Objectives. Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Certain genes have been identified as important clinical risk factors for AD, and technological advances in genomic research, such as genome-wide association studies (GWAS), allow for analysis of polymorphisms and have been widely applied to studies of AD. However, shortcomings of GWAS include sensitivity to sample size and hereditary deletions, which result in low classification and predictive accuracy. Therefore, this paper proposes a novel deep-learning genomics approach and applies it to multitasking classification of AD progression, with the goal of identifying novel genetic biomarkers overlooked by traditional GWAS analysis. Methods. In this study, we selected genotype data from 1461 subjects enrolled in the Alzheimer’s Disease Neuroimaging Initiative, including 622 AD, 473 mild cognitive impairment (MCI), and 366 healthy control (HC) subjects. The proposed deep-learning genomics (DLG) approach consists of three steps: quality control, coding of single-nucleotide polymorphisms, and classification. The ResNet framework was used for the DLG model, and the results were compared with classifications by simple convolutional neural network structure. All data were randomly assigned to one training/validation group and one test group at a ratio of 9 : 1. And fivefold cross-validation was used. Results. We compared classification results from the DLG model to those from traditional GWAS analysis among the three groups. For the AD and HC groups, the accuracy, sensitivity, and specificity of classification were, respectively, 98.78 ± 1.50 % , 98.39 % ± 2.50 % , and 99.44 % ± 1.11 % using the DLG model, while 71.38 % ± 0.63 % , 63.13 % ± 2.87 % , and 85.59 % ± 6.66 % using traditional GWAS. Similar results were obtained from the other two intergroup classifications. Conclusion. The DLG model can achieve higher accuracy and sensitivity when applied to progression of AD. More importantly, we discovered several novel genetic biomarkers of AD progression, including rs6311 and rs6313 in HTR2A, rs1354269 in NAV2, and rs690705 in RFC3. The roles of these novel loci in AD should be explored in future research.
Background: Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease associated with progressive joint damage and disability. There is a lack of effective methods in the treatment of RA currently. Many clinical trials have proved that traditional Chinese medicine (TCM) has obvious advantages in the treatment of RA. In this systematic review, we intend to evaluate the efficacy and safety of TCM for active RA. Methods: We will search PubMed, the Cochrane Library, Embase, Web of Science, the Chinese National Knowledge Infrastructure Database, Wanfang Data, and Chinese Science and Technology Periodical Database. Simultaneously we will retrieval relevant meeting minutes, eligible research reference lists, symposium abstracts, and grey literatures. Included criteria are randomized controlled trials (RCTs) about TCM for active RA to assess its efficacy and safety. We will use the Revman 5.3 and Stata 13.0 software for data synthesis, sensitivity analysis, meta regression, subgroup analysis, and risk of bias assessment. The Grading of Recommendations Assessment, Development, and Evaluation standard will be used to evaluate the quality of evidence. Results: This systematic review will provide a synthesis of TCM for patients with active RA from various evaluation aspects including tender joint count, swollen joint count, RF, CRP, ESR, DAS28, TCM syndrome evaluation criteria, and adverse events. Conclusion: The systematic review will provide evidence to assess the efficacy and safety of TCM in the treatment of patients with active RA. PROSPERO registration number: PROSPERO CRD42019146726
The present study focuses on determining the suitability of servant-teacher leadership as a leadership approach for instructional purposes. The study specifically looks at how servant leadership affects followers' cognitive learning by considering the mediating role of follower psychological empowerment. Using a stratified random sampling technique, data was collected through a questionnaire from seven hundred graduates from various HEC-recognized universities. The results of the study show that servant teaching significantly improves students' cognitive learning and sense of empowerment. Additionally, the study found that psychological empowerment mediates the link between follower cognitive learning and servant leadership. The findings of this study provide new insight to instructors seeking to develop a learning environment that fosters students' learning outcomes. However, it is also important to consider how to ensure students' commitment and that they are being challenged. Future research may explore the impact of cultural values on students' behavior toward servant teachers.
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