A rare case of coronary artery complication in a child with systemic juvenile idiopathic arthritis and macrophage activation syndrome: case report and literature review.

A rare case of coronary artery involvement in a child with Systemic Juvenile Idiopathic Arthritis (sJIA) complicated by Macrophage Activation Syndrome (MAS) is reported. The patient initially received an inaccurate diagnosis of Kawasaki Disease, sepsis, and mycoplasma infection and showed no improvement after Intravenous Immune Globulin (IVIG) treatment. Upon admission, symptoms included diffuse red rash, swelling of the limbs, lymph node enlargement, and hepatosplenomegaly. Post investigations, a diagnosis of sJIA and MAS was confirmed, and treatment involved a combination of hormones (methylprednisolone) and immunosuppressive drugs (methotrexate). The revealed widened coronary artery diameter was managed with a disease-specific treatment plan and prophylactic plus low-dose aspirin anti-coagulation therapy. Under this management, MAS was well controlled, and follow-ups showed normalization of the child's coronary artery structure and function. This case and the associated literature review underscore the importance of early recognition, diagnosis, treatment, and long-term monitoring for children presenting with sJIA and MAS complicated by coronary artery involvement.

Uncertainty-inspired open set learning for retinal anomaly identification.

Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies. We establish an uncertainty-inspired open set (UIOS) model, which is trained with fundus images of 9 retinal conditions. Besides assessing the probability of each category, UIOS also calculates an uncertainty score to express its confidence. Our UIOS model with thresholding strategy achieves an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set, external target categories (TC)-JSIEC dataset and TC-unseen testing set, respectively, compared to the F1 score of 92.20%, 80.69% and 64.74% by the standard AI model. Furthermore, UIOS correctly predicts high uncertainty scores, which would prompt the need for a manual check in the datasets of non-target categories retinal diseases, low-quality fundus images, and non-fundus images. UIOS provides a robust method for real-world screening of retinal anomalies.

Cardiovascular involvement in Epstein-Barr virus infection.

Cardiovascular involvement is an uncommon but severe complication of Epstein-Barr virus (EBV) infection caused by direct damage and immune injury. Recently, it has drawn increasing attention due to its dismal prognosis. It can manifest in various ways, including coronary artery dilation (CAD), coronary artery aneurysm (CAA), myocarditis, arrhythmias, and heart failure, among others. If not treated promptly, cardiovascular damage can progress over time and even lead to death, which poses a challenge to clinicians. Early diagnosis and treatment can improve the prognosis and reduce mortality. However, there is a lack of reliable large-scale data and evidence-based guidance for the management of cardiovascular damage. Consequently, in this review, we attempt to synthesize the present knowledge of cardiovascular damage associated with EBV and to provide an overview of the pathogenesis, classification, treatment, and prognosis, which may enhance the recognition of cardiovascular complications related to EBV and may be valuable to their clinical management.

AutoPX: a new software package to process X-ray diffraction data from biomacromolecular crystals.

A new software package, autoPX, for processing X-ray diffraction data from biomacromolecular crystals is reported. This processing software package is designed on the basis of novel methods such as the location of diffraction spots by an improved Canny operator, indexing by a modified Fourier transform, a novel definition of mosaicity that expresses the dispersion state of reciprocal diffraction spots, and the correction of predicted diffraction spot coordinates by homography transform. New programming of some traditional algorithms necessary for integration and scaling is also included. Several examples of crystal structure determination using data from the SSRF beamlines reduced using autoPX, HKL-2000, DIALS and XDS are also demonstrated, and indicate that autoPX is capable of processing diffraction data from biomacromolecular crystals and providing adequate solutions to problems encountered at the SSRF beamlines.

DHNet: High-resolution and hierarchical network for cross-domain OCT speckle noise reduction.

