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Secundum atrial septal defect (ASD) is one of the most common congenital heart diseases (CHDs). This study aims to evaluate the feasibility and accuracy of automatic detection of ASD in children based on color Doppler echocardiographic images using convolutional neural networks. In this study, we propose a fully automatic detection system for ASD, which includes three stages. The first stage is used to identify four target echocardiographic views (that is, the subcostal view focusing on the atrium septum, the apical four-chamber view, the low parasternal four-chamber view, and the parasternal short-axis view). These four echocardiographic views are most useful for the diagnosis of ASD clinically. The second stage aims to segment the target cardiac structure and detect candidates for ASD. The third stage is to infer the final detection by utilizing the segmentation and detection results of the second stage. The proposed ASD detection system was developed and validated using a training set of 4,031 cases containing 370,057 echocardiographic images and an independent test set of 229 cases containing 203,619 images, of which 105 cases with ASD and 124 cases with intact atrial septum. Experimental results showed that the proposed ASD detection system achieved accuracy, recall, precision, specificity, and F1 score of 0.8833, 0.8545, 0.8577, 0.9136, and 0.8546, respectively on the image-level averages of the four most clinically useful echocardiographic views. The proposed system can automatically and accurately identify ASD, laying a good foundation for the subsequent artificial intelligence diagnosis of CHDs.

Background Barth syndrome (BTHS) is a rare X-linked recessive disease characterized by cardiomyopathy, neutropenia, skeletal myopathy and growth delay. Early diagnosis and appropriate treatment may improve the prognosis of this disease. The purpose of this study is to determine the role of targeted next-generation sequencing (NGS) in the early diagnosis of BTHS in children with cardiomyopathy. Methods During the period between 2012 and 2015, a gene panel-based NGS approach was used to search for potentially disease-causing genetic variants in all patients referred to our institution with a clinical diagnosis of primary cardiomyopathy. NGS was performed using the Illumina sequencing system. Results A total of 180 Chinese pediatric patients (114 males and 66 females) diagnosed with primary cardiomyopathy were enrolled in this study. TAZ mutations were identified in four of the male index patients, including two novel mutations (c.527A > G, p.H176R and c.134_136delinsCC, p.H45PfsX38). All four probands and two additional affected male family members were born at full term with a median birth weight of 2350 g (range, 2000–2850 g). The median age at diagnosis of cardiomyopathy was 3.0 months (range, 1.0–20.0 months). The baseline echocardiography revealed prominent dilation and trabeculations of the left ventricle with impaired systolic function in the six patients, four of which fulfilled the diagnostic criteria of left ventricular noncompaction. Other aspects of their clinical presentations included hypotonia (6/6), growth delay (6/6), neutropenia (3/6) and 3-methylglutaconic aciduria (4/5). Five patients died at a median age of 7.5 months (range, 7.0–12.0 months). The cause of death was heart failure associated with infection in three patients and cardiac arrhythmia in two patients. The remaining one patient survived beyond infancy but had fallen into a persistent vegetative state after suffering from cardiac arrest. Conclusions This is the first report of systematic mutation screening of TAZ in a large cohort of pediatric patients with primary cardiomyopathy using the NGS approach. TAZ mutations were found in 4/114 (3.5%) male patients with primary cardiomyopathy. Our findings indicate that the inclusion of TAZ gene testing in cardiomyopathy genetic testing panels may contribute to the early diagnosis of BTHS.

Recently, the United States Geological Survey (USGS) has released a new dataset, called Landsat Analysis Ready Data (ARD), which is designed specifically for facilitating time series analysis. In this study, we evaluated the temporal consistency of this new dataset and recommended several processing streamlines for improving data consistency. Specifically, we examined the impacts of data resampling, cloud/cloud shadow detection, Bidirectional Reflectance Distribution Function (BRDF) correction, and topographic correction on the temporal consistency of the Landsat Time Series (LTS). We have four major observations. First, single-resampled data (ARD) are generally more consistent than double-resampled data (re-projected Collection 1 data), but the difference is very minor. Second, the improved cloud and cloud shadow detection approach (e.g., Fmask 4.0 vs. 3.3) moderately increased data consistency. Third, BRDF correction contributed the most in making LTS consistent. Finally, we corrected the topographic effects by using several widely used algorithms, including Sun-Canopy-Sensor (SCS), a semiempirical SCS (SCS+C), and Illumination Correction (IC) algorithms, however they were found to have very limited or even negative impacts on the consistency of LTS. Therefore, we recommend using Landsat ARD with the improved cloud and cloud shadow detection approach (Fmask 4.0), and with BRDF correction for routine time series analysis.

