Leadwise clustering multi-branch network for multi-label ECG classification.

The 12-lead electrocardiogram (ECG) is widely used for diagnosing cardiovascular diseases in clinical practice. Recently, deep learning methods have become increasingly effective for automatically classifying ECG signals. However, most current research simply combines the 12-lead ECG signals into a matrix without fully considering the intrinsic relationships between the leads and the heart's structure. To better utilize medical domain knowledge, we propose a multi-branch network for multi-label ECG classification and introduce an intuitive and effective lead grouping strategy. Correspondingly, we design multi-branch networks where each branch employs a multi-scale convolutional network structure to extract more comprehensive features, with each branch corresponding to a lead combination. To better integrate features from different leads, we propose a feature weighting fusion module. We evaluate our method on the PTB-XL dataset for classifying 4 arrhythmia types and normal rhythm, and on the China Physiological Signal Challenge 2018 (CPSC2018) database for classifying 8 arrhythmia types and normal rhythm. Experimental results on multiple multi-label datasets demonstrate that our proposed multi-branch network outperforms state-of-the-art networks in multi-label classification tasks.

Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA.

Arrhythmias are irregular heartbeat rhythms caused by various conditions. Automated ECG signal classification aids in diagnosing and predicting arrhythmias. Current studies mostly focus on 1D ECG signals, overlooking the fusion of multiple ECG modalities for enhanced analysis. We converted ECG signals into modal images using RP, GAF, and MTF, inputting them into our classification model. To optimize detail retention, we introduced a CNN-based model with FCA for multimodal ECG tasks. Achieving 99.6% accuracy on the MIT-BIH arrhythmia database for five arrhythmias, our method outperforms prior models. Experimental results confirm its reliability for ECG classification tasks.

Heartbeat classification method combining multi-branch convolutional neural networks and transformer.

The detection and classification of arrhythmias are crucial steps in diagnosing cardiovascular diseases. However, current deep learning-based classification methods often fail to consider both the morpho-logical and temporal features of the electrocardiogram (ECG) simultaneously. Therefore, we propose a hybrid heartbeat classification method that combines Transformer and multi branch convolutional neural networks (CNNs). Then, use the fusion module to stitch the features obtained from different classifiers. We performed three different heartbeat classification protocols on the MIT-BIH arrhythmia (MIT-BIH-AR) database and analyzed performance on SVEB and VEB classes to validate our method. The first was an intra-patient protocol with an overall accuracy of 99.5%, with 92.4% and 99.9% for Sen and Spe on SVEB and 98.2% and 99.9% for Sen and Spe on VEB. The latter two were inter-patient protocols, and we divided the training and test sets using different records, and the results showed an overall accuracy of 98.8% and 97.2%, respectively.

ECG data enhancement method using generate adversarial networks based on Bi-LSTM and CBAM.

Objective.The classification performance of electrocardiogram (ECG) classification algorithms is easily affected by data imbalance, which often leads to poor model prediction performance for a few classes and a consequent decrease in the overall performance of the model.Approach.To address this problem, this paper proposed an ECG data augmentation method based on a generative adversarial network (GAN) that combines bidirectional long short-term memory (Bi-LSTM) networks and convolutional block attention mechanism (CBAM) to improve the overall performance of ECG classification models. In this paper, we used two ECG databases, namely the MIT-BIH arrhythmia (MIT-BIH-AR) database and the Chinese cardiovascular disease database (CCDD). The quality of the ECG signals produced by the generated models was assessed using the percent relative difference, root mean square error, Frechet distance, dynamic time warping (DTW), and Pearson correlation metrics. In addition, we also validated the impact of our proposed data augmentation method on ECG classification performance on MIT-BIH-AR database and CCDD.Main results.On the MIT-BIH-AR database, the overall accuracy of the data-enhanced balanced dataset was improved to 99.46% for 15 types of heartbeat classification task. On the CCDD, which focuses on the detection of ventricular precession (PVC), the overall accuracy of PVC detection improved to 99.15% after performing data enhancement.Significance.The experimental results indicate that the data augmentation method proposed in this paper can further improve the ECG classification performance.

Comparative transcriptomics analysis reveals a calcineurin B-like gene to positively regulate constitutive and acclimated freezing tolerance in potato.

Freezing stress is a major limiting factor in crop production. To increase frost-hardiness of crops via breeding, deciphering the genes conferring freezing-tolerance is vital. Potato cultivars (Solanum tuberosum) are generally freezing-sensitive, but some potato wild species are freezing-tolerant, including Solanum commersonii, Solanum malmeanum and Solanum acaule. However, the underlying molecular mechanisms conferring the freezing-tolerance to the wild species remain to be deciphered. In this study, five representative genotypes of the above-mentioned species with distinct freezing-tolerance were investigated. Comparative transcriptomics analysis showed that SaCBL1-like (calcineurin B-like protein) was upregulated substantially in all of the freezing-tolerant genotypes. Transgenic overexpression and known-down lines of SaCBL1-like were examined. SaCBL1-like was shown to confer freezing-tolerance without significantly impacting main agricultural traits. A functional mechanism analysis showed that SaCBL1-like increases the expression of the C-repeat binding factor-regulon as well as causes a prolonged higher expression of CBF1 after exposure to cold conditions. Furthermore, SaCBL1-like was found to only interact with SaCIPK3-1 (CBL-interacting protein kinase) among all apparent cold-responsive SaCIPKs. Our study identifies SaCBL1-like to play a vital role in conferring freezing tolerance in potato, which may provide a basis for a targeted potato breeding for frost-hardiness.

Silver nanoparticle@carbon quantum dot composite as an antibacterial agent.