PURPOSE: Optical coherence tomography (OCT) imaging uses the principle of Michelson interferometry to obtain high-resolution images by coherent superposing of multiple forward and backward scattered light waves with random phases. This process inevitably produces speckle noise that severely compromises visual quality of OCT images and degrades performances of subsequent image analysis tasks. In addition, datasets obtained by different OCT scanners have distribution shifts, making a speckle noise suppression model difficult to be generalized across multiple datasets. In order to solve the above issues, we propose a novel end-to-end denoising framework for OCT images collected by different scanners. METHODS: The proposed model utilizes the high-resolution network (HRNet) as backbone for image restoration, which reconstructs high-fidelity images by maintaining high-resolution representations throughout the entire learning process. To compensate distribution shifts among datasets collected by different scanners, we develop a hierarchical adversarial learning strategy for domain adaption. The proposed model is trained using datasets with clean ground truth produced by two commercial OCT scanners, and then applied to suppress speckle noise in OCT images collected by our recently developed OCT scanner, BV-1000 (China Bigvision Corporation). We name the proposed model as DHNet (Double-H-Net, High-resolution and Hierarchical Network). RESULTS: We compare DHNet with state-of-the-art methods and experiment results show that DHNet improves signal-to-noise ratio by a large margin of 18.137 dB as compared to the best of our previous method. In addition, DHNet achieves a testing time of 25 ms, which satisfies the real-time processing requirement for the BV-1000 scanner. We also conduct retinal layer segmentation experiment on OCT images before and after denoising and show that DHNet can also improve segmentation. CONCLUSIONS: The proposed DHNet can compensate domain shifts between different data sets while significantly improve speckle noise suppression. The HRNet backbone is utilized to carry low- and high-resolution information to recover fidelity images. Domain adaptation is achieved by a hierarchical module through adversarial learning. In addition, DHNet achieved a testing time of 25 ms, which satisfied the real-time processing requirement.

AFENet: Attention Fusion Enhancement Network for Optic Disc Segmentation of Premature Infants.

Retinopathy of prematurity and ischemic brain injury resulting in periventricular white matter damage are the main causes of visual impairment in premature infants. Accurate optic disc (OD) segmentation has important prognostic significance for the auxiliary diagnosis of the above two diseases of premature infants. Because of the complexity and non-uniform illumination and low contrast between background and the target area of the fundus images, the segmentation of OD for infants is challenging and rarely reported in the literature. In this article, to tackle these problems, we propose a novel attention fusion enhancement network (AFENet) for the accurate segmentation of OD in the fundus images of premature infants by fusing adjacent high-level semantic information and multiscale low-level detailed information from different levels based on encoder-decoder network. Specifically, we first design a dual-scale semantic enhancement (DsSE) module between the encoder and the decoder inspired by self-attention mechanism, which can enhance the semantic contextual information for the decoder by reconstructing skip connection. Then, to reduce the semantic gaps between the high-level and low-level features, a multiscale feature fusion (MsFF) module is developed to fuse multiple features of different levels at the top of encoder by using attention mechanism. Finally, the proposed AFENet was evaluated on the fundus images of preterm infants for OD segmentation, which shows that the proposed two modules are both promising. Based on the baseline (Res34UNet), using DsSE or MsFF module alone can increase Dice similarity coefficients by 1.51 and 1.70%, respectively, whereas the integration of the two modules together can increase 2.11%. Compared with other state-of-the-art segmentation methods, the proposed AFENet achieves a high segmentation performance.

Joint Segmentation of Multi-Class Hyper-Reflective Foci in Retinal Optical Coherence Tomography Images.

Hyper-reflective foci (HRF) refers to the spot-shaped, block-shaped areas with characteristics of high local contrast and high reflectivity, which is mostly observed in retinal optical coherence tomography (OCT) images of patients with fundus diseases. HRF mainly appears hard exudates (HE) and microglia (MG) clinically. Accurate segmentation of HE and MG is essential to alleviate the harm in retinal diseases. However, it is still a challenge to segment HE and MG simultaneously due to similar pathological features, various shapes and location distribution, blurred boundaries, and small morphology dimensions. To tackle these problems, in this paper, we propose a novel global information fusion and dual decoder collaboration-based network (GD-Net), which can segment HE and MG in OCT images jointly. Specifically, to suppress the interference of similar pathological features, a novel global information fusion (GIF) module is proposed, which can aggregate the global semantic information efficiently. To further improve the segmentation performance, we design a dual decoder collaborative workspace (DDCW) to comprehensively utilize the semantic correlation between HE and MG while enhancing the mutual influence on them by feedback alternately. To further optimize GD-Net, we explore a joint loss function which integrates pixel-level with image-level. The dataset of this study comes from patients diagnosed with diabetic macular edema at the department of ophthalmology, University Medical Center Groningen, The Netherlands. Experimental results show that our proposed method performs better than other state-of-the-art methods, which suggests the effectiveness of the proposed method and provides research ideas for medical applications.

