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BACKGROUND: In radiography procedures, radiographers' suboptimal positioning and exposure parameter settings may necessitate image retakes, subjecting patients to unnecessary ionizing radiation exposure. Reducing retakes is crucial to minimize patient X-ray exposure and conserve medical resources. OBJECTIVE: We propose a Digital Radiography (DR) Pre-imaging All-round Assistant (PIAA) that leverages Artificial Intelligence (AI) technology to enhance traditional DR. METHODS: PIAA consists of an RGB-Depth (RGB-D) multi-camera array, an embedded computing platform, and multiple software components. It features an Adaptive RGB-D Image Acquisition (ARDIA) module that automatically selects the appropriate RGB camera based on the distance between the cameras and patients. It includes a 2.5D Selective Skeletal Keypoints Estimation (2.5D-SSKE) module that fuses depth information with 2D keypoints to estimate the pose of target body parts. Thirdly, it also uses a Domain expertise (DE) embedded Full-body Exposure Parameter Estimation (DFEPE) module that combines 2.5D-SSKE and DE to accurately estimate parameters for full-body DR views. RESULTS: Optimizes DR workflow, significantly enhancing operational efficiency. The average time required for positioning patients and preparing exposure parameters was reduced from 73 seconds to 8 seconds. CONCLUSIONS: PIAA shows significant promise for extension to full-body examinations.

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Flower endophytic fungi play a major role in plant reproduction, stress resistance, and growth and development. However, little is known about how artificial cultivation affects the endophytic fungal community found in the tepals of rare horticultural plants. In this research, we used high-throughput sequencing technology combined with bioinformatics analysis to reveal the endophytic fungal community of tepals in Lirianthe delavayi and the effects of artificial cultivation on the community composition and function of these plants, using tepals of L. delavayi from wild habitat, cultivated campus habitat, and cultivated field habitat as research objects. The results showed that the variety of endophytic fungi in the tepals of L. delavayi was abundant, with a total of 907 Amplicon sequencing variants (ASVs) obtained from all the samples, which were further classified into 4 phyla, 23 classes, 51 orders, 97 families, 156 genera, and 214 species. We also found that artificial cultivation had a significant impact on the community composition of endophytic fungi. Although there was no significant difference at the phylum level, with Ascomycota and Basidiomycota being the main phyla, there were significant differences in dominant and unique genera. Artificial cultivation has led to the addition of new pathogenic fungal genera, such as Phaeosphaeria, Botryosphaeria, and Paraconiothyrium, increasing the risk of disease in L. delavayi. In addition, the abundance of the endophytic fungus Rhodotorula, which is typical in plant reproductive organs, decreased. Artificial cultivation also altered the metabolic pathways of endophytic fungi, decreasing their ability to resist pests and diseases and reducing their ability to reproduce. A comparison of endophytic fungi in tepals and leaves revealed significant differences in community composition and changes in the endophytic diversity caused by artificial cultivation. To summarize, our results indicate that endophytic fungi in the tepals of L. delavayi mainly consist of pathogenic and saprophytic fungi. Simultaneously, artificial cultivation introduces a great number of pathogenic fungi that alter the metabolic pathways associated with plant resistance to disease and pests, as well as reproduction, which can increase the risk of plant disease and reduce plant reproductive capacity. Our study provides an important reference for the conservation and breeding of rare horticultural plants.

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The widespread use of silica nanoparticles (SiNPs) has increased the risk of human exposure, which raised concerns about their adverse effects on human health, especially the reproductive system. Previous studies have shown that SiNPs could cause damage to reproductive organs, but the specific mechanism is still unclear. In this study, to investigate the underlying mechanism of male reproductive toxicity induced by SiNPs, 40 male mice at the age of 8 weeks were divided into two groups and then intraperitoneally injected with vehicle control or 10 mg/kg SiNPs per day for one week. The results showed that SiNPs could damage testicular structure, perturb spermatogenesis and reduce serum testosterone levels, leading to a decrease in sperm quality and quantity. In addition, the ROS level in the testis of exposed mice was significantly increased, followed by imbalance of the oxidative redox status. Further study revealed that exposure to SiNPs led to cell cycle arrest and apoptosis, as shown by downregulation of the expression of positive cell cycle regulators and the activation of TNF-α/TNFR Ⅰ-mediated apoptotic pathway. The results demonstrated that SiNPs could cause testicles injure via inducing oxidative stress and DNA damage which led to cell cycle arrest and apoptosis, and thereby resulting in spermatogenic dysfunction.

