FAK inhibitors in cancer, a patent review - an update on progress.

INTRODUCTION: Focal adhesion kinase (FAK) is a cytoplasmic non-receptor tyrosine kinase over-expressed in various malignancies which is related to various cellular functions such as adhesion, metastasis and proliferation. AREAS COVERED: There is growing evidence that FAK is a promising therapeutic target for designing inhibitors by regulating the downstream pathways of FAK. Some potential FAK inhibitors have entered clinical phase research. EXPERT OPINION: FAK could be an effective target in medicinal chemistry research and there were a variety of FAKIs have been patented recently. Here, we updated an overview of design, synthesis and structure-activity relationship of chemotherapeutic FAK inhibitors (FAKIs) from 2017 until now based on our previous work. We hope our efforts can broaden the understanding of FAKIs and provide new ideas and insights for future cancer treatment from medicinal chemistry point of view.

Integrated network pharmacology and metabolomics to reveal the mechanism of Pinellia ternata inhibiting non-small cell lung cancer cells.

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.

Coronary artery segmentation in CCTA images based on multi-scale feature learning.

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.

An end-to-end multi-scale airway segmentation framework based on pulmonary CT image.

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 asa prioriinformation. 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.

A review of traditional Chinese medicine diagnosis using machine learning: Inspection, auscultation-olfaction, inquiry, and palpation.

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.

Patient-specific non-invasive estimation of the aortic blood pressure waveform by ultrasound and tonometry.

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.

Adaptive convolutional dictionary learning for denoising seismocardiogram to enhance the classification performance of aortic stenosis.

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.

ST-GAN: A Swin Transformer-Based Generative Adversarial Network for Unsupervised Domain Adaptation of Cross-Modality Cardiac Segmentation.

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.

COACT: Coronary artery centerline tracker.

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.

Automatic coronary artery segmentation of CCTA images using UNet with a local contextual transformer.

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%.

Multi-constraint point set registration with redundant point removal for the registration of coronary arteries.

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.

BAP31 depletion inhibited adipogenesis, repressed lipolysis and promoted lipid droplets abnormal growth via attenuating Perilipin1 proteasomal degradation.

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. ß3-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.

Factors that contribute to false-negative results in CT-guided transthoracic lung core-needle biopsy.

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.

Central Aortic Blood Pressure Waveform Estimation with a Temporal Convolutional Network.

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.

Wave reflection quantification analysis and personalized flow wave estimation based on the central aortic pressure waveform.

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.

A U-Shaped Network Based on Multi-level Feature and Dual-Attention Coordination Mechanism for Coronary Artery Segmentation of CCTA Images.

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.

Personalized aortic pressure waveform estimation from brachial pressure waveform using an adaptive transfer function.

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.

Pulmonary arteries segmentation from CT images using PA-Net with attention module and contour loss.

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.

Cognitive impairment in diffuse axonal injury patients with favorable outcome.

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.