DrugMGR: a deep bioactive molecule binding method to identify compounds targeting proteins.

MOTIVATION: Understanding the intermolecular interactions of ligand-target pairs is key to guiding the optimization of drug research on cancers, which can greatly mitigate overburden workloads for wet labs. Several improved computational methods have been introduced and exhibit promising performance for these identification tasks, but some pitfalls restrict their practical applications: (i) first, existing methods do not sufficiently consider how multigranular molecule representations influence interaction patterns between proteins and compounds; and (ii) second, existing methods seldom explicitly model the binding sites when an interaction occurs to enable better prediction and interpretation, which may lead to unexpected obstacles to biological researchers. RESULTS: To address these issues, we here present DrugMGR, a deep multigranular drug representation model capable of predicting binding affinities and regions for each ligand-target pair. We conduct consistent experiments on three benchmark datasets using existing methods and introduce a new specific dataset to better validate the prediction of binding sites. For practical application, target-specific compound identification tasks are also carried out to validate the capability of real-world compound screen. Moreover, the visualization of some practical interaction scenarios provides interpretable insights from the results of the predictions. The proposed DrugMGR achieves excellent overall performance in these datasets, exhibiting its advantages and merits against state-of-the-art methods. Thus, the downstream task of DrugMGR can be fine-tuned for identifying the potential compounds that target proteins for clinical treatment. AVAILABILITY AND IMPLEMENTATION: https://github.com/lixiaokun2020/DrugMGR.

Boosting knowledge diversity, accuracy, and stability via tri-enhanced distillation for domain continual medical image segmentation.

Domain continual medical image segmentation plays a crucial role in clinical settings. This approach enables segmentation models to continually learn from a sequential data stream across multiple domains. However, it faces the challenge of catastrophic forgetting. Existing methods based on knowledge distillation show potential to address this challenge via a three-stage process: distillation, transfer, and fusion. Yet, each stage presents its unique issues that, collectively, amplify the problem of catastrophic forgetting. To address these issues at each stage, we propose a tri-enhanced distillation framework. (1) Stochastic Knowledge Augmentation reduces redundancy in knowledge, thereby increasing both the diversity and volume of knowledge derived from the old network. (2) Adaptive Knowledge Transfer selectively captures critical information from the old knowledge, facilitating a more accurate knowledge transfer. (3) Global Uncertainty-Guided Fusion introduces a global uncertainty view of the dataset to fuse the old and new knowledge with reduced bias, promoting a more stable knowledge fusion. Our experimental results not only validate the feasibility of our approach, but also demonstrate its superior performance compared to state-of-the-art methods. We suggest that our innovative tri-enhanced distillation framework may establish a robust benchmark for domain continual medical image segmentation.

TTN: Topological Transformer Network for Automated Coronary Artery Branch Labeling in Cardiac CT Angiography.

OBJECTIVE: Existing methods for automated coronary artery branch labeling in cardiac CT angiography face two limitations: 1) inability to model overall correlation of branches, since differences between branches cannot be captured directly. 2) a serious class imbalance between main and side branches. METHODS AND PROCEDURES: Inspired by the application of Transformer in sequence data, we propose a topological Transformer network (TTN), which solves the vessel branch labeling from a novel perspective of sequence labeling learning. TTN detects differences between branches by establishing their overall correlation. A topological encoding that represents the positions of vessel segments in the artery tree, is proposed to assist the model in classifying branches. Also, a segment-depth loss is introduced to solve the class imbalance between main and side branches. RESULTS: On a dataset with 325 CCTA, our method obtains the best overall result on all branches, the best result on side branches, and a competitive result on main branches. CONCLUSION: TTN solves two limitations in existing methods perfectly, thus achieving the best result in coronary artery branch labeling task. It is the first Transformer based vessel branch labeling method and is notably different from previous methods. CLINICAL IMPACT: This Pre-Clinical Research can be integrated into a computer-aided diagnosis system to generate cardiovascular disease diagnosis report, assisting clinicians in locating the atherosclerotic plaques.

ColonNet: A novel polyp segmentation framework based on LK-RFB and GPPD.

