Journal of Healthcare Engineering

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ISSN / EISSN : 2040-2295 / 2040-2309
Published by: Hindawi Limited (10.1155)
Total articles ≅ 2,836
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Latest articles in this journal

, Jie Chen
Published: 3 October 2022
Journal of Healthcare Engineering, Volume 2022, pp 1-7; https://doi.org/10.1155/2022/8343452

Abstract:
The life satisfaction of the elderly is the key to subjective well-being and healthy aging. Many related studies are focused on the affected factors, including health status, economic level, social support, pension mode, social security, and intergenerational support, etc., but few are based on the macro perspective of healthy aging. This study constructed a healthy aging evaluation system with 6 dimensions, including 15 primary indicators and 57 secondary indicators, to evaluate the relationship between healthy aging and elderly life satisfaction. Results showed that the 13168 participants were, mainly, female (53.76%), 80–99 years old (47.99%), lived in rural areas (77.00%), married and living with their spouse (43.70%), and widowed (52.15%). 80.32% lived with household members. 70.37% elderly were satisfied with their lives. Specifically, there was no gender difference in life satisfaction of the elderly ( p=0.273 ), but there were significant differences between groups of urban and rural ( p<0.001 ), age groups of 65–79 and 80 older ( p<0.001 ), marriage groups of unmarried and married ( p<0.001 ), and types of elderly care of living alone and with others ( p<0.001 ), respectively. Among the six dimensions of healthy aging, healthcare performed best and living environment dimension was the worst, which was an area that urgently needed to strengthen. The odds ratios (ORs) showed that the dimensions of social participation/social equity and economic finance played important roles in the well-being of the elderly. Under the macro background of healthy aging, how to take measures from the micro perspective of the healthy aging evaluation index system and ultimately improve the life satisfaction of the elderly and still needs to be explored in-depth.
, Bin Li, Hua Yin, Bo Xu
Published: 29 September 2022
Journal of Healthcare Engineering, Volume 2022, pp 1-15; https://doi.org/10.1155/2022/6727957

Abstract:
Automatic and accurate segmentation of ground glass opacity (GGO) nodules still remains challenging due to inhomogeneous interiors, irregular shapes, and blurred boundaries from different patients. Despite successful applications in the image processing domains, the random walk has some limitations for segmentation of GGO pulmonary nodules. In this paper, an improved random walker method is proposed for the segmentation of GGO nodules. To calculate a new affinity matrix, intensity, spatial, and texture features are incorporated. It strengthens discriminative power between two adjacent nodes on the graph. To address the problem of robustness in seed acquisition, the geodesic distance is introduced and a novel local search strategy is presented to automatically acquire reliable seeds. For segmentation, a label constraint term is introduced to the energy function of original random walker, which alleviates the accumulation of errors caused by the initial seeds acquisition. Massive experiments conducted on Lung Images Dataset Consortium (LIDC) demonstrate that the proposed method achieves visually satisfactory results without user interactions. Both qualitative and quantitative evaluations also demonstrate that the proposed method obtains better performance compared with conventional random walker method and state-of-the-art segmentation methods in terms of the overlap score and F-measure.
, Ling Chen, Kelong Chen, Bin Li, Zhimin Wu
Published: 28 September 2022
Journal of Healthcare Engineering, Volume 2022, pp 1-13; https://doi.org/10.1155/2022/4336622

Abstract:
Objective. The aim of the study is to investigate the influencing factors of quality of life in adult patients with epilepsy in Wenzhou in China. Methods. A total of 190 patients who visited our hospital from July 2019 to February 2021 were included in the study. Demographic data and disease status were collected. Moreover, QOLIE-31, PSQI, ESS, HAMD-17, and GAD-7 were used in the questionnaire survey. Structural equation model fitting was used to analyze the influencing factors of quality of life in adult patients with epilepsy. Results. The scores of the dimension of onset worry in men were greater than those of women (P = 0.049), and the scores of the dimension of life satisfaction were lower than women (P = 0.047). The scores of cognitive function decreased with age (P = 0.047). The scores of quality of life of unemployed and drinking patients significantly decreased P<0.05 . The score of quality of life positively correlated with good economic status and family relations P<0.05 . The score of emotional health increased first and then decreased with the course of the disease. With the decrease in seizure frequency and the extension of months without a seizure, the score of quality of life gradually increased. Furthermore, the structural equation model showed that health status was directly correlated to the quality of life of patients with epilepsy. Conclusion. Male, unemployment, drinking, older age, and disease are negatively related to the quality of life in patients with epilepsy. However, good economic status, good family relations, and good colleague relationships are positively related to the quality of life.
Guibin Liang, Jiuang Li, Shiqian Pu,
Published: 26 September 2022
Journal of Healthcare Engineering, Volume 2022, pp 1-10; https://doi.org/10.1155/2022/6788569

