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Falls among the elderly are a common and serious health risk that can lead to physical injuries and other complications. To promptly detect and respond to fall events, radar-based fall detection systems have gained widespread attention. In this paper, a deep learning model is proposed based on the frequency spectrum of radar signals, called the convolutional bidirectional long short-term memory (CB-LSTM) model. The introduction of the CB-LSTM model enables the fall detection system to capture both temporal sequential and spatial features simultaneously, thereby enhancing the accuracy and reliability of the detection. Extensive comparison experiments demonstrate that our model achieves an accuracy of 98.83% in detecting falls, surpassing other relevant methods currently available. In summary, this study provides effective technical support using the frequency spectrum and deep learning methods to monitor falls among the elderly through the design and experimental validation of a radar-based fall detection system, which has great potential for improving quality of life for the elderly and providing timely rescue measures.

Lung cancer is a malignant tumor with highly heterogeneous characteristics. A classic Chinese medicine, Pinellia ternata (PT), was shown to exert therapeutic effects on lung cancer cells. However, its chemical and pharmacological profiles are not yet understood. In the present study, we aimed to reveal the mechanism of PT in treating lung cancer cells through metabolomics and network pharmacology. Metabolomic analysis of two strains of lung cancer cells treated with Pinellia ternata extracts (PTE) was used to identify differentially abundant metabolites, and the metabolic pathways associated with the DEGs were identified by MetaboAnalyst. Then, network pharmacology was applied to identify potential targets against PTE-induced lung cancer cells. The integrated network of metabolomics and network pharmacology was constructed based on Cytoscape. PTE obviously inhibited the proliferation, migration and invasion of A549 and NCI-H460 cells. The results of the cellular metabolomics analysis showed that 30 metabolites were differentially expressed in the lung cancer cells of the experimental and control groups. Through pathway enrichment analysis, 5 metabolites were found to be involved in purine metabolism, riboflavin metabolism and the pentose phosphate pathway, including D-ribose 5-phosphate, xanthosine, 5-amino-4-imidazolecarboxyamide, flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD). Combined with network pharmacology, 11 bioactive compounds were found in PT, and networks of bioactive compound-target gene-metabolic enzyme-metabolite interactions were constructed. In conclusion, this study revealed the complicated mechanisms of PT against lung cancer. Our work provides a novel paradigm for identifying the potential mechanisms underlying the pharmacological effects of natural compounds., (© 2024. The Author(s).)

The aim of this study was to investigated the effects of forsythiaside A (FA) on acute lung injury (ALI). The lung tissue pathological was detected by hematoxylin-eosin staining (HE) staining. Wet weight/dry weight (w/d) of the lung in mice was measured. Cytokine such as interleukin 1β (IL-1β), IL-6 and tumor necrosis factor-α (TNF-α) were also detected. Compared with the vector group, the protein expression levels of TRAF6 and TAK1 the RNF99 group were significantly reduced. Ubiquitinated TRAF6 protein was increased after knockdown of RNF99. Finally, it was found that FA significantly ameliorated ALI via regulation of RNF99/TRAF6/NF-κB signal pathway. In conclusion, RNF99 was an important biomarker in ALI and FA alleviated ALI via RNF99/ TRAF6/NF-κB signal pathway., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Objective . Automatic and accurate airway segmentation is necessary for lung disease diagnosis. The complex tree-like structures leads to gaps in the different generations of the airway tree, and thus airway segmentation is also considered to be a multi-scale problem. In recent years, convolutional neural networks have facilitated the development of medical image segmentation. In particular, 2D CNNs and 3D CNNs can extract different scale features. Hence, we propose a two-stage and 2D + 3D framework for multi-scale airway tree segmentation. Approach . In stage 1, we use a 2D full airway SegNet(2D FA-SegNet) to segment the complete airway tree. Multi-scale atros spatial pyramid and Atros Residual Skip connection modules are inserted to extract different scales feature. We designed a hard sample selection strategy to increase the proportion of intrapulmonary airway samples in stage 2. 3D airway RefineNet (3D ARNet) as stage 2 takes the results of stage 1 as a priori information. Spatial information extracted by 3D convolutional kernel compensates for the loss of in 2D FA-SegNet. Furthermore, we added false positive losses and false negative losses to improve the segmentation performance of airway branches within the lungs. Main results . We performed data enhancement on the publicly available dataset of ISICDM 2020 Challenge 3, and on which evaluated our method. Comprehensive experiments show that the proposed method has the highest dice similarity coefficient (DSC) of 0.931, and IoU of 0.871 for the whole airway tree and DSC of 0.699, and IoU of 0.543 for the intrapulmonary bronchi tree. In addition, 3D ARNet proposed in this paper cascaded with other state-of-the-art methods to increase detected tree length rate by up to 46.33% and detected tree branch rate by up to 42.97%. Significance . The quantitative and qualitative evaluation results show that our proposed method performs well in segmenting the airway at different scales., (© 2024 Institute of Physics and Engineering in Medicine.)

Background: The curved planar reformation (CPR) technique is one of the most commonly used methods in clinical practice to locate coronary arteries in medical images., Purpose: The artery centerline is the cornerstone for the generation of the CPR image. Here, we describe the development of a new fully automatic artery centerline tracker with the aim of increasing the efficiency and accuracy of the process., Methods: We propose a COronary artery Centerline Tracker (COACT) framework which consists of an ostium point finder (OPFinder) model, an intersection point detector (IPDetector) model and a set of centerline tracking strategies. The output of OPFinder is the ostium points. The function of the IPDetector is to predict the intersections of a sample sphere and the centerlines. The centerline tracking process starts from two ostium points detected by the OPFinder, and combines the results of the IPDetector with a series of strategies to gradually reconstruct the coronary artery centerline tree., Results: Two coronary CT angiography (CCTA) datasets were used to validate the models. Dataset1 contains 160 cases (32 for test and 128 for training) and dataset2 contains 70 cases (20 for test and 50 for training). The results show that the average distance between the ostium points predicted by the OPFinder and the manually annotated ostium points was 0.88 mm, which is similar to the differences between the results obtained by two observers (0.85 mm). For the IPDetector, the average overlap of the predicted and ground truth intersection points was 97.82% and this is also close to the inter-observer agreement of 98.50%. For the entire coronary centerline tree, the overlap between the results obtained by COACT and the gold standard was 94.33%, which is slightly lower than the inter-observer agreement, 98.39%., Conclusions: We have developed a fully automatic centerline tracking method for CCTA scans and achieved a satisfactory result. The proposed algorithms are also incorporated in the medical image analysis platform TIMESlice (https://slice-doc.netlify.app) for further studies., (© 2023 American Association of Physicists in Medicine.)