Colorectal cancer (CRC) holds the distinction of being the most prevalent malignant tumor affecting the digestive system. It is a formidable global health challenge, as it ranks as the fourth leading cause of cancer-related fatalities around the world. Despite considerable advancements in comprehending and addressing colorectal cancer (CRC), the likelihood of recurring tumors and metastasis remains a major cause of high morbidity and mortality rates during treatment. Currently, colonoscopy is the predominant method for CRC screening. Artificial intelligence has emerged as a promising tool in aiding the diagnosis of polyps, which have demonstrated significant potential. Unfortunately, most segmentation methods face challenges in terms of limited accuracy and generalization to different datasets, especially the slow processing and analysis speed has become a major obstacle. In this study, we propose a fast and efficient polyp segmentation framework based on the Large-Kernel Receptive Field Block (LK-RFB) and Global Parallel Partial Decoder(GPPD). Our proposed ColonNet has been extensively tested and proven effective, achieving a DICE coefficient of over 0.910 and an FPS of over 102 on the CVC-300 dataset. In comparison to the state-of-the-art (SOTA) methods, ColonNet outperforms or achieves comparable performance on five publicly available datasets, establishing a new SOTA. Compared to state-of-the-art methods, ColonNet achieves the highest FPS (over 102 FPS) while maintaining excellent segmentation results, achieving the best or comparable performance on the five public datasets. The code will be released at: https://github.com/SPECTRELWF/ColonNet.

Ambiguity-aware breast tumor cellularity estimation via self-ensemble label distribution learning.

In this work, we address the task of tumor cellularity (TC) estimation with a novel framework based on the label distribution learning (LDL) paradigm. We propose a self-ensemble label distribution learning framework (SLDL) to resolve the challenges of existing LDL-based methods, including difficulties for inter-rater ambiguity exploitation, proper and flexible label distribution generation, and accurate TC value recovery. The proposed SLDL makes four main contributions which have been demonstrated to be quite effective in numerous experiments. First, we propose an expertness-aware conditional VAE for diversified single-rater modeling and an attention-based multi-rater fusion strategy that enables effective inter-rater ambiguity exploitation. Second, we propose a template-based label distribution generation method that is tailored for the TC estimation task and constructs label distributions based on the annotation priors. Third, we propose a novel restricted distribution loss, significantly improving the TC value estimation by effectively regularizing the learning with unimodal loss and regression loss. Fourth, to the best of our knowledge, we are the first to simultaneously leverage inter-rater and intra-rater variability to address the label ambiguity issue in the breast tumor cellularity estimation tasks. The experimental results on the public BreastPathQ dataset demonstrate that the SLDL outperforms the existing methods by a large margin and achieves new state-of-the-art results in the TC estimation task. The code will be available from https://github.com/PerceptionComputingLab/ULTRA.

Curriculum label distribution learning for imbalanced medical image segmentation.

Label distribution learning (LDL) has the potential to resolve boundary ambiguity in semantic segmentation tasks. However, existing LDL-based segmentation methods suffer from severe label distribution imbalance: the ambiguous label distributions contain a small fraction of the data, while the unambiguous label distributions occupy the majority of the data. The imbalanced label distributions induce model-biased distribution learning and make it challenging to accurately predict ambiguous pixels. In this paper, we propose a curriculum label distribution learning (CLDL) framework to address the above data imbalance problem by performing a novel task-oriented curriculum learning strategy. Firstly, the region label distribution learning (R-LDL) is proposed to construct more balanced label distributions and improves the imbalanced model learning. Secondly, a novel learning curriculum (TCL) is proposed to enable easy-to-hard learning in LDL-based segmentation by decomposing the segmentation task into multiple label distribution estimation tasks. Thirdly, the prior perceiving module (PPM) is proposed to effectively connect easy and hard learning stages based on the priors generated from easier stages. Benefiting from the balanced label distribution construction and prior perception, the proposed CLDL effectively conducts a curriculum learning-based LDL and significantly improves the imbalanced learning. We evaluated the proposed CLDL using the publicly available BRATS2018 and MM-WHS2017 datasets. The experimental results demonstrate that our method significantly improves different segmentation metrics compared to many state-of-the-art methods. The code will be available.1.

