Exploring atherosclerosis imaging with contrast-enhanced MRI using PEGylated ultrasmall iron oxide nanoparticles

Plaque rupture is a critical concern due to its potential for severe outcomes such as cerebral infarction and myocardial infarction, underscoring the urgency of noninvasive early diagnosis. Magnetic resonance imaging (MRI) has gained prominence in plaque imaging, leveraging its noninvasiveness, high spatial resolution, and lack of ionizing radiation. Ultrasmall iron oxides, when modified with polyethylene glycol, exhibit prolonged blood circulation and passive targeting toward plaque sites, rendering them conducive for MRI. In this study, we synthesized ultrasmall iron oxide nanoparticles of approximately 3 nm via high-temperature thermal decomposition. Subsequent surface modification facilitated the creation of a dual-modality magnetic resonance/fluorescence probe. Upon intravenous administration of the probes, MRI assessment of atherosclerotic plaques and diagnostic evaluation were conducted. The application of Flash-3D sequence imaging revealed vascular constriction at lesion sites, accompanied by a gradual signal amplification postprobe injection. T1-weighted imaging of the carotid artery unveiled a progressive signal ratio increase between plaques and controls within 72 h post-administration. Fluorescence imaging of isolated carotid arteries exhibited incremental lesion-to-control signal ratios. Additionally, T1 imaging of the aorta demonstrated an evolving signal enhancement over 48 h. Therefore, the ultrasmall iron oxide nanoparticles hold immense promise for early and noninvasive diagnosis of plaques, providing an avenue for dynamic evaluation over an extended time frame

Biosynthetic Nanobubble-Mediated CRISPR/Cas9 Gene Editing of Cdh2 Inhibits Breast Cancer Metastasis

The epithelial-mesenchymal transition (EMT), a process in which epithelial cells undergo a series of biochemical changes to acquire a mesenchymal phenotype, has been linked to tumor metastasis. Here, we present a novel strategy for knocking out the EMT-related Cdh2 gene, which encodes N-cadherin through CRISPR/Cas9-mediated gene editing by an ultrasound combined with biosynthetic nanobubbles (Gas Vesicles, GVs). Polyethyleneimine were employed as a gene delivery vector to deliver sgRNA into 4T1 cells that stably express the Cas9 protein, resulting in the stable Cdh2 gene- knockout cell lines. The Western blotting assay confirmed the absence of an N-cadherin protein in these Cdh2 gene-knockout 4T1 cell lines. Significantly reduced tumor cell migration was observed in the Cdh2 gene-knockout 4T1 cells in comparison with the wild-type cells. Our study demonstrated that an ultrasound combined with GVs could effectively mediate CRISPR/Cas9 gene editing of a Cdh2 gene to inhibit tumor invasion and metastasis

Earth gravity field solution with combining CHAMP and GRACE data

Satellite gravity data fusion with multi-type and huge-amount is one of the hot topics in physical geodesy. After a brief review of dynamic approach, the CHAMP-only and GRACE-only gravity fields by using HL-SST and LL-SST data from 2003 to 2009 are recovered respectively. An combination strategy of CHAMP and GRACE data by using Helmert variance component estimation (VCE) is proposed based on normal equation level fusion. Three gravity field models with 150° and order by CHAMP-only data, GRACE-only data and combining CHAMP and GRACE data from 2003 to 2009 are recovered. The comparisons between our recovered models and those latest released models were performed. The external accuracy validations using marine gravity anomalies from DTU13 products and height anomalies from GPS/leveling data are also conducted in this paper. The results show that long-term CHAMP data do contribute to the accuracy improvement of gravity field solution. The accuracy of the combined model using CHAMP and GRACE data is better than those of the individuals and comparative to the models published by international groups

An Efficient Retrieval System Framework for Fabrics Based on Fine-Grained Similarity

In the context of “double carbon”, as a traditional high energy consumption industry, the textile industry is facing the severe challenges of energy saving and emission reduction. To improve production efficiency in the textile industry, we propose the use of content-based image retrieval technology to shorten the fabric production cycle. However, fabric retrieval has high requirements for results, which makes it difficult for common retrieval methods to be directly applied to fabric retrieval. This paper presents a novel method for fabric image retrieval. Firstly, we define a fine-grained similarity to measure the similarity between two fabric images. Then, a convolutional neural network with a compact structure and cross-domain connections is designed to narrow the gap between fabric images and similarities. To overcome the problems of probabilistic missing and difficult training in classical hashing, we introduce a variational network module and structural module into the hashing model, which is called DVSH. We employ list-wise learning to perform similarity embedding. The experimental results demonstrate the superiority and efficiency of the proposed hashing model, DVSH