Background and Objective: Aortic blood pressure (ABP) is a more effective prognostic indicator of cardiovascular disease than peripheral blood pressure. A highly accurate algorithm for non-invasively deriving the ABP wave, based on ultrasonic measurement of aortic flow combined with peripheral pulse wave measurements, has been proposed elsewhere. However, it has remained at the proof-of-concept stage because it requires a priori knowledge of the ABP waveform to calculate aortic pulse wave velocity (PWV). The objective of this study is to transform this proof-of-concept algorithm into a clinically feasible technique., Methods: We used the Bramwell-Hill equation to non-invasively calculate aortic PWV which was then used to reconstruct the ABP waveform from non-invasively determined aortic blood flow velocity, aortic diameter, and radial pressure. The two aortic variables were acquired by an ultrasound system from 90 subjects, followed by recordings of radial pressure using a SphygmoCor device. The ABPs estimated by the new algorithm were compared with reference values obtained by cardiac catheterization (invasive validation, 8 subjects aged 62.3 ± 12.7 years) and a SphygmoCor device (non-invasive validation, 82 subjects aged 45.0 ± 17.8 years)., Results: In the invasive comparison, there was good agreement between the estimated and directly measured pressures: the mean error in systolic blood pressure (SBP) was 1.4 ± 0.8 mmHg; diastolic blood pressure (DBP), 0.9 ± 0.8 mmHg; mean blood pressure (MBP), 1.8 ± 1.2 mmHg and pulse pressure (PP), 1.4 ± 1.1 mmHg. In the non-invasive comparison, the estimated and directly measured pressures also agreed well: the errors being: SBP, 2.0 ± 1.4 mmHg; DBP, 0.8 ± 0.1 mmHg; MBP, 0.1 ± 0.1 mmHg and PP, 2.3 ± 1.6 mmHg. The significance of the differences in mean errors between calculated and reference values for SBP, DBP, MBP and PP were assessed by paired t-tests. The agreement between the reference methods and those obtained by applying the new approach was also expressed by correlation and Bland-Altman plots., Conclusion: The new method proposed here can accurately estimate ABP, allowing this important variable to be obtained non-invasively, using standard, well validated measurement techniques. It thus has the potential to relocate ABP estimation from a research environment to more routine use in the cardiac clinic., Short Abstract: A highly accurate algorithm for non-invasively deriving the ABP wave has been proposed elsewhere. However, it has remained at the proof-of-concept stage because it requires a priori knowledge of the ABP waveform to calculate aortic pulse wave velocity (PWV). This study aims to transform this proof-of-concept algorithm into a clinically feasible technique. We used the Bramwell-Hill equation to non-invasively calculate aortic PWV which was then used to reconstruct the ABP waveform. The ABPs estimated by the new algorithm were compared with reference values obtained by cardiac catheterization or a SphygmoCor device. The results showed that there was good agreement between the estimated and directly measured pressures. The new method proposed can accurately estimate ABP, allowing this important variable to be obtained non-invasively, using standard, well validated measurement techniques. It thus has the potential to relocate ABP estimation from a research environment to more routine use in the cardiac clinic., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Traditional Chinese medicine (TCM) is an essential part of the Chinese medical system and is recognized by the World Health Organization as an important alternative medicine. As an important part of TCM, TCM diagnosis is a method to understand a patient's illness, analyze its state, and identify syndromes. In the long-term clinical diagnosis practice of TCM, four fundamental and effective diagnostic methods of inspection, auscultation-olfaction, inquiry, and palpation (IAOIP) have been formed. However, the diagnostic information in TCM is diverse, and the diagnostic process depends on doctors' experience, which is subject to a high-level subjectivity. At present, the research on the automated diagnosis of TCM based on machine learning is booming. Machine learning, which includes deep learning, is an essential part of artificial intelligence (AI), which provides new ideas for the objective and AI-related research of TCM. This paper aims to review and summarize the current research status of machine learning in TCM diagnosis. First, we review some key factors for the application of machine learning in TCM diagnosis, including data, data preprocessing, machine learning models, and evaluation metrics. Second, we review and summarize the research and applications of machine learning methods in TCM IAOIP and the synthesis of the four diagnostic methods. Finally, we discuss the challenges and research directions of using machine learning methods for TCM diagnosis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Unsupervised domain adaptation (UDA) methods have shown great potential in cross-modality medical image segmentation tasks, where target domain labels are unavailable. However, the domain shift among different image modalities remains challenging, because the conventional UDA methods are based on convolutional neural networks (CNNs), which tend to focus on the texture of images and cannot establish the global semantic relevance of features due to the locality of CNNs. This paper proposes a novel end-to-end Swin Transformer-based generative adversarial network (ST-GAN) for cross-modality cardiac segmentation. In the generator of ST-GAN, we utilize the local receptive fields of CNNs to capture spatial information and introduce the Swin Transformer to extract global semantic information, which enables the generator to better extract the domain-invariant features in UDA tasks. In addition, we design a multi-scale feature fuser to sufficiently fuse the features acquired at different stages and improve the robustness of the UDA network. We extensively evaluated our method with two cross-modality cardiac segmentation tasks on the MS-CMR 2019 dataset and the M&Ms dataset. The results of two different tasks show the validity of ST-GAN compared with the state-of-the-art cross-modality cardiac image segmentation methods.

Background: Coronary artery segmentation is a prerequisite in computer-aided diagnosis of Coronary Artery Disease (CAD). However, segmentation of coronary arteries in Coronary Computed Tomography Angiography (CCTA) images faces several challenges. The current segmentation approaches are unable to effectively address these challenges and existing problems such as the need for manual interaction or low segmentation accuracy., Objective: A Multi-scale Feature Learning and Rectification (MFLR) network is proposed to tackle the challenges and achieve automatic and accurate segmentation of coronary arteries., Methods: The MFLR network introduces a multi-scale feature extraction module in the encoder to effectively capture contextual information under different receptive fields. In the decoder, a feature correction and fusion module is proposed, which employs high-level features containing multi-scale information to correct and guide low-level features, achieving fusion between the two-level features to further improve segmentation performance., Results: The MFLR network achieved the best performance on the dice similarity coefficient, Jaccard index, Recall, F1-score, and 95% Hausdorff distance, for both in-house and public datasets., Conclusion: Experimental results demonstrate the superiority and good generalization ability of the MFLR approach. This study contributes to the accurate diagnosis and treatment of CAD, and it also informs other segmentation applications in medicine.

Background: Aortic stenosis (AS) is the most prevalent type of valvular heart disease (VHD), traditionally diagnosed using echocardiogram or phonocardiogram. Seismocardiogram (SCG), an emerging wearable cardiac monitoring modality, is proved to be feasible in non-invasive and cost-effective AS diagnosis. However, SCG waveforms acquired from patients with heart diseases are typically weak, making them more susceptible to noise contamination. While most related researches focus on motion artifacts, sensor noise and quantization noise have been mostly overlooked. These noises pose additional challenges for extracting features from the SCG, especially impeding accurate AS classification., Method: To address this challenge, we present a convolutional dictionary learning-based method. Based on sparse modeling of SCG, the proposed method generates a personalized adaptive-size dictionary from noisy measurements. The dictionary is used for sparse coding of the noisy SCG into a transform domain. Reconstruction from the domain removes the noise while preserving the individual waveform pattern of SCG., Results: Using two self-collected SCG datasets, we established optimal dictionary learning parameters and validated the denoising performance. Subsequently, the proposed method denoised SCG from 50 subjects (25 AS and 25 non-AS). Leave-one-subject-out cross-validation (LOOCV) was applied to 5 machine learning classifiers. Among the classifiers, a bi-layer neural network achieved a moderate accuracy of 90.2%, with an improvement of 13.8% from the denoising., Conclusions: The proposed sparsity-based denoising technique effectively removes stochastic sensor noise and quantization noise from SCG, consequently improving AS classification performance. This approach shows promise for overcoming instrumentation constraints of SCG-based diagnosis., Competing Interests: Declaration of competing interest None declared., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background: Coronary artery disease (CAD) is the leading cause of death worldwide. The registration of the coronary artery at different phases can help radiologists explore the motion patterns of the coronary artery and assist in the diagnosis of CAD. However, there is no automatic and easy-to-execute method to solve the missing data problem that occurs at the endpoints of the coronary artery tree. This paper proposed a non-rigid multi-constraint point set registration with redundant point removal (MPSR-RPR) algorithm to tackle this challenge., Methods: Firstly, the MPSR-RPR algorithm roughly registered two coronary artery point sets with the pre-set smoothness regularization parameter and Gaussian filter width value. The moving coherent, local feature, and the corresponding relationship between bifurcation point pairs were exploited as the constraints. Next, the spatial geometry information of the coronary artery was utilized to automatically recognize the vessel endpoints and to delete the redundant points of the coronary artery. Finally, the algorithm continued carrying out the multi-constraint registration with another group of the pre-set parameters to improve the alignment performance., Results: The experimental results demonstrated that the MPSR-RPR algorithm achieved a significantly lower mean value of the modified Hausdorff distance (MHD) compared to the other state-of-the-art methods for addressing the serious missing data in the left and right coronary arteries., Conclusion: This study demonstrated the effectiveness of the proposed algorithm in aligning coronary arteries, providing significant value in assisting in the diagnosis of coronary artery and myocardial lesions., Competing Interests: Declaration of competing interest No conflict of interest exists in the submission of this manuscript, and the manuscript is approved by all authors for publication in this journal. I would like to declare on behalf of my co-authors that the work described is original research that has not been published previously, and it is not under consideration for publication elsewhere, in whole or in part., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Coronary artery segmentation is an essential procedure in the computer-aided diagnosis of coronary artery disease. It aims to identify and segment the regions of interest in the coronary circulation for further processing and diagnosis. Currently, automatic segmentation of coronary arteries is often unreliable because of their small size and poor distribution of contrast medium, as well as the problems that lead to over-segmentation or omission. To improve the performance of convolutional-neural-network (CNN) based coronary artery segmentation, we propose a novel automatic method, DR-LCT-UNet, with two innovative components: the Dense Residual (DR) module and the Local Contextual Transformer (LCT) module. The DR module aims to preserve unobtrusive features through dense residual connections, while the LCT module is an improved Transformer that focuses on local contextual information, so that coronary artery-related information can be better exploited. The LCT and DR modules are effectively integrated into the skip connections and encoder-decoder of the 3D segmentation network, respectively. Experiments on our CorArtTS2020 dataset show that the dice similarity coefficient (DSC), Recall, and Precision of the proposed method reached 85.8%, 86.3% and 85.8%, respectively, outperforming 3D-UNet (taken as the reference among the 6 other chosen comparison methods), by 2.1%, 1.9%, and 2.1%., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Wang, Xu, Wang, Yang, Sun, Yang and Greenwald.)