Abstract:
Background and objectives. Sepsis is a life-threatening organ dysfunction caused by the imbalance of the bodys response to infection. Delay in sepsis diagnosis has become a primary cause of patient death. This study aims to identify potential biomarkers of sepsis based on bioinformatics data analysis, so as to provide new gene biomarkers for the diagnosis and treatment of sepsis. Methods. Gene expression profiles of GSE13904, GSE26378, GSE26440, GSE65682, and GSE69528 were obtained from the National Center for Biotechnology Information (NCBI). The differentially expressed genes (DEGs) were searched using limma software package. Gene Ontology (GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis were performed to elucidate molecular mechanisms of DEGs and screen hub genes. Results. A total of 108 DEGs were identified in the study, of which 67 were upregulated and 41 were downregulated. 15 superlative diagnostic biomarkers (CCL5, CCR7, CD2, CD27, CD274, CD3D, GNLY, GZMA, GZMH, GZMK, IL2RB, IL7R, ITK, KLRB1, and PRF1) for sepsis were identified by bioinformatics analysis. Conclusion. 15 hub genes (CCL5, CCR7, CD2, CD27, CD274, CD3D, GNLY, GZMA, GZMH, GZMK, IL2RB, IL7R, ITK, KLRB1, and PRF1) have been elucidated in this study, and these biomarkers may be helpful in the diagnosis and therapy of patients with sepsis.
Kangjian Wang, Zongkai Zou, Haolin Shen, Guimei Huang, Shuping Yang
Published: 26 September 2022
Journal of Healthcare Engineering, Volume 2022, pp 1-9; https://doi.org/10.1155/2022/9336185

Abstract:
Previous studies suggest that triple-negative breast cancer (TNBC) may have unique imaging characteristics, however, studies focused on the imaging characteristics of TNBC are still limited. The aim of the present study is to analyze the ultrasonic characteristics of TNBC and to provide more reliable information on imaging diagnosis of TNBC. This retrospective study was performed including 162 TNBC patients with 184 TNBC lesions. 174 non-TNBC cases with 196 lesions were used as the control group. The median size of TNBC lesions and non-TNBC lesions were 23 mm × 16 mm and 21 mm × 15 mm, respectively. The shape of most breast cancer lesions was irregular. However, 15.30% (28/183) TNBC lesions and 16.84% (33/196) non-TNBC lesions were oval-shaped. Most breast cancer lesions (79.78% TNBC & 85.71% non-TNBC) were ill-defined. In comparison to non-TNBC, the distinctive ultrasonic characteristics of TNBC were summarized as three features: calcifications, posterior acoustic, and blood flow. Microcalcifications was less common in non-TNBC. The remarkable posterior acoustic characteristics on TNBC were no posterior acoustic features (136, 73.91%). Avascular pattern (21.74%) was also more common in TNBC. The other feature of TNBC was markedly hypoechoic lesions (23.91%). The above-mentioned differences between TNBC and non-TNBC were significant. 93.48% TBNC and 94.39% non–TNBC lesions were in BI-RADS-US category of 4A-5. The results indicate that TNBC has some distinctive ultrasound characteristics. Ultrasound is a useful adjunct in early detection of breast cancer. A combination of ultrasound with mammography is excellent for detecting breast cancer.
, Muhammad Ibrahim Khalil, Ghadah Alwakid, N. Z. Jhanjhi
Published: 26 September 2022
Journal of Healthcare Engineering, Volume 2022, pp 1-11; https://doi.org/10.1155/2022/7028717

