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BACKGROUND: Despite improvement in treatment strategies for atrial fibrillation (AF), a significant proportion of patients still experience recurrence after ablation. This study aims to propose a novel algorithm based on Transformer using surface electrocardiogram (ECG) signals and clinical features can predict AF recurrence. METHODS: Between October 2018 to December 2021, patients who underwent index radiofrequency ablation for AF with at least one standard 10-second surface ECG during sinus rhythm were enrolled. An end-to-end deep learning framework based on Transformer and a fusion module was used to predict AF recurrence using ECG and clinical features. Model performance was evaluated using areas under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy and F1-score. RESULTS: A total of 920 patients (median age 61 [IQR 14] years, 66.3% male) were included. After a median follow-up of 24 months, 253 patients (27.5%) experienced AF recurrence. A single deep learning enabled ECG signals identified AF recurrence with an AUROC of 0.769, sensitivity of 75.5%, specificity of 61.1%, F1 score of 55.6% and overall accuracy of 65.2%. Combining ECG signals and clinical features increased the AUROC to 0.899, sensitivity to 81.1%, specificity to 81.7%, F1 score to 71.7%, and overall accuracy to 81.5%. CONCLUSIONS: The Transformer algorithm demonstrated excellent performance in predicting AF recurrence. Integrating ECG and clinical features enhanced the models' performance and may help identify patients at low risk for AF recurrence after index ablation.

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OBJECTIVE: This study aims to develop an artificial intelligence-based method to screen patients with left ventricular ejection fraction (LVEF) of 50% or lesser using electrocardiogram (ECG) data alone. METHODS: Convolutional neural network (CNN) is a class of deep neural networks, which has been widely used in medical image recognition. We collected standard 12-lead ECG and transthoracic echocardiogram (TTE) data including the LVEF value. Then, we paired the ECG and TTE data from the same individual. For multiple ECG-TTE pairs from a single individual, only the earliest data pair was included. All the ECG-TTE pairs were randomly divided into the training, validation, or testing data set in a ratio of 9:1:1 to create or evaluate the CNN model. Finally, we assessed the screening performance by overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS: We retrospectively enrolled a total of 26 786 ECG-TTE pairs and randomly divided them into training (n = 21 732), validation (n = 2 530), and testing data set (n = 2 530). In the testing set, the CNN algorithm showed an overall accuracy of 73.9%, sensitivity of 69.2%, specificity of 70.5%, positive predictive value of 70.1%, and negative predictive value of 69.9%. CONCLUSION: Our results demonstrate that a well-trained CNN algorithm may be used as a low-cost and noninvasive method to identify patients with left ventricular dysfunction.

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Coptis chinensis is a widely used traditional Chinese herbal medicine. In this work, 6 new alkaloids (coptisine A-F, 1-6) and 26 known alkaloids (7-32) were isolated from the chloroform extract of the rhizomes of C. chinensis. Compounds 1-3 are α-carbonylated benzylisoquinolines, and 4-6 are berberidic acid type alkaloids. Their structures were elucidated on the basis of extensive NMR and MS analyses. Seven compounds (7, 20, 22, 23, 25, 26, 29) exhibited significant AChE inhibitory activities at 10 µM (inhibition rates >80%).

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Once rice straw has been bioconverted into biofuels, it is difficult to further biodegrade or decompose the saccharification residue (mainly lignin). Taking into account the pyrolysis characteristics of lignin, in this study the saccharification residue was used as a raw material for the preparation of biochar (biochar-SR), a potential soil amendment. Biochar was prepared directly from rice straw (biochar-O) with a yield of 32.45 g/100 g rice straw, whereas 30.14 g biochar-SR and 30.46 g monosaccharides (including 20.46 g glucose, 9.11 g xylose, and 0.89 g arabinose) were obtained from 100 g of rice straw. When added to liquid soil extracts as a soil amendment, almost nothing was released from biochar-SR, whereas numerous dissolved solids (about 70 mg/L) were released from biochar-O. Adding a mixture of biochar-SR and autotrophic bacteria improved soil total organic carbon 1.8-fold and increased the transcription levels of cbbL and cbbM, which were 4.76 × 103 and 3.76 × 105 times those of the initial blank, respectively. By analyzing the soil microbial community, it was clear that the above mixture favored the growth of CO2-fixing bacteria such as Ochrobactrum. Compared with burning rice straw or preparing biochar-O, the preparation of biochar-SR reduced CO2 emissions by 67.53% or 37.13%, respectively. These results demonstrate that biochar-SR has potential applications in reducing the cost of sustainable energy and addressing environmental issues.