Background: Pulmonary embolism is a kind of cardiovascular disease that threatens human life and health. Since pulmonary embolism exists in the pulmonary artery, improving the segmentation accuracy of pulmonary artery is the key to the diagnosis of pulmonary embolism. Traditional medical image segmentation methods have limited effectiveness in pulmonary artery segmentation. In recent years, deep learning methods have been gradually adopted to solve complex problems in the field of medical image segmentation., Purpose: Due to the irregular shape of the pulmonary artery and the adjacent-complex tissues, the accuracy of the existing pulmonary artery segmentation methods based on deep learning needs to be improved. Therefore, the purpose of this paper is to develop a segmentation network, which can obtain higher segmentation accuracy and further improve the diagnosis effect., Methods: In this study, the pulmonary artery segmentation performance from the network model and loss function is improved, proposing a pulmonary artery segmentation network (PA-Net) to segment the pulmonary artery region from 2D CT images. Reverse Attention and edge attention are used to enhance the expression ability of the boundary. In addition, to better use feature information, the channel attention module is introduced in the decoder to highlight the important channel features and suppress the unimportant channels. Due to blurred boundaries, pixels near the boundaries of the pulmonary artery may be difficult to segment. Therefore, a new contour loss function based on the active contour model is proposed in this study to segment the target region by assigning dynamic weights to false positive and false negative regions and accurately predict the boundary structure., Results: The experimental results show that the segmentation accuracy of this proposed method is significantly improved in comparison with state-of-the-art segmentation methods, and the Dice coefficient is 0.938 ± 0.035, which is also confirmed from the 3D reconstruction results., Conclusions: Our proposed method can accurately segment pulmonary artery structure. This new development will provide the possibility for further rapid diagnosis of pulmonary artery diseases such as pulmonary embolism. Code is available at https://github.com/Yuanyan19/PA-Net., (© 2023 American Association of Physicists in Medicine.)

A novel temporal convolutional network (TCN) model is utilized to reconstruct the central aortic blood pressure (aBP) waveform from the radial blood pressure waveform. The method does not need manual feature extraction as traditional transfer function approaches. The data acquired by the SphygmoCor CVMS device in 1,032 participants as a measured database and a public database of 4,374 virtual healthy subjects were used to compare the accuracy and computational cost of the TCN model with the published convolutional neural network and bi-directional long short-term memory (CNN-BiLSTM) model. The TCN model was compared with CNN-BiLSTM in the root mean square error (RMSE). The TCN model generally outperformed the existing CNN-BiLSTM model in terms of accuracy and computational cost. For the measured and public databases, the RMSE of the waveform using the TCN model was 0.55 ± 0.40 mmHg and 0.84 ± 0.29 mmHg, respectively. The training time of the TCN model was 9.63 min and 25.51 min for the entire training set; the average test time was around 1.79 ms and 8.58 ms per test pulse signal from the measured and public databases, respectively. The TCN model is accurate and fast for processing long input signals, and provides a novel method for measuring the aBP waveform. This method may contribute to the early monitoring and prevention of cardiovascular disease.

Purpose: Computed tomography coronary angiography (CCTA) images provide optimal visualization of coronary arteries to aid in diagnosing coronary heart disease (CHD). With the deep convolutional neural network, this work aims to develop an intelligent and lightweight coronary artery segmentation algorithm that can be deployed in hospital systems to assist clinicians in quantitatively analyzing CHD., Methods: With the multi-level feature fusion, we proposed Dual-Attention Coordination U-Net (DAC-UNet) that achieves automated coronary artery segmentation in 2D CCTA images. The coronary artery occupies a small region, and the foreground and background are extremely unbalanced. For this reason, the more original information can be retained by fusing related features between adjacent layers, which is conducive to recovering the small coronary artery area. The dual-attention coordination mechanism can select valid information and filter redundant information. Moreover, the complementation and coordination of double attention factors can enhance the integrity of features of coronary arteries, reduce the interference of non-coronary arteries, and prevent over-learning. With gradual learning, the balanced character of double attention factors promotes the generalization ability of the model to enhance coronary artery localization and contour detail segmentation., Results: Compared with existing related segmentation methods, our method achieves a certain degree of improvement in 2D CCTA images for the segmentation accuracy of coronary arteries with a mean Dice index of 0.7920. Furthermore, the method can obtain relatively accurate results even in a small sample dataset and is easy to implement and deploy, which is promising. The code is available at: https://github.com/windfly666/Segmentation ., Conclusion: Our method can capture the coronary artery structure end-to-end, which can be used as a fundamental means for automatic detection of coronary artery stenosis, blood flow reserve fraction analysis, and assisting clinicians in diagnosing CHD., (© 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.)

BAP31 expression was robustly decreased in obese white adipose tissue (WAT). To investigate the roles of BAP31 in lipid metabolism, adipocyte-specific conditional knockout mice (BAP31-ASKO) were generated. BAP31-ASKO mice grow normally as controls, but exhibited reduced lipid accumulation in WAT. Histomorphometric analysis reported increased adipocyte size in BAP31-ASKO mice. Mouse embryonic fibroblasts (MEFs) were induced to differentiation to adipocytes, showed reduced induction of adipogenic markers and attenuated adipogenesis in BAP31-deficient MEFs. BAP31-deficiency inhibited fasting-induced PKA signaling activation and the fasting response. β3-adrenergic receptor agonist-induced lipolysis also was reduced, accompanied by reduced free-fatty acids and glycerol release, and impaired agonist-induced lipolysis from primary adipocytes and adipose explants. BAP31 interacts with Perilipin1 via C-terminal cytoplasmic portion on lipid droplets (LDs) surface. Depletion of BAP31 repressed Perilipin1 proteasomal degradation, enhanced Perilipin1 expression and blocked LDs degradation, which promoted LDs abnormal growth and supersized LDs formation, resulted in adipocyte expansion, thus impaired insulin signaling and aggravated pro-inflammation in WAT. BAP31-deficiency increased phosphatidylcholine/phosphatidylethanolamine ratio, long chain triglycerides and most phospholipids contents. Overall, BAP31-deficiency inhibited adipogenesis and lipid accumulation in WAT, decreased LDs degradation and promoted LDs abnormal growth, pointing the critical roles in modulating LDs dynamics and homeostasis via proteasomal degradation system in adipocytes., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Background and Objective: The aortic pressure waveform (APW) provides reliable information for the diagnosis of cardiovascular disease. APW is often measured using a generalized transfer function (GTF) applied to the peripheral pressure waveform acquired noninvasively, to avoid the significant risks of invasive APW acquisition. However, the GTF ignores various physiological conditions, which affects the accuracy of the estimated APW. To solve this problem, this study utilized an adaptive transfer function (ATF) combined with a tube-load model to achieve personalized and accurate estimation of APW from the brachial pressure waveform (BPW)., Methods: The proposed method was validated using APWs and BPWs from 34 patients. The ATF was defined using a tube-load model in which pulse transit time and reflection coefficients were determined from, respectively, the diastolic-exponential-pressure-decay of the APW and a piece-wise constant approximation. The root-mean-square-error of overall morphology, mean absolute errors of common hemodynamic indices (systolic blood pressure, diastolic blood pressure and pulse pressure) were used to evaluate the ATF., Results: The proposed ATF performed better in estimating diastolic blood pressure and pulse pressure (1.63 versus 1.94 mmHg, and 2.37 versus 3.10 mmHg, respectively, both P < 0.10), and produced similar errors in overall morphology and systolic blood pressure (3.91 versus 4.24 mmHg, and 2.83 versus 2.91 mmHg, respectively, both P > 0.10) compared to GTF., Conclusion: Unlike the GTF which uses fixed parameters trained on existing clinical datasets, the proposed method can achieve personalized estimation of APW. Hence, it provides accurate pulsatile hemodynamic measures for the evaluation of cardiovascular function., Competing Interests: Declaration of competing interest No conflict of interests., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Pulse wave reflections reflect cardiac afterload and perfusion, which yield valid indicators for monitoring cardiovascular status. Accurate quantification of pressure wave reflections requires the measurement of aortic flow wave. However, direct flow measurement involves extra equipment and well-trained operator. In this study, the personalized aortic flow waveform was estimated from the individual central aortic pressure waveform (CAPW) based on pressure-flow relations. The separated forward and backward pressure waves were used to calculate wave reflection indices such as reflection index (RI) and reflection magnitude (RM), as well as the central aortic pulse transit time (PTT). The effectiveness and feasibility of the method were validated by a set of clinical data (13 participants) and the Nektar1D Pulse Wave Database (4,374 subjects). The performance of the proposed personalized flow waveform method was compared with the traditional triangular flow waveform method and the recently proposed lognormal flow waveform method by statistical analyses. Results show that the root mean square error calculated by the personalized flow waveform approach is smaller than that of the typical triangular and lognormal flow methods, and the correlation coefficient with the measured flow waveform is higher. The estimated personalized flow waveform based on the characteristics of the CAPW can estimate wave reflection indices more accurately than the other two methods. The proposed personalized flow waveform method can be potentially used as a convenient alternative for the measurement of aortic flow waveform., Competing Interests: Author LX was employed by Neusoft Research of Intelligent Healthcare Technology, Co. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Sun, Yao, Liu, Zhou, Du, Tan, Yu, Xu and Avolio.)