Abstract:
Medical image recognition plays an essential role in the forecasting and early identification of serious diseases in the field of identification. Medical pictures are essential to a patient’s health record since they may be used to control, manage, and treat illnesses. On the other hand, image categorization is a difficult problem in diagnostics. This paper provides an enhanced classifier based on the outstanding Feature Selection oriented Clinical Classifier using the Deep Learning (DL) model, which incorporates preprocessing, extraction of features, and classifying. The paper aims to develop an optimum feature extraction model for successful medical imaging categorization. The proposed methodology is based on feature extraction with the pretrained EfficientNetB0 model. The optimum features enhanced the classifier performance and raised the precision, recall, F1 score, accuracy, and detection of medical pictures to improve the effectiveness of the DL classifier. The paper aims to develop an optimum feature extraction model for successful medical imaging categorization. The optimum features enhanced the classifier performance and raised the result parameters for detecting medical pictures to improve the effectiveness of the DL classifier. Experiment findings reveal that our presented approach outperforms and achieves 98% accuracy.
Tasnimul Hasan, Faiyed Bin Karim, Mahin Khan Mahadi, ,
Published: 25 September 2022
Journal of Healthcare Engineering, Volume 2022, pp 1-18; https://doi.org/10.1155/2022/6963891

Abstract:
The endeavor to detect human activities and behaviors is targeted as a real-time detection mechanism that tends to predict the form of human motions and actions. Though sensors like accelerometer and gyroscopes are noticeable in human motion detection, categorizing unique and individual human gestures require software-based assistance. With the widespread implementation of machine learning algorithms, human actions can be distinguished into multiple classes. Several state-of-the-art machine learning algorithms can be applied to this specified field which will give suitable outcomes, yet due to the bulk of the dataset, complexity can be made apparent, which will reduce the efficiency of the model. In our proposed research, ensemble learning methods have been established by assembling several trained and tuned machine learning models. The adopted dataset for the model has been preprocessed through PCA (principal component analysis), SMOTE oversampling (synthetic minority oversampling technique), and K-means clustering, which reduced the dataset to essentials, keeping the weight of the features intact and reducing complexity. Maximum accuracy of 99.36% was achieved from both stacking and voting ensemble methods.
Xuewei Yin, Chunyi Lyu, Zonghong Li, Qian Wang, Yi Ding, Yan Wang, Yan Qiu, Siyuan Cui, ,
Published: 23 September 2022
Journal of Healthcare Engineering, Volume 2022, pp 1-15; https://doi.org/10.1155/2022/2669114

Abstract:
Acyl-CoA thioesterase (ACOT) plays a considerable role in lipid metabolism, which is closely related to the occurrence and development of cancer, nevertheless, its role has not been fully elucidated in acute myeloid leukemia (AML). To explore the role of ACOT2 in AML and to provide a potential therapeutic target for AML, the expression pattern of ACOT was investigated based on the TNMplot, Gene Expression Profiling Interactive Analysis (GEPIA), and Cancer Cell Line Encyclopedia (CCLE) database, and diagnostic value, prognostic value, and clinical phenotype of ACOT were explored based on data from The Cancer Genome Atlas (TCGA). Functional annotation and enrichment analysis of the common targets between ACOT2 coexpressed and AML-related genes were further performed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) analyses. The protein-protein interaction (PPI) network of ACOT2 coexpressed genes and functional ACOT2-related metabolites association network were constructed based on GeneMANIA and Human Metabolome Database. Among ACOTs, ACOT2 was highly expressed in AML compared to normal control subjects according to TNMplot, GEPIA, and CCLE database, which was significantly associated with poor overall survival (OS) in AML ( P=0.003 ). Moreover, ACOT2 exhibited excellent diagnostic efficiency for AML (AUC: 1.000) and related to French-American-British (FAB) classification and cytogenetics. GO, KEGG, and GSEA analyses of 71 common targets between ACOT2 coexpressed and AML-related genes revealed that ACOT2 is closely related to ACOT1, ACOT4, enoyl-acyl carrier protein reductase, mitochondrial (MECR), puromycin-sensitive aminopeptidase (NPEPPS), SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily B member 1 (SMARCB1), and long-chain fatty acid-CoA ligase 1 (ACSL1) in PPI network, and plays a significant role in lipid metabolism, that is, involved in fatty acid elongation and biosynthesis of unsaturated fatty acids. Collectively, the increase of ACOT2 may be an important characteristic of worse OS and abnormal lipid metabolism, suggesting that ACOT2 may become a potential therapeutic target for AML.
Published: 19 September 2022
Journal of Healthcare Engineering, Volume 2022, pp 1-18; https://doi.org/10.1155/2022/6711592