MF-Net: Multi-Scale Information Fusion Network for CNV Segmentation in Retinal OCT Images.

Choroid neovascularization (CNV) is one of the blinding ophthalmologic diseases. It is mainly caused by new blood vessels growing in choroid and penetrating Bruch's membrane. Accurate segmentation of CNV is essential for ophthalmologists to analyze the condition of the patient and specify treatment plan. Although many deep learning-based methods have achieved promising results in many medical image segmentation tasks, CNV segmentation in retinal optical coherence tomography (OCT) images is still very challenging as the blur boundary of CNV, large morphological differences, speckle noise, and other similar diseases interference. In addition, the lack of pixel-level annotation data is also one of the factors that affect the further improvement of CNV segmentation accuracy. To improve the accuracy of CNV segmentation, a novel multi-scale information fusion network (MF-Net) based on U-Shape architecture is proposed for CNV segmentation in retinal OCT images. A novel multi-scale adaptive-aware deformation module (MAD) is designed and inserted into the top of the encoder path, aiming at guiding the model to focus on multi-scale deformation of the targets, and aggregates the contextual information. Meanwhile, to improve the ability of the network to learn to supplement low-level local high-resolution semantic information to high-level feature maps, a novel semantics-details aggregation module (SDA) between encoder and decoder is proposed. In addition, to leverage unlabeled data to further improve the CNV segmentation, a semi-supervised version of MF-Net is designed based on pseudo-label data augmentation strategy, which can leverage unlabeled data to further improve CNV segmentation accuracy. Finally, comprehensive experiments are conducted to validate the performance of the proposed MF-Net and SemiMF-Net. The experiment results show that both proposed MF-Net and SemiMF-Net outperforms other state-of-the-art algorithms.

Semi-MsST-GAN: A Semi-Supervised Segmentation Method for Corneal Ulcer Segmentation in Slit-Lamp Images.

Corneal ulcer is a common leading cause of corneal blindness. It is difficult to accurately segment corneal ulcers due to the following problems: large differences in the pathological shapes between point-flaky and flaky corneal ulcers, blurred boundary, noise interference, and the lack of sufficient slit-lamp images with ground truth. To address these problems, in this paper, we proposed a novel semi-supervised multi-scale self-transformer generative adversarial network (Semi-MsST-GAN) that can leverage unlabeled images to improve the performance of corneal ulcer segmentation in fluorescein staining of slit-lamp images. Firstly, to improve the performance of segmenting the corneal ulcer regions with complex pathological features, we proposed a novel multi-scale self-transformer network (MsSTNet) as the MsST-GAN generator, which can guide the model to aggregate the low-level weak semantic features with the high-level strong semantic information and adaptively learn the spatial correlation in feature maps. Then, to further improve the segmentation performance by leveraging unlabeled data, the semi-supervised approach based on the proposed MsST-GAN was explored to solve the problem of the lack of slit-lamp images with corresponding ground truth. The proposed Semi-MsST-GAN was comprehensively evaluated on the public SUSTech-SYSU dataset, which contains 354 labeled and 358 unlabeled fluorescein staining slit-lamp images. The results showed that, compared with other state-of-the-art methods, our proposed method achieves better performance with comparable efficiency.

DW-Net: Dynamic Multi-Hierarchical Weighting Segmentation Network for Joint Segmentation of Retina Layers With Choroid Neovascularization.

