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  Subjects -> SOCIAL SERVICES AND WELFARE (Total: 224 journals)
Showing 1 - 135 of 135 Journals sorted by number of followers
Journal of Personality and Social Psychology     Full-text available via subscription   (Followers: 313)
Personality and Social Psychology Bulletin     Hybrid Journal   (Followers: 171)
Journal of Public Health     Hybrid Journal   (Followers: 143)
Social Policy and Society     Hybrid Journal   (Followers: 134)
Journal of Social Work     Hybrid Journal   (Followers: 82)
Violence and Victims     Hybrid Journal   (Followers: 81)
British Journal of Social Work     Hybrid Journal   (Followers: 72)
New Zealand Journal of Occupational Therapy     Full-text available via subscription   (Followers: 69)
International Journal of Sociology and Social Policy     Hybrid Journal   (Followers: 67)
Health and Social Work     Hybrid Journal   (Followers: 63)
International Journal of Social Research Methodology     Hybrid Journal   (Followers: 63)
Journal of Applied Social Psychology     Hybrid Journal   (Followers: 57)
Safer Communities     Hybrid Journal   (Followers: 51)
Personality and Social Psychology Review     Hybrid Journal   (Followers: 50)
Health & Social Care In the Community     Hybrid Journal   (Followers: 48)
Critical Social Policy     Hybrid Journal   (Followers: 45)
Quality in Ageing and Older Adults     Hybrid Journal   (Followers: 44)
Annals of the American Academy of Political and Social Science     Hybrid Journal   (Followers: 44)
European Journal of Social Psychology     Hybrid Journal   (Followers: 43)
Journal of Social Policy     Hybrid Journal   (Followers: 41)
Basic and Applied Social Psychology     Hybrid Journal   (Followers: 40)
Social Work     Hybrid Journal   (Followers: 38)
Journal of European Social Policy     Hybrid Journal   (Followers: 36)
Mental Health and Social Inclusion     Hybrid Journal   (Followers: 36)
Global Social Policy     Hybrid Journal   (Followers: 36)
European Journal of Work and Organizational Psychology     Hybrid Journal   (Followers: 35)
Qualitative Research     Hybrid Journal   (Followers: 34)
European Journal of Social Work     Hybrid Journal   (Followers: 33)
Advances in Social Work     Open Access   (Followers: 31)
Research on Social Work Practice     Hybrid Journal   (Followers: 30)
Social Policy & Administration     Hybrid Journal   (Followers: 30)
Journal of Evidence-Based Social Work     Hybrid Journal   (Followers: 28)
Clinical Social Work Journal     Hybrid Journal   (Followers: 27)
Journal of Social Philosophy     Hybrid Journal   (Followers: 27)
Journal of Occupational Science     Hybrid Journal   (Followers: 27)
Social Philosophy and Policy     Full-text available via subscription   (Followers: 25)
Social Work Research     Hybrid Journal   (Followers: 24)
Science and Public Policy     Hybrid Journal   (Followers: 24)
Mental Health and Substance Use: dual diagnosis     Hybrid Journal   (Followers: 24)
Human Service Organizations Management, Leadership and Governance     Hybrid Journal   (Followers: 23)
Social Justice Research     Hybrid Journal   (Followers: 23)
Community, Work & Family     Hybrid Journal   (Followers: 23)
Philosophy & Social Criticism     Hybrid Journal   (Followers: 22)
Ethics and Social Welfare     Hybrid Journal   (Followers: 22)
The Milbank Quarterly     Hybrid Journal   (Followers: 22)
International Social Science Journal     Hybrid Journal   (Followers: 22)
Death Studies     Hybrid Journal   (Followers: 21)
Journal of Family Issues     Hybrid Journal   (Followers: 21)
Critical and Radical Social Work     Hybrid Journal   (Followers: 21)
Counseling Psychology and Psychotherapy     Open Access   (Followers: 20)
Australian Journal of Emergency Management     Full-text available via subscription   (Followers: 20)
Community Development     Hybrid Journal   (Followers: 20)
Qualitative Social Work     Hybrid Journal   (Followers: 20)
Housing Policy Debate     Hybrid Journal   (Followers: 19)
International Social Work     Hybrid Journal   (Followers: 19)
Self and Identity     Hybrid Journal   (Followers: 19)
Research on Language and Social Interaction     Hybrid Journal   (Followers: 19)
Social Cognition     Full-text available via subscription   (Followers: 19)
Social Work & Social Sciences Review     Open Access   (Followers: 19)
International Journal of Social Work     Open Access   (Followers: 19)
Journal of Ethnic & Cultural Diversity in Social Work     Hybrid Journal   (Followers: 18)
Journal of Language and Social Psychology     Hybrid Journal   (Followers: 18)
Journal of Integrated Care     Hybrid Journal   (Followers: 18)
International Journal of Social Welfare     Hybrid Journal   (Followers: 18)
Social and Personality Psychology Compass     Hybrid Journal   (Followers: 17)
Journal of Social Issues     Hybrid Journal   (Followers: 17)
Asian Journal of Social Science     Hybrid Journal   (Followers: 17)
Practice: Social Work in Action     Hybrid Journal   (Followers: 16)
Adoption & Fostering     Hybrid Journal   (Followers: 16)
Social Work Review     Full-text available via subscription   (Followers: 16)
Journal of Comparative Social Welfare     Hybrid Journal   (Followers: 16)
Developing Practice : The Child, Youth and Family Work Journal     Full-text available via subscription   (Followers: 15)
Critical Policy Studies     Hybrid Journal   (Followers: 15)
European Review of Social Psychology     Hybrid Journal   (Followers: 14)
Journal of Social Work in Disability & Rehabilitation     Hybrid Journal   (Followers: 14)
Journal of Public Mental Health     Hybrid Journal   (Followers: 14)
Society and Mental Health     Hybrid Journal   (Followers: 14)
Journal of Social Work Education     Hybrid Journal   (Followers: 13)
Journal of Community & Applied Social Psychology     Partially Free   (Followers: 13)
Grief Matters : The Australian Journal of Grief and Bereavement     Full-text available via subscription   (Followers: 13)
Research in Social Stratification and Mobility     Hybrid Journal   (Followers: 13)
Social Work Education: The International Journal     Hybrid Journal   (Followers: 13)
Canadian Social Work Review     Full-text available via subscription   (Followers: 13)
Policy Sciences     Hybrid Journal   (Followers: 13)
Australian Social Work     Hybrid Journal   (Followers: 13)
Journal of Religion & Spirituality in Social Work: Social Thought     Hybrid Journal   (Followers: 12)
Counseling Outcome Research and Evaluation     Hybrid Journal   (Followers: 12)
Social Behavior and Personality : An International Journal     Full-text available via subscription   (Followers: 12)
Journal of Accessibility and Design for All     Open Access   (Followers: 12)
Contemporary Rural Social Work     Open Access   (Followers: 12)
Du Bois Review: Social Science Research on Race     Full-text available via subscription   (Followers: 11)
Journal of Social Work Practice in the Addictions     Hybrid Journal   (Followers: 11)
Journal of Community Practice     Hybrid Journal   (Followers: 11)
Social Science Japan Journal     Hybrid Journal   (Followers: 11)
Journal of Forensic Social Work     Hybrid Journal   (Followers: 11)
Journal of Social Service Research     Hybrid Journal   (Followers: 11)
Families in Society : The Journal of Contemporary Social Services     Full-text available via subscription   (Followers: 11)
