StrucGP: de novo structural sequencing of site-specific N-glycan on glycoproteins using a modularization strategy.

Precision mapping of glycans at structural and site-specific level is still one of the most challenging tasks in the glycobiology field. Here, we describe a modularization strategy for de novo interpretation of N-glycan structures on intact glycopeptides using tandem mass spectrometry. An algorithm named StrucGP is also developed to automate the interpretation process for large-scale analysis. By dividing an N-glycan into three modules and identifying each module using distinct patterns of Y ions or a combination of distinguishable B/Y ions, the method enables determination of detailed glycan structures on thousands of glycosites in mouse brain, which comprise four types of core structure and 17 branch structures with three glycan subtypes. Owing to the database-independent glycan mapping strategy, StrucGP also facilitates the identification of rare/new glycan structures. The approach will be greatly beneficial for in-depth structural and functional study of glycoproteins in the biomedical research.

Mechanistic insight into the functional role of human sinoatrial node conduction pathways and pacemaker compartments heterogeneity: A computer model analysis.

The sinoatrial node (SAN), the primary pacemaker of the heart, is responsible for the initiation and robust regulation of sinus rhythm. 3D mapping studies of the ex-vivo human heart suggested that the robust regulation of sinus rhythm relies on specialized fibrotically-insulated pacemaker compartments (head, center and tail) with heterogeneous expressions of key ion channels and receptors. They also revealed up to five sinoatrial conduction pathways (SACPs), which electrically connect the SAN with neighboring right atrium (RA). To elucidate the role of these structural-molecular factors in the functional robustness of human SAN, we developed comprehensive biophysical computer models of the SAN based on 3D structural, functional and molecular mapping of ex-vivo human hearts. Our key finding is that the electrical insulation of the SAN except SACPs, the heterogeneous expression of If, INa currents and adenosine A1 receptors (A1R) across SAN pacemaker-conduction compartments are required to experimentally reproduce observed SAN activation patterns and important phenomena such as shifts of the leading pacemaker and preferential SACP. In particular, we found that the insulating border between the SAN and RA, is required for robust SAN function and protection from SAN arrest during adenosine challenge. The heterogeneity in the expression of A1R within the human SAN compartments underlies the direction of pacemaker shift and preferential SACPs in the presence of adenosine. Alterations of INa current and fibrotic remodelling in SACPs can significantly modulate SAN conduction and shift the preferential SACP/exit from SAN. Finally, we show that disease-induced fibrotic remodeling, INa suppression or increased adenosine make the human SAN vulnerable to pacing-induced exit blocks and reentrant arrhythmia. In summary, our computer model recapitulates the structural and functional features of the human SAN and can be a valuable tool for investigating mechanisms of SAN automaticity and conduction as well as SAN arrhythmia mechanisms under different pathophysiological conditions.

Machine learning-based prediction of postpartum hemorrhage after vaginal delivery: combining bleeding high risk factors and uterine contraction curve.