Objective: To investigate the possibility of false-negative occurrence of non-specific benign pathological results on CT-guided transthoracic lung core-needle biopsy and identify risk factors for false-negative results., Methods: The clinical, imaging, and surgical data of 403 lung biopsy patients were retrospectively analyzed. Patients were divided into true-negative and false-negative (FN) groups according to the final diagnosis. Univariate analysis was used to compare the variables in two groups for statistical differences, and multivariate analysis was used to clarify the risk factors associated with FN results., Results: Of the 403 lesions, 332 were finally confirmed as benign and 71 to be malignant, with a FN rate of 17.6%. Older patient age (P = 0.01), burr sign (P = 0.00), and pleural traction sign (P = 0.02) were independent risk factors for FN results. The area under the receiver operating characteristic (ROC) curve's area under curve (AUC) was 0.73., Conclusion: CT-guided transthoracic lung core-needle biopsy has a high diagnostic accuracy and low rate of FN results. Older patient age, the burr sign, and the pleural traction sign are independent risk factors for FN results that should be monitored prior to surgery to reduce the risk of FN results., Competing Interests: None

Background and Purpose: Traumatic brain injury (TBI), especially the severe TBI are often followed by persistent cognitive sequalae, including decision-making difficulties, reduced neural processing speed and memory deficits. Diffuse axonal injury (DAI) is classified as one of the severe types of TBI. Part of DAI patients are marginalized from social life due to cognitive impairment, even if they are rated as favorable outcome. The purpose of this study was to elucidate the specific type and severity of cognitive impairment in DAI patients with favorable outcome., Methods: The neurocognition of 46 DAI patients with favorable outcome was evaluated by the Chinese version of the Montreal Cognitive Assessment Basic (MoCA-BC), and the differences in the domains of cognitive impairment caused by different grades of DAI were analyzed after data conversion of scores of nine cognitive domains of MoCA-BC by Pearson correlation analysis., Results: Among the 46 DAI patients with favorable outcome, eight had normal cognitive function (MoCA-BC ≥ 26), and 38 had cognitive impairment (MoCA-BC < 26). The MoCA-BC scores were positively correlated with pupillary light reflex ( r = 0.361, p = 0.014), admission Glasgow Coma Scale (GCS) ( r = 0.402, p = 0.006), and years of education ( r = 0.581, p < 0.001). Return of consciousness ( r = -0.753, p < 0.001), Marshall CT ( r = -0.328, p = 0.026), age ( r = -0.654, p < 0.001), and DAI grade ( r = -0.403, p = 0.006) were found to be negatively correlated with the MoCA-BC scores. In patients with DAI grade 1, the actually deducted scores (Ads) of memory ( r = 0.838, p < 0.001), abstraction ( r = 0.843, p < 0.001), and calculation ( r = 0.782, p < 0.001) were most related to the Ads of MoCA-BC. The Ads of nine cognitive domains and MoCA-BC were all proved to be correlated, among patients with DAI grade 2. However, In the DAI grade 3 patients, the highest correlation with the Ads of MoCA-BC were the Ads of memory ( r = 0.904, p < 0.001), calculation ( r = 0.799, p = 0.006), orientation ( r = 0.801, p = 0.005), and executive function ( r = 0.869, p = 0.001)., Conclusion: DAI patients with favorable outcome may still be plagued by cognitive impairment, and different grades of DAI cause different domains of cognitive impairment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Chen, Yao, Li, Huang, Zhu, Chen, Su, Huang, Xu, Sun, Song, Jiang and Wang.)

Coronavirus disease 2019 (COVID-19) epidemic has devastating effects on personal health around the world. It is significant to achieve accurate segmentation of pulmonary infection regions, which is an early indicator of disease. To solve this problem, a deep learning model, namely, the content-aware pre-activated residual UNet (CAPA-ResUNet), was proposed for segmenting COVID-19 lesions from CT slices. In this network, the pre-activated residual block was used for down-sampling to solve the problems of complex foreground and large fluctuations of distribution in datasets during training and to avoid gradient disappearance. The area loss function based on the false segmentation regions was proposed to solve the problem of fuzzy boundary of the lesion area. This model was evaluated by the public dataset (COVID-19 Lung CT Lesion Segmentation Challenge-2020) and compared its performance with those of classical models. Our method gains an advantage over other models in multiple metrics. Such as the Dice coefficient, specificity (Spe), and intersection over union (IoU), our CAPA-ResUNet obtained 0.775 points, 0.972 points, and 0.646 points, respectively. The Dice coefficient of our model was 2.51% higher than Content-aware residual UNet (CARes-UNet). The code is available at https://github.com/malu108/LungInfectionSeg., Competing Interests: The authors declare no conflicts of interest., (© 2022 Wiley Periodicals LLC.)

The radial artery pulse wave contains a wealth of physiological and pathological information about the human body, and non-invasive studies of the radial artery pulse wave can assess arterial vascular elasticity in different age groups.The piezoelectric pulse wave transducers were used to non-invasively acquire radial artery pulse waves at different contact pressures in young and middle-aged and elderly populations. The radial artery waveforms were decomposed using a triangular blood flow model fitting method to obtain forward and reflected waves and calculate reflection parameters. Finally a correlation analysis and regression analysis of the contact pressure P sensor with the reflection parameters was carried out. The results showed that the reflection parameters RM , RI and R d had a strong negative correlation with P sensor in both types of subjects, and the correlation coefficients and slopes of the regression curves were significantly different between the two types of subjects ( P <0.05). Based on the results of this study, excessive contact pressure on the transducer should be avoided when detecting radial artery reflection waves in clinical practice. The results also show that the magnitude of the slope of the regression curve between the reflection parameters and the transducer contact pressure may be a potentially useful indicator for quantifying the elastic properties of the vessel.

Objective. Our previous study has shown that low-intensity focused ultrasound stimulation (FUS) of the vagus nerve could modulate blood pressure (BP), but its underlying mechanisms remain unclear. We hypothesized that low-intensity FUS of the vagus nerve would regulate autonomic function and thus BP. Approach. 17 anesthetized spontaneously hypertensive rats were treated with low-intensity FUS of the left vagus nerve for 15 min each trial. Continuous BP, heart rate, respiration rate (RR), and core body temperature were simultaneously recorded to evaluate the effects on BP and other physiological parameters. Heart rate variability (HRV), systolic BP variability, and baroreflex sensitivity were computed to evaluate the autonomic modulation function. A Control-sham group without stimulation and another Control-FUS group with non-target stimulation were also examined to exclude the influence of potential confounding factors on autonomic modulation. Main results. A prolonged significant decrease in BP, pulse pressure, RR, the normalized low-frequency power of HRV, and the low-to-high frequency power ratio of HRV were found after the low-intensity FUS of the left vagus nerve in comparison with the baseline and those of the control groups, demonstrating that activities of the sympathetic nervous system were inhibited. The prolonged significant increase of the normalized high-frequency power of HRV suggested the activation of parasympathetic activity. Significance. Low-intensity FUS of the left vagus nerve effectively improved the autonomic function by activating parasympathetic efferent and inhibiting sympathetic efferent, which contributes to BP reduction. The findings shed light on the hypotensive mechanism underlying FUS., (Creative Commons Attribution license.)

Background and Objective: The left ventricular pressure waveform is indispensable for the construction of the pressure strain loop when investigating coronary artery disease (CAD) patients. In previous studies by others, exclusion of CAD patients has not allowed a reliable estimation of the left ventricular pressure waveform from the pressure strain loop of these patients. To remedy this, we propose a patient-specific noninvasive method for the estimation of left ventricular pressure., Methods: A simplified systemic circulation model consisting primarily of a single fiber model and a 1D simulation of the arterial tree was used. Sensitivity analysis based on the Morris method was performed to select a subset of the important parameters. Following this, the important parameter subset and the set of all the parameters were identified in the model using the pressure waveform of a peripheral artery as input, in a two-step process. In addition, the left ventricular pressure waveform was estimated using the set of all parameters., Results: Reducing the size of the parameter subset significantly decreases the computational cost of parameter optimization in the first step and greatly simplifies the identification of the full parameter set in the second step. Comparison with the reference left ventricular pressure waveform from CAD patients, showed that the proposed method provides a good estimate of the reference left ventricular pressure waveform. The correlation coefficients between the estimated and reference were r = 0.907, r = 0.904 and r = 0.780 for systolic blood pressure, pulse pressure and mean blood pressure, respectively., Conclusions: This work may provide a convenient surrogate for the estimation of the left ventricular pressure waveform., Competing Interests: Declaration of Competing Interest All authors declare no conflict of interest., (Copyright © 2022. Published by Elsevier B.V.)