Social Choice and Welfare     Hybrid Journal   (Followers: 11)
Journal of Investigative Psychology and Offender Profiling     Hybrid Journal   (Followers: 11)
Learning in Health and Social Care     Hybrid Journal   (Followers: 11)
Race and Social Problems     Hybrid Journal   (Followers: 10)
Psychoanalytic Social Work     Hybrid Journal   (Followers: 10)
Journal of the Society for Social Work and Research     Full-text available via subscription   (Followers: 10)
Sexual Abuse in Australia and New Zealand     Full-text available via subscription   (Followers: 9)
International Social Security Review     Hybrid Journal   (Followers: 9)
Service social     Full-text available via subscription   (Followers: 9)
Journal of Health Care for the Poor and Underserved     Full-text available via subscription   (Followers: 9)
Journal of Prevention & Intervention Community     Hybrid Journal   (Followers: 9)
Partner Abuse     Hybrid Journal   (Followers: 9)
Health and Social Care Chaplaincy     Hybrid Journal   (Followers: 9)
Mortality: Promoting the interdisciplinary study of death and dying     Hybrid Journal   (Followers: 9)
Research on Economic Inequality     Hybrid Journal   (Followers: 9)
Aboriginal and Islander Health Worker Journal     Full-text available via subscription   (Followers: 8)
Asia Pacific Journal of Social Work and Development     Hybrid Journal   (Followers: 8)
Asian Social Work and Policy Review     Hybrid Journal   (Followers: 8)
Journal of HIV/AIDS & Social Services     Hybrid Journal   (Followers: 7)
Journal of Social Development in Africa     Full-text available via subscription   (Followers: 7)
Australasian Policing     Full-text available via subscription   (Followers: 7)
Journal of Policy Practice     Hybrid Journal   (Followers: 7)
Social Semiotics     Hybrid Journal   (Followers: 7)
Social Work With Groups     Hybrid Journal   (Followers: 7)
Journal of Care Services Management     Hybrid Journal   (Followers: 7)
Journal of Evidence-Informed Social Work     Hybrid Journal   (Followers: 7)
Global Social Welfare     Hybrid Journal   (Followers: 6)
Nordic Social Work Research     Hybrid Journal   (Followers: 6)
Northwestern Journal of Law & Social Policy     Open Access   (Followers: 6)
Third World Planning Review     Hybrid Journal   (Followers: 6)
European Journal of Social Security     Full-text available via subscription   (Followers: 6)
Social Influence     Hybrid Journal   (Followers: 6)
African Security     Hybrid Journal   (Followers: 6)
Australian Journal of Social Issues     Hybrid Journal   (Followers: 6)
Just Policy: A Journal of Australian Social Policy     Full-text available via subscription   (Followers: 6)
Care Management Journals     Hybrid Journal   (Followers: 5)
Nouvelles pratiques sociales     Full-text available via subscription   (Followers: 5)
Australian Ageing Agenda     Full-text available via subscription   (Followers: 5)
Social Compass     Hybrid Journal   (Followers: 5)
ACOSS Papers     Full-text available via subscription   (Followers: 4)
Measurement and Evaluation in Counseling and Development     Hybrid Journal   (Followers: 4)
African Safety Promotion     Full-text available via subscription   (Followers: 4)
Communities, Children and Families Australia     Full-text available via subscription   (Followers: 4)
Review of Social Economy     Hybrid Journal   (Followers: 3)
Journal of Comparative Social Work     Open Access   (Followers: 3)
Third Sector Review     Full-text available via subscription   (Followers: 3)
Journal of Social Distress and the Homeless     Hybrid Journal   (Followers: 3)
Journal of Healthcare Engineering     Open Access   (Followers: 3)
Public Policy and Aging Report     Hybrid Journal   (Followers: 3)
Youth Studies Australia     Full-text available via subscription   (Followers: 3)
Hong Kong Journal of Social Work, The     Hybrid Journal   (Followers: 3)
Counsellor (The)     Full-text available via subscription   (Followers: 3)
Journal of Benefit-Cost Analysis     Hybrid Journal   (Followers: 2)
Sociedade e Estado     Open Access   (Followers: 2)
Journal of Human Rights and Social Work     Hybrid Journal   (Followers: 2)
Social Action : The Journal for Social Action in Counseling and Psychology     Free   (Followers: 2)
Social Work and Society     Open Access   (Followers: 2)
International Journal of Disability Management Research     Full-text available via subscription   (Followers: 2)
National Emergency Response     Full-text available via subscription   (Followers: 2)
African Journal of Social Work     Open Access   (Followers: 2)
Parity     Full-text available via subscription   (Followers: 2)
International Journal of East Asian Studies     Open Access   (Followers: 2)
Geopolitical, Social Security and Freedom Journal     Open Access   (Followers: 1)
HOLISTICA ? Journal of Business and Public Administration     Open Access   (Followers: 1)
Groupwork     Full-text available via subscription   (Followers: 1)
Journal for Specialists in Group Work     Hybrid Journal   (Followers: 1)
Australasian Journal of Human Security     Full-text available via subscription   (Followers: 1)
Merrill-Palmer Quarterly     Full-text available via subscription   (Followers: 1)
Mundos do Trabalho     Open Access   (Followers: 1)
Em Pauta : Teoria Social e Realidade Contemporânea     Open Access   (Followers: 1)
Australian Journal on Volunteering     Full-text available via subscription   (Followers: 1)
Islamic Counseling : Jurnal Bimbingan Konseling Islam     Open Access  
Tidsskriftet Norges Barnevern     Full-text available via subscription  
Tidsskrift for velferdsforskning     Open Access  
Tidsskrift for omsorgsforskning     Open Access  
Nordisk välfärdsforskning | Nordic Welfare Research     Open Access  
Socialinė teorija, empirija, politika ir praktika     Open Access  
Revista Serviço Social em Perspectiva     Open Access  
ConCienciaSocial     Open Access  
Bakti Budaya     Open Access  
Voces desde el Trabajo Social     Open Access  
Janus Sosiaalipolitiikan ja sosiaalityön tutkimuksen aikakauslehti     Open Access  
Finnish Journal of eHealth and eWelfare : Finjehew     Open Access  
Leidfaden : Fachmagazin für Krisen, Leid, Trauer     Hybrid Journal  
Kontext : Zeitschrift für Systemische Therapie und Familientherapie     Hybrid Journal  
Prospectiva : Revista de Trabajo Social e Intervención Social     Open Access  
International Journal of Care and Caring     Hybrid Journal  
Volunteer Management Report     Full-text available via subscription  
Social Work / Maatskaplike Werk     Open Access  
Argumentum     Open Access  
Indonesian Journal of Guidance and Counseling     Open Access  
Trabajo Social Global - Global Social Work     Open Access  
Journal of Danubian Studies and Research     Open Access  
Maltrattamento e abuso all’infanzia     Full-text available via subscription  
unsere jugend     Full-text available via subscription  
Pedagogia i Treball Social : Revista de Cičncies Socials Aplicades     Open Access  
Cuadernos de Trabajo Social     Open Access  
Developmental Child Welfare     Hybrid Journal  
Nusantara of Research: Jurnal Hasil-hasil Penelitian Universitas Nusantara PGRI Kediri     Open Access  
Revista Internacional De Seguridad Social     Hybrid Journal  
L'Orientation scolaire et professionnelle     Open Access  
Soziale Passagen     Hybrid Journal  
Tempo Social     Open Access  