A AgNPs@S,N-CQDs composite was synthesized by a one-step approach. It possessed AgNPs naturally surrounded by S,N-CQDs, and the size of the particles was found to be uniform and stable via a series of characterization methods. The antibacterial properties of the composite material were studied, and it had good antibacterial properties against S. aureus, E. coli, MRSA and C. albicans. The minimum inhibitory concentrations were 63 µg mL-1 against S. aureus and MRSA and 32 µg mL-1 against E. coli and C. albicans. In addition, the AgNPs@S,N-CQDs composite had an antibacterial effect via the generation of ROS, which was verified using the DCFH-DA kit. Finally, HepG2 cells were used to study its biocompatibility. The antibacterial properties and biocompatibility results show that the AgNPs@S,N-CQDs composite material can serve as a promising antibacterial agent.

Salicylic Acid Improves the Constitutive Freezing Tolerance of Potato as Revealed by Transcriptomics and Metabolomics Analyses.

Freezing severely impacts potato production. Deciphering the pathways and metabolites that regulate the freezing tolerance of potato is useful in cultivation and breeding for hardiness. In the present study, Solanum acaule was identified to be more freezing tolerant than S. tuberosum. Furthermore, the two genotypes before/after exposure to 4 °C for 7 d with additional -1 °C for 12 h were analysed by RNA-seq and metabolomics, and the results were compared with the previous -1 °C for 12 h. The results showed that S. acaule activated numerous genes that differed from those of S. tuberosum. Among the genes, five pathways, such as the hormone signalling pathway, which includes salicylic acid, were enriched. Further metabolomics analysis showed that the content of salicylic acid was improved in S. acaule in response to -1 °C for 12 h. Moreover, exogenous application of 0.1 mM salicylic acid to potato was shown to improve constitutive freezing tolerance and increase the expression of HSFC1. Following transcriptome and metabolome analyses, it was documented that the content of SA that increased in freezing-tolerant S. acaule after exposure to cold condition, associated with the SA signalling pathway, enhanced potato freezing tolerance, probably through HSFC1.

Sulfhydryl functionalized carbon quantum dots as a turn-off fluorescent probe for sensitive detection of Hg2.

Mercury ion (Hg2+) is one of the most toxic heavy metal ions and lowering the detection limit of Hg2+ is always a challenge in analytical chemistry and environmental analysis. In this work, sulfhydryl functionalized carbon quantum dots (HS-CQDs) were synthesized through a one-pot hydrothermal method. The obtained HS-CQDs were able to detect mercury ions Hg2+ rapidly and sensitively through fluorescence quenching, which may be ascribed to the formation of nonfluorescent ground-state complexes and electron transfer reaction between HS-CQDs and Hg2+. A modification of the HS-CQD surface by -SH was confirmed using Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). The HS-CQDs sensing system obtained a good linear relationship over a Hg2+ concentration ranging from 0.45 µM to 2.1 µM with a detection limit of 12 nM. Delightfully, the sensor has been successfully used to detect Hg2+ in real samples with satisfactory results. This means that the sensor has the potential to be used for testing actual samples.

Premature ventricular contraction detection combining deep neural networks and rules inference.

Premature ventricular contraction (PVC), which is a common form of cardiac arrhythmia caused by ectopic heartbeat, can lead to life-threatening cardiac conditions. Computer-aided PVC detection is of considerable importance in medical centers or outpatient ECG rooms. In this paper, we proposed a new approach that combined deep neural networks and rules inference for PVC detection. The detection performance and generalization were studied using publicly available databases: the MIT-BIH arrhythmia database (MIT-BIH-AR) and the Chinese Cardiovascular Disease Database (CCDD). The PVC detection accuracy on the MIT-BIH-AR database was 99.41%, with a sensitivity and specificity of 97.59% and 99.54%, respectively, which were better than the results from other existing methods. To test the generalization capability, the detection performance was also evaluated on the CCDD. The effectiveness of the proposed method was confirmed by the accuracy (98.03%), sensitivity (96.42%) and specificity (98.06%) with the dataset over 140,000 ECG recordings of the CCDD.

Electroacupuncture Inhibits the Activation of p38MAPK in the Central Descending Facilitatory Pathway in Rats with Inflammatory Pain.

The mitogen-activated protein kinases (MAPKs), especially p38MAPK, play a pivotal role in chronic pain. Electroacupuncture (EA) relieves inflammatory pain underlying the descending pathway, that is, the periaqueductal gray (PAG), the rostral ventromedial medulla (RVM), and the spinal cord dorsal horn (SCDH). However, whether EA antagonizes inflammatory pain through regulation of p38MAPK in this descending facilitatory pathway is unclear. Complete Freund's adjuvant (CFA) was injected into the hind paw of rats to establish inflammatory pain model. EA was administrated for 30 min at Zusanli and Kunlun acupoints at 0.5, 24.5, 48.5, and 72.5 h, respectively. The paw withdrawal threshold (PWT), paw edema, and Phosphor-p38MAPK-Immunoreactivity (p-p38MAPK-IR) cells were measured before (0 h) and at 1, 3, 5, 7, 25, and 73 h after CFA or saline injection. EA increased PWT at 1, 3, 25, and 73 h and inhibited paw edema at 25 and 73 h after CFA injection. Moreover, the increasing number of p-p38MAPK-IR cells which was induced by CFA was suppressed by EA stimulation in PAG and RVM at 3 and 5 h and in SCDH at 5, 7, 25, and 73 h. These results suggest that EA suppresses inflammation-induced hyperalgesia probably through inhibiting p38MAPK activation in the descending facilitatory pathway.