The rapid growth in demand for portable and intelligent hardware has caused tremendous pressure on signal sampling, transfer, and storage resources. As an emerging signal acquisition technology, compressed sensing (CS) has promising application prospects in low-cost wireless sensor networks. To achieve reduced energy consumption and maintain a longer acquisition duration for high sample rate electromyogram (EMG) signals, this paper comprehensively analyzes the compressed sensing method using EMG. A fair comparison is carried out on the performances of 52 ordinary wavelet sparse bases and five widely applied reconstruction algorithms at different compression levels. The experimental results show that the db2 wavelet basis can sparse EMG signals so that the compressed EMG signals are reconstructed properly, thanks to its low percentage root mean square distortion (PRD) values at most compression ratios. In addition, the basis pursuit (BP) reconstruction algorithm can provide a more efficient reconstruction process and better reconstruction performance by comparison. The experiment records and comparative analysis screen out the suitable sparse bases and reconstruction algorithms for EMG signals, acting as prior experiments for further practical applications and also a benchmark for future academic research.

Background: Many cardiovascular diseases are closely related to the composition of epicardial adipose tissue (EAT). Accurate segmentation of EAT can provide a reliable reference for doctors to diagnose the disease. The distribution and composition of EAT often have significant individual differences, and the traditional segmentation methods are not effective. In recent years, deep learning method has been gradually introduced into EAT segmentation task., Purpose: The existing EAT segmentation methods based on deep learning have a large amount of computation and the segmentation accuracy needs to be improved. Therefore, the purpose of this paper is to develop a lightweight EAT segmentation network, which can obtain higher segmentation accuracy with less computation and further alleviate the problem of false-positive segmentation., Methods: First, the obtained computed tomography was preprocessed. That is, the threshold range of EAT was determined to be -190, -30 HU according to prior knowledge, and the non-adipose pixels were excluded by threshold segmentation to reduce the difficulty of training. Second, the image obtained after thresholding was input into the lightweight RDU-Net network to perform the training, validating, and testing process. RDU-Net uses a residual multi-scale dilated convolution block in order to extract a wider range of information without changing the current resolution. At the same time, the form of residual connection is adopted to avoid the problem of gradient expansion or gradient explosion caused by too deep network, which also makes the learning easier. In order to optimize the training process, this paper proposes PNDiceLoss, which takes both positive and negative pixels as learning targets, fully considers the class imbalance problem, and appropriately highlights the status of positive pixels., Results: In this paper, 50 CCTA images were randomly selected from the hospital, and the commonly used Dice similarity coefficient (DSC), Jaccard similarity, accuracy (ACC), specificity (SP), precision (PC), and Pearson correlation coefficient are used as evaluation metrics. Bland-Altman analysis results show that the extracted EAT volume is consistent with the actual volume. Compared with the existing methods, the segmentation results show that the proposed method achieves better performance on these metrics, achieving the DSC of 0.9262. The number of false-positive pixels has been reduced by more than half. Pearson correlation coefficient reached 0.992, and linear regression coefficient reached 0.977 when measuring the volume of EAT obtained. In order to verify the effectiveness of the proposed method, experiments are carried out in the cardiac fat database of VisualLab. On this database, the proposed method also achieved good results, and the DSC value reached 0.927 in the case of only 878 slices., Conclusions: A new method to segment and quantify EAT is proposed. Comprehensive experiments show that compared with some classical segmentation algorithms, the proposed method has the advantages of shorter time-consuming, less memory required for operations, and higher segmentation accuracy. The code is available at https://github.com/lvanlee/EAT&#95;Seg/tree/main/EAT&#95;seg., (© 2022 American Association of Physicists in Medicine.)

In neuroscience, protein activity characterizes neuronal excitability in response to a diverse array of external stimuli and represents the cell state throughout the development of brain diseases. Importantly, it is necessary to characterize the proteins involved in disease progression, nuclear function determination, stimulation method effect, and other aspects. Therefore, the quantification of protein activity is indispensable in neuroscience. Currently, ImageJ software and manual counting are two of the most commonly used methods to quantify proteins. To improve the efficiency of quantitative protein statistics, the you-only-look-once-v5 (YOLOv5) model was proposed. In this study, c-Fos immunofluorescence images data set as an example to verify the efficacy of the system using protein quantitative statistics. The results indicate that YOLOv5 was less time-consuming or obtained higher accuracy than other methods (time: ImageJ software: 80.12 ± 1.67 s, manual counting: 3.41 ± 0.25 s, YOLOv5: 0.0251 ± 0.0003 s, p < 0.0001, n = 83; simple linear regression equation: ImageJ software: Y = 1.013 × X + 0.776, R 2 = 0.837; manual counting: Y = 1.0* X + 0, R 2 = 1; YOLOv5: Y = 0.9730* X + 0.3821, R 2 = 0.933, n = 130). The findings suggest that the YOLOv5 algorithm provides feasible methods for quantitative statistical analysis of proteins and has good potential for application in detecting target proteins in neuroscience., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Pang, Liu, Lin, Chen, Liu, Pan, Shi, Xiao and Xu.)

Flexible, lightweight sensors with a wide strain-sensing range are showing increasing significance in structural health monitoring compared with conventional hard sensors, which typically have a small strain range, are heavyweight, and have a large volume. In this work, salt particle precipitation and mechanical coating methods are used to fabricate porous graphene nanoplatelet (GNP)/polydimethylsiloxane (PDMS) flexible sensors for tension monitoring in structural health applications. The signal transformation through the Back Propagation (BP) algorithm is integrated to provide monitoring data that are comparable with other sensors. The results reveal that the flexible sensors with a low content of GNPs (0.1-0.25 wt%) possess better flexibility, allowing tensile strains over 200% to be measured. In addition, due to the enhanced deformation capacity of the pore structures, they can achieve high sensitivity (1-1000) under 65% strain, and a fast response time (70 ms) under 10% strain at 60 mm min -1 . They also show high performance in the fatigue test (20 000 cycles) under 5% strain, and can effectively respond to bending and torsion. In addition, the sensors show an obvious response to temperature. Overall, the prepared flexible composite sensors in this work have the advantages of a wide strain-sensing range, a full-coverage conductive network, and being lightweight, and show potential for structural health monitoring in the near future., Competing Interests: The authors declare no competing financial interest., (This journal is © The Royal Society of Chemistry.)

Automatic coronary artery segmentation is of great value in diagnosing coronary disease. In this paper, we propose an automatic coronary artery segmentation method for coronary computerized tomography angiography (CCTA) images based on a deep convolutional neural network. The proposed method consists of three steps. First, to improve the efficiency and effectiveness of the segmentation, a 2D DenseNet classification network is utilized to screen out the non-coronary-artery slices. Second, we propose a coronary artery segmentation network based on the 3D-UNet, which is capable of extracting, fusing and rectifying features efficiently for accurate coronary artery segmentation. Specifically, in the encoding process of the 3D-UNet network, we adapt the dense block into the 3D-UNet so that it can extract rich and representative features for coronary artery segmentation; In the decoding process, 3D residual blocks with feature rectification capability are applied to improve the segmentation quality further. Third, we introduce a Gaussian weighting method to obtain the final segmentation results. This operation can highlight the more reliable segmentation results at the center of the 3D data blocks while weakening the less reliable segmentations at the block boundary when merging the segmentation results of spatially overlapping data blocks. Experiments demonstrate that our proposed method achieves a Dice Similarity Coefficient (DSC) value of 0.826 on a CCTA dataset constructed by us. The code of the proposed method is available at https://github.com/alongsong/3D&#95;CAS.

Background: Low back pain is a common medical condition among the general population that is associated with many adverse health effects when comorbid with depressive symptoms. However, little is known about depressive symptoms in the population with low back pain in China. Our study evaluated the prevalence of depressive symptoms and analyzed the factors associated with this condition in the Chinese population with low back pain., Methods: We conducted a cross-sectional analysis of data from the 2018 China Health and Retirement Longitudinal Study. We collected low back pain information for each participant and identified depressive symptoms using the brief version of the Center for Epidemiologic Studies Depression Scale. A wide range of sociodemographic and health-related characteristics of the subjects were extracted. We measured the prevalence of depressive symptoms comorbid with low back pain and analyzed the associated factors by multiple logistic regression., Results: A total of 5,779 respondents aged 45 and over with low back pain formed the sample, 41.8% of whom reported depressive symptoms. Multiple logistic regression analysis indicated greater vulnerability to depressive symptoms among females (OR = 1.41, 95% CI, 1.16-1.73), relatively younger persons (60-74 years: OR = 0.72, 95% CI, 0.63-0.83; ≥ 75 years: OR = 0.62, 95% CI, 0.49-0.79, reference: 45-59 years), those from the central and western regions (central: OR = 1.39, 95% CI, 1.18-1.64; western: OR = 1.56, 95% CI, 1.33-1.83), participants with extremely short sleep duration (OR = 2.74, 95% CI, 2.33-3.23), those with poor self-perceived health status (OR = 2.91, 95% CI, 2.34-3.63,), multisite pain (OR = 1.54, 95% CI, 1.20-1.98) and disability in activities of daily living (Basic: OR = 1.70, 95% CI, 1.47-1.98; Instrumental: OR = 1.95, 95% CI, 1.70-2.24)., Conclusion: Depressive symptoms were highly prevalent in the Chinese population ≥ 45 years with low back pain. More attention should be paid to the individuals at high-risk confirmed by this study to facilitate early identification and intervention against depressive symptoms., (Copyright © 2022 He, Chen, Guo, Xu, Liu, Zhang and Hu.)