Trajectory-Aware Adaptive Imaging Clue Analysis for Guidewire Artifact Removal in Intravascular Optical Coherence Tomography.

Guidewire Artifact Removal (GAR) involves restoring missing imaging signals in areas of IntraVascular Optical Coherence Tomography (IVOCT) videos affected by guidewire artifacts. GAR helps overcome imaging defects and minimizes the impact of missing signals on the diagnosis of CardioVascular Diseases (CVDs). To restore the actual vascular and lesion information within the artifact area, we propose a reliable Trajectory-aware Adaptive imaging Clue analysis Network (TAC-Net) that includes two innovative designs: (i) Adaptive clue aggregation, which considers both texture-focused original (ORI) videos and structure-focused relative total variation (RTV) videos, and suppresses texture-structure imbalance with an active weight-adaptation mechanism; (ii) Trajectory-aware Transformer, which uses a novel attention calculation to perceive the attention distribution of artifact trajectories and avoid the interference of irregular and non-uniform artifacts. We provide a detailed formulation for the procedure and evaluation of the GAR task and conduct comprehensive quantitative and qualitative experiments. The experimental results demonstrate that TAC-Net reliably restores the texture and structure of guidewire artifact areas as expected by experienced physicians (e.g., SSIM: 97.23%). We also discuss the value and potential of the GAR task for clinical applications and computer-aided diagnosis of CVDs.

SAGDTI: self-attention and graph neural network with multiple information representations for the prediction of drug-target interactions.

Motivation: Accurate identification of target proteins that interact with drugs is a vital step in silico, which can significantly foster the development of drug repurposing and drug discovery. In recent years, numerous deep learning-based methods have been introduced to treat drug-target interaction (DTI) prediction as a classification task. The output of this task is binary identification suggesting the absence or presence of interactions. However, existing studies often (i) neglect the unique molecular attributes when embedding drugs and proteins, and (ii) determine the interaction of drug-target pairs without considering biological interaction information. Results: In this study, we propose an end-to-end attention-derived method based on the self-attention mechanism and graph neural network, termed SAGDTI. The aim of this method is to overcome the aforementioned drawbacks in the identification of DTI. SAGDTI is the first method to sufficiently consider the unique molecular attribute representations for both drugs and targets in the input form of the SMILES sequences and three-dimensional structure graphs. In addition, our method aggregates the feature attributes of biological information between drugs and targets through multi-scale topologies and diverse connections. Experimental results illustrate that SAGDTI outperforms existing prediction models, which benefit from the unique molecular attributes embedded by atom-level attention and biological interaction information representation aggregated by node-level attention. Moreover, a case study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shows that our model is a powerful tool for identifying DTIs in real life. Availability and implementation: The data and codes underlying this article are available in Github at https://github.com/lixiaokun2020/SAGDTI.

A contrastive consistency semi-supervised left atrium segmentation model.

Accurate segmentation for the left atrium (LA) is a key process of clinical diagnosis and therapy for atrial fibrillation. In clinical, the semantic-level segmentation of LA consumes much time and labor. Although supervised deep learning methods can somewhat solve this problem, a high-efficient deep learning model requires abundant labeled data that is hard to acquire. Therefore, the research on automatic LA segmentation of leveraging unlabeled data is highly required. In this paper, we propose a semi-supervised LA segmentation framework including a segmentation model and a classification model. The segmentation model takes volumes from both labeled and unlabeled data as input and generates predictions of LAs. And then, a classification model maps these predictions to class-vectors for each input. Afterward, to leverage the class information, we construct a contrastive consistency loss function based on these class-vectors, so that the model can enlarge the discrepancy of the inter-class and compact the similarity of the intra-class for learning more distinguishable representation. Moreover, we set the class-vectors from the labeled data as references to the class-vectors from the unlabeled data to relieve the influence of the unreliable prediction for the unlabeled data. At last, we evaluate our semi-supervised LA segmentation framework on a public LA dataset using four universal metrics and compare it with recent state-of-the-art models. The proposed model achieves the best performance on all metrics with a Dice Score of 89.81 %, Jaccard of 81.64 %, 95 % Hausdorff distance of 7.15 mm, and Average Surface Distance of 1.82 mm. The outstanding performance of the proposed framework shows that it may have a significant contribution to assisting the therapy of patients with atrial fibrillation. Code is available at: https://github.com/PerceptionComputingLab/SCC.