Vortex-induced vibration (VIV) is a common fluid–structure interaction phenomenon in practical engineering with significant research value. Traditional methods to solve VIV issues include experimental studies and numerical simulations. However, experimental studies are costly and time-consuming, while numerical simulations are constrained by low Reynolds numbers and simplified models. Deep learning (DL) can successfully capture VIV patterns and generate accurate predictions by using a large amount of training data. The Physics-Informed Neural Network (PINN), a subfield of DL, introduces physics equations into the loss function to reduce the need for large data. Nevertheless, PINN loss functions often include multiple loss terms, which may interact with each other, causing imbalanced training speeds and a potentially inferior overall performance. To address this issue, this study proposes an Adaptive Weight Physics-Informed Neural Network (AW-PINN) algorithm built upon a gradient normalization method (GradNorm) from multi-task learning. The AW-PINN regulates the weights of each loss term by computing the gradient norms on the network weights, ensuring the norms of the loss terms match predefined target values. This ensures balanced training speeds for each loss term and improves both the prediction precision and robustness of the network model. In this study, a VIV dataset of a cylindrical body with different degrees of freedom is used to compare the performance of the PINN and three PINN optimization algorithms. The findings suggest that, compared to a standard PINN, the AW-PINN lowers the mean squared error (MSE) on the test set by 50%, significantly improving the prediction accuracy. The AW-PINN also demonstrates an enhanced stability across different datasets, confirming its robustness and reliability for VIV modeling. Compared with existing methods in the literature, the AW-PINN achieves a comparable lift prediction accuracy using merely 1% of the training data, while simultaneously improving the prediction accuracy of the peak lift.

Background The accurate diagnosis of local soft tissue recurrence (LR) in primary bone tumors is crucial for guiding clinical management and predicting patient outcomes. However, standardized postoperative surveillance protocols remain undefined. This study aims to compare the diagnostic efficacy of ultrasound (US) versus magnetic resonance imaging (MRI) in detecting LR following primary bone tumor surgery and to characterize the sonographic features of osteosarcoma recurrence. Methods We conducted a retrospective review of medical records from patients who underwent postoperative surveillance for primary bone tumors at our institution between 01/06/2016 to 01/09/2023. Diagnostic performance was compared using McNemar's test for paired variables. Sonographic characteristics were analyzed using logistic regression analysis, with statistical significance set at p  < 0.05. Results Comparative analysis revealed no statistically significant differences ( p > 0.05) in sensitivity, specificity, or accuracy between MRI and US, and the exact values for these parameters are provided in Table 1. Key sonographic features predictive of osteosarcoma recurrence included tumor size and anatomical location. The diagnostic model demonstrated excellent discriminative ability, with an area under the receiver operating characteristic (ROC) curve of 0.973. The diagnostic parameters were as follows: sensitivity (96.6%), specificity (90.9%), accuracy (94.6%), positive predictive value (95.0%), and negative predictive value (93.8%). Conclusion The findings from this study support the role of ultrasonography as a valuable tool in tumor surveillance paradigms, providing a scientific rationale for optimizing integrated management strategies in bone oncology.

The oil & gas transport and storage (OGTS) engineering, from the upstream of gathering and processing in the oil & gas fields, to the midstream long-distance pipelines, and the downstream tanks and LNG terminals, while using supply chains to connect each part, is exploring its way to reduce energy consumption and carbon footprints. This work provides an overview of current methods and technological improvements and the latest trends in OGTS to show how this industry strives to achieve sustainable development goals. The critical analyses are from increasing flexibility, energy saving, emission reduction, and changing energy structure. The study shows the need to focus on improving energy efficiency further, reducing energy/water/material consumption and emissions, and maintaining safety for such an extensive oil & gas network.