The central aortic pressure (CAP) provides insights into the prediction, prevention, diagnosis, and treatment of cardiovascular disease, but can't be directly measured non-invasively. Therefore, the development of a noninvasive CAP estimation method based on the non-invasively measured peripheral pressure waveform is critical for clinical decisions based on the CAP. Some existing widely applied methods, such as the generalized transfer function (GTF) method relating measured peripheral pressure to the CAP, do not or only partly account for inter-subject or intra-subject variability of the cardiovascular system. To overcome this pitfall, we propose a subject-specific central aortic pressure estimation method in this paper. The novel method presented can derive an accurate aortic pressure from the peripheral pressure based on an individualized pulse wave propagation model using a GTF method as a first guess. To develop a strategy to personalize a pulse wave propagation model one usually needs to optimize many input parameters. Therefore, we present a two-step approach with the screening method of Morris and the adaptive sparse generalized polynomial chaos expansion (agPCE) algorithm for the sensitivity analysis of the wave propagation model. First, for a-priori defined output of the model, a subset of important parameters is identified using the screening method of Morris. Next, a quantitative variance-based sensitivity analysis is performed using agPCE. This approach is applied to a 1D pulse wave propagation model to get the personalized parameters of the pulse wave propagation model for the estimation of a subject-specific central aortic pressure waveform and is validated with 26 patients. Compared with the GTF method, the proposed method showed better performance in estimating the central aortic pulse wave and predicting the parameters., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background and Objective: Arterial stiffness, commonly assessed by carotid-femoral pulse wave velocity (cfPWV), is an independent biomarker for cardiovascular disease. The measurement of cfPWV, however, has been considered impractical for routine clinical application. Pulse wave analysis using a single pulse wave measurement in the radial artery is a convenient alternative. This study aims to identify pulse wave features for a more accurate estimation of cfPWV from a single radial pulse wave measurement., Methods: From a dataset of 140 subjects, cfPWV was measured and the radial pulse waveform was recorded for 30 s twice in succession. Features were extracted from the waveforms in the time and frequency domains, as well as by wave separation analysis. All-possible regressions with bootstrapping, McHenry's select algorithm, and support vector regression were applied to compute models for cfPWV estimation., Results: The correlation coefficients between the measured and estimated cfPWV were r = 0.81, r = 0.81, and r = 0.8 for all-possible regressions, McHenry's select algorithm, and support vector regression, respectively. The features selected by all-possible regressions are physiologically interpretable. In particular, the amplitude ratio of the diastolic peak to the notch of the radial pulse waveform (R n,d r,P ) is shown to be correlated with cfPWV. This correlation was further evaluated and found to be independent of wave reflections using a dataset (n = 3,325) of simulated pulse waves., Conclusions: The proposed method may serve as a convenient surrogate for the measurement of cfPWV. R n,d r,P is associated with aortic pulse wave velocity and this association may not be dependent on wave reflection., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background and Objective: Aortic pressure (P a ) is important for the diagnosis of cardiovascular disease. However, its direct measurement is invasive, not risk-free, and relatively costly. In this paper, a new simplified Kalman filter (SKF) algorithm is employed for the reconstruction of the P a waveform using dual peripheral artery pressure waveforms., Methods: P a waveforms obtained in a previous study were collected from 25 patients. Simultaneously, radial and femoral pressure waveforms were generated from two simulation experiments, using transfer functions. In the first, the transfer function is a known finite impulse response; and in the second, it is derived from a tube-load model. To analyze the performance of the proposed SKF algorithm, variable amounts of noise were added to the observed output signal, to give a range of signal-to-noise ratios (SNRs). Additionally, central aortic, brachial and femoral pressure waveforms were simultaneously collected from 2 Sprague-Dawley rats and the measured and reconstructed P a waveforms were compared., Results: The proposed SKF algorithm outperforms canonical correlation analysis (CCA), which is the current state-of-the-art blind system identification method for the non-invasive estimation of central aortic blood pressure. It is also shown that the proposed SKF algorithm is more noise-tolerant than the CCA algorithm over a wide range of SNRs., Conclusion: The simulations and animal experiments illustrate that the proposed SKF algorithm is accurate and stable in the face of low SNRs. Improved methods for estimating central blood pressure as a measure of cardiac load adds to their value as a prognostic and diagnostic tool., (Copyright © 2022. Published by Elsevier B.V.)

Purpose: Coronary heart disease is a serious disease that endangers human health and life. In recent years, the incidence and mortality of coronary heart disease have increased rapidly. The quantification of the coronary artery is critical in diagnosing coronary heart disease., Methods: In this paper, we improve the coronary arteries segmentation performance from two aspects of network model and algorithm. We proposed a U-shaped network based on spatio-temporal feature fusion structure to segment coronary arteries from 2D slices of computed tomography angiography (CTA) heart images. The spatio-temporal feature combines features of multiple levels and different receptive fields separately to get more precise boundaries. It is easy to cause over-segmented for the small proportion of coronary arteries in CTA images. For this reason, a combo loss function was designed to deal with the notorious imbalance between inputs and outputs that plague learning models. Input imbalance refers to the class imbalance in the input training samples. The output imbalance refers to the imbalance between the false positive and false negative of the inference model. The two imbalances in training and inference were divided and conquered with our combo loss function. Specifically, a gradient harmonizing mechanism (GHM) loss was employed to balance the gradient contribution of the input samples and at the same time punish false positives/negatives using another sensitivity-precision loss term to learn better model parameters gradually., Results: Compared with some existing methods, our proposed method improves the segmentation accuracy significantly, achieving the mean Dice coefficient of 0.87. In addition, accurate results can be obtained with little data using our method. Code is available at: https://github.com/Ariel97-star/FFNet-CoronaryArtery-Segmentation ., Conclusions: Our method can intelligently capture coronary artery structure and achieve accurate flow reserve fraction (FFR) analysis. Through a series of steps such as CPR curved reconstruction, the detection of coronary heart disease can be achieved without affecting the patient's body. In addition, our work can be used as an effective means to assist in the detection of stenosis. In the screening of coronary heart disease among high-risk cardiovascular factors, automatic detection of luminal stenosis can be performed based on the application of later algorithm transformation., (© 2021. Biomedical Engineering Society.)

Central aortic blood pressure (CABP) is a better predictor for cardiovascular events than brachial blood pressure. However, direct CABP measurement is invasive. The objective of this paper is to develop an ultrasound-based method using individualized Windkessel (WK) models for non-invasive estimation of CABP. Three WK models (with two-, three- and four-element WK, named, WK2, WK3 and WK4, respectively) were created and the model parameters were individualized based on aortic flow velocity and diameter waveforms measured by ultrasound (US). Experimental data were acquired in 42 subjects aged 21-67 years. The CABP estimated by WK models was compared with the reference CABP obtained using a commercial system. The results showed that the overall performance of the WK3 and WK4 models was similar, outperforming the WK2 model. The estimated CABP based on WK3/WK4 model showed good agreement with the reference CABP: the absolute errors of systolic blood pressure (SBP), 2.4 ± 2.1/2.4 ± 2.0 mmHg; diastolic blood pressure (DBP), 1.4 ± 1.1/1.7 ± 1.5 mmHg; mean blood pressure (MBP), 1.3 ± 0.8/1.3 ± 0.8 mmHg; pulse pressure (PP), 3.0 ± 2.3/3.2 ± 2.6 mmHg; the root mean square error (RMSE) of the waveforms, 2.5 ± 1.0/2.6 ± 1.1 mmHg. Therefore, the proposed method can provide a non-invasive CABP estimation during routine cardiac US examination., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Arterial stiffness, as measured by pulse wave velocity, for the early non-invasive screening of cardiovascular disease is becoming ever more widely used and is an independent prognostic indicator for a variety of pathologies including arteriosclerosis. Carotid-femoral pulse wave velocity (cfPWV) is regarded as the gold standard for aortic stiffness. Existing algorithms for cfPWV estimation have been shown to have good repeatability and accuracy, however, further assessment is needed, especially when signal quality is compromised. We propose a method for calculating cfPWV based on a simplified tube-load model, which allows for the propagation and reflection of the pulse wave. In-vivo cfPWV measurements from 57 subjects and numerical cfPWV data based on a one-dimensional model were used to assess the method and its performance was compared to three other existing approaches (waveform matching, intersecting tangent, and cross-correlation). The cfPWV calculated using the simplified tube-load model had better repeatability than the other methods (Intra-group Correlation Coefficient, ICC = 0.985). The model was also more accurate than other methods (deviation, 0.13 ms -1 ) and was more robust when dealing with noisy signals. We conclude that the determination of cfPWV based on the proposed model can accurately and robustly evaluate arterial stiffness., (© 2022. The Author(s).)