Choroid neovascularization (CNV) is one of the blinding factors. The early detection and quantitative measurement of CNV are crucial for the establishment of subsequent treatment. Recently, many deep learning-based methods have been proposed for CNV segmentation. However, CNV is difficult to be segmented due to the complex structure of the surrounding retina. In this paper, we propose a novel dynamic multi-hierarchical weighting segmentation network (DW-Net) for the simultaneous segmentation of retinal layers and CNV. Specifically, the proposed network is composed of a residual aggregation encoder path for the selection of informative feature, a multi-hierarchical weighting connection for the fusion of detailed information and abstract information, and a dynamic decoder path. Comprehensive experimental results show that our proposed DW-Net achieves better performance than other state-of-the-art methods.

Value of PTEN and Echocardiography in Predicting the Efficacy of Trimetazidine Combined with Metoprolol in the Treatment of Heart Failure.

OBJECTIVE: To investigate the predictive value of PTEN and echocardiography in the treatment of heart failure with trimetazidine combined with metoprolol. METHODS: A total of 100 patients with coronary heart disease and HF who admitted to our hospital from August 2018 to August 2020 were enrolled into research. All patients received routine treatment according to the guidelines and were treated with trimetazidine and metoprolol for a total course of 6 months. Echocardiographic parameters and PTEN levels were measured at baseline and after treatment. The patients were divided into groups according to the quartile of basic PTEN level, and the total effective rates were compared. The echocardiographic parameters of patients with different prognosis were analyzed. Bivariate correlation analysis was used to evaluate the correlation between PTEN, echocardiography and treatment effect. RESULTS: Compared with that before treatment, the level of PTEN increased significantly after treatment (P < 0.01). According to the quartile of basic PTEN level, the total effective rate of patients with different levels of basic PTEN was was statistically different (P < 0.01). There was a linear correlation between the level of basic PTEN and the treatment effect, and the total effective rate of patients with high level of basic PTEN was higher than that of patients with low level of PTEN. Compared with before treatment, LVEF, SV, E/A and lvfs increased significantly after treatment (P < 0.01). There was a correlation between the basic echocardiographic parameters and the treatment effect of patients. The basic echocardiographic parameters of patients with poor prognosis were worse than those with good prognosis. PTEN expression in patients' serum was only positively correlated with E/A, but not with LVFE, SV and LVFS (P < 0.01). CONCLUSION: PTEN and echocardiographic parameters serve as a good method to evaluate the short-term therapeutic effect of trimetazidine combined with metoprolol in patients with heart failure.

Susceptibility of Dermanyssus gallinae from China to acaricides and functional analysis of glutathione S-transferases associated with beta-cypermethrin resistance.

Dermanyssus gallinae poses a significant threat to poultry production, and the resistance to pyrethroids has been identified worldwide. Periodic monitoring of acaricide resistance in D. gallinae is very important for its control, and molecular mechanism associated with beta-cypermethrin resistance in D. gallinae is not fully clear. Results showed, four field isolates of CBP-1, CBP-2, CBP-5 and CBY-1 from China remained either susceptible or with decreased susceptibility (resistance ratio < 5.0) to phoxim, amitraz, propoxur and carbaryl. Four field isolates of CBP-1, CBP-3, CBY-2 and CBH-1 had developed high or extremely high level of resistance (resistance ratio ≥ 40.0) to beta-cypermethrin or permethrin. Detoxification enzyme activity of GSTs was significantly higher in beta-cypermethrin resistant (RS) than susceptible strain (SS), indicating that GSTs are probably involved in beta-cypermethrin resistance in D. gallinae. The recombinant GSTs (rGST-1, 2, 3) showed a pronounced activity toward the conjugates of 1-chloro-2, 4 dinitrobenzene (CDNB) and glutathione (GSH), with rGST-1 presenting the highest enzymatic activity. Constitutive over-expression of Deg-GST-2 was detected in RS strain, and GSTs genes were all inducible with the treatment of beta-cypermethrin in SS and RS strains. More importantly, knocking down Deg-GST-2 gene expression by RNAi increased the susceptibility of RS strain to beta-cypermethrin. HPLC analysis indicated that rGST-1 protein could metabolize phoxim directly, but rGSTs could not directly metabolize beta-cypermethrin. Our results indicated that some field isolates of D. gallinae from China had developed high level of resistance to pyrethroids, and elevated GSTs activity as well as increased GSTs expression levels were involved in beta-cypermethrin resistance, but the three evaluated GSTs did not play a direct role in the metabolism of beta-cypermethrin.