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Journal Cover
Journal of Healthcare Engineering
Journal Prestige (SJR): 0.28
Citation Impact (citeScore): 1
Number of Followers: 3  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2040-2295 - ISSN (Online) 2040-2309
Published by Hindawi Homepage  [339 journals]
  • Superlative Feature Selection Based Image Classification Using Deep
           Learning in Medical Imaging

    • 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.
      PubDate: Mon, 26 Sep 2022 17:50:01 +000
  • Screening of Sepsis Biomarkers Based on Bioinformatics Data Analysis

    • Abstract: Background and objectives. Sepsis is a life-threatening organ dysfunction caused by the imbalance of the body’s 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.
      PubDate: Mon, 26 Sep 2022 17:50:00 +000
  • Calcification, Posterior Acoustic, and Blood Flow: Ultrasonic
           Characteristics of Triple-Negative Breast Cancer

    • 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.
      PubDate: Mon, 26 Sep 2022 07:05:00 +000
  • Employment of Ensemble Machine Learning Methods for Human Activity

    • 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.
      PubDate: Sun, 25 Sep 2022 10:50:01 +000
  • High Expression of ACOT2 Predicts Worse Overall Survival and Abnormal
           Lipid Metabolism: A Potential Target for Acute Myeloid Leukemia

    • 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 (). 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.
      PubDate: Fri, 23 Sep 2022 05:35:00 +000
  • Using the Laney p’ Control Chart for Monitoring COVID-19 Cases in

    • 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.
      PubDate: Mon, 19 Sep 2022 08:35:00 +000
  • Exploring Potential Biomarkers, Ferroptosis Mechanisms, and Therapeutic
           Targets Associated with Cutaneous Squamous Cell Carcinoma via Integrated
           Transcriptomic Analysis

    • 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.
      PubDate: Mon, 19 Sep 2022 08:05:00 +000
  • Glucose Determination by a Single 1535 nm Pulsed Photoacoustic
           Technique: A Multiple Calibration for the External Factors

    • Abstract: Photoacoustic spectroscopy has been proved to be a potential method for noninvasive blood glucose detection. We used 1535 nm pulsed laser to excite photoacoustic signal in glucose solution and then explored the influence of different glucose concentration on photoacoustic signal to analyze the sensitivity of photoacoustic signal to glucose at this wavelength. We designed a simple photoacoustic cell structure, which used a focused ultrasonic transducer to receive signals, so as to reduce signal attenuation. In terms of the results, we have found that for high-concentration glucose solutions, the results have strong linearity and discrimination, and when the concentration is close to the human body level, the signal difference is not so obvious. Therefore, we explore the external factors affecting the photoacoustic signal in detail and propose a calibration method. Through calibration, the signal generated by the low-concentration glucose solution also has a good linearity.
      PubDate: Mon, 19 Sep 2022 05:50:00 +000
  • Connecting to Nature through 360° Videos during COVID-19 Confinement: A
           Pilot Study of a Brief Psychological Intervention

    • Abstract: Psychological interventions have been shown to be beneficial in mitigating stress related to COVID-19 confinement. According to theories of restorative environments, exposure to natural surroundings has positive effects on well-being and stress through its restorative qualities. With 360° video-based Virtual Reality (VR), people can be exposed to nature and so better manage the consequences associated with mobility restrictions during confinement. The main aim of this pilot study was to examine whether a 360° video-based VR intervention composed of five 13-minute sessions (once a day) has positive effects on affect, well-being, and stress. The sample was made up of 10 participants (4 men and 6 women; age : M = 46.5, SD = 11.7) who were confined at home (voluntarily or not) during the COVID-19 pandemic. Participants were instructed to watch a 360° video each day (of a “beach” or “lake” environment) using their smartphone and VR glasses sent to them by mail. Participants responded with several self-reports before and/or after each session (emotions and sense of presence) and before and/or after the intervention (affect, well-being, perceived stress, perceived restorativeness of nature, and the usefulness and acceptability of the intervention). Results showed a tendency to improve positive (e.g., happiness) and negative (e.g., anxiousness) emotions and experience a high sense of presence after each session. Moreover, perceived restorative qualities of the environment and their cognitive and behavioral effects were high. A significant decrease in negative affect was found after the intervention. Usefulness and acceptability were also high. This is the first study to show that an affordable and accessible technology can be used to overcome the negative consequences of confinement and counteract its harmful psychological effects.
      PubDate: Wed, 14 Sep 2022 07:35:01 +000
  • Stacking Ensemble Method for Gestational Diabetes Mellitus Prediction in
           Chinese Pregnant Women: A Prospective Cohort Study

