) was prepared through a Mannich reaction and characterized via FT-IR, TG, SEM and XPS in this study. Subsequently, the adsorption capacity of AELS for methyl blue (MB) was evaluated under various conditions such as pH, adsorbent dosage, contact time, initial concentration and temperature. The adsorption kinetics, isotherms and thermodynamics of AELS for methyl blue were investigated and analyzed. The results were found to closely adhere to the pseudo-second-order kinetic model and Langmuir isotherm model, suggesting a single-molecular-layer adsorption process. Notably, the maximum adsorption capacity of AELS for methyl blue (153.42 mg g-1) was achieved under the specified conditions (T = 298 K, MAELS = 0.01 g, pH = 6, VMB = 25 mL, C0 = 300 mg L-1). The adsorption process was determined to be spontaneous and endothermic. Following five adsorption cycles, the adsorption capacity exhibited a minimal reduction from 118.99 mg g-1 to 114.33 mg g-1, indicating good stability. This study contributes to the advancement of utilizing natural resources effectively and sustainably.

Theoretical studies on the photoelectron and absorption spectra of MnO4(-) and TcO4(-).

The tetraoxo pertechnetate anion (TcO4(-)) is of great interest for nuclear waste management and radiopharmceuticals. To elucidate its electronic structure and to compare with that of its lighter congener MnO4(-), the photoelectron and electronic absorption spectra of MnO4(-) and TcO4(-) are investigated with density functional theory (DFT) and ab initio wave function theory (WFT). The vertical electron detachment energies (VDEs) of MnO4(-) obtained with the CR-EOM-CCSD(T) method are in good agreement with the lowest two experimental VDEs; the differences are less than 0.1 eV, representing a significant improvement over the IP-EOM-CCSD(T) result in the literature. Combining our CCSD(T) and CR-EOM-CCSD(T) results, the first five VDEs of TcO4(-) are estimated between 5 and 10 eV with an estimated accuracy of about ±0.2 eV. The vertical excitation energies are determined by using TD-DFT, CR-EOM-CCSD(T), and RAS-PT2 methods. The excitation energies and the assignments of the spectra are analyzed and partly improved. They are compared with reported SAC-CI results and available experimental data. Both dynamic and nondynamic electron correlations are important in the ground and excited states of MnO4(-) and TcO4(-). Nondynamical correlations are particularly relevant in TcO4(-) for reliable prediction of excitation energies. In TcO4(-) one Rydberg state interlaces but does not mix with the valence excited states, and it disappears in the condensed phase.

Probing the electronic structure and chemical bonding in tricoordinate uranyl complexes UO2X3- (X = F, Cl, Br, I): competition between Coulomb repulsion and U-X bonding.

While uranyl halide complexes [UO2(halogen)n](2-n) (n = 1, 2, 4) are ubiquitous, the tricoordinate species have been relatively unknown until very recently. Here photoelectron spectroscopy and relativistic quantum chemistry are used to investigate the bonding and stability of a series of gaseous tricoordinate uranyl complexes, UO2X3(-) (X = F, Cl, Br, I). Isolated UO2X3(-) ions are produced by electrospray ionization and observed to be highly stable with very large adiabatic electron detachment energies: 6.25, 6.64, 6.27, and 5.60 eV for X = F, Cl, Br, and I, respectively. Theoretical calculations reveal that the frontier molecular orbitals are mainly of uranyl U-O bonding character in UO2F3(-), but they are from the ligand valence np lone pairs in the heavier halogen complexes. Extensive bonding analyses are carried out for UO2X3(-) as well as for the doubly charged tetracoordinate complexes (UO2X4(2-)), showing that the U-X bonds are dominated by ionic interactions with weak covalency. The U-X bond strength decreases down the periodic table from F to I. Coulomb barriers and dissociation energies of UO2X4(2-) → UO2X3(-) + X(-) are calculated, revealing that all gaseous dianions are in fact metastable. The dielectric constant of the environment is shown to be the key in controlling the thermodynamic and kinetic stabilities of the tetracoordinate uranyl complexes via modulation of the ligand-ligand Coulomb repulsions.

[An analysis of factors affecting the prognosis of patients with cardiac amyloidosis].

OBJECTIVE: To analyze the clinical characteristics, diagnosis, treatment and outcome of patients with cardiac amyloidosis (CA). METHODS: Clinical data from 18 patients diagnosed as CA by endomyocardial biopsy (EMB) from 1995 to 2005 were retrospectively analyzed. RESULTS: Among the 18 patients with CA, all patients had reduced diastolic dysfunction; 12 had mitral valve early diastolic blood flow peak velocity/late diastolic blood flow peak velocity (E/A) > 2.0 and ventricular diastolic early filling deceleration time (DT) < 150 ms; 12 had left ventricular ejection fraction (LVEF) < 50%; and 13 had New York Heart Association (NYHA) classification III or IV. The 1-year, 3-year and 5-year survival rates of 18 patients with CA were 67%, 44% and 17%, respectively. Kaplan-Meier analysis showed, NYHA functional class > II, E/A > 2.0 and DT < 150 ms were associated with increased mortality (log-rank statistic P = 0.026 and 0.001, respectively). CA patients with chemotherapy before heart failure were associated with decreased mortality and extend survival. CONCLUSIONS: The mortality rate goes up and survival rate gradually descends as prolonged onset time. NYHA functional class >IIand E/A > 2.0 (DT< 150 ms) are associated with mortality.