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  Subjects -> SOCIAL SERVICES AND WELFARE (Total: 243 journals)
Showing 1 - 135 of 135 Journals sorted alphabetically
Aboriginal and Islander Health Worker Journal     Full-text available via subscription   (Followers: 18)
ACOSS Papers     Full-text available via subscription   (Followers: 4)
Adoption & Fostering     Hybrid Journal   (Followers: 25)
Advances in Neurodevelopmental Disorders     Hybrid Journal   (Followers: 5)
Advances in Social Work     Open Access   (Followers: 40)
African Journal of Social Work     Open Access   (Followers: 2)
African Security     Hybrid Journal   (Followers: 7)
Annals of the American Academy of Political and Social Science     Hybrid Journal   (Followers: 52)
Argumentum     Open Access  
Asia Pacific Journal of Social Work and Development     Hybrid Journal   (Followers: 12)
Asian Social Work and Policy Review     Hybrid Journal   (Followers: 10)
Australasian Journal of Human Security     Full-text available via subscription   (Followers: 1)
Australasian Policing     Full-text available via subscription   (Followers: 8)
Australian Ageing Agenda     Full-text available via subscription   (Followers: 7)
Australian Journal of Emergency Management     Full-text available via subscription   (Followers: 30)
Australian Journal of Social Issues     Hybrid Journal   (Followers: 7)
Australian Journal on Volunteering     Full-text available via subscription   (Followers: 3)
Australian Social Work     Hybrid Journal   (Followers: 13)
AZARBE : Revista Internacional de Trabajo Social y Bienestar     Open Access   (Followers: 6)
Bakti Budaya     Open Access   (Followers: 1)
Basic and Applied Social Psychology     Hybrid Journal   (Followers: 47)
British Journal of Social Work     Hybrid Journal   (Followers: 104)
Campbell Systematic Reviews     Open Access   (Followers: 3)
Canadian Social Work Review     Full-text available via subscription   (Followers: 11)
Care Management Journals     Hybrid Journal   (Followers: 5)
Clinical Social Work Journal     Hybrid Journal   (Followers: 33)
Columbia Social Work Review     Open Access   (Followers: 1)
Communities, Children and Families Australia     Full-text available via subscription   (Followers: 4)
Community Development     Hybrid Journal   (Followers: 26)
Community, Work & Family     Hybrid Journal   (Followers: 26)
Comunitania : Revista Internacional de Trabajo Social y Ciencias Sociales     Open Access   (Followers: 1)
ConCienciaSocial     Open Access  
Contemporary Rural Social Work     Open Access   (Followers: 17)
Counseling Outcome Research and Evaluation     Hybrid Journal   (Followers: 13)
Counseling Psychology and Psychotherapy     Open Access   (Followers: 19)
Counsellor (The)     Full-text available via subscription   (Followers: 3)
Critical and Radical Social Work     Hybrid Journal   (Followers: 22)
Critical Policy Studies     Hybrid Journal   (Followers: 15)
Critical Social Policy     Hybrid Journal   (Followers: 49)
Critical Social Work : An Interdisciplinary Journal Dedicated to Social Justice     Open Access   (Followers: 2)
Cuadernos de Trabajo Social     Open Access   (Followers: 1)
Death Studies     Hybrid Journal   (Followers: 24)
Developing Practice : The Child, Youth and Family Work Journal     Full-text available via subscription   (Followers: 21)
Developmental Child Welfare     Hybrid Journal  
Du Bois Review: Social Science Research on Race     Full-text available via subscription   (Followers: 12)
Em Pauta : Teoria Social e Realidade Contemporânea     Open Access   (Followers: 1)
Ethics and Social Welfare     Hybrid Journal   (Followers: 25)
European Journal of Social Psychology     Hybrid Journal   (Followers: 46)
European Journal of Social Security     Full-text available via subscription   (Followers: 8)
European Journal of Social Work     Hybrid Journal   (Followers: 38)
European Journal of Work and Organizational Psychology     Hybrid Journal   (Followers: 37)
European Review of Social Psychology     Hybrid Journal   (Followers: 17)
Families in Society : The Journal of Contemporary Social Services     Full-text available via subscription   (Followers: 12)
Finnish Journal of eHealth and eWelfare : Finjehew     Open Access  
Geopolitical, Social Security and Freedom Journal     Open Access   (Followers: 1)