    • Abstract: Gestational diabetes mellitus (GDM) is closely related to adverse pregnancy outcomes and other diseases. Early intervention in pregnant women who are at high risk of developing GDM could help prevent adverse health consequences. The study aims to develop a simple model using the stacking ensemble method to predict GDM for women in the first trimester based on easily available factors. We used the data from the Chinese Pregnant Women Cohort Study from July 2017 to November 2018. A total of 6,848 pregnant women in the first trimester were included in the analysis. Logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) were considered as base learners. Optimal feature subsets for each learner were chosen by using recursive feature elimination cross-validation. Then, we built a pipeline to process imbalance data, tune hyperparameters, and evaluate model performance. The learners with the best hyperparameters were employed in the first layer of the proposed stacking method. Their predictions were obtained using optimal feature subsets and served as meta-learner’s inputs. Another LR was used as a meta-learner to obtain the final prediction results. Accuracy, specificity, error rate, and other metrics were calculated to evaluate the performance of the models. A paired samples t-test was performed to compare the model performance. In total, 967 (14.12%) women developed GDM. For base learners, the RF model had the highest accuracy (0.638 (95% confidence interval (CI) 0.628–0.648)) and specificity (0.683 (0.669–0.698)) and lowest error rate (0.362 (0.352–0.372)). The stacking method effectively improved the accuracy (0.666 (95% CI 0.663–0.670)) and specificity (0.725 (0.721–0.729)) and decreased the error rate (0.333 (0.330–0.337)). The differences in the performance between the stacking method and RF were statistically significant. Our proposed stacking method based on easily available factors has better performance than other learners such as RF.
      PubDate: Tue, 13 Sep 2022 11:50:00 +000
  • Prognostic Implication of a Cuproptosis-Related miRNA Signature in
           Hepatocellular Carcinoma

    • Abstract: Background. Hepatocellular carcinoma (HCC) is one of the most frequently diagnosed malignancies globally, accounting for the third cause of cancer mortality. Cuproptosis, a copper-induced cell death, was recently reported in Science. The purpose of this study was to evaluate the prognostic implication of cuproptosis-related miRNAs (CRMs) in HCC. Methods. Transcriptomic data and clinicopathological features of patients with HCC were extracted from the Cancer Genome Atlas (TCGA) database. Prognostic CRM signature was established by utilizing univariate Cox regression and LASSO analyses. To validate the accuracy of prediction, the Kaplan-Meier (K-M) and time-dependent receiver operating characteristic (ROC) analyses were adopted. A nomogram comprising clinical characteristics and the miRNA signature was developed to improve the prediction of patient outcomes. Finally, functional enrichment analysis and immune infiltration analysis were carried out. Results. Of CRMs, 14 were obtained to construct a prognostic miRNA signature. This CRM signature was an independent factor for predicting overall survival (OS). Kaplan-Meier curves demonstrated a noteworthy difference in survival rates between different risk subgroups (). The robust prognostic capacity of this signature was exhibited by sampling verification and stratified survival analysis. Functional analysis indicated that the high-risk group was mainly enriched in signaling pathways and different levels of immune infiltration were revealed between the two risk groups. The potential interaction of the model with the immune checkpoint activities was also detected. Conclusion. The CRM signature could act as an independent predictor to guide individual treatment strategies, which could provide fundamental insights for further studies.
      PubDate: Tue, 13 Sep 2022 07:35:00 +000
  • Construction of a Prediction Model for the Mortality of Elderly Patients
           with Diabetic Nephropathy

    • Abstract: To construct a prediction model for all-cause mortality in elderly diabetic nephropathy (DN) patients, in this cohort study, the data of 511 DN patients aged ≥65 years were collected and the participants were divided into the training set (n = 358) and the testing set (n = 153). The median survival time of all participants was 2 years. The data in the training set were grouped into the survival group (n = 203) or the death group (n = 155). Variables with P ≤ 0.1 between the two groups were selected as preliminary predictors and involved into the multivariable logistic regression model and the covariables were gradually adjusted. The receiver operator characteristic (ROC), Kolmogorov-Smirnov (KS), and calibration curves were plotted for evaluating the predictive performance of the model. Internal validation of the performance of the model was verified in the testing set. The predictive values of the model were also conducted in terms of people with different genders and ages or accompanied with chronic kidney disease (CKD) or cardiovascular diseases (CVD), respectively. In total, 216 (42.27%) elderly DN patients were dead within 2 years. The prediction model for the 2-year mortality of elderly patients with DN was established based on length of stay (LOS), temperature, heart rate, peripheral oxygen saturation (SpO2), serum creatinine (Scr), red cell distribution width (RDW), the simplified acute physiology score-II (SAPS-II), hyperlipidemia, and the Chronic Kidney Disease Epidemiology Collaboration equation for estimated glomerular filtration rate (eGFR-CKD-EPI). The AUC of the model was 0.78 (95% CI: 0.73–0.83) in the training set and 0.72 (95% CI: 0.63–0.80) in the testing set. The AUC of the model was 0.78 (95% CI: 0.65–0.91) in females and 0.78 (95%CI: 0.68–0.88) in patients ≤75 years. The AUC of the model was 0.74 (95% CI: 0.64–0.84) in patients accompanied with CKD. The model had good predictive value for the mortality of elderly patients with DN within 2 years. In addition, the model showed good predictive values for female DN patients, DN patients ≤75 years, and DN patients accompanied with CKD.
      PubDate: Mon, 12 Sep 2022 10:35:00 +000
  • Low Testosterone Level and Risk of Adverse Clinical Events among Male
           Patients with Chronic Kidney Disease: A Systematic Review and
           Meta-Analysis of Cohort Studies

    • Abstract: The phenomenon of low testosterone level is extremely common in male patients with chronic kidney diseases (CKDs). This meta-analysis aimed to evaluate whether the low circulating testosterone could independently predict adverse outcomes among male patients with chronic kidney diseases (CKDs). The data till May 2022 were systematically searched from Pubmed, Web of Science, and Embase from inception. Studies meeting the PICOS (population, intervention/exposure, control/comparison, outcomes, and study design) principles were included in this meta-analysis. Study-specific effect estimates were pooled using fixed-effects (I2 > 50%) or random-effects models (I2 
      PubDate: Sat, 10 Sep 2022 07:20:01 +000
  • Deep Learning Model for Predicting Rhythm Outcomes after Radiofrequency
           Catheter Ablation in Patients with Atrial Fibrillation