PURPOSE: This work used a machine learning model to improve the accuracy of predicting postpartum hemorrhage in vaginal delivery. METHODS: Among the 25,098 deliveries in the obstetrics department of the First Hospital of Jinan University recorded from 2016 to 2020, 10,520 were vaginal deliveries with complete study data. Further review selected 850 cases of postpartum hemorrhage (amount of bleeding > 500 mL) and 54 cases of severe postpartum hemorrhage (amount of bleeding > 1000 mL). Indicators of clinical risk factors for postpartum hemorrhage were retrieved from electronic medical records. Features of the uterine contraction curve were extracted 2 h prior to vaginal delivery and modeled using a 49-variable machine learning with 90% of study cases used in the training set and 10% of study cases used in the test set. Accuracy was compared among the assessment table, classical statistical models, and machine learning models used to predict postpartum hemorrhage to assess their clinical use. The assessment table contained 16 high-risk factor scores to predict postpartum hemorrhage. The classical statistical model used was Logistic Regression (LR). The machine learning models were Random Forest (RF), K Nearest Neighbor (KNN), and the one integrated with Lightgbm (LGB) and LR. The effect of model prediction was evaluated by area under the receiver operating characteristic curve (AUC), namely, C-static, calibration curve Brier score, decision curve, F-measure, sensitivity (SE), and specificity (SP). RESULTS: 1: Among the tested tools, the machine learning model LGB + LR has the best performance in predicting postpartum hemorrhage. Its Brier, AUC, and F-measure scores are better than those of other models in each group, and its SE and SP reach 0.694 and 0.800, respectively. The predictive performance of the classical statistical model LR is AUC: 0.729, 95%CI [0.702-0.756]). 2: Verification on the testing set reveals that the features of uterine contraction contribute to the improved accuracy of the model prediction. 3: LGB + LR model suggested that among the 49 indicators for predicting severe postpartum hemorrhage, the importance of the first 10 characteristics in descending order is as follows: hematocrit (%), shock index, frequency of contractions (min-1), white blood cell count, gestational hypertension, neonatal weight (kg), time of second labor (min), mean area of contractions (mmHg s), total amniotic fluid (mL), and body mass index (BMI). The prediction effect is close to that of the model after training with all 49 features. The predictive effect was close to that of the model after training using all 49 features. 4: Contraction frequency and intensity Mean_Area (representing effective contractions) have a high predictive value for severe postpartum hemorrhage. 5: Blood loss amount within 2 h has a high warning effect on postpartum hemorrhage, and the increase in AUC to 0.95 indicates that postpartum bleeding mostly occurs within 2 h after delivery. CONCLUSION: Machine learning models incorporated with uterine contraction features can further improve the accuracy of postpartum hemorrhage prediction in vaginal delivery and provide a reference for clinicians to intervene early and reduce adverse pregnancy outcomes.

In silico investigation of the mechanisms underlying atrial fibrillation due to impaired Pitx2.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is a major cause of stroke and morbidity. Recent genome-wide association studies have shown that paired-like homeodomain transcription factor 2 (Pitx2) to be strongly associated with AF. However, the mechanisms underlying Pitx2 modulated arrhythmogenesis and variable effectiveness of antiarrhythmic drugs (AADs) in patients in the presence or absence of impaired Pitx2 expression remain unclear. We have developed multi-scale computer models, ranging from a single cell to tissue level, to mimic control and Pitx2-knockout atria by incorporating recent experimental data on Pitx2-induced electrical and structural remodeling in humans, as well as the effects of AADs. The key findings of this study are twofold. We have demonstrated that shortened action potential duration, slow conduction and triggered activity occur due to electrical and structural remodelling under Pitx2 deficiency conditions. Notably, the elevated function of calcium transport ATPase increases sarcoplasmic reticulum Ca2+ concentration, thereby enhancing susceptibility to triggered activity. Furthermore, heterogeneity is further elevated due to Pitx2 deficiency: 1) Electrical heterogeneity between left and right atria increases; and 2) Increased fibrosis and decreased cell-cell coupling due to structural remodelling slow electrical propagation and provide obstacles to attract re-entry, facilitating the initiation of re-entrant circuits. Secondly, our study suggests that flecainide has antiarrhythmic effects on AF due to impaired Pitx2 by preventing spontaneous calcium release and increasing wavelength. Furthermore, our study suggests that Na+ channel effects alone are insufficient to explain the efficacy of flecainide. Our study may provide the mechanisms underlying Pitx2-induced AF and possible explanation behind the AAD effects of flecainide in patients with Pitx2 deficiency.

Understanding PITX2-Dependent Atrial Fibrillation Mechanisms through Computational Models.

Atrial fibrillation (AF) is a common arrhythmia. Better prevention and treatment of AF are needed to reduce AF-associated morbidity and mortality. Several major mechanisms cause AF in patients, including genetic predispositions to AF development. Genome-wide association studies have identified a number of genetic variants in association with AF populations, with the strongest hits clustering on chromosome 4q25, close to the gene for the homeobox transcription PITX2. Because of the inherent complexity of the human heart, experimental and basic research is insufficient for understanding the functional impacts of PITX2 variants on AF. Linking PITX2 properties to ion channels, cells, tissues, atriums and the whole heart, computational models provide a supplementary tool for achieving a quantitative understanding of the functional role of PITX2 in remodelling atrial structure and function to predispose to AF. It is hoped that computational approaches incorporating all we know about PITX2-related structural and electrical remodelling would provide better understanding into its proarrhythmic effects leading to development of improved anti-AF therapies. In the present review, we discuss advances in atrial modelling and focus on the mechanistic links between PITX2 and AF. Challenges in applying models for improving patient health are described, as well as a summary of future perspectives.

