Qingdao University

About: Qingdao University is a education organization based out in Qingdao, China. It is known for research contribution in the topics: Cancer & Apoptosis. The organization has 35675 authors who have published 27275 publications receiving 374908 citations. The organization is also known as: Qīngdǎo Dàxué.

Fuzzy System Based Medical Image Processing for Brain Disease Prediction

Abstract: The present work aims to explore the performance of fuzzy system-based medical image processing for brain disease prediction. The imaging mechanism of NMR (Nuclear Magnetic Resonance) and the complexity of human brain tissues cause the brain MRI (Magnetic Resonance Imaging) images to present varying degrees of noise, weak boundaries, and artifacts. Hence, improvements are made over the fuzzy clustering algorithm. While ensuring the model safety performance, a brain image processing and brain disease diagnosis prediction model is designed based on improved fuzzy clustering and HPU-Net (Hybrid Pyramid U-Net Model for Brain Tumor Segmentation). Brain MRI images collected from the Department of Brain Oncology, XX Hospital, are employed in simulation experiments to validate the performance of the proposed algorithm. Moreover, CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), FCM (Fuzzy C-Means), LDCFCM (Local Density Clustering Fuzzy C-Means), and AFCM (Adaptive Fuzzy C-Means) are included in simulation experiments for performance comparison. Results demonstrated that the proposed algorithm has more nodes, lower energy consumption, and more stable changes than other models under the same conditions. Regarding the overall network performance, the proposed algorithm can complete the data transmission tasks the fastest, basically maintaining at about 4.5 seconds on average, which performs remarkably better than other models. A further prediction performance analysis reveals that the proposed algorithm provides the highest prediction accuracy for the Whole Tumor under the DSC coefficient, reaching 0.936. Besides, its Jaccard coefficient is 0.845, proving its superior segmentation accuracy over other models. To sum up, the proposed algorithm can provide higher accuracy while ensuring energy consumption, a more apparent denoising effect, and the best segmentation and recognition effect than other models, which can provide an experimental basis for the feature recognition and predictive diagnosis of brain images.

Genome-wide serum microRNA expression profiling identifies serum biomarkers for Alzheimer's disease.

Abstract: Recent findings that human serum contains stably expressed microRNAs (miRNAs) have revealed a great potential of serum miRNA signature as disease fingerprints to diagnosis. Here we used genome-wide serum miRNA expression analysis to investigate the value of serum miRNAs as biomarkers for the diagnosis of Alzheimer's disease (AD). Illumina HiSeq 2000 sequencing followed by individual quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) assays was used to test the difference in levels of serum miRNAs between 50 AD patients and 50 controls in the screening stages. The detected serum miRNAs then were validated by qRT-PCR in 158 patients and 155 controls. MiR-98-5p, miR-885-5p, miR-483-3p, miR-342-3p, miR-191-5p, and miR-let-7d-5p displayed significantly different expression levels in AD patients compared with controls. Among the 6 miRNAs, miR-342-3p has the best sensitivity (81.5%) and specificity (70.1%) and was correlated to Mini-Mental State Examination score. This study identified six serum miRNAs that distinguish AD patients from healthy controls with high sensitivity and specificity. Serum miRNA panel (or miR-342-3p alone) may serve as a novel, noninvasive biomarker for AD.

Attribute-Based Encryption With Parallel Outsourced Decryption for Edge Intelligent IoV

Abstract: Edge intelligence is an emerging concept referring to processes in which data are collected and analyzed and insights are delivered close to where the data are captured in a network using a selection of advanced intelligent technologies. As a promising solution to solve the problems of insufficient computing capacity and transmission latency, the edge intelligence-empowered Internet of Vehicles (IoV) is being widely investigated in both academia and industry. However, data sharing security in edge intelligent IoV is a challenge that should be solved with priority. Although attribute-based encryption (ABE) is capable of addressing this challenge, many time-consuming modular exponential operations and bilinear pair operations as well as serial computing cause ABE to have a slow decryption speed. Consequently, it cannot address the response time requirement of edge intelligent IoV. Given this problem, an ABE model with parallel outsourced decryption for edge intelligent IoV, called ABEM-POD , is proposed. It includes a generic parallel outsourced decryption method for ABE based on Spark and MapReduce. This method is applicable to all ABE schemes with a tree access structure and can be applied to edge intelligent IoV. Any ABE scheme based on the proposed model not only supports parallel outsourced decryption but also has the same security as the original scheme. In this paper, ABEM-POD has been applied to three representative ABE schemes, and the experiments show that the proposed ABEM-POD is efficient and easy to use. This approach can significantly improve the speed of outsourced decryption to address the response time requirement for edge intelligent IoV.

Long term effects of the implantation of Wharton’s jelly-derived mesenchymal stem cells from the umbilical cord for newly-onset type 1 diabetes mellitus

Abstract: Type 1 diabetes mellitus (T1DM) is an autoimmune disorder resulted from T cell-mediated destruction of pancreatic β-cells, how to regenerate β-cells and prevent the autoimmune destruction of remnant and neogenetic β-cells is a tough problem. Immunomodulatory propertity of mesenchymal stem cell make it illuminated to overcome it. We assessed the long-term effects of the implantation of Wharton's jelly-derived mesenchymal stem cells (WJ-MSCs) from the umbilical cord for Newly-onset T1DM. Twenty-nine patients with newly onset T1DM were randomly divided into two groups, patients in group I were treated with WJ-MSCs and patients in group II were treated with normal saline based on insulin intensive therapy. Patients were followed-up after the operation at monthly intervals for the first 3 months and thereafter every 3 months for the next 21 months, the occurrence of any side effects and results of laboratory examinations were evaluated. There were no reported acute or chronic side effects in group I compared with group II, both the HbA1c and C peptide in group I patients were significantly better than either pretherapy values or group II patients during the follow-up period. These data suggested that the implantation of WJ-MSCs for the treatment of newly-onset T1DM is safe and effective. This therapy can restore the function of islet β cells in a longer time, although precise mechanisms are unknown, the implantation of WJ-MSCs is expected to be an effective strategy for treatment of type1 diabetes.