    • Abstract: Current guidelines on atrial fibrillation (AF) emphasized that radiofrequency catheter ablation (RFCA) should be decided after fully considering its prognosis. However, a robust prediction model reflecting the complex interactions between the features affecting prognosis remains to be developed. In this paper, we propose a deep learning model for predicting the late recurrence after RFCA in patients with AF. Aiming to predict the late recurrence (LR) of AF within 1 year after pulmonary vein isolation, we designed a multimodal model based on the multilayer perceptron architecture. For quantitative evaluation, we conducted 4-fold cross-validation on data from 177 AF patients including 47 LR patients. The proposed model (area under the receiver operating characteristic curve-AUROC, 0.766) outperformed the acute patient physiologic and laboratory evaluation (APPLE) score (AUROC, 0.605), CHA2DS2-VASc score (AUROC, 0.595), linear regression (AUROC, 0.541), logistic regression (AUROC, 0.546), extreme gradient boosting (AUROC, 0.608), and support vector machine (AUROC, 0.638). The proposed model exhibited better performance than clinical indicators (APPLE and CHA2DS2-VASc score) and machine learning techniques (linear regression, logistic regression, extreme gradient boosting, and support vector machine). The model will support clinical decision-making for selecting good responders to the RFCA intervention.
      PubDate: Sat, 10 Sep 2022 07:20:01 +000
  • A Deep Neural Network Ensemble Classifier with Focal Loss for Automatic
           Arrhythmia Classification

    • Abstract: Automated electrocardiogram classification techniques play an important role in assisting physicians in diagnosing arrhythmia. Among these, the automatic classification of single-lead heartbeats has received wider attention due to the urgent need for portable ECG monitoring devices. Although many heartbeat classification studies performed well in intrapatient assessment, they do not perform as well in interpatient assessment. In particular, for supraventricular ectopic heartbeats (S), most models do not classify them well. To solve these challenges, this article provides an automated arrhythmia classification algorithm. There are three key components of the algorithm. First, a new heartbeat segmentation method is used, which improves the algorithm’s capacity to classify S substantially. Second, to overcome the problems created by data imbalance, a combination of traditional sampling and focal loss is applied. Finally, using the interpatient evaluation paradigm, a deep convolutional neural network ensemble classifier is built to perform classification validation. The experimental results show that the overall accuracy of the method is 91.89%, the sensitivity is 85.37%, the positive productivity is 59.51%, and the specificity is 93.15%. In particular, for the supraventricular ectopic heartbeat(s), the method achieved a sensitivity of 80.23%, a positivity of 49.40%, and a specificity of 96.85%, exceeding most existing studies. Even without any manually extracted features or heartbeat preprocessing, the technique achieved high classification performance in the interpatient assessment paradigm.
      PubDate: Fri, 09 Sep 2022 07:20:00 +000
  • A Framework on Performance Analysis of Mathematical Model-Based
           Classifiers in Detection of Epileptic Seizure from EEG Signals with
           Efficient Feature Selection

    • Abstract: Epilepsy is one of the neurological conditions that are diagnosed in the vast majority of patients. Electroencephalography (EEG) readings are the primary tool that is used in the process of diagnosing and analyzing epilepsy. The epileptic EEG data display the electrical activity of the neurons and provide a significant amount of knowledge on pathology and physiology. As a result of the significant amount of time that this method requires, several automated classification methods have been developed. In this paper, three wavelets such as Haar, dB4, and Sym 8 are employed to extract the features from A–E sets of the Bonn epilepsy dataset. To select the best features of epileptic seizures, a Particle Swarm Optimization (PSO) technique is applied. The extracted features are further classified using seven classifiers like linear regression, nonlinear regression, Gaussian Mixture Modeling (GMM), K-Nearest Neighbor (KNN), Support Vector Machine (SVM-linear), SVM (polynomial), and SVM Radial Basis Function (RBF). Classifier performances are analyzed through the benchmark parameters, such as sensitivity, specificity, accuracy, F1 Score, error rate, and g-means. The SVM classifier with RBF kernel in sym 8 wavelet features with PSO feature selection method attains a higher accuracy rate of 98% with an error rate of 2%. This classifier outperforms all other classifiers.
      PubDate: Tue, 06 Sep 2022 03:20:00 +000
  • Bioinformatics-Based Analysis: Noncoding RNA-Mediated COL10A1 Is
           Associated with Poor Prognosis and Immune Cell Infiltration in Pancreatic

    • Abstract: Background. Collagen type X alpha 1 (COL10A1) is a structural component of the extracellular matrix that is aberrantly expressed in a variety of cancer tissues. However, its role in pancreatic cancer progression is not well understood. Methods. The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Gene Expression Profiling Interaction Analysis (GEPIA) data were employed to explore the expression of COL10A1 in normal and tumor tissues and its prognostic value in pancreatic adenocarcinoma. The clinical data of pancreatic cancer in TCGA were used to explore the relationship between COL10A1 and clinical features. Genes coexpressed with COL10A1 were explored using multiple databases and analyzed for functional enrichment. In addition, the lncRNA/miRNA/COL10A1 axis that may be involved in COL10A1 regulation in pancreatic cancer was explored by constructing a competitive endogenous RNA (ceRNA) regulatory axis. Finally, COL10A1 was analyzed for correlation with immune cell infiltration and various immune checkpoint molecules in pancreatic cancer. Results. It was found that the expression of COL10A1 was significantly increased in pancreatic cancer tissues. High expression of COL10A1 was related to the clinicopathological characteristics and the worse prognosis of pancreatic cancer patients. The TUG1/miR-144-3p/COL10A1 axis was identified as the most likely upstream noncoding RNA pathway for COL10A1 in pancreatic cancer. Besides, in pancreatic adenocarcinoma, the expression level of COL10A1 showed a significant positive correlation with tumor immune cell infiltration, biomarkers of immune cells, and expression of immune checkpoint molecules. Conclusion. COL10A1 is an early diagnostic marker, and its high expression correlates with immune infiltration in pancreatic cancer. The TUG1/miR-144-3p/COL10A1 axis was identified as the most likely upstream noncoding RNA pathway for COL10A1 in pancreatic cancer.
      PubDate: Mon, 05 Sep 2022 06:35:01 +000
  • Clinicopathological Characteristics and Prognostic Factors of Primary
           Bladder Signet Ring Cell Carcinoma