In Silico Assessment of Class I Antiarrhythmic Drug Effects on Pitx2-Induced Atrial Fibrillation: Insights from Populations of Electrophysiological Models of Human Atrial Cells and Tissues.

Electrical remodelling as a result of homeodomain transcription factor 2 (Pitx2)-dependent gene regulation was linked to atrial fibrillation (AF) and AF patients with single nucleotide polymorphisms at chromosome 4q25 responded favorably to class I antiarrhythmic drugs (AADs). The possible reasons behind this remain elusive. The purpose of this study was to assess the efficacy of the AADs disopyramide, quinidine, and propafenone on human atrial arrhythmias mediated by Pitx2-induced remodelling, from a single cell to the tissue level, using drug binding models with multi-channel pharmacology. Experimentally calibrated populations of human atrial action po-tential (AP) models in both sinus rhythm (SR) and Pitx2-induced AF conditions were constructed by using two distinct models to represent morphological subtypes of AP. Multi-channel pharmaco-logical effects of disopyramide, quinidine, and propafenone on ionic currents were considered. Simulated results showed that Pitx2-induced remodelling increased maximum upstroke velocity (dVdtmax), and decreased AP duration (APD), conduction velocity (CV), and wavelength (WL). At the concentrations tested in this study, these AADs decreased dVdtmax and CV and prolonged APD in the setting of Pitx2-induced AF. Our findings of alterations in WL indicated that disopyramide may be more effective against Pitx2-induced AF than propafenone and quinidine by prolonging WL.

Afterdepolarizations and abnormal calcium handling in atrial myocytes with modulated SERCA uptake: a sensitivity analysis of calcium handling channels.

Delayed afterdepolarizations (DADs) and spontaneous depolarizations (SDs) are typically triggered by spontaneous diastolic Ca2+ release from the sarcoplasmic reticulum (SR) which is caused by an elevated SR Ca2+-ATPase (SERCA) uptake and dysfunctional ryanodine receptors. However, recent studies on the T-box transcription factor gene (TBX5) demonstrated that abnormal depolarizations could occur despite a reduced SERCA uptake. Similar findings have also been reported in experimental or clinical studies of diabetes and heart failure. To investigate the sensitivity of SERCA in the genesis of DADs/SDs as well as its dependence on other Ca2+ handling channels, we performed systematic analyses using the Maleckar et al. model. Results showed that the modulation of SERCA alone cannot trigger abnormal depolarizations, but can instead affect the interdependency of other Ca2+ handling channels in triggering DADs/SDs. Furthermore, we discovered the existence of a threshold value for the intracellular concentration of Ca2+ ([Ca2+]i) for abnormal depolarizations, which is modulated by the maximum SERCA uptake and the concentration of Ca2+ in the uptake and release compartments in the SR ([Ca2+]up and [Ca2+]rel). For the first time, our modelling study reconciles different mechanisms of abnormal depolarizations in the setting of 'lone' AF, reduced TBX5, diabetes and heart failure, and may lead to more targeted treatment for these patients. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

PSFHSP-Net: an efficient lightweight network for identifying pubic symphysis-fetal head standard plane from intrapartum ultrasound images.