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Silica nanoparticles (SiNPs), one of the most produced nanoparticles (NPs) in the world, are used in all aspects of life. The increased application of SiNPs, especially in medicine, has raised considerable concern regarding their toxicological impact. Previous studies have shown that SiNPs can pass through the reproductive barrier and cause reproductive organ dysfunction by destroying Sertoli cells, Leydig cells, and germ cells. However, little is known about the mechanism of SiNPs-induced reproductive toxicity. In the present study, 5-week-old male mice were intraperitoneally administered SiNPs per day for 1 week at a dose of 0.2 mg per mouse. The results showed that SiNPs could cause damage to the structure of the testis and the epididymis and change the reproductive organ coefficients, leading to decreases of 56.1% and 55.3% in the rates of sperm concentration and motility and an increase of 168.8% in the rate of sperm abnormality. Moreover, the serum testosterone level obviously decreased from 18.77 to 5.23 µg/ml after exposure, and the transcription statuses of some key genes involved in the synthesis and transport of testosterone in the testis were also affected. Additional experiments showed that SiNPs exposure during puberty induced oxidative stress and an inflammatory response, as shown by the changed activity of superoxide dismutase (SOD), increased contents of malondialdehyde (MDA), and excess expression of proinflammatory factors, including TNF-α and IL-1ß. Furthermore, the administration of SiNPs caused DNA damage and cell apoptosis, which were presented by the increased apoptotic cells in the sections of testis and epididymis and activation of the TNF-α/TNFR I-mediated pro-apoptotic pathway. In conclusion, these results indicate that SiNPs exposure during puberty significantly damaged the structure and function of the testis and epididymis by inducing oxidative stress and cell apoptosis. This study provides novel insight into SiNPs-induced reproductive toxicity during puberty, which warrants a more careful assessment of SiNPs before their application in juvenile supplies.

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Automatic and accurate esophageal lesion classification and segmentation is of great significance to clinically estimate the lesion statuses of the esophageal diseases and make suitable diagnostic schemes. Due to individual variations and visual similarities of lesions in shapes, colors, and textures, current clinical methods remain subject to potential high-risk and time-consumption issues. In this paper, we propose an Esophageal Lesion Network (ELNet) for automatic esophageal lesion classification and segmentation using deep convolutional neural networks (DCNNs). The underlying method automatically integrates dual-view contextual lesion information to extract global features and local features for esophageal lesion classification and lesion-specific segmentation network is proposed for automatic esophageal lesion annotation at pixel level. For the established clinical large-scale database of 1051 white-light endoscopic images, ten-fold cross-validation is used in method validation. Experiment results show that the proposed framework achieves classification with sensitivity of 0.9034, specificity of 0.9718, and accuracy of 0.9628, and the segmentation with sensitivity of 0.8018, specificity of 0.9655, and accuracy of 0.9462. All of these indicate that our method enables an efficient, accurate, and reliable esophageal lesion diagnosis in clinics.

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BACKGROUND: Accurate segmentation of brain tissues from magnetic resonance imaging (MRI) is of significant importance in clinical applications and neuroscience research. Accurate segmentation is challenging due to the tissue heterogeneity, which is caused by noise, bias filed and partial volume effects. METHODS: To overcome this limitation, this paper presents a novel algorithm for brain tissue segmentation based on supervoxel and graph filter. Firstly, an effective supervoxel method is employed to generate effective supervoxels for the 3D MRI image. Secondly, the supervoxels are classified into different types of tissues based on filtering of graph signals. RESULTS: The performance is evaluated on the BrainWeb 18 dataset and the Internet Brain Segmentation Repository (IBSR) 18 dataset. The proposed method achieves mean dice similarity coefficient (DSC) of 0.94, 0.92 and 0.90 for the segmentation of white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) for BrainWeb 18 dataset, and mean DSC of 0.85, 0.87 and 0.57 for the segmentation of WM, GM and CSF for IBSR18 dataset. CONCLUSIONS: The proposed approach can well discriminate different types of brain tissues from the brain MRI image, which has high potential to be applied for clinical applications.

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