Non-metallic gold nanoclusters for oxygen activation and aerobic oxidation

Display omitte

Non-metallic gold nanoclusters for oxygen activation and aerobic oxidation

Display omitte

Optimization and Coordination of the Fresh Agricultural Product Supply Chain Considering the Freshness-Keeping Effort and Information Sharing

To solve freshness-keeping problems and analyse a retailer’s information sharing strategies in the fresh agricultural product supply chain (FAPSC), often confronted with challenges in keeping agri-products fresh in an uncertain market, we study an FAPSC via a decentralized mode in which the supplier or retailer exerts the freshness-keeping effort while the retailer decides its information sharing strategies regarding private demand forecasting. We consider a contract coordination mode including three incentive contracts, cost-sharing (cs), revenue-sharing (re) and revenue-and-cost-sharing (rc), to facilitate supply chain coordination. The results show that, as opposed to the case where the supplier takes on the freshness-keeping effort, the optimal freshness-keeping effort level, wholesale price and retail price are not only affected by the retailer’s information sharing strategy but also the freshness-keeping efficiency as the retailer exerts the freshness-keeping effort. Regarding the information sharing strategy, when the freshness-keeping effort is undertaken by the retailer, sharing information sometimes benefits the supplier; however, information sharing is never preferable for the retailer. Consequently, it is necessary to explore the supply chain coordination mode via effective incentive contracts which can improve the supplier and retailer’s profit. We also numerically analyze the effects of freshness-keeping efficiency on equilibrium decisions and expected profits in the decentralized mode, and the effects of the three contract parameters on the expected profits in equilibrium in the coordination mode

Yarn-Dyed Fabric Defect Detection Based On Autocorrelation Function And GLCM

In this study, a new detection algorithm for yarn-dyed fabric defect based on autocorrelation function and grey level co-occurrence matrix (GLCM) is put forward. First, autocorrelation function is used to determine the pattern period of yarn-dyed fabric and according to this, the size of detection window can be obtained. Second, GLCMs are calculated with the specified parameters to characterise the original image. Third, Euclidean distances of GLCMs between being detected images and template image, which is selected from the defect-free fabric, are computed and then the threshold value is given to realise the defect detection. Experimental results show that the algorithm proposed in this study can achieve accurate detection of common defects of yarn-dyed fabric, such as the wrong weft, weft crackiness, stretched warp, oil stain and holes

Infections Caused by Extended-Spectrum β-Lactamase Producing Escherichia Coli in Systemic Lupus Erythematosus Patients: Prevalence, Risk Factors, and Predictive Model

Objective. To investigate the prevalence and risk factors of infections caused by Extended-Spectrum β-Lactamase (ESBL) producing Escherichia coli (E. coli) in systemic lupus erythematosus (SLE) patients and develop a predictive model. Methods. Three hundred and eighty-four consecutive SLE patients with E. coli infection were enrolled in this retrospective case control study from January 2012 to December 2017. Prevalence and antimicrobial susceptibility pattern of ESBL producing E. coli were analyzed. Multivariate analysis was performed to determine the risk factors. Sensitivity and specificity were obtained at various point cutoffs and area under the receiver operator characteristic curve (AuROC) was determined to confirm the prediction power of the model. Results. Of the total 384 E. coli strains tested, 212 (55.2%) produced ESBL. The majority of these isolates were from urine (44.3%). Carbapenems (>80%) and amikacin (89.6%) had good activity against ESBL producing E. coli. Eleven variables were identified as independent risk factors for ESBL producing E. coli infection including Enterobacteriaceae colonization or infection in preceding year (OR=8.15, 95%CI 5.12–21.71), daily prednisone dose > 30mg (OR=5.48, 95%CI 3.12–13.72), low C3 levels (OR=2.17, 95%CI 1.62–6.71), nosocomial acquired infection (OR=4.12, 95%CI 1.98–8.85), etc. The model developed to predict ESBL producing E. coli infection was effective, with the AuROC of 0.840 (95% CI 0.799-0.876). Conclusions. The prevalence of ESBL producing E. coli was increasing with high antibiotics resistance in patients with SLE. The model revealed excellent predictive performance and exhibited a good discrimination