The accurate selection of the ultrasound plane for the fetal head and pubic symphysis is critical for precisely measuring the angle of progression. The traditional method depends heavily on sonographers manually selecting the imaging plane. This process is not only time-intensive and laborious but also prone to variability based on the clinicians' expertise. Consequently, there is a significant need for an automated method driven by artificial intelligence. To enhance the efficiency and accuracy of identifying the pubic symphysis-fetal head standard plane (PSFHSP), we proposed a streamlined neural network, PSFHSP-Net, based on a modified version of ResNet-18. This network comprises a single convolutional layer and three residual blocks designed to mitigate noise interference and bolster feature extraction capabilities. The model's adaptability was further refined by expanding the shared feature layer into task-specific layers. We assessed its performance against both traditional heavyweight and other lightweight models by evaluating metrics such as F1-score, accuracy (ACC), recall, precision, area under the ROC curve (AUC), model parameter count, and frames per second (FPS). The PSFHSP-Net recorded an ACC of 0.8995, an F1-score of 0.9075, a recall of 0.9191, and a precision of 0.9022. This model surpassed other heavyweight and lightweight models in these metrics. Notably, it featured the smallest model size (1.48 MB) and the highest processing speed (65.7909 FPS), meeting the real-time processing criterion of over 24 images per second. While the AUC of our model was 0.930, slightly lower than that of ResNet34 (0.935), it showed a marked improvement over ResNet-18 in testing, with increases in ACC and F1-score of 0.0435 and 0.0306, respectively. However, precision saw a slight decrease from 0.9184 to 0.9022, a reduction of 0.0162. Despite these trade-offs, the compression of the model significantly reduced its size from 42.64 to 1.48 MB and increased its inference speed by 4.4753 to 65.7909 FPS. The results confirm that the PSFHSP-Net is capable of swiftly and effectively identifying the PSFHSP, thereby facilitating accurate measurements of the angle of progression. This development represents a significant advancement in automating fetal imaging analysis, promising enhanced consistency and reduced operator dependency in clinical settings.

PSFHS: Intrapartum ultrasound image dataset for AI-based segmentation of pubic symphysis and fetal head.

During the process of labor, the intrapartum transperineal ultrasound examination serves as a valuable tool, allowing direct observation of the relative positional relationship between the pubic symphysis and fetal head (PSFH). Accurate assessment of fetal head descent and the prediction of the most suitable mode of delivery heavily rely on this relationship. However, achieving an objective and quantitative interpretation of the ultrasound images necessitates precise PSFH segmentation (PSFHS), a task that is both time-consuming and demanding. Integrating the potential of artificial intelligence (AI) in the field of medical ultrasound image segmentation, the development and evaluation of AI-based models rely significantly on access to comprehensive and meticulously annotated datasets. Unfortunately, publicly accessible datasets tailored for PSFHS are notably scarce. Bridging this critical gap, we introduce a PSFHS dataset comprising 1358 images, meticulously annotated at the pixel level. The annotation process adhered to standardized protocols and involved collaboration among medical experts. Remarkably, this dataset stands as the most expansive and comprehensive resource for PSFHS to date.

USP20 deubiquitinates and stabilizes the reticulophagy receptor RETREG1/FAM134B to drive reticulophagy.

