Photo-functionalized TiO2 nanotubes decorated with multifunctional Ag nanoparticles for enhanced vascular biocompatibility

Titanium dioxide (TiO2) has a long history of application in blood contact materials, but it often suffers from insufficient anticoagulant properties. Recently, we have revealed the photocatalytic effect of TiO2 also induces anticoagulant properties. However, for long-term vascular implant devices such as vascular stents, besides anticoagulation, also anti-inflammatory, anti-hyperplastic properties, and the ability to support endothelial repair, are desired. To meet these requirements, here, we immobilized silver nanoparticles (AgNPs) on the surface of TiO2 nanotubes (TiO2-NTs) to obtain a composite material with enhanced photo-induced anticoagulant property and improvement of the other requested properties. The photo-functionalized TiO2-NTs showed protein-fouling resistance, causing the anticoagulant property and the ability to suppress cell adhesion. The immobilized AgNPs increased the photocatalytic activity of TiO2-NTs to enhances its photo-induced anticoagulant property. The AgNP density was optimized to endow the TiO2-NTs with anti-inflammatory property, a strong inhibitory effect on smooth muscle cells (SMCs), and low toxicity to endothelial cells (ECs). The in vivo test indicated that the photofunctionalized composite material achieved outstanding biocompatibility in vasculature via the synergy of photo-functionalized TiO2-NTs and the multifunctional AgNPs, and therefore has enormous potential in the field of cardiovascular implant devices. Our research could be a useful reference for further designing of multifunctional TiO2 materials with high vascular biocompatibility

The pattern of late gadolinium enhancement by cardiac MRI in fulminant myocarditis and its prognostic implication: a two-year follow-up study

BackgroundMyocardial fibrosis, as quantified by late gadolinium enhancement (LGE) in cardiac magnetic resonance (CMR), provides valuable prognostic information for patients with myocarditis. However, due to the low incidence rate of fulminant myocarditis (FM) and accordingly small sample size, the knowledge about the role of LGE to patients with FM is limited.Methods and resultsA total of 44 adults with viral-FM receiving the Chinese treating regimen were included in this retrospective study. They were divided into the low LGE group and the high LGE group according to the ratio of LGE to left ventricular mass (LGE mass%). CMR exams and LGE were performed after hemodynamic assistance at discharge in all patients with FM. Routine echocardiography parameters and global longitudinal strain (GLS) at discharge and at 2-year follow-up were obtained and then compared. Both left ventricular ejection fraction (LVEF) and GLS showed no significant difference in both groups at discharge, whereas significant differences were observed at 2-year follow-up between two groups. Moreover, there were significant improvements of LVEF and GLS in the low LGE group, but not in the high LGE group during the 2-year period. Furthermore, LGE mass% was negatively correlated with GLS and LVEF.ConclusionsThere were two distinct forms of LGE presentation in patients with FM. Moreover, the cardiac function of patients with low LGE was significantly better than those with high LGE at 2-year follow-up. LGE mass% at discharge provided significant prognosis information about cardiac function of patients with FM

Identification of microtubule-associated biomarkers in diffuse large B-cell lymphoma and prognosis prediction

Background: Diffuse large B-cell lymphoma (DLBCL) is a genetically heterogeneous disease with a complicated prognosis. Even though various prognostic evaluations have been applied currently, they usually only use the clinical factors that overlook the molecular underlying DLBCL progression. Therefore, more accurate prognostic assessment needs further exploration. In the present study, we constructed a novel prognostic model based on microtubule associated genes (MAGs).Methods: A total of 33 normal controls and 1360 DLBCL samples containing gene-expression from the Gene Expression Omnibus (GEO) database were included. Subsequently, the univariate Cox, the least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis were used to select the best prognosis related genes into the MAGs model. To validate the model, Kaplan-Meier curve, and nomogram were analyzed.Results: A risk score model based on fourteen candidate MAGs (CCDC78, CD300LG, CTAG2, DYNLL2, MAPKAPK2, MREG, NME8, PGK2, RALBP1, SIGLEC1, SLC1A1, SLC39A12, TMEM63A, and WRAP73) was established. The K-M curve presented that the high-risk patients had a significantly inferior overall survival (OS) time compared to low-risk patients in training and validation datasets. Furthermore, knocking-out TMEM63A, a key gene belonging to the MAGs model, inhibited cell proliferation noticeably.Conclusion: The novel MAGs prognostic model has a well predictive capability, which may as a supplement for the current assessments. Furthermore, candidate TMEM63A gene has therapeutic target potentially in DLBCL

Morphological segmentation analysis and texture-based support vector machines classification on mice liver fibrosis microscopic images

Background To reduce the intensity of the work of doctors, pre-classification work needs to be issued. In this paper, a novel and related liver microscopic image classification analysis method is proposed. Objective For quantitative analysis, segmentation is carried out to extract the quantitative information of special organisms in the image for further diagnosis, lesion localization, learning and treating anatomical abnormalities and computer-guided surgery. Methods in the current work, entropy based features of microscopic fibrosis mice’ liver images were analyzed using fuzzy c-cluster, k-means and watershed algorithms based on distance transformations and gradient. A morphological segmentation based on a local threshold was deployed to determine the fibrosis areas of images. Results the segmented target region using the proposed method achieved high effective microscopy fibrosis images segmenting of mice liver in terms of the running time, dice ratio and precision. The image classification experiments were conducted using Gray Level Co-occurrence Matrix (GLCM). The best classification model derived from the established characteristics was GLCM which performed the highest accuracy of classification using a developed Support Vector Machine (SVM). The training model using 11 features was found to be as accurate when only trained by 8 GLCMs. Conclusion The research illustrated the proposed method is a new feasible research approach for microscopy mice liver image segmentation and classification using intelligent image analysis techniques. It is also reported that the average computational time of the proposed approach was only 2.335 seconds, which outperformed other segmentation algorithms with 0.8125 dice ratio and 0.5253 precision