Pan-Cancer Methylated Dysregulation of Long Non-coding RNAs Reveals Epigenetic Biomarkers.

Different cancer types not only have common characteristics but also have their own characteristics respectively. The mechanism of these specific and common characteristics is still unclear. Pan-cancer analysis can help understand the similarities and differences among cancer types by systematically describing different patterns in cancers and identifying cancer-specific and cancer-common molecular biomarkers. While long non-coding RNAs (lncRNAs) are key cancer modulators, there is still a lack of pan-cancer analysis for lncRNA methylation dysregulation. In this study, we integrated lncRNA methylation, lncRNA expression and mRNA expression data to illuminate specific and common lncRNA methylation patterns in 23 cancer types. Then, we screened aberrantly methylated lncRNAs that negatively regulated lncRNA expression and mapped them to the ceRNA relationship for further validation. 29 lncRNAs were identified as diagnostic biomarkers for their corresponding cancer types, with lncRNA AC027601 was identified as a new KIRC-associated biomarker, and lncRNA ACTA2-AS1 was regarded as a carcinogenic factor of KIRP. Two lncRNAs HOXA-AS2 and AC007228 were identified as pan-cancer biomarkers. In general, the cancer-specific and cancer-common lncRNA biomarkers identified in this study may aid in cancer diagnosis and treatment.

Inter-subject registration-based one-shot segmentation with alternating union network for cardiac MRI images.

Medical image segmentation based on deep-learning networks makes great progress in assisting disease diagnosis. However, currently, the training of most networks still requires a large amount of data with labels. In reality, labeling a considerable number of medical images is challenging and time-consuming. In order to tackle this challenge, a new one-shot segmentation framework for cardiac MRI images based on an inter-subject registration model called Alternating Union Network (AUN) is proposed in this study. The label of the source image is warped with deformation fields discovered from AUN to segment target images directly. Initially, the volumes are pre-processed by aligning affinely and adjusting the global intensity to simplify subsequent deformation registration. AUN consists of two kinds of subnetworks trained alternately to optimize segmentation gradually. The first kind of subnetwork takes a pair of volumes as inputs and registers them using global intensity similarity. The second kind of subnetwork, which takes the predicted labels generated from the previous subnetwork and the labels refined using the information of intrinsic anatomical structures of interest as inputs, is intensity-independent and focuses attention on registering structures of interest. Specifically, the input of AUN is a pair of a labeled image with the texture in regions of interest removed and a target image. Additionally, a new similarity measurement more appropriate for registering such image pair is defined as Local Squared Error (LSE). The proposed registration-based one-shot segmentation pays attention to the problem of the lack of labeled medical images. In AUN, only one labeled volume is required and a large number of unlabeled ones can be leveraged to improve segmentation performance, which has great advantages in clinical application. In addition, the intensity-independent subnetwork and LSE proposed in this study empower the framework to segment medical images with complicated intensity distribution.

Uncertainty-guided symmetric multilevel supervision network for 3D left atrium segmentation in late gadolinium-enhanced MRI.