The endoplasmic reticulum (ER) serves as a hub for various cellular processes, and maintaining ER homeostasis is essential for cell function. Reticulophagy is a selective process that removes impaired ER subdomains through autophagy-mediatedlysosomal degradation. While the involvement of ubiquitination in autophagy regulation is well-established, its role in reticulophagy remains unclear. In this study, we screened deubiquitinating enzymes (DUBs) involved in reticulophagy and identified USP20 (ubiquitin specific peptidase 20) as a key regulator of reticulophagy under starvation conditions. USP20 specifically cleaves K48- and K63-linked ubiquitin chains on the reticulophagy receptor RETREG1/FAM134B (reticulophagy regulator 1), thereby stabilizing the substrate and promoting reticulophagy. Remarkably, despite lacking a transmembrane domain, USP20 is recruited to the ER through its interaction with VAPs (VAMP associated proteins). VAPs facilitate the recruitment of early autophagy proteins, including WIPI2 (WD repeat domain, phosphoinositide interacting 2), to specific ER subdomains, where USP20 and RETREG1 are enriched. The recruitment of WIPI2 and other proteins in this process plays a crucial role in facilitating RETREG1-mediated reticulophagy in response to nutrient deprivation. These findings highlight the critical role of USP20 in maintaining ER homeostasis by deubiquitinating and stabilizing RETREG1 at distinct ER subdomains, where USP20 further recruits VAPs and promotes efficient reticulophagy.Abbreviations: ACTB actin beta; ADRB2 adrenoceptor beta 2; AMFR/gp78 autocrine motility factor receptor; ATG autophagy related; ATL3 atlastin GTPase 3; BafA1 bafilomycin A1; BECN1 beclin 1; CALCOCO1 calcium binding and coiled-coil domain 1; CCPG1 cell cycle progression 1; DAPI 4',6-diamidino-2-phenylindole; DTT dithiothreitol; DUB deubiquitinating enzyme; EBSS Earle's Balanced Salt Solution; FFAT two phenylalanines (FF) in an acidic tract; GABARAP GABA type A receptor-associated protein; GFP green fluorescent protein; HMGCR 3-hydroxy-3-methylglutaryl-CoA reductase; IL1B interleukin 1 beta; LIR LC3-interacting region; MAP1LC3/LC3 microtubule associated protein 1 light chain 3; PIK3C3/Vps34 phosphatidylinositol 3-kinase catalytic subunit type 3; RB1CC1/FIP200 RB1 inducible coiled-coil 1; RETREG1/FAM134B reticulophagy regulator 1; RFP red fluorescent protein; RHD reticulon homology domain; RIPK1 receptor interacting serine/threonine kinase 1; RTN3L reticulon 3 long isoform; SEC61B SEC61 translocon subunit beta; SEC62 SEC62 homolog, preprotein translocation factor; SIM super-resolution structured illumination microscopy; SNAI2 snail family transcriptional repressor 2; SQSTM1/p62 sequestosome 1; STING1/MITA stimulator of interferon response cGAMP interactor 1; STX17 syntaxin 17; TEX264 testis expressed 264, ER-phagy receptor; TNF tumor necrosis factor; UB ubiquitin; ULK1 unc-51 like autophagy activating kinase 1; USP20 ubiquitin specific peptidase 20; USP33 ubiquitin specific peptidase 33; VAMP8 vesicle associated membrane protein 8; VAPs VAMP associated proteins; VMP1 vacuole membrane protein 1; WIPI2 WD repeat domain, phosphoinositide interacting 2; ZFYVE1/DFCP1 zinc finger FYVE-type containing 1.

RAS Dataset: A 3D Cardiac LGE-MRI Dataset for Segmentation of Right Atrial Cavity.

The current challenge in effectively treating atrial fibrillation (AF) stems from a limited understanding of the intricate structure of the human atria. The objective and quantitative interpretation of the right atrium (RA) in late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) scans relies heavily on its precise segmentation. Leveraging the potential of artificial intelligence (AI) for RA segmentation presents a promising solution. However, the successful implementation of AI in this context necessitates access to a substantial volume of annotated LGE-MRI images for model training. In this paper, we present a comprehensive 3D cardiac dataset comprising 50 high-resolution LGE-MRI scans, each meticulously annotated at the pixel level. The annotation process underwent rigorous standardization through crowdsourcing among a panel of medical experts, ensuring the accuracy and consistency of the annotations. Our dataset represents a significant contribution to the field, providing a valuable resource for advancing RA segmentation methods.

Fetal Head and Pubic Symphysis Segmentation in Intrapartum Ultrasound Image Using a Dual-Path Boundary-Guided Residual Network.

Accurate segmentation of the fetal head and pubic symphysis in intrapartum ultrasound images and measurement of fetal angle of progression (AoP) are critical to both outcome prediction and complication prevention in delivery. However, due to poor quality of perinatal ultrasound imaging with blurred target boundaries and the relatively small target of the public symphysis, fully automated and accurate segmentation remains challenging. In this paper, we propse a dual-path boundary-guided residual network (DBRN), which is a novel approach to tackle these challenges. The model contains a multi-scale weighted module (MWM) to gather global context information, and enhance the feature response within the target region by weighting the feature map. The model also incorporates an enhanced boundary module (EBM) to obtain more precise boundary information. Furthermore, the model introduces a boundary-guided dual-attention residual module (BDRM) for residual learning. BDRM leverages boundary information as prior knowledge and employs spatial attention to simultaneously focus on background and foreground information, in order to capture concealed details and improve segmentation accuracy. Extensive comparative experiments have been conducted on three datasets. The proposed method achieves average Dice score of 0.908 ±0.05 and average Hausdorff distance of 3.396 ±0.66 mm. Compared with state-of-the-art competitors, the proposed DBRN achieves better results. In addition, the average difference between the automatic measurement of AoPs based on this model and the manual measurement results is 6.157 °, which has good consistency and has broad application prospects in clinical practice.