How Digital Technologies are Transforming the Grocery Retailers? A Case Study of Consumer Repurchase Behavior with Instacart Data

The surge of grocery delivery has changed the grocery retailing industry. Especially due to the current severe situation of COVID-19, people cannot do regular grocery shopping. More and more consumers are turning to the companies in the sharing economy for cost-eective access to goods and services including groceries. In this paper, I use Instacart, an online food-delivery company that does sameday delivery, as an example. I mainly discuss the impact of Instacart on grocery retailers. Specically, I will develop machine learning to build a model to show how to understand the consumer behavior better. In addition, I show some possible solutions with the aid of big data for grocery retailers to problems like balancing demands and supplies

A Transfer Residual Neural Network Based on ResNet-50 for Detection of Steel Surface Defects

With the increasing popularity of deep learning, enterprises are replacing traditional inefficient and non-robust defect detection methods with intelligent recognition technology. This paper utilizes TL (transfer learning) to enhance the model’s recognition performance by integrating the Adam optimizer and a learning rate decay strategy. By comparing the TL-ResNet50 model with other classic CNN models (ResNet50, VGG19, and AlexNet), the superiority of the model used in this paper was fully demonstrated. To address the current lack of understanding regarding the internal mechanisms of CNN models, we employed an interpretable algorithm to analyze pre-trained models and visualize the learned semantic features of defects across various models. This further confirms the efficacy and reliability of CNN models in accurately recognizing different types of defects. Results showed that the TL-ResNet50 model achieved an overall accuracy of 99.4% on the testing set and demonstrated good identification ability for defect features

Identification of NOL-Ring Composite Materials’ Damage Mechanism Based on the STOA-VMD Algorithm

This research utilized the sooty tern optimization algorithm–variational mode decomposition (STOA-VMD) optimization algorithm to extract the acoustic emission (AE) signal associated with damage in fiber-reinforced composite materials. The effectiveness of this optimization algorithm was validated through a tensile experiment on glass fiber/epoxy NOL-ring specimens. To solve the problems of a high degree of aliasing, high randomness, and a poor robustness of AE data of NOL-ring tensile damage, the signal reconstruction method of optimized variational mode decomposition (VMD) was first used to reconstruct the damage signal and the parameters of VMD were optimized by the sooty tern optimization algorithm. The optimal decomposition mode number K and penalty coefficient α were introduced to improve the accuracy of adaptive decomposition. Second, a typical single damage signal feature was selected to construct the damage signal feature sample set and a recognition algorithm was used to extract the feature of the AE signal of the glass fiber/epoxy NOL-ring breaking experiment to evaluate the effectiveness of the damage mechanism recognition. The results showed that the recognition rates of the algorithm in matrix cracking, fiber fracture, and delamination damage were 94.59%, 94.26%, and 96.45%, respectively. The damage process of the NOL-ring was characterized and the findings indicated that it was highly efficient in the feature extraction and recognition of polymer composite damage signals

Electrospun Functional Materials toward Food Packaging Applications: A Review

Electrospinning is an effective and versatile method to prepare continuous polymer nanofibers and nonwovens that exhibit excellent properties such as high molecular orientation, high porosity and large specific surface area. Benefitting from these outstanding and intriguing features, electrospun nanofibers have been employed as a promising candidate for the fabrication of food packaging materials. Actually, the electrospun nanofibers used in food packaging must possess biocompatibility and low toxicity. In addition, in order to maintain the quality of food and extend its shelf life, food packaging materials also need to have certain functionality. Herein, in this timely review, functional materials produced from electrospinning toward food packaging are highlighted. At first, various strategies for the preparation of polymer electrospun fiber are introduced, then the characteristics of different packaging films and their successful applications in food packaging are summarized, including degradable materials, superhydrophobic materials, edible materials, antibacterial materials and high barrier materials. Finally, the future perspective and key challenges of polymer electrospun nanofibers for food packaging are also discussed. Hopefully, this review would provide a fundamental insight into the development of electrospun functional materials with high performance for food packaging

Flexible and Highly Sensitive Hydrogen Sensor Based on Organic Nanofibers Decorated by Pd Nanoparticles

A highly sensitive and flexible hydrogen sensor based on organic nanofibers decorated by Pd nanoparticles (NPs) was designed and fabricated for low-concentration hydrogen detection. Pd NPs were deposited on organic nanofiber materials by DC magnetron sputtering. The temperature dependence of the sensitivity at 25 ppm H2 was characterized and discussed, and the maximum response of the sensor increased linearly with increasing measurement temperature. Performances of the hydrogen sensor were investigated with hydrogen concentration ranging from 5 ppm to 50 ppm. This sensor exhibits high sensitivity, with the response up to 6.55% for H2 as low as 5 ppm, and the output response of the hydrogen sensor increased linearly with the square root of hydrogen concentration. A cycling test between pure nitrogen and 25 ppm hydrogen concentration was performed, and the hydrogen sensor exhibited excellent consistency