Global Social Policy     Hybrid Journal   (Followers: 36)
Global Social Welfare     Hybrid Journal   (Followers: 8)
Grief Matters : The Australian Journal of Grief and Bereavement     Full-text available via subscription   (Followers: 12)
Health & Social Care In the Community     Hybrid Journal   (Followers: 55)
Health and Social Care Chaplaincy     Hybrid Journal   (Followers: 11)
Health and Social Work     Hybrid Journal   (Followers: 72)
HOLISTICA ? Journal of Business and Public Administration     Open Access  
Hong Kong Journal of Social Work, The     Hybrid Journal   (Followers: 3)
Housing Policy Debate     Hybrid Journal   (Followers: 21)
Human Service Organizations Management, Leadership and Governance     Hybrid Journal   (Followers: 28)
Indonesian Journal of Guidance and Counseling     Open Access  
International Journal of Ageing and Later Life     Open Access   (Followers: 4)
International Journal of Care and Caring     Hybrid Journal  
International Journal of Disability Management Research     Full-text available via subscription   (Followers: 3)
International Journal of East Asian Studies     Open Access   (Followers: 5)
International Journal of School Social Work     Open Access   (Followers: 5)
International Journal of Social Research Methodology     Hybrid Journal   (Followers: 79)
International Journal of Social Welfare     Hybrid Journal   (Followers: 19)
International Journal of Social Work     Open Access   (Followers: 26)
International Journal of Sociology and Social Policy     Hybrid Journal   (Followers: 68)
International Journal on Child Maltreatment : Research, Policy and Practice     Hybrid Journal   (Followers: 2)
International Social Science Journal     Hybrid Journal   (Followers: 25)
International Social Security Review     Hybrid Journal   (Followers: 9)
International Social Work     Hybrid Journal   (Followers: 22)
Islamic Counseling : Jurnal Bimbingan Konseling Islam     Open Access   (Followers: 1)
Janus Sosiaalipolitiikan ja sosiaalityön tutkimuksen aikakauslehti     Open Access  
Journal for Specialists in Group Work     Hybrid Journal   (Followers: 2)
Journal of Accessibility and Design for All     Open Access   (Followers: 15)
Journal of Applied Social Psychology     Hybrid Journal   (Followers: 61)
Journal of Baccalaureate Social Work     Full-text available via subscription   (Followers: 2)
Journal of Benefit-Cost Analysis     Hybrid Journal   (Followers: 4)
Journal of Care Services Management     Hybrid Journal   (Followers: 7)
Journal of Child and Adolescent Counseling     Hybrid Journal  
Journal of Community & Applied Social Psychology     Partially Free   (Followers: 15)
Journal of Community Practice     Hybrid Journal   (Followers: 12)
Journal of Comparative Social Welfare     Hybrid Journal   (Followers: 19)
Journal of Comparative Social Work     Open Access   (Followers: 2)
Journal of Danubian Studies and Research     Open Access  
Journal of Ethnic & Cultural Diversity in Social Work     Hybrid Journal   (Followers: 20)
Journal of European Social Policy     Hybrid Journal   (Followers: 34)
Journal of Evidence-Based Social Work     Hybrid Journal   (Followers: 28)
Journal of Evidence-Informed Social Work     Hybrid Journal   (Followers: 6)
Journal of Family Issues     Hybrid Journal   (Followers: 24)
Journal of Forensic Social Work     Hybrid Journal   (Followers: 7)
Journal of Health Care for the Poor and Underserved     Full-text available via subscription   (Followers: 9)
Journal of Healthcare Engineering     Open Access   (Followers: 3)
Journal of HIV/AIDS & Social Services     Hybrid Journal   (Followers: 10)
Journal of Human Rights and Social Work     Hybrid Journal  
Journal of Integrated Care     Hybrid Journal   (Followers: 22)
Journal of International and Comparative Social Policy     Hybrid Journal   (Followers: 3)
Journal of Investigative Psychology and Offender Profiling     Hybrid Journal   (Followers: 10)
Journal of Language and Social Psychology     Hybrid Journal   (Followers: 17)
Journal of Occupational Science     Hybrid Journal   (Followers: 30)
Journal of Personality and Social Psychology     Full-text available via subscription   (Followers: 401)
Journal of Policy Practice     Hybrid Journal   (Followers: 6)