RTSeg-net: A lightweight network for real-time segmentation of fetal head and pubic symphysis from intrapartum ultrasound images.

The segmentation of the fetal head (FH) and pubic symphysis (PS) from intrapartum ultrasound images plays a pivotal role in monitoring labor progression and informing crucial clinical decisions. Achieving real-time segmentation with high accuracy on systems with limited hardware capabilities presents significant challenges. To address these challenges, we propose the real-time segmentation network (RTSeg-Net), a groundbreaking lightweight deep learning model that incorporates innovative distribution shifting convolutional blocks, tokenized multilayer perceptron blocks, and efficient feature fusion blocks. Designed for optimal computational efficiency, RTSeg-Net minimizes resource demand while significantly enhancing segmentation performance. Our comprehensive evaluation on two distinct intrapartum ultrasound image datasets reveals that RTSeg-Net achieves segmentation accuracy on par with more complex state-of-the-art networks, utilizing merely 1.86 M parameters-just 6 % of their hyperparameters-and operating seven times faster, achieving a remarkable rate of 31.13 frames per second on a Jetson Nano, a device known for its limited computing capacity. These achievements underscore RTSeg-Net's potential to provide accurate, real-time segmentation on low-power devices, broadening the scope for its application across various stages of labor. By facilitating real-time, accurate ultrasound image analysis on portable, low-cost devices, RTSeg-Net promises to revolutionize intrapartum monitoring, making sophisticated diagnostic tools accessible to a wider range of healthcare settings.

PSFHS challenge report: Pubic symphysis and fetal head segmentation from intrapartum ultrasound images.

Segmentation of the fetal and maternal structures, particularly intrapartum ultrasound imaging as advocated by the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) for monitoring labor progression, is a crucial first step for quantitative diagnosis and clinical decision-making. This requires specialized analysis by obstetrics professionals, in a task that i) is highly time- and cost-consuming and ii) often yields inconsistent results. The utility of automatic segmentation algorithms for biometry has been proven, though existing results remain suboptimal. To push forward advancements in this area, the Grand Challenge on Pubic Symphysis-Fetal Head Segmentation (PSFHS) was held alongside the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). This challenge aimed to enhance the development of automatic segmentation algorithms at an international scale, providing the largest dataset to date with 5,101 intrapartum ultrasound images collected from two ultrasound machines across three hospitals from two institutions. The scientific community's enthusiastic participation led to the selection of the top 8 out of 179 entries from 193 registrants in the initial phase to proceed to the competition's second stage. These algorithms have elevated the state-of-the-art in automatic PSFHS from intrapartum ultrasound images. A thorough analysis of the results pinpointed ongoing challenges in the field and outlined recommendations for future work. The top solutions and the complete dataset remain publicly available, fostering further advancements in automatic segmentation and biometry for intrapartum ultrasound imaging.

Dilated convolution network with edge fusion block and directional feature maps for cardiac MRI segmentation.

Cardiac magnetic resonance imaging (MRI) segmentation task refers to the accurate segmentation of ventricle and myocardium, which is a prerequisite for evaluating the soundness of cardiac function. With the development of deep learning in medical imaging, more and more heart segmentation methods based on deep learning have been proposed. Due to the fuzzy boundary and uneven intensity distribution of cardiac MRI, some existing methods do not make full use of multi-scale characteristic information and have the problem of ambiguity between classes. In this paper, we propose a dilated convolution network with edge fusion block and directional feature maps for cardiac MRI segmentation. The network uses feature fusion module to preserve boundary information, and adopts the direction field module to obtain the feature maps to improve the original segmentation features. Firstly, multi-scale feature information is obtained and fused through dilated convolutional layers of different scales while downsampling. Secondly, in the decoding stage, the edge fusion block integrates the edge features into the side output of the encoder and concatenates them with the upsampled features. Finally, the concatenated features utilize the direction field to improve the original segmentation features and generate the final result. Our propose method conducts comprehensive comparative experiments on the automated cardiac diagnosis challenge (ACDC) and myocardial pathological segmentation (MyoPS) datasets. The results show that the proposed cardiac MRI segmentation method has better performance compared to other existing methods.