Ballistocardiography (BCG) is considered a good alternative to HRV analysis with its non-contact and unobtrusive acquisition characteristics. However, consensus about its validity has not yet been established. In this study, 50 healthy subjects (26.2 ± 5.5 years old, 22 females, 28 males) were invited. Comprehensive statistical analysis, including Coefficients of Variation (CV), Lin’s Concordance Correlation Coefficient (LCCC), and Bland-Altman analysis (BA ratio), were utilized to analyze the consistency of BCG and ECG signals in HRV analysis. If the methods gave different answers, the worst case was taken as the result. Measures of consistency such as Mean, SDNN, LF gave good agreement (the absolute value of CV difference < 2%, LCCC > 0.99, BA ratio < 0.1) between J-J (BCG) and R-R intervals (ECG). pNN50 showed moderate agreement (the absolute value of CV difference < 5%, LCCC > 0.95, BA ratio < 0.2), while RMSSD, HF, LF/HF indicated poor agreement (the absolute value of CV difference ≥ 5% or LCCC ≤ 0.95 or BA ratio ≥ 0.2). Additionally, the R-R intervals were compared with P-P intervals extracted from the pulse wave (PW). Except for pNN50, which exhibited poor agreement in this comparison, the performances of the HRV indices estimated from the PW and the BCG signals were similar.

Cell fusion is an essential event in many biological processes and has gained increasing attention in the field of biotechnology. In this study, we demonstrate an effective and convenient strategy for cell capture, pairing, and fusion based on oscillating bubbles within an acoustofluidic device. Multirectangular structures of the same size were fabricated at the sidewall of polydimethylsiloxane to generate monodisperse microbubbles. These microbubbles oscillated with a similar amplitude under single-frequency acoustic excitation. Cells were simultaneously captured and paired on the surface of the oscillating bubbles within 40 ms, and the efficiency reached approximately 90%. Homotypic or heterotypic cell membrane fusion was achieved within 15 and 20 min, respectively. More importantly, the homotypic fused cells enabled migration and proliferation at 24 h, indicating that the important biological functions were not altered.

Background: At present, coronary artery disease (CAD) is the leading cause of death worldwide. Many studies have shown that CAD is strongly associated with the motion characteristics of the coronary arteries. Although cardiovascular imaging technology has been widely used for the diagnosis of CAD, the motion parameters of the heart and coronary arteries cannot be directly calculated from the images. In this paper, we propose a point set registration method with global and local topology constraints to quantify coronary artery movement., Methods: The global constraint is the motion coherence of the point set which enforces the smoothness of the displacement field. The local linear embedding based topological structure and the local feature descriptor i.e., the 3D shape context, are designed to retain the local structure of the point set. We incorporate these constraints into a maximum likelihood framework and derive an expectation-maximization algorithm to obtain the transformation function between the two point sets. The proposed method was compared with four existing algorithms using simulated data and applied to the real data obtained from 4D CT angiograms., Results: For the simulation data, the proposed method achieves a lower registration error than the comparison algorithms. For the real data, the proposed method shows that, in most cases, the right coronary artery achieves a larger velocity than the left anterior descending and left circumflex branches, and there are three well-defined velocity peaks, during the cardiac cycle for these branches., Conclusion: The proposed approach is feasible and effective in quantifying coronary artery movement and thus adds to the diagnostic power of coronary imaging., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Goal: This paper proposes and validates a completely adaptive transfer function (CATF) based on an autoregressive exogenous (ARX) model which adjusts the gain and phase of a generalized transfer function (GTF) simultaneously to estimate the aortic pressure waveform from a brachial pressure waveform., Methods: Invasive aortic and brachial pressure waveforms were recorded from 34 subjects for the validation of the proposed method. Individual transfer functions (ITFs) were trained based on the pressure waveforms using an ARX model. The GTF was derived by averaging the ITFs. CATF was then obtained by adjusting both the gain and phase of the GTF using regression formulas calculated from the ITFs and brachial hemodynamic parameters. Meanwhile the quantitative contributions of the adaption of gain and phase of the GTF were investigated respectively. The root-mean-square-error of the total waveform and absolute errors of common hemodynamic indices including systolic and diastolic blood pressures (SBP and DBP, respectively), pulse pressure (PP) and augmentation index were used to evaluate the performance of the proposed method in the data divided into low, middle and high PP amplification groups., Results: The CATF achieved lower errors for DBP and PP in the low PP amplification group (1.79 versus 2.10 mmHg and 5.08 versus 6.23 mmHg, respectively, both P < 0.05) and PP in the middle amplification group (1.43 versus 1.92 mmHg, P < 0.05) compared with the GTF., Significance: The proposed method provides a step towards the development of an improved and clinically useful non-invasive approach for estimating the aortic pressure waveform from a peripheral pressure waveform., (Copyright © 2021. Published by Elsevier Ltd.)

Purpose: Myocardial infarction (MI) is one of the most common cardiovascular diseases, frequently resulting in death. Early and accurate diagnosis is therefore important, and the electrocardiogram (ECG) is a simple and effective method for achieving this. However, it requires assessment by a specialist; so many recent works have focused on the automatic assessment of ECG signals., Methods: For the detection and localization of MI, deep learning models have been proposed, but the diagnostic accuracy of this approaches still need to be improved. Moreover, with deep learning methods the way in which a given result was achieved lacks interpretability. In this study, ECG data was obtained from the PhysioBank open access database, and was analyzed as follows. Firstly, the 12-lead ECG signal was preprocessed to identify each beat and obtain each heart interval. Secondly, a multi-scale deep learning model combined with a residual network and attention mechanism was proposed, where the input was the 12-lead ECG recording. Through the SENet model and the Grad-CAM algorithm, the weighting of each lead was calculated and visualized. Using existing knowledge of the way in which different types of MI gave characteristic patterns in specific ECG leads, the model was used to provisionally diagnose the type of MI according to the characteristics of each of the 12 ECG leads., Results: Ten types of MI anterior, anterior lateral, anterior septal, inferior, inferior lateral, inferior posterior, inferior posterior lateral, lateral, posterior, and posterior lateral were diagnosed. The average accuracy, sensitivity, and specificity for MI detection of all lesion types was 99.98, 99.94, and 99.98%, respectively; and the average accuracy, sensitivity, and specificity for MI localization was 99.79, 99.88, and 99.98%, respectively., Conclusion: When compared to existing models based on traditional machine learning methods, convolutional neural networks and recurrent neural networks, the results showed that the proposed model had better diagnostic performance, being superior in accuracy, sensitivity, and specificity., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Cao, Liu, Zhang, Xu, Zhu, Cui, Geng, Han and Greenwald.)

Sonoporation employs ultrasound accompanied by microbubble (MB) cavitation to induce the reversible disruption of cell membranes and has been exploited as a promising intracellular macromolecular delivery strategy. Due to the damage to cells resulting from strong cavitation, it is difficult to balance efficient delivery and high survival rates. In this paper, a traveling surface acoustic wave (TSAW) device, consisting of a TSAW chip and a polydimethylsiloxane (PDMS) channel, was designed to explore single-cell sonoporation using targeted microbubbles (TMBs) in a non-cavitation regime. A TSAW was applied to precisely manipulate the movement of the TMBs attached to MDA-MB-231 cells, leading to sonoporation at a single-cell level. The impact of input voltage and the number of TMBs on cell sonoporation was investigated. In addition, the physical mechanisms of bubble cavitation or the acoustic radiation force (ARF) for cell sonoporation were analyzed. The TMBs excited by an ARF directly propelled cell membrane deformation, leading to reversible perforation in the cell membrane. When two TMBs adhered to the cell surface and the input voltage was 350 mVpp, the cell sonoporation efficiency went up to 83%.

Background: NCCN guidelines recommend a dose of 100 μg/kg or a fixed dose of 6 mg pegylated recombinant human granulocyte colony-stimulating factor (PEG rhG-CSF) for chemotherapy-induced neutropenia. However, a single dose of 60 μg/kg or 100 μg/kg produced a similar neutrophil response among patients with chemotherapy-induced neutropenia (CIN). Thus, this prospective randomized study was designed to investigate the efficacy of 3 mg PEG rhG-CSF in preventing acute lower respiratory tract infection (ALRTI) after chemotherapy., Methods: Patients with stage IIIB/IVA lung cancer who underwent chemotherapy were randomly divided into a (i) control group, and (ii) treatment group subject to 3 mg PEG rhG-CSF after chemotherapy. Patients in the control group were administered rhG-CSF (5 μg/kg) when decreased absolute neutrophil count (ANC) reached grade 3 of adverse events. The primary outcome was incidence of ALRTI, and the secondary outcomes included ANC, febrile neutropenia (FN), incidence of delayed chemotherapy, infection-related medical expenses and adverse reactions., Results: Compared with the control group, there was a significant decrease in the incidence of ALRTI (9.6% vs. 24.6%, p < 0.01), FN (1.7% vs. 7.3%, p < 0.001) and neutropenia (8.3% vs. 23.3%, p < 0.01) in the PEG-rhG-CSF group. The incidence of ALRTI was significantly correlated with the grade of CTCAE on ANC. The main adverse reactions of PEG-rhG-CSF were pain and fatigue, among which three cases showed pain of ≥ grade 3. The cost of infection-associated medical expenditure in the treatment group was greatly reduced compared with the control group (p < 0.001)., Conclusions: ALRTI could well be prevented after prophylactic application of PEG-rhG-CSF (3 mg), and was related to the reduced neutropenia., (© 2021 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd.)