PURPOSE: Atrial fibrillation (AF) is a common arrhythmia and requires volumetric imaging to guide the therapy procedure. Late gadolinium-enhanced magnetic resonance imaging (LGE MRI) is an efficient noninvasive technology for imaging the diseased heart. Three-dimensional segmentation of the left atrium (LA) in LGE MRI is a fundamental step for guiding the therapy of patients with AF. However, the low contrast and fuzzy surface of the LA in LGE MRI make accurate and objective LA segmentation a challenge. The purpose of this study is to propose an automatic and efficient LA segmentation model based on a convolutional neural network to obtain a more accurate predicted surface and improve the LA segmentation results. METHODS: In this study, we proposed an uncertainty-guided symmetric multilevel supervision (SML) network for 3D LA segmentation in LGE MRI. First, we constructed an SML structure to combine the corresponding features from the encoding and decoding stages to learn the multiscale representation of LA. Second, we formulated the discrepancy of predictions of our model as model uncertainty. Then we proposed an uncertainty-guided objective function to further increase the segmentation accuracy on the surface. RESULTS: We evaluated our proposed model on the public LA segmentation database using four universal metrics. The proposed model achieved Hausdorff Distance (HD) of 11.68 mm, average symmetric surface distance of 0.92 mm, Dice score of 0.92, and Jaccard of 0.85. Compared with state-of-the-art models, our model achieved the best HD that is sensitive to surface accuracy. For the other three metrics, our model also achieved better or comparable performance. CONCLUSIONS: We proposed an efficient automatic LA segmentation model that consisted of an SML structure and an uncertainty-guided objective function. Compared to other models, we designed an additional supervision branch in the encoding stage to learn more detailed representations of LA while learning global context information through the multilevel structure of each supervision branch. To address the fuzzy surface challenge of LA segmentation in LGE MRI, we leveraged the model uncertainty to enhance the distinguishing ability of the model on the surface, thereby the predicted accuracy of the LA surface can be further increased. We conducted extensive ablation and comparative experiments with state-of-the-art models. The experiment results demonstrated that our proposed model could handle the complex structure of LA and had superior advantages in improving the segmentation performance on the surface.

Generalizable Beat-by-Beat Arrhythmia Detection by Using Weakly Supervised Deep Learning.

Beat-by-beat arrhythmia detection in ambulatory electrocardiogram (ECG) monitoring is critical for the evaluation and prognosis of cardiac arrhythmias, however, it is a highly professional demanding and time-consuming task. Current methods for automatic beat-by-beat arrhythmia detection suffer from poor generalization ability due to the lack of large-sample and finely-annotated (labels are given to each beat) ECG data for model training. In this work, we propose a weakly supervised deep learning framework for arrhythmia detection (WSDL-AD), which permits training a fine-grained (beat-by-beat) arrhythmia detector with the use of large amounts of coarsely annotated ECG data (labels are given to each recording) to improve the generalization ability. In this framework, heartbeat classification and recording classification are integrated into a deep neural network for end-to-end training with only recording labels. Several techniques, including knowledge-based features, masked aggregation, and supervised pre-training, are proposed to improve the accuracy and stability of the heartbeat classification under weak supervision. The developed WSDL-AD model is trained for the detection of ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB) on five large-sample and coarsely-annotated datasets and the model performance is evaluated on three independent benchmarks according to the recommendations from the Association for the Advancement of Medical Instrumentation (AAMI). The experimental results show that our method improves the F 1 score of supraventricular ectopic beats detection by 8%-290% and the F1 of ventricular ectopic beats detection by 4%-11% on the benchmarks compared with the state-of-the-art methods of supervised learning. It demonstrates that the WSDL-AD framework can leverage the abundant coarsely-labeled data to achieve a better generalization ability than previous methods while retaining fine detection granularity. Therefore, this framework has a great potential to be used in clinical and telehealth applications. The source code is available at https://github.com/sdnjly/WSDL-AD.

Mechanisms of ventricular arrhythmias elicited by coexistence of multiple electrophysiological remodeling in ischemia: A simulation study.

Myocardial ischemia, injury and infarction (MI) are the three stages of acute coronary syndrome (ACS). In the past two decades, a great number of studies focused on myocardial ischemia and MI individually, and showed that the occurrence of reentrant arrhythmias is often associated with myocardial ischemia or MI. However, arrhythmogenic mechanisms in the tissue with various degrees of remodeling in the ischemic heart have not been fully understood. In this study, biophysical detailed single-cell models of ischemia 1a, 1b, and MI were developed to mimic the electrophysiological remodeling at different stages of ACS. 2D tissue models with different distributions of ischemia and MI areas were constructed to investigate the mechanisms of the initiation of reentrant waves during the progression of ischemia. Simulation results in 2D tissues showed that the vulnerable windows (VWs) in simultaneous presence of multiple ischemic conditions were associated with the dynamics of wave propagation in the tissues with each single pathological condition. In the tissue with multiple pathological conditions, reentrant waves were mainly induced by two different mechanisms: one is the heterogeneity along the excitation wavefront, especially the abrupt variation in conduction velocity (CV) across the border of ischemia 1b and MI, and the other is the decreased safe factor (SF) for conduction at the edge of the tissue in MI region which is attributed to the increased excitation threshold of MI region. Finally, the reentrant wave was observed in a 3D model with a scar reconstructed from MRI images of a MI patient. These comprehensive findings provide novel insights for understanding the arrhythmic risk during the progression of myocardial ischemia and highlight the importance of the multiple pathological stages in designing medical therapies for arrhythmias in ischemia.