A hybrid attentional guidance network for tumors segmentation of breast ultrasound images.

PURPOSE: In recent years, breast cancer has become the greatest threat to women. There are many studies dedicated to the precise segmentation of breast tumors, which is indispensable in computer-aided diagnosis. Deep neural networks have achieved accurate segmentation of images. However, convolutional layers are biased to extract local features and tend to lose global and location information as the network deepens, which leads to a decrease in breast tumors segmentation accuracy. For this reason, we propose a hybrid attention-guided network (HAG-Net). We believe that this method will improve the detection rate and segmentation of tumors in breast ultrasound images. METHODS: The method is equipped with multi-scale guidance block (MSG) for guiding the extraction of low-resolution location information. Short multi-head self-attention (S-MHSA) and convolutional block attention module are used to capture global features and long-range dependencies. Finally, the segmentation results are obtained by fusing multi-scale contextual information. RESULTS: We compare with 7 state-of-the-art methods on two publicly available datasets through five random fivefold cross-validations. The highest dice coefficient, Jaccard Index and detect rate ([Formula: see text]%, [Formula: see text]%, [Formula: see text]% and [Formula: see text]%, [Formula: see text]%, [Formula: see text]%, separately) obtained on two publicly available datasets(BUSI and OASUBD), prove the superiority of our method. CONCLUSION: HAG-Net can better utilize multi-resolution features to localize the breast tumors. Demonstrating excellent generalizability and applicability for breast tumors segmentation compare to other state-of-the-art methods.

Comparison of fetal heart rate baseline estimation by the cardiotocograph network and clinicians: a multidatabase retrospective assessment study.

Background: This study aims to compare the fetal heart rate (FHR) baseline predicted by the cardiotocograph network (CTGNet) with that estimated by clinicians. Material and methods: A total of 1,267 FHR recordings acquired with different electrical fetal monitors (EFM) were collected from five datasets: 84 FHR recordings acquired with F15 EFM (Edan, Shenzhen, China) from the Guangzhou Women and Children's Medical Center, 331 FHR recordings acquired with SRF618B5 EFM (Sanrui, Guangzhou, China), 234 FHR recordings acquired with F3 EFM (Lian-Med, Guangzhou, China) from the NanFang Hospital of Southen Medical University, 552 cardiotocographys (CTG) recorded using STAN S21 and S31 (Neoventa Medical, Mölndal, Sweden) and Avalon FM40 and FM50 (Philips Healthcare, Amsterdam, The Netherlands) from the University Hospital in Brno, Czech Republic, and 66 FHR recordings acquired using Avalon FM50 fetal monitor (Philips Healthcare, Amsterdam, The Netherlands) at St Vincent de Paul Hospital (Lille, France). Each FHR baseline was estimated by clinicians and CTGNet, respectively. And agreement between CTGNet and clinicians was evaluated using the kappa statistics, intra-class correlation coefficient, and the limits of agreement. Results: The number of differences <3 beats per minute (bpm), 3-5 bpm, 5-10 bpm and ≥10 bpm, is 64.88%, 15.94%, 14.44% and 4.74%, respectively. Kappa statistics and intra-class correlation coefficient are 0.873 and 0.969, respectively. Limits of agreement are -6.81 and 7.48 (mean difference: 0.36 and standard deviation: 3.64). Conclusion: An excellent agreement was found between CTGNet and clinicians in the baseline estimation from FHR recordings with different signal loss rates.

Mechanisms of pulmonary arterial hypertension-induced atrial fibrillation: insights from multi-scale models of the human atria.