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Microbial interactions are important for metabolism as they can improve or reduce metabolic efficiency. To improve lignocellulolytic enzyme activity, a series of synergistic microbial consortia of increasing diversity and complexity were devised using fungal strains, including Trichoderma reesei, Penicillium decumbens, Aspergillus tubingensis, and Aspergillus niger. However, when a screened microbial community with cellulolytic capacity was added to the consortia to increase the number of strains, it engendered more microbial interactions with the above strains and universally improved the ß-glucosidase activity of the consortia. Analysis of the microbial community structure revealed that the bacteria in the consortia are more important for lignocellulolytic enzyme activity than the fungi. One fungal and 16 bacterial genera in the consortia may interact with T. reesei and are potential members of a devised synergistic microbial consortium. Such devised microbial consortia may potentially be applied to effectively and economically degrade lignocellulose.

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Alstonia scholaris has been used in "Dai" ethnic medicine to treat chronic respiratory diseases for a long history, and the major bioactive constituents are alkaloids. An alkaloidal extract of A. scholaris leaves (AAS) has been developed into an investigational new drug, and has been approved for phase I/II clinical trials by China Food and Drug Administration. However, little is known on the chemical composition and in vivo metabolism of AAS, thus far. In this study, an ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC/qTOF-MS) method was established to characterize the chemical constituents of AAS. Samples were separated on an ACQUITY UPLC CSH column (2.1×100mm, 1.7µm) with acetonitrile and water containing 0.3% formic acid as the mobile phase. On the basis of high-accuracy mass spectral analysis, a total of 35 alkaloids were characterized from AAS, including 11 scholaricine-type, 9 vallesamine-type, 12 picrinine-type, and 3 tubotaiwine-type alkaloids. Furthermore, the metabolic pathways of 4 representative alkaloids in rats were studied. They mainly undertook hydroxylation and glucuronidation reactions. Based on the above metabolic pathways, the metabolism of AAS (10mg/kg) in rats after oral administration was studied by LC/MS. A total of 33 compounds in plasma, 40 compounds in urine, and 38 compounds in feces were characterized. The results indicated that scholaricine-type alkaloids could get into circulation more readily than the other types. This is the first systematic study on chemical profiling and metabolites identification of AAS.

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The roots of Glycyrrhiza yunnanensis Cheng f. et L. K. Dai ex P. C. Li are used as licorice in traditional medicine of Southwest China. Triterpene saponins are the major chemical constituents. In this study, one new oleanane-type triterpenoid, glyyunnansapogenin I (I), seven new triterpene saponins, yunganosides E3 (II), L (III), M (IV), N1 (V), O (VI), P (VII) and N2 (VIII), together with four known saponins (IX-XII) were isolated from the roots of G. yunnanensis by preparative chromatography. Their structures were identified by spectroscopic analysis including NMR and HR-MS. Based on (-)-ESI-MS/MS fragmentation behaviors of these reference standards, an LC/MS/MS method using neutral loss scan and precursor ion scan on a triple quadrupole mass spectrometer was established to rapidly and comprehensively analyze triterpene saponins in G. yunnanensis. Combined with high-accuracy qTOF mass spectrometry analysis, a total of 50 saponins were detected, and their structures were identified or tentatively characterized. This is the first systematic study on triterpene saponins in G. yunnanensis.

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Terahertz (THz) signals measured by means of the spectral-encoding technique with different temporal discrepancies between probe pulses and THz signals are investigated. It is found that imperfect synchronization between the chirped probe and THz pulses induce a distortion and this distortion affects significantly the retrieved THz spectrum if the temporal discrepancy is large. The distortion becomes more prominent if the probe pulse length is less than the optimal chirped probe pulse duration. A simple approach is proposed to realize the synchronization and minimize the distortion. THz signals from a high-voltage-biased air plasma filament are measured with this approach and distortion similar to the simulation results is observed.