Performance of novel deep learning network with the incorporation of the automatic segmentation network for diagnosis of breast cancer in automated breast ultrasound.

OBJECTIVE: To develop novel deep learning network (DLN) with the incorporation of the automatic segmentation network (ASN) for morphological analysis and determined the performance for diagnosis breast cancer in automated breast ultrasound (ABUS). METHODS: A total of 769 breast tumors were enrolled in this study and were randomly divided into training set and test set at 600 vs. 169. The novel DLNs (Resent v2, ResNet50 v2, ResNet101 v2) added a new ASN to the traditional ResNet networks and extracted morphological information of breast tumors. The accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristic (ROC) curve (AUC), and average precision (AP) were calculated. The diagnostic performances of novel DLNs were compared with those of two radiologists with different experience. RESULTS: The ResNet34 v2 model had higher specificity (76.81%) and PPV (82.22%) than the other two, the ResNet50 v2 model had higher accuracy (78.11%) and NPV (72.86%), and the ResNet101 v2 model had higher sensitivity (85.00%). According to the AUCs and APs, the novel ResNet101 v2 model produced the best result (AUC 0.85 and AP 0.90) compared with the remaining five DLNs. Compared with the novice radiologist, the novel DLNs performed better. The F1 score was increased from 0.77 to 0.78, 0.81, and 0.82 by three novel DLNs. However, their diagnostic performance was worse than that of the experienced radiologist. CONCLUSIONS: The novel DLNs performed better than traditional DLNs and may be helpful for novice radiologists to improve their diagnostic performance of breast cancer in ABUS. KEY POINTS: • A novel automatic segmentation network to extract morphological information was successfully developed and implemented with ResNet deep learning networks. • The novel deep learning networks in our research performed better than the traditional deep learning networks in the diagnosis of breast cancer using ABUS images. • The novel deep learning networks in our research may be useful for novice radiologists to improve diagnostic performance.

Hematoma Expansion Context Guided Intracranial Hemorrhage Segmentation and Uncertainty Estimation.

Accurate segmentation of the Intracranial Hemorrhage (ICH) in non-contrast CT images is significant for computer-aided diagnosis. Although existing methods have achieved remarkable 1 1 The code will be available from https://github.com/JohnleeHIT/SLEX-Net. results, none of them incorporated ICH's prior information in their methods. In this work, for the first time, we proposed a novel SLice EXpansion Network (SLEX-Net), which incorporated hematoma expansion in the segmentation architecture by directly modeling the hematoma variation among adjacent slices. Firstly, a new module named Slice Expansion Module (SEM) was built, which can effectively transfer contextual information between two adjacent slices by mapping predictions from one slice to another. Secondly, to perceive contextual information from both upper and lower slices, we designed two information transmission paths: forward and backward slice expansion, and aggregated results from those paths with a novel weighing strategy. By further exploiting intra-slice and inter-slice context with the information paths, the network significantly improved the accuracy and continuity of segmentation results. Moreover, the proposed SLEX-Net enables us to conduct an uncertainty estimation with one-time inference, which is much more efficient than existing methods. We evaluated the proposed SLEX-Net and compared it with some state-of-the-art methods. Experimental results demonstrate that our method makes significant improvements in all metrics on segmentation performance and outperforms other existing uncertainty estimation methods in terms of several metrics.

Automatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding.