Body temperature plays a critical role in rehabilitation, and numerous studies proved that the regulation of body temperature contributes to the sensorimotor recovery of patients with brain diseases such as stroke. The hypothalamus plays a key role in thermoregulation. Ultrasound deep brain stimulation (UDBS) can noninvasively modulate deep brain nuclei and have potential applications in the treatment of Parkinson's disease, Alzheimer's disease, and depression, among others. The purpose of this study was to investigate whether ultrasound stimulation of the hypothalamus could regulate body temperature in free-moving mice. Results showed that thermoregulation was related to ultrasonic parameters (pulse repetition frequency (PRF), duty cycle, total time, and acoustic pressure). UDBS of the preoptic area of the anterior hypothalamus at 500 Hz PRF could significantly reduce body temperature ( [Formula: see text] at t = 5 min, [Formula: see text] at t = 10 min, [Formula: see text] at t = 15 min). Meanwhile, UDBS of the dorsomedial hypothalamus at 10 Hz PRF triggered a significant increase in body temperature ( [Formula: see text] at t = 5 min, [Formula: see text] at t = 10 min). These results suggest that UDBS, as a noninvasive neuromodulation tool, may play a key role in the future clinical treatment of malignant hyperthermia and hypothermia.

Nanoplatforms are nano-scale systems that can transport different small molecular anticancer drugs or chemosensitization motif to accumulate in tumor cells without obvious side-effect in normal cells and achieve a synergistic therapy. In this paper the new self-assembled nanoparticles (NPs) merging doxorubicin (DOX) and myricetin (MYR) with ferric ions (Fe 3+ ) and polyphenol was employed for forming the DOX@MYR-Fe 3+ NP (FDMP NP). The FDMP NPs could reduce the DOX-induced toxicity in blood; and they could not cause damage to the heart and kidney tissues by the reasons that the MYR could enhance the anti-oxidation capability in normal cells, which resulted in preventing ROS-induced damage. Additionally, the FDMP NPs were characteristic of small size (37.70 ± 6.30 nm), high DOX loading efficiency (46.67 ± 1.58%), pH-controlled release and excellent stable pharmacokinetics, that inducing drug release and enhancing drug accumulation in the tumor. Moreover, the FDMP NPs could inhibit the expression of the hypoxia-inducible factor-1 α (HIF-1 α ) and the key angiogenesis mediator vascular endothelial growth factor (VEGF) both in vitro and in vivo, which succeed in preventing the generation of new blood vessel networks; that is the mechanism of the synergistic effect against tumors induced by FDMP NPs.

Background: The aim of the study was to assess the efficacy and side-effects of intrapleural treatment in non-small cell lung cancer (NSCLC) patients with malignant pleural effusions (MPEs)., Methods: The medical records of NSCLC patients with MPEs diagnosed in four Chinese hospitals from October 2014 to December 2019 were searched. The Kaplan-Meier method is used to calculate median overall survival (MOS) and subgroup analyses are done., Results: A total of 285 patients were evaluated; 81.1% of patients received intrapleural treatment, and no patients received talc pleurodesis. MOS of the whole cohort was 21 months. Patients were divided into three groups: erythromycin group (EG; intrapleural treatment with drugs and erythromycin); intrathoracic treatment group (ITG; intrapleural treatment with drugs); control group (CG; no drug treatment in the pleural cavity). The MOS of patients in the EG, ITG and CG was 20, 22, and 19 months, respectively. Among patients who received only chemotherapy as systemic therapy, the MOS of intrathoracic administration group (IAG; i.e., EG and ITG) was longer than that of CG (12 vs. 6 months; p = 0.034), and the MOS of patients with a ratio of carcinoembryonic antigen in pleural effusion (PE-CEA): CEA in blood (B-CEA) ≤1 is worse than that of patients with a ratio >1 (4 vs. 12 months, p = 0.021) and that of CG (4 vs. 6 months, p = 0.442)., Conclusions: Intrapleural treatment can prolong the survival of NSCLC patients with MPE who do not receive targeted treatment or who only receive chemotherapy. The PE-CEA: B-CEA ratio can be used to predict the efficacy if intrapleural treatment is indicated., (© 2021 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd.)

Our recent study showed that low-intensity focused ultrasound stimulation (FUS) of the vagus nerve is capable of lowering blood pressure (BP). However, it remains unknown that what is the underlying mechanisms of BP modulation with FUS. In our preliminary experiments, we noticed that there was temperature elevation accompanied the FUS. Thus, to verify whether the thermal effect of ultrasound contributes in the BP-lowering effect, this study compared the BP response under the FUS (with thermal effect and mechanical effect) and the alternative heating source treatment (AHST) (with thermal effect only) of left vagus nerve. Six Sprague Dawley rats were randomly divided into two groups (FUS, n=3 and AHST, n=3). In vivo temperature measurements were conducted to evaluate the heating performance of the FUS and the AHST. Blood pressure (BP) waveform was continuously recorded from the right common artery and was used for analyzing systolic BP (SBP), diastolic BP (DBP), mean BP (MBP), and heart rate (HR). The results showed that the SBP, DBP, MBP and HR decreased during the 15-min FUS. However, most of the SBP, DBP, MBP and HR increased during the 15-min AHST, which had the approximate temperature elevation of the FUS. Thus, the thermal effect of ultrasound probably does not contribute in the BP-lowering effect induced by low-intensity FUS of the vagus nerve.

Objective. Aortic stiffness is associated with risk of cardiovascular events. Carotid-femoral pulse wave velocity (cfPWV) is the current noninvasive gold standard for assessing aortic stiffness. However, the cfPWV measurement is challenging, requiring simultaneous signals at the carotid and femoral sites. Approach. In this study, the aortic PWV is estimated using a single radial pressure waveform and compared with cfPWV. 111 subjects' aortic PWVs are estimated from the decomposition of the derived central aortic pressure waveform based on three types of reconstructed flow waveform: the peak of triangular flow waveform based on 30% ejection time ( Q 30%tri ), the peak of triangular flow waveform based on inflection point ( Q tri ), and averaged flow waveform ( Q avg ). The central aortic pressure waveform is derived from a radial pressure waveform via a validated transfer function. Main results. The Q avg is used for estimating aortic PWV without the determination of the peak point of the triangular flow waveforms. The estimated aortic PWV shows good agreement with cfPWV. The mean difference ± SD is 0.29 ± 1.50 m s -1 ( r 2  = 0.29, p < 0.001) for the Q 30%tri ; 0.27 ± 1.40 m s -1 ( r 2  = 0.38, p  < 0.001) for the Q tri ; 0.23 ± 1.39 m s -1 ( r 2  = 0.40, p  < 0.001) for the Q avg . The correlation between estimated aortic PWV based on Q 30%tri and measured cfPWV is weak. The results of Q tri and Q avg show no obvious difference. Significance. The proposed method can be used as a less complex way than conventional measurement of cfPWV to further assess arterial stiffness and predict cardiovascular risks or events., (© 2021 Institute of Physics and Engineering in Medicine.)

Carotid-femoral pulse transit time (cfPTT) is a widely accepted measure of central arterial stiffness. The cfPTT is commonly calculated from two synchronized pressure waves. However, measurement of synchronized pressure waves is technically challenging. In this paper, a method of decomposing the radial pressure wave is proposed for estimating cfPTT. From the radial pressure wave alone, the pressure wave can be decomposed into forward and backward waves by fitting a double triangular flow wave. The first zero point of the second derivative of the radial pressure wave and the peak of the dicrotic segment of radial pressure wave are used as the peaks of the fitted double triangular flow wave. The correlation coefficient between the measured wave and the estimated forward and backward waves based on the decomposition of the radial pressure wave was 0.98 and 0.75, respectively. Then from the backward wave, cfPTT can be estimated. Because it has been verified that the time lag estimation based on of backward wave has strong correlation with the measured cfPTT. The corresponding regression function between the time lag estimation of backward wave and measured cfPTT is y = 0.96x + 5.50 (r = 0.77; p < 0.001). The estimated cfPTT using radial pressure wave decomposition based on the proposed double triangular flow wave is more accurate and convenient than the decomposition of the aortic pressure wave based on the triangular flow wave. The significance of this study is that arterial stiffness can be directly estimated from a noninvasively measured radial pressure wave., (© 2021. The Author(s).)