Journal of Policy Practice and Research     Hybrid Journal   (Followers: 3)
Journal of Prevention & Intervention Community     Hybrid Journal   (Followers: 9)
Journal of Professional Counseling: Practice, Theory & Research     Hybrid Journal  
Journal of Public Health     Hybrid Journal   (Followers: 240)
Journal of Public Mental Health     Hybrid Journal   (Followers: 16)
Journal of Religion & Spirituality in Social Work: Social Thought     Hybrid Journal   (Followers: 12)
Journal of Social Development in Africa     Full-text available via subscription   (Followers: 8)
Journal of Social Distress and the Homeless     Hybrid Journal   (Followers: 6)
Journal of Social Issues     Hybrid Journal   (Followers: 22)
Journal of Social Philosophy     Hybrid Journal   (Followers: 28)
Journal of Social Policy     Hybrid Journal   (Followers: 45)
Journal of Social Service Research     Hybrid Journal   (Followers: 14)
Journal of Social Work     Hybrid Journal   (Followers: 210)
Journal of Social Work Education     Hybrid Journal   (Followers: 16)
Journal of Social Work in Disability & Rehabilitation     Hybrid Journal   (Followers: 20)
Journal of Social Work in the Global Community     Open Access   (Followers: 5)
Journal of Social Work Practice in the Addictions     Hybrid Journal   (Followers: 14)
Journal of the Society for Social Work and Research     Full-text available via subscription   (Followers: 14)
Jurnal Guidena : Journal of Guidance and counseling, Psychology and Education     Open Access   (Followers: 3)
Jurnal Karya Abdi Masyarakat     Open Access  
Just Policy: A Journal of Australian Social Policy     Full-text available via subscription   (Followers: 18)
Kontext : Zeitschrift für Systemische Therapie und Familientherapie     Hybrid Journal  
L'Orientation scolaire et professionnelle     Open Access   (Followers: 1)
Learning in Health and Social Care     Hybrid Journal   (Followers: 16)
Leidfaden : Fachmagazin für Krisen, Leid, Trauer     Hybrid Journal  
Links to Health and Social Care     Open Access   (Followers: 1)
Maltrattamento e abuso all’infanzia     Full-text available via subscription  
Measurement and Evaluation in Counseling and Development     Hybrid Journal   (Followers: 4)
Mental Health and Social Inclusion     Hybrid Journal   (Followers: 43)
Mental Health and Substance Use: dual diagnosis     Hybrid Journal   (Followers: 32)
Merrill-Palmer Quarterly     Full-text available via subscription   (Followers: 1)
Mortality: Promoting the interdisciplinary study of death and dying     Hybrid Journal   (Followers: 11)
Mundos do Trabalho     Open Access  
National Emergency Response     Full-text available via subscription   (Followers: 4)
New Zealand Journal of Occupational Therapy     Full-text available via subscription   (Followers: 71)
Nordic Social Work Research     Hybrid Journal   (Followers: 7)
Nordisk välfärdsforskning | Nordic Welfare Research     Open Access  
Northwestern Journal of Law & Social Policy     Open Access   (Followers: 6)
Nouvelles pratiques sociales     Full-text available via subscription   (Followers: 5)
Nusantara of Research: Jurnal Hasil-hasil Penelitian Universitas Nusantara PGRI Kediri     Open Access   (Followers: 2)
Parity     Full-text available via subscription   (Followers: 4)
Partner Abuse     Hybrid Journal   (Followers: 10)
Pedagogia i Treball Social : Revista de Cičncies Socials Aplicades     Open Access  
Personality and Social Psychology Bulletin     Hybrid Journal   (Followers: 243)
Personality and Social Psychology Review     Hybrid Journal   (Followers: 51)
Philosophy & Social Criticism     Hybrid Journal   (Followers: 25)
Policy Sciences     Hybrid Journal   (Followers: 13)
Practice: Social Work in Action     Hybrid Journal   (Followers: 20)
Prospectiva : Revista de Trabajo Social e Intervención Social     Open Access   (Followers: 2)
Psikopedagogia : Jurnal Bimbingan dan Konseling     Open Access   (Followers: 2)
Psychoanalytic Social Work     Hybrid Journal   (Followers: 10)
Public Policy and Aging Report     Hybrid Journal   (Followers: 3)
Qualitative Research     Hybrid Journal   (Followers: 35)
Qualitative Social Work     Hybrid Journal   (Followers: 26)
Quality in Ageing and Older Adults     Hybrid Journal   (Followers: 46)
Race and Social Problems     Hybrid Journal   (