Automatic electrocardiogram (ECG) classification is a promising technology for the early screening and follow-up management of cardiovascular diseases. It is, by nature, a multi-label classification task owing to the coexistence of different kinds of diseases, and is challenging due to the large number of possible label combinations and the imbalance among categories. Furthermore, the task of multi-label ECG classification is cost-sensitive, a fact that has usually been ignored in previous studies on the development of the model. To address these problems, in this work, we propose a novel deep learning model-based learning framework and a thresholding method, namely category imbalance and cost-sensitive thresholding (CICST), to incorporate prior knowledge about classification costs and the characteristic of category imbalance in designing a multi-label ECG classifier. The learning framework combines a residual convolutional network with a class-wise attention mechanism. We evaluate our method with a cost-sensitive metric on multiple realistic datasets. The results show that CICST achieved a cost-sensitive metric score of 0.641 ± 0.009 in a 5-fold cross-validation, outperforming other commonly used thresholding methods, including rank-based thresholding, proportion-based thresholding, and fixed thresholding. This demonstrates that, by taking into account the category imbalance and predefined cost information, our approach is effective in improving the performance and practicability of multi-label ECG classification models.

The Functional Role of Hyperpolarization Activated Current (I f) on Cardiac Pacemaking in Human vs. in the Rabbit Sinoatrial Node: A Simulation and Theoretical Study.

The cardiac hyperpolarization-activated "funny" current (I f), which contributes to sinoatrial node (SAN) pacemaking, has a more negative half-maximal activation voltage and smaller fully-activated macroscopic conductance in human than in rabbit SAN cells. The consequences of these differences for the relative roles of I f in the two species, and for their responses to the specific bradycardic agent ivabradine at clinical doses have not been systematically explored. This study aims to address these issues, through incorporating rabbit and human I f formulations developed by Fabbri et al. into the Severi et al. model of rabbit SAN cells. A theory was developed to correlate the effect of I f reduction with the total inward depolarising current (I total) during diastolic depolarization. Replacing the rabbit I f formulation with the human one increased the pacemaking cycle length (CL) from 355 to 1,139 ms. With up to 20% I f reduction (a level close to the inhibition of I f by ivabradine at clinical concentrations), a modest increase (~5%) in the pacemaking CL was observed with the rabbit I f formulation; however, the effect was doubled (~12.4%) for the human I f formulation, even though the latter has smaller I f density. When the action of acetylcholine (ACh, 0.1 nM) was considered, a 20% I f reduction markedly increased the pacemaking CL by 37.5% (~27.3% reduction in the pacing rate), which is similar to the ivabradine effect at clinical concentrations. Theoretical analysis showed that the resultant increase of the pacemaking CL is inversely proportional to the magnitude of I total during diastolic depolarization phase: a smaller I f in the model resulted in a smaller I total amplitude, resulting in a slower pacemaking rate; and the same reduction in I f resulted in a more significant change of CL in the cell model with a smaller I total. This explained the mechanism by which a low dose of ivabradine slows pacemaking rate more in humans than in the rabbit. Similar results were seen in the Fabbri et al. model of human SAN cells, suggesting our observations are model-independent. Collectively, the results of study explain why low dose ivabradine at clinically relevant concentrations acts as an effective bradycardic agent in modulating human SAN pacemaking.

Effects of long-term fasting and confinement on the cardiovascular activity.

Fasting has been demonstrated to improve health and slow aging in human and other species; however, its impact on the human body in the confined environment is still unclear. This work studies the effects of long-term fasting and confined environment on the cardiovascular activities of human via a 10-day fasting experiment with two groups of subjects being in confined (6 subjects) and unconfined (7 subjects) environments respectively and undergoing the same four-stage fasting/feeding process. It is found that the confinement has significant influences on the autonomic regulation to the heart rate during the fasting process by altering the activity of the parasympathetic nervous system, which is manifested by the significant higher pNN50, rMSSD, and Ln-HF of heart rate variability (HRV) (p < 0.05) and slower heart rate (p < 0.01) in the confined group than that in the unconfined group. Furthermore, the long-term fasting induces a series of changes in both groups, including reduced level of serum sodium (p < 0.01), increased the serum calcium (p < 0.05), prolonged QTc intervals (p < 0.05), and reduced systolic blood pressures (p < 0.05). These effects are potentially negative to human health and therefore need to be treated with caution. Study of the effects of fasting and confinement on the cardiovascular activities.