    • Abstract: Introduction. The aim of this study is to examine the treatment pattern and predictors of long-term survival of patients with primary signet ring cell carcinoma (PSRCC) of the urinary bladder based on the analysis of the SEER database. Methods. The 3-year and 5-year overall survival (OS) and cancer-specific survival (CSS) were calculated using the Kaplan–Meier method. Then, we compared the CSS curves by the log-rank test. The independent risk factors were determined using univariate and multivariate Cox regression. Results. The 3-year OS and CSS rates for PSRCC of the bladder were 25.3% and 33.3%. The 5-year OS and CSS rates for the entire cohort were 16.4% and 25.2%. The CSS rates, respectively, were 0, 25.0, 66.7, 33.2, 42.4, and 31.7% at 3 years and 0, 25.0, 34.3, 24.1, 27.2, and 31.7% at 5 years for none, transurethral resection of the bladder (TURB), partial cystectomy, radical cystectomy with reconstruction, pelvic exenteration, and other surgeries (P = 0.001). Multivariate analyses showed independent risk factors only including T stage, M stage, lymph node removal, and surgical approach. Conclusions. T stage, M stage, lymph node removal, and surgical approach are independent risk factors of PSRCC of the urinary bladder. TURB and radical cystectomy with reconstruction appear to provide a better outcome.
      PubDate: Mon, 05 Sep 2022 06:35:01 +000
  • A Study on the Association between Korotkoff Sound Signaling and Chronic
           Heart Failure (CHF) Based on Computer-Assisted Diagnoses

    • Abstract: Background. Korotkoff sound (KS) is an important indicator of hypertension when monitoring blood pressure. However, its utility in noninvasive diagnosis of Chronic heart failure (CHF) has rarely been studied. Purpose. In this study, we proposed a method for signal denoising, segmentation, and feature extraction for KS, and a Bayesian optimization-based support vector machine algorithm for KS classification. Methods. The acquired KS signal was resampled and denoised to extract 19 energy features, 12 statistical features, 2 entropy features, and 13 Mel Frequency Cepstrum Coefficient (MFCCs) features. A controlled trial based on the VALSAVA maneuver was carried out to investigate the relationship between cardiac function and KS. To classify these feature sets, the K-Nearest Neighbors (KNN), decision tree (DT), Naive Bayes (NB), ensemble (EM) classifiers, and the proposed BO-SVM were employed and evaluated using the accuracy (Acc), sensitivity (Se), specificity (Sp), Precision (Ps), and F1 score (F1). Results. The ALSAVA maneuver indicated that the KS signal could play an important role in the diagnosis of CHF. Through comparative experiments, it was shown that the best performance of the classifier was obtained by BO-SVM, with Acc (85.0%), Se (85.3%), and Sp (84.6%). Conclusions. In this study, a method for noise reduction, segmentation, and classification of KS was established. In the measured data set, our method performed well in terms of classification accuracy, sensitivity, and specificity. In light of this, we believed that the methods described in this paper can be applied to the early, noninvasive detection of heart disease as well as a supplementary monitoring technique for the prognosis of patients with CHF.
      PubDate: Thu, 01 Sep 2022 09:50:00 +000
  • Application of Image-Fusion 3D Printing Model in Total En Bloc
           Spondylectomy for Spinal Malignant Tumors

    • Abstract: Purpose. To examine the effects of 3D printing model in total en bloc spondylectomy (TES). Methods. We performed a retrospective chart review of 41 cases of spinal tumors at our institution between 2017 and 2020, in which TES was applied. There were 19 cases with 3D printing model and 22 cases without 3D printing model. Operation time, intraoperative blood loss, excision range, complications, VAS, and ASIA grades were recorded. Statistical methods were used to analyze the data. KaplanMeier survival curve was made to evaluate the survival. Result. There were significant differences in intraoperative blood loss between the two groups. The rate of R0 resection and tumor envelope preservation were higher in 3D group than that in non-3D group. In 3D group, the complications included surgical site infection (5.2%) and cerebrospinal fluid leak (15.7%). In non-3D group, the complications included cerebrospinal fluid leak (27.3%) and nerve root injury (13.6%). The pain and neurological dysfunction were significantly relieved before and after surgery in 3D group. However, the neurological relief in non-3D group patients was not complete. The VAS scores of non-3D group at 6 months after surgery were much higher than that of 3D group. Conclusion. The application of 3D printing model not only helps surgeons observe the morphology, invasion range, and anatomic relationship of the tumor preoperatively, but also assists surgeons to judge, locate, and separate the tumor intraoperatively. For spinal malignancies, the 3D printing model is worth promoting.
      PubDate: Wed, 31 Aug 2022 07:20:01 +000
  • The Identification of Chinese Herbal Medicine Combination Association Rule
           Analysis Based on an Improved Apriori Algorithm in Treating Patients with
           COVID-19 Disease

    • Abstract: In this work, an improved Apriori algorithm is proposed. The main goal is to improve the processing efficiency of the algorithm, and the idea and process of the Apriori algorithm are optimized. The proposed method is compared with the classical association rule algorithm to verify its effectiveness. Traditional Chinese medicine plays a certain role in the prevention and treatment of COVID-19. In order to deeply mine the association rules between Chinese herbal medicines for the prevention and treatment of COVID-19, this improved Apriori algorithm is applied from the retrieved published scientific literature and the guidelines for the prevention and treatment of COVID-19 published all over China. Based on the representation of traditional Chinese medicine data in binary form, the potential core traditional Chinese medicine combinations in the treatment of COVID-19 are identified. The results of association rules of Chinese herbal medicine data obtained from the real database provide an important reference for the analysis of COVID-19 combined treatment of Chinese herbal medicine.
      PubDate: Wed, 31 Aug 2022 07:20:00 +000
  • TCM Constitution Analysis Method Based on Parallel FP-Growth Algorithm in
           Hadoop Framework

    • Abstract: This work is devoted to establishing a comparatively accurate classification model between symptoms, constitutions, and regimens for traditional Chinese medicine (TCM) constitution analysis to provide preliminary screening and decision support for clinical diagnosis. However, for the analysis of massive distributed medical data in a cloud platform, the traditional data mining methods have the problems of low mining efficiency and large memory consumption, and long tuning time, an association rules method for TCM constitution analysis (ARA-TCM) is proposed that based on FP-growth algorithm and the open-source distributed file system in Hadoop framework (HDFS) to make full use of its powerful parallel processing capability. Firstly, the proposed method was used to explore the association rules between the 9 kinds of TCM constitutions and symptoms, as well as the regimen treatment plans, so as to discover the rules of typical clinical symptoms and treatment rules of different constitutions and to conduct an evidence-based medical evaluation of TCM effects in constitution-related chronic disease health management. Secondly, experiments were applied on a self-built TCM clinical records database with a total of 30,071 entries and it is found that the top three constitutions are mid constitution (42.3%), hot and humid constitution (31.3%), and inherited special constitution (26.2%), respectively. What is more, there are obvious promotions in the precision and recall rate compared with the Apriori algorithm, which indicates that the proposed method is suitable for the classification of TCM constitutions. This work is mainly focused on uncovering the rules of “disease symptoms constitution regimen” in TCM medical records, but tongue image and pulse signal are also very important to TCM constitution analysis. Therefore, this additional information should be considered into further studies to be more in line with the actual clinical needs.
      PubDate: Tue, 30 Aug 2022 10:35:00 +000
  • Sparse Dictionary-Based Magnetic Resonance Superresolution Imaging with
           Joint Loss Function Learning