Abstract:
In this research, we examine the use of the Laney p’ control chart and the application of test rules to assess governmental interventions throughout the COVID-19 pandemic and understand how certain activities and events that took place affected the infection rate. Data for the infection rate (IR) were collected between October 31, 2020, and March 19, 2022. The IR was calculated by dividing the number of confirmed cases by the number of PCR (polymerase chain reaction) tests performed. The IR data were subsequently plotted on the Laney p’ control charts using the Minitab software. The charts thereby allowed us to study the effects on infection rates of the government’s moves to restrict the movements and activities of the population, as well as the results of easing these restrictions. The restrictive measures proved to be effective in decreasing the infection rate, whereas relaxing these measures had the opposite effect. Typically, test signals are considered as an indication of a change in a process, although in some situations we have observed that slight changes are not accompanied by a signal. Regardless, the analysis shows cases where using test rules rapidly detected patterns and changes in IR, and allowing remedial action to be taken without delay. In this study, we use the Laney p’ control chart to monitor the COVID-19 IR and compare its performance with that of the EWMA control chart. In addition, we analyze the performance of various test rules in detecting IR changes. Comparing the Laney p’ control chart with the EWMA control chart, the data showed that in most cases, the Laney p’ control chart was able to identify the change of IR faster. Comparing the performance of different tests in detecting changes in the IR, one can see that no particular test outperformed the others in all cases. We also recommend analyzing the data points in both single-stage and multistage analyses in accordance with this new perspective rather than the traditional one used in process improvement projects. Accordingly, the single-stage analysis gives a complete picture of how the infection rate is changing overall, whereas the multistage analysis is more sensitive to small changes.
Wenxing Su, Biao Huang, Qingyi Zhang, Wei Han, Lu An, Yi Guan, Jiang Ji, Daojiang Yu
Published: 19 September 2022
Journal of Healthcare Engineering, Volume 2022, pp 1-14; https://doi.org/10.1155/2022/3524022

Abstract:
Background. Cutaneous squamous cell carcinoma (cSCC) is the leading cause of death in patients with nonmelanoma skin cancers (NMSC). However, the unclear pathogenesis of cSCC limits the application of molecular targeted therapy. Methods. Three microarray datasets (GSE2503, GSE45164, and GSE66359) were downloaded from the Gene Expression Omnibus (GEO). After identifying the differentially expressed genes (DEGs) in tumor and nontumor tissues, five kinds of analyses, namely, functional annotation, protein-protein interaction (PPI) network, hub gene selection, TF-miRNA-mRNA regulatory network analysis, and ferroptosis mechanism, were performed. Results. A total of 146 DEGs were identified with significant differences, including 113 upregulated genes and 33 downregulated genes. The enriched functions and pathways of the DEGs included microtubule-based movement, ATP binding, cell cycle, P53 signaling pathway, oocyte meiosis, and PLK1 signaling events. Nine hub genes were identified (CDK1, AURKA, RRM2, CENPE, CCNB1, KIAA0101, ZWINT, TOP2A, and ASPM). Finally, RRM2, AURKA, and SAT1 were identified as significant ferroptosis-related genes in cSCC. The differential expression of these genes has been verified in two other independent datasets. Conclusions. By integrated bioinformatic analysis, the hub genes identified in this study elucidated the molecular mechanism of the pathogenesis and progression of cSCC and are expected to become future biomarkers or therapeutic targets.
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