This study aimed to use multi-scale atrial models to investigate pulmonary arterial hypertension (PAH)-induced atrial fibrillation mechanisms. The results of our computer simulations revealed that, at the single-cell level, PAH-induced remodelling led to a prolonged action potential (AP) (ΔAPD: 49.6 ms in the right atria (RA) versus 41.6 ms in the left atria (LA)) and an increased calcium transient (CaT) (ΔCaT: 7.5 × 10-2 µM in the RA versus 0.9 × 10-3 µM in the LA). Moreover, heterogeneous remodelling increased susceptibility to afterdepolarizations, particularly in the RA. At the tissue level, we observed a significant reduction in conduction velocity (CV) (ΔCV: -0.5 m s-1 in the RA versus -0.05 m s-1 in the LA), leading to a shortened wavelength in the RA, but not in the LA. Additionally, afterdepolarizations in the RA contributed to enhanced repolarization dispersion and facilitated unidirectional conduction block. Furthermore, the increased fibrosis in the RA amplified the likelihood of excitation wave breakdown and the occurrence of sustained re-entries. Our results indicated that the RA is characterized by increased susceptibility to afterdepolarizations, slow conduction, reduced wavelength and upregulated fibrosis. These findings shed light on the underlying factors that may promote atrial fibrillation in patients with PAH.

The segmentation effect of style transfer on fetal head ultrasound image: a study of multi-source data.

The generalization ability of the fetal head segmentation method is reduced due to the data obtained by different machines, settings, and operations. To keep the generalization ability, we proposed a Fourier domain adaptation (FDA) method based on amplitude and phase to achieve better multi-source ultrasound data segmentation performance. Given the source/target image, the Fourier domain information was first obtained using fast Fourier transform. Secondly, the target information was mapped to the source Fourier domain through the phase adjustment parameter α and the amplitude adjustment parameter ß. Thirdly, the target image and the preprocessed source image obtained through the inverse discrete Fourier transform were used as the input of the segmentation network. Finally, the dice loss was computed to adjust α and ß. In the existing transform methods, the proposed method achieved the best performance. The adaptive-FDA method provides a solution for the automatic preprocessing of multi-source data. Experimental results show that it quantitatively improves the segmentation results and model generalization performance.

Chamber-specific wall thickness features in human atrial fibrillation.

Persistent atrial fibrillation (AF) is not effectively treated due to a lack of adequate tools for identifying patient-specific AF substrates. Recent studies revealed that in 30-50% of patients, persistent AF is maintained by localized drivers not only in the left atrium (LA) but also in the right atrium (RA). The chamber-specific atrial wall thickness (AWT) features underlying AF remain elusive, though the important role of AWT in AF is widely acknowledged. We aimed to provide direct evidence of the existence of distinguished RA and LA AWT features underlying AF drivers by analysing functionally and structurally mapped human hearts ex vivo. Coronary-perfused intact human atria (n = 7, 47 ± 14 y.o.; two female) were mapped using panoramic near-infrared optical mapping during pacing-induced AF. Then the hearts were imaged at approximately 170 µm3 resolution by 9.4 T gadolinium-enhanced MRI. The heart was segmented, and 3D AWT throughout atrial chambers was estimated and analysed. Optical mapping identified six localized RA re-entrant drivers in four hearts and four LA drivers in three hearts. All RA AF drivers were anchored to the pectinate muscle junctions with the crista terminalis or atrial walls. The four LA AF drivers were in the posterior LA. RA (n = 4) with AF drivers were thicker with greater AWT variation than RA (n = 3) without drivers (5.4 ± 2.6 mm versus 5.0 ± 2.4 mm, T-test p < 0.05; F-test p < 0.05). Furthermore, AWT in RA driver regions was thicker and varied more than in RA non-driver regions (5.1 ± 2.5 mm versus 4.4 ± 2.2 mm, T-test p < 0.05; F-test p < 0.05). On the other hand, LA (n = 3) with drivers was thinner than the LA (n = 4) without drivers. In particular, LA driver regions were thinner than the rest of LA regions (3.4 ± 1.0 mm versus 4.2 ± 1.0 mm, T-test p < 0.05). This study demonstrates chamber-specific AWT features of AF drivers. In RA, driver regions are thicker and have more variable AWT than non-driver regions. By contrast, LA drivers are thinner than non-drivers. Robust evaluation of patient-specific AWT features should be considered for chamber-specific targeted ablation.