    • Abstract: Magnetic resonance image has important application value in disease diagnosis. Due to the particularity of its imaging mechanism, the resolution of hardware imaging needs to be improved by increasing radiation intensity and radiation time. Excess radiation can cause the body to overheat and, in severe cases, inactivate the protein. This problem is expected to be solved by the image superresolution method based on joint dictionary learning, which has good superresolution performance. In the process of dictionary learning, the loss function will directly affect the dictionary performance. The general method only uses the cascade error as the optimization function in dictionary training, and the method does not consider the individual reconstruction error of high- and low-resolution image dictionary. In order to solve the above problem, In this paper, the loss function of dictionary learning is optimized. While ensuring that the coefficients are sufficiently sparse, the high- and low-resolution dictionaries are trained separately to reduce the error generated by the joint high- and low-resolution dictionary block pair and increase the high-resolution reconstruction error. Experiments on neck and ankle MR images show that the proposed algorithm has better superresolution reconstruction performance on ×2 and ×4 compared with bicubic interpolation, nearest neighbor, and original dictionary learning algorithms.
      PubDate: Mon, 29 Aug 2022 15:35:01 +000
  • Study on the Mechanism of Huanglian Jiedu Decoction in Treating
           Dyslipidemia Based on Network Pharmacology

    • Abstract: Objective. This study aimed to determine the active ingredients of Huanglian Jiedu decoction (HLJDD) and the targets for treating dyslipidemia through network pharmacology to facilitate further application of HJJDD in the treatment of dyslipidemia. Methods. Potential drug targets for dyslipidemia were identified with a protein-protein interaction network. Gene ontology (GO) enrichment analysis and KEGG pathway analysis were performed to elucidate the biological function and major pathways involved in the HLJDD-mediated treatment of dyslipidemia. Results. This approach revealed 22 components, 234 targets of HLJDD, and 221 targets of dyslipidemia. There were 14 components and 31 common targets between HLJDD and dyslipidemia treatment. GO enrichment analysis showed that these targets were mainly associated with the response to DNA-binding transcription factor activity, lipid localization and storage, reactive oxygen species metabolic process, and inflammatory response. The results of KEGG analysis indicated that the AGE-RAGE, NF-κB, HIF-1, IL-17, TNF, FoxO, and PPAR signalling pathways were enriched in the antidyslipidemic action of HLJDD. Conclusion. This study expounded the pharmacological actions and molecular mechanisms of HLJDD in treating dyslipidemia from a holistic perspective, which may provide a scientific basis for the clinical application of HLJDD.
      PubDate: Wed, 24 Aug 2022 18:20:00 +000
  • Automated Detection and Characterization of Colon Cancer with Deep
           Convolutional Neural Networks

    • Abstract: Colon cancer is a momentous reason for illness and death in people. The conclusive diagnosis of colon cancer is made through histological examination. Convolutional neural networks are being used to analyze colon cancer via digital image processing with the introduction of whole-slide imaging. Accurate categorization of colon cancers is necessary for capable analysis. Our objective is to promote a system for detecting and classifying colon adenocarcinomas by applying a deep convolutional neural network (DCNN) model with some preprocessing techniques on digital histopathology images. It is a leading cause of cancer-related death, despite the fact that both traditional and modern methods are capable of comparing images that may encompass cancer regions of various sorts after looking at a significant number of colon cancer images. The fundamental problem for colon histopathologists is differentiating benign from malignant illnesses to having some complicated factors. A cancer diagnosis can be automated through artificial intelligence (AI), enabling us to appraise more patients in less time and at a decreased cost. Modern deep learning (MDL) and digital image processing (DIP) approaches are used to accomplish this. The results indicate that the proposed structure can accurately analyze cancer tissues to a maximum of 99.80%. By implementing this approach, medical practitioners will establish an automated and reliable system for detecting various forms of colon cancer. Moreover, CAD systems will be built in the near future to extract numerous aspects from colonoscopic images for use as a preprocessing module for colon cancer diagnosis.
      PubDate: Wed, 24 Aug 2022 15:50:00 +000
  • Effect of Unilateral Knee Extension Restriction on the Lumbar Region
           during Gait

    • Abstract: Unilateral knee extension restriction might change trunk alignment and increase mechanical load on the lumbar region during walking. We aimed to clarify lumbar region mechanical load during walking with restricted knee extension using a musculoskeletal model simulation. Seventeen healthy adult males were enrolled in this study. Participants walked 10 m at a comfortable velocity with and without restricted right knee extension of 15° and 30° using a knee brace. L4–5 joint moment, joint reaction force, and muscle forces around the lumbar region during walking were calculated for each condition. Peaks of kinetic data were compared among three gait conditions during 0%–30% and 50%–80% of the right gait cycle. Lumbar extension moment at early stance of the bilateral lower limbs was significantly increased in the 30° restricted condition (). Muscle force of the multifidus showed peaks at stance phase of the contralateral side during walking, and the erector spinae showed force peaks at early stance of the bilateral lower limb. Muscle force of the multifidus and erector spinae increased with increasing degree of knee flexion (), with a large effect size (η2 = 0.273–0.486). The joint force acting on L4–5 showed two peaks at early stance of the bilateral lower limbs during the walking cycle. The anterior and vertical joint force on L4–5 increased by 14.2%–36.5% and 10.0%–23.0% in walking with restricted knee extension, respectively (), with a large effect size (η2 = 0.149–0.425). Restricted knee joint extension changed trunk alignment and increased the muscle force and the vertical and anterior joint force on the L4–5 joint during walking; this tendency became more obvious with increased restriction angle. Our results provide important information for therapists engaged in the rehabilitation of patients with knee contracture.
      PubDate: Mon, 22 Aug 2022 17:50:01 +000
  • Effects of Ultrasound-Guided Stellate Ganglion Block on Postoperative
           Quality of Recovery in Patients Undergoing Breast Cancer Surgery: A
           Randomized Controlled Clinical Trial

