Activation of Neural Modeling-Related Genes in the Heart of Mice after Gamma Irradiation

Retracted: Activation of Neural Modeling-Related Genes in the Heart of Mice after Gamma Irradiation

[This retracts the article DOI: 10.1155/2021/8522417.].

easyMF: A Web Platform for Matrix Factorization-Based Gene Discovery from Large-scale Transcriptome Data

With the development of high-throughput experimental technologies, large-scale RNA sequencing (RNA-Seq) data have been and continue to be produced, but have led to challenges in extracting relevant biological knowledge hidden in the produced high-dimensional gene expression matrices. Here, we develop easyMF ( https://github.com/cma2015/easyMF ), a web platform that can facilitate functional gene discovery from large-scale transcriptome data using matrix factorization (MF) algorithms. Compared with existing MF-based software packages, easyMF exhibits several promising features, such as greater functionality, flexibility and ease of use. The easyMF platform is equipped using the Big-Data-supported Galaxy system with user-friendly graphic user interfaces, allowing users with little programming experience to streamline transcriptome analysis from raw reads to gene expression, carry out multiple-scenario MF analysis, and perform multiple-way MF-based gene discovery. easyMF is also powered with the advanced packing technology to enhance ease of use under different operating systems and computational environments. We illustrated the application of easyMF for seed gene discovery from temporal, spatial, and integrated RNA-Seq datasets of maize (Zea mays L.), resulting in the identification of 3,167 seed stage-specific, 1,849 seed compartment-specific, and 774 seed-specific genes, respectively. The present results also indicated that easyMF can prioritize seed-related genes with superior prediction performance over the state-of-art network-based gene prioritization system MaizeNet. As a modular, containerized and open-source platform, easyMF can be further customized to satisfy users' specific demands of functional gene discovery and deployed as a web service for broad applications.

Integrating functional data analysis with case-based reasoning for hypertension prognosis and diagnosis based on real-world electronic health records

BACKGROUND

Hypertension is the fifth chronic disease causing death worldwide. The early prognosis and diagnosis are critical in the hypertension care process. Inspired by human philosophy, CBR is an empirical knowledge reasoning method for early detection and intervention of hypertension by only reusing electronic health records. However, the traditional similarity calculation method often ignores the internal characteristics and potential information of medical examination data.

METHODS

In this paper, we first calculate the weights of input attributes by a random forest algorithm. Then, the risk value of hypertension from each medical examination can be evaluated according to the input data and the attribute weights. By fitting the risk values into a risk curve of hypertension, we calculate the similarity between different community residents, and obtain the most similar case according to the similarity. Finally, the diagnosis and treatment protocol of the new case can be given.

RESULTS

The experiment data comes from the medical examination of Tianqiao Community (Tongling City, Anhui Province, China) from 2012 to 2021. It contains 4143 community residents and 43,676 medical examination records. We first discuss the effect of the influence factor and the decay factor on similarity calculation. Then we evaluate the performance of the proposed FDA-CBR algorithm against the GRA-CBR algorithm and the CS-CBR algorithm. The experimental results demonstrate that the proposed algorithm is highly efficient and accurate.

CONCLUSIONS

The experiment results show that the proposed FDA-CBR algorithm can effectively describe the variation tendency of the risk value and always find the most similar case. The accuracy of FDA-CBR algorithm is higher than GRA-CBR algorithm and CS-CBR algorithm, increasing by 9.94 and 16.41%, respectively.

TREM2 ameliorates anesthesia and surgery-induced cognitive impairment by regulating mitophagy and NLRP3 inflammasome in aged C57/BL6 mice

Postoperative cognitive dysfunction (POCD) is a major postoperative complication. Triggering receptor expressed on myeloid cells 2 (TREM2) exerts a neuroprotective function against neuro-inflammatory responses. The present study investigated the role of TREM2 in anesthesia and surgery-induced cognitive impairment and the potential related mechanism. Our results revealed that TREM2 was downregulated, coupled with activation of the NLRP3 inflammasome and subsequent IL-1β expression on postoperative day 3. A corresponding decline in PSD-95 and BDNF was found at the same time point. The key regulator of mitophagy PINK1 and Parkin protein levels were significantly decreased following surgery and anesthesia. TREM2 overexpression partially reversed postoperative cognitive impairment and enhanced PSD-95 and BDNF expression. TREM2 overexpression also improved mitophagy function and inhibited activation of the NLRP3 inflammasome and associated production of IL-1β. Our findings demonstrate that TREM2 rescues anesthesia and surgery-induced spatial learning and memory impairment and neuro-inflammation in aged C57/BL6 mice, which may be at least partially mediated through the activation of mitophagy and subsequent inhibition of the NLRP3 inflammasome.