Paternal nicotine exposure induces hyperactivity in next-generation via down-regulating the expression of DAT.

Many substances in cigarette smoke can induce changes in DNA methylation. Our previous studies have confirmed paternal nicotine exposure causes hyperactivity in the offspring via mmu-miR-15b. The main aim of the present study is to explore the molecular mechanism underlying the cross-generation effects of paternal nicotine exposure more comprehensively. The male C57BL/6 mice were exposed to 2 mg/kg/d nicotine for 5 weeks, and then mated with wild-type females. The offspring male mice were subjected to behavioral tests at 8 weeks after birth. The results suggested that, paternal nicotine exposure led to hyperactivity in the offspring. An analysis of the changes in DNA methylation revealed that nicotine exposure induced a rise in the total DNA methylation level of Dat in murine spermatozoa, and the hyper-methylation could imprint in the brains of the offspring mice. Then these epigenetic modifications reduced the expression of DAT in the brain of the offspring, resulting in a rise in the level of extracellular dopamine. The activation of D2 receptors caused the dephosphorylation of AKT, which led to increased activation of GSK3α/ß, and ultimately caused hyperactivity in the offspring mice. Further, in wild-type mice, injection of DAT inhibitors simulated this hyperactive phenotype, while the injection of D2s inhibitors reversed the hyperactivity of the offspring caused by paternal nicotine exposure. In conclusion, all results indicated that paternal nicotine exposure could induce hyperactivity in the offspring via the hyper-methylation of Dat. Consequently, Dat may be one of the genes that mediate the cross-generation effects of nicotine besides mmu-mmiR-15b.

Darkness increases the population growth rate of the poultry red mite Dermanyssus gallinae.

BACKGROUND: The poultry red mite (PRM), Dermanyssus gallinae, is one of the most economically deleterious ectoparasites affecting egg-laying hens worldwide. It may be possible to control D. gallinae populations by manipulating lighting regimes within poultry units. However, no studies have clearly shown the effects of darkness on the population growth rate of D. gallinae. METHODS: The effect of darkness on the population growth rate of D. gallinae was investigated, together with the first description of the molecular identity of the mite from China. Mite variables under two lighting regimens (1:23 h L:D and 12:12 h L:D) were compared, including number of mites and eggs, survival and feeding rates, engorgement, oviposition, hatchability and the life-cycle of D. gallinae. RESULTS: The results showed that the number of mites (13,763 ± 956) and eggs (5424 ± 317) in the rearing system with prolonged darkness of 1:23 h L:D at 4th week were 2.4- and 3.6-fold higher than those under a conventional lighting regimen of 12:12 h L:D, respectively. The feeding rates of mites under prolonged darkness ranged from 36.7 ± 1.1% to 52.0 ± 7.0%, which were significantly higher than those under conventional lighting regimen (ranging from 22.6 ± 1.9% to 37.3 ± 1.6%). The mean weight of engorged females (0.26 ± 0.01 mg) and the mean number of eggs per female (on average 5.87 ± 0.36) under prolonged darkness were significantly higher than those under conventional lighting regimen (0.22 ± 0.01 mg and 3.62 ± 0.31, respectively). However, the survival rate ranging from 98.07 ± 0.10% to 98.93 ± 0.19%, hatchability of 97.93 ± 0.01% and the life-cycle of D. gallinae (9 days) was not affected by the lighting period. CONCLUSIONS: Our findings demonstrated that prolonged darkness significantly promoted the proliferation levels of D. gallinae, resulting in increased number of mites and eggs in the rearing system. The promoted population growth of D. gallinae was found to be related to the increased feeding rate, engorgement level and oviposition level of mites under prolonged darkness. The egg hatchability, the survival rates and the duration of life-cycle of D. gallinae were not affected by the light regimes.