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      Evaluation of Functional Magnetic Resonance Imaging under Artificial Intelligence Algorithm on Plan-Do-Check-Action Home Nursing for Patients with Diabetic Nephropathy

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          Abstract

          This study aimed to evaluate the effect of functional magnetic resonance imaging (fMRI) under the fuzzy C-means (FCM) clustering algorithm on plan-do-check-action (PDCA) home nursing for patients with diabetic nephropathy (DN). As the characteristics of fMRI image data were combined, the FCM algorithm was improved and applied into the clustering processing of fMRI activation regions of patients. 64 patients with DN were chosen as the research objects and were divided into the research group with PDCA home nursing and the control group with routine home nursing. The patients were randomly divided into the research group ( n = 32) and the control group ( n = 32). The curative effect, nursing satisfaction, and quality of life of patients after nursing were compared. The results showed that the coverage of fMRI activation points was significantly higher as being detected by the FCM algorithm, and the running time was shortened by 33.6 min. After nursing, the total effective rates in the research group and the control group were 87.5% vs. 34.4% in 3 months, 93.8% vs. 68.8% in 6 months, and 96.9% vs. 75.0% in 12 months, respectively; those in the research group were significantly higher than those in the control group ( P < 0.05). The nursing satisfaction score (91.3 ± 4.5 vs. 80.9 ± 5.2) and nursing service quality score (89.7 ± 6.6 vs. 80.3 ± 7.1) in the research group were also significantly higher than those in the control group ( P < 0.05). Meanwhile, the scores of each item after nursing in the research group were significantly higher than those in the control group ( P < 0.05). The improved FCM algorithm detected the activation regions in the fMRI images more effectively, which could provide help for diagnosis and reduce error and misdiagnosis. At the same time, the PDCA home nursing also offered great help to the recovery of patients with DN, which was more superior for the curative effect of hospitalization, the promotion of recovery, and the improvement of patients' quality of life.

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          SF-36 total score as a single measure of health-related quality of life: Scoping review

          Liliane Lins, Fernando Martins Carvalho (2016)
          According to the 36-Item Short Form Health Survey questionnaire developers, a global measure of health-related quality of life such as the “SF-36 Total/Global/Overall Score” cannot be generated from the questionnaire. However, studies keep on reporting such measure. This study aimed to evaluate the frequency and to describe some characteristics of articles reporting the SF-36 Total/Global/Overall Score in the scientific literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses method was adapted to a scoping review. We performed searches in PubMed, Web of Science, SCOPUS, BVS, and Cochrane Library databases for articles using such scores. We found 172 articles published between 1997 and 2015; 110 (64.0%) of them were published from 2010 onwards; 30.0% appeared in journals with Impact Factor 3.00 or greater. Overall, 129 (75.0%) out of the 172 studies did not specify the method for calculating the “SF-36 Total Score”; 13 studies did not specify their methods but referred to the SF-36 developers’ studies or others; and 30 articles used different strategies for calculating such score, the most frequent being arithmetic averaging of the eight SF-36 domains scores. We concluded that the “SF-36 Total/Global/Overall Score” has been increasingly reported in the scientific literature. Researchers should be aware of this procedure and of its possible impacts upon human health.
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            Inflammation and the pathogenesis of diabetic nephropathy.

            Jun Wada, Hirofumi Makino (2013)
            The most problematic issue in clinical nephrology is the relentless and progressive increase in patients with ESRD (end-stage renal disease) worldwide. The impact of diabetic nephropathy on the increasing population with CKD (chronic kidney disease) and ESRD is enormous. Three major pathways showing abnormality of intracellular metabolism have been identified in the development of diabetic nephropathy: (i) the activation of polyol and PKC (protein kinase C) pathways; (ii) the formation of advanced glycation end-products; and (iii) intraglomerular hypertension induced by glomerular hyperfiltration. Upstream of these three major pathways, hyperglycaemia is the major driving force of the progression to ESRD from diabetic nephropathy. Downstream of the three pathways, microinflammation and subsequent extracellular matrix expansion are common pathways for the progression of diabetic nephropathy. In recent years, many researchers have been convinced that the inflammation pathways play central roles in the progression of diabetic nephropathy, and the identification of new inflammatory molecules may link to the development of new therapeutic strategies. Various molecules related to the inflammation pathways in diabetic nephropathy include transcription factors, pro-inflammatory cytokines, chemokines, adhesion molecules, Toll-like receptors, adipokines and nuclear receptors, which are candidates for the new molecular targets for the treatment of diabetic nephropathy. Understanding of these molecular pathways of inflammation would translate into the development of anti-inflammation therapeutic strategies.
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              Fuzzy System Based Medical Image Processing for Brain Disease Prediction

              Mandong Hu, Yi Zhong, Shuxuan Xie … (2021)
              The present work aims to explore the performance of fuzzy system-based medical image processing for predicting the brain disease. 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. 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) to ensure the model safety performance. Brain MRI images collected from a 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 demonstrate 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 s 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 DSC (Dice Similarity Coefficient), reaching 0.936. Besides, its Jaccard coefficient is 0.845, proving its superior segmentation accuracy over other models. In a word, the proposed algorithm can provide higher accuracy, a more apparent denoising effect, and the best segmentation and recognition effect than other models while ensuring energy consumption. The results can provide an experimental basis for the feature recognition and predictive diagnosis of brain images.
              Article 0 comments Cited 167 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Qianqian Du:
                ORCID: https://orcid.org/0000-0003-3556-882X
                Journal
                Journal ID (nlm-ta): Contrast Media Mol Imaging
                Journal ID (iso-abbrev): Contrast Media Mol Imaging
                Journal ID (publisher-id): CMMI
                Title: Contrast Media & Molecular Imaging
                Publisher: Hindawi
                ISSN (Print): 1555-4309
                ISSN (Electronic): 1555-4317
                Publication date Collection: 2022
                Publication date (Electronic): 25 March 2022
                Volume: 2022
                Electronic Location Identifier: 9882532
                Affiliations
                1Department of Endocrinology, the Second Hospital of Shijiazhuang, Shijiazhuang 050000, Hebei, China
                2Community Health Service Centre of Zhentou, Shijiazhuang 050000, Hebei, China
                3Department of Nursing, the Second Hospital of Shijiazhuang, Shijiazhuang 050000, Hebei, China
                4Department of Nephrology, the Second Hospital of Shijiazhuang, Shijiazhuang 050000, Hebei, China
                5Department of Peripheral Vascular Surgery, the Second Hospital of Shijiazhuang, Shijiazhuang 050000, Hebei, China
                Author notes

                Academic Editor: M Pallikonda Rajasekaran

                Author information
                https://orcid.org/0000-0003-3556-882X
                https://orcid.org/0000-0002-6055-6049
                https://orcid.org/0000-0001-7032-3030
                https://orcid.org/0000-0002-7794-3408
                https://orcid.org/0000-0001-5441-8910
                Article
                DOI: 10.1155/2022/9882532
                PMC ID: 8975661
                PubMed ID: 35399221
                SO-VID: f4336cba-6e7e-49bf-8fba-fec01ffdc5e8
                Copyright © Copyright © 2022 Qianqian Du et al.
                License:

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 31 December 2021
                Date revision received : 25 February 2022
                Date accepted : 28 February 2022
                Funding
                Funded by: Hebei Youth Science and technology project
                Award ID: 20160768
                Categories
                Subject: Research Article

                Data availability:

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