Owing to its high mortality rate, lung cancer (LC) remains the most common cancer worldwide, with the highest malignancy diagnosis rate. The phosphatidylinositol-3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling (PAM) pathway is a critical intracellular pathway involved in various cellular functions and regulates numerous cellular processes, including growth, survival, proliferation, metabolism, apoptosis, invasion, and angiogenesis. This review aims to highlight preclinical and clinical studies focusing on the PAM signaling pathway in LC and underscore the potential of natural products targeting it. Additionally, this review synthesizes the existing literature and discusses combination therapy and future directions for LC treatment while acknowledging the ongoing challenges in the field. Continuous development of novel therapeutic agents, technologies, and precision medicine offers an increasingly optimistic outlook for the treatment of LC.

Polymeric fibres with small radii (such as 125 nm) are delicate to handle and should be laid down on a solid substrate to obtain practical devices. However, placing these nanofibres on commonly used glass substrates prevents them from guiding light. In this study, we numerically and experimentally demonstrate that when the nanofibre is placed on a suitable dielectric multilayer, it supports a guided mode, a Bloch surface wave (BSW) confined in one dimension. The physical origin of this new mode is discussed in comparison with the typical two-dimensional BSW mode. Polymeric nanofibres are easily fabricated to contain fluorophores, which make the dielectric nanofibre and multilayer configuration suitable for developing a large range of new nanometric scale devices, such as processor–memory interconnections, devices with sensitivity to target analytes, incident polarization and multi-colour BSW modes. Typically nanofibres need to be placed on solid substrates for the next generation of devices, but this prevents light guiding. Here Wang et al . numerically and experimentally demonstrate that when a nanofibre is placed on a dielectric multilayer, it supports a Bloch surface wave confined in one dimension.

Objective To investigate the expression and significance of Fractalkine (CX3CL1, FKN) in serum and renal tissue of myeloperoxidase and anti-neutrophil cytoplasmic antibody associated vasculitis (MPO-AAV) rats. Methods Thirty Wistar-Kyoto (WKY) rats were randomly divided into: Control group, MPO-AAV group (400 µg/kg MPO mixed with Freund’s complete adjuvant i.p), MPO-AAV + Anti-FKN group (400 µg/kg MPO mixed with Freund’s complete adjuvant i.p), anti-FKN group (1 µg/ rat /day, i.p) after 6 weeks. MPO-AAV associated glomerulonephritis model was established by intraperitoneal injection of MPO + Freund’s complete adjuvant with 10 mice in each group. The concentration of MPO-ANCA and FKN in serum was detected by Enzyme-linked immunosorbent assay (ELISA). Hematoxylin-eosin (HE) staining was used to detect pathological changes of kidney tissue. Western blot and immunofluorescence staining were used to detect the expression and localization of FKN protein in kidney tissue. Renal function test indicators: 24-hour urinary protein (UAER), blood urea nitrogen (BUN), serum creatinine (Scr). The expression levels of p65NF-κB and IL-6 was detected by Immunohistochemical assays. Results Compared with the control group, the serum MPO-ANCA antibody expression level in the MPO-AAV group was significantly increased ( P < 0.01 ), and the contents of UAER, BUN and Scr were significantly up-regulated at 24 h ( P < 0.01 ). Compared with the control group, the glomeruli in the MPO-AAV group had different degrees of damage, infiltration of inflammatory cell, and membrane cell hyperplasia and renal tubule edema. Compared with the control group, rats in the MPO-AAV group had significantly higher levels of FKN in serum and renal tissues ( P < 0.01 ), and high expression of p65NF-κB and IL-6 in renal tissues ( P < 0.01 ) ( P < 0.05 ), whereas anti-FKN reversed the expression of the above factors. In MPO-AAV renal tissue, FKN was mainly expressed in the cytoplasm of renal tubular epithelial cells and glomerular podocytes. In addition, the contents of 24 h UAER, BUN and Scr of renal function in MPO-AAV rats were significantly decreased ( P < 0.01 ) and the damage of renal tissue was significantly ameliorated after the administration of antagonistic FKN. Conclusion FKN may play a key role in the pathogenesis of MPO-AAV associated glomerulonephritis.