    • Abstract: Surgery has been the primary treatment for breast cancer. However, instant postoperative complications, such as sleep disorder and pain, dramatically impair early postoperative quality of recovery, resulting in more extended hospital stays and higher costs. Recent clinical trials indicated that stellate ganglion block (SGB) could prolong sleep time and improve sleep quality in breast cancer survivors. Moreover, during the perioperative period, SGB enhanced the recovery of gastrointestinal functions in patients with laparoscopic colorectal cancer surgery and thoracolumbar spinal surgery. Furthermore, perioperative SGB decreased intraoperative requirements for anesthetics and analgesics in patients with complex regional pain syndrome. However, information is scarce regarding the effects of SGB on postoperative quality recovery in patients with breast cancer surgery. Therefore, we investigated the effects of SGB on the postoperative quality of recovery of patients undergoing breast cancer surgery. Sixty patients who underwent an elective unilateral modified radical mastectomy were randomized into two 30-patient groups that received either an ultrasound-guided right-sided SGB with 6 ml 0.25% ropivacaine (SGB group) or no block (control group). The primary outcome was the quality of postoperative recovery 24 hours after surgery, assessed with a Chinese version of the 40-item Quality of Recovery (QoR-40) questionnaire. Secondary outcomes were intraoperative requirements of propofol and opioids, rest pain at two, four, eight, and 24 hours after surgery, patient satisfaction score, and the incidence of postoperative abdominal distension. At 24 hours after surgery, global QoR-40 scores were higher in the SGB group than in the control group. Besides, in the SGB group, patients needed less propofol, had a lower incidence of postoperative abdominal bloating, and had higher satisfaction scores. Ultrasound-guided SGB could improve the quality of postoperative recovery in patients undergoing breast cancer surgery by less intraoperatively need for propofol and better postoperative recovery of sleep and gastrointestinal function.
      PubDate: Mon, 22 Aug 2022 17:35:00 +000
  • Nomogram Models Based on the Gene Expression in Prediction of Breast
           Cancer Bone Metastasis

    • Abstract: Objective. The aim of this study is to design a weighted co-expression network and build gene expression signature-based nomogram (GESBN) models for predicting the likelihood of bone metastasis in breast cancer (BC) patients. Methods. Dataset GSE124647 was used as a training set, while GSE16446, GSE45255, and GSE14020 were taken as validation sets. In the training cohort, the limma package in R was adopted to obtain differentially expressed genes (DEGs) between BC nonbone metastasis and bone metastasis patients, which were used for functional enrichment analysis. After weighted co-expression network analysis (WGCNA), univariate Cox regression and Kaplan–Meier plotter analyses were performed to screen potential prognosis-related genes. Then, GESBN models were constructed and evaluated. The prognostic value of the GESBN models was investigated in the GSE124647 dataset, which was validated in GSE16446 and GSE45255 datasets. Further, the expression levels of genes in the models were explored in the training set, which was validated in GSE14020. Finally, the expression and prognostic value of hub genes in BC were explored. Results. A total of 1858 DEGs were obtained. The WGCNA result showed that the blue module was most significantly related to bone metastasis and prognosis. After survival analyses, GAJ1, SLC24A3, ITGBL1, and SLC44A1 were subjected to construct a GESBN model for overall survival (OS). While GJA1, IGFBP6, MDFI, TGFBI, ANXA2, and SLC24A3 were subjected to build a GESBN model for progression-free survival (PFS). Kaplan–Meier plotter and receiver operating characteristic analyses presented the reliable prediction ability of the models. Cox regression analysis further revealed that GESBN models were independent prognostic predictors for OS and PFS in BC patients. Besides, GJA1, IGFBP6, ITGBL1, SLC44A1, and TGFBI expressions were significantly different between the two groups in GSE124647 and GSE14020. The hub genes had a significant impact on patient prognosis. Conclusion. Both the four-gene signature and six-gene signature could accurately predict patient prognosis, which may provide novel treatment insights for BC bone metastasis.
      PubDate: Mon, 22 Aug 2022 17:35:00 +000
  • Numerical Analysis of Nasal Flow Characteristics with Microparticles

    • Abstract: This study was to investigate the airflow characteristics in nasal cavity under different conditions and analyze the effects of different respiratory intensity, particle diameter, and particle density on the deposition of particles carried by airflow in the nasal cavity, respectively. The three-dimensional geometric model of the nasal cavity was established based on typical medical images. The SST k-ω turbulence model in the computational fluid dynamics (CFD) was used to simulate the airflow in the nasal cavity, and the deposition of particles in the airflow was analyzed with the Lagrange discrete phase model. The results showed that the air in the nasal cavity flows in the left and right nasal passages through the perforation in front of the nasal septum and forms a vortex structure at the perforation site, and the particle deposition efficiencies (DE) under perforation nasal cavity are higher than that under normal nasal cavity. Different parameters had different effects on the particle DE. The results showed that the DE of particles with smaller size (≤2.5 μm) is lower; the higher the respiration intensity, the greater the influence on the DE of the larger particle size; and the larger particle density (>1550 kg·m−3) has little effect on the DE of larger particle size (DP = 10 μm). The results agree well with the corresponding research data.
      PubDate: Mon, 22 Aug 2022 11:35:01 +000
  • Machine Learning for Predicting Hyperglycemic Cases Induced by PD-1/PD-L1

    • Abstract: Objective. Immune checkpoint inhibitors, such as programmed death-1/ligand-1 (PD-1/L1), exhibited autoimmune-like disorders, and hyperglycemia was on the top of grade 3 or higher immune-related adverse events. Machine learning is a model from past data for future data prediction. From post-marketing monitoring, we aimed to construct a machine learning algorithm to efficiently and rapidly predict hyperglycemic adverse reaction in patients using PD-1/L1 inhibitors. Methods. In original data downloaded from Food and Drug Administration Adverse Event Reporting System (US FAERS), a multivariate pattern classification of support vector machine (SVM) was used to construct a classifier to separate adverse hyperglycemic reaction patients. With correct core SVM function, a 10-fold 3-time cross validation optimized parameter value composition in model setup with R language software. Results. The SVM prediction model was set up from the number type/number optimization method, as well as the kernel and type of “rbf” and “nu-regression” composition. Two key values (nu and gamma) and case number displayed high adjusted r2 in curve regressions (). This SVM model with computable parameters greatly improved the assessing indexes (accuracy, F1 score, and kappa) as well as coequal sensitivity and the area under the curve (AUC). Conclusion. We constructed an effective machine learning model based on compositions of exact kernels and computable parameters; the SVM prediction model can noninvasively and precisely predict hyperglycemic adverse drug reaction (ADR) in patients treated with PD-1/L1 inhibitors, which could greatly help clinical practitioners to identify high-risk patients and perform preventive measurements in time. Besides, this model setup process provided an analytic conception for promotion to other ADR prediction, such ADR information is vital for outcome improvement by identifying high-risk patients, and this machine learning algorithm can eventually add value to clinical decision making.
      PubDate: Fri, 19 Aug 2022 07:20:00 +000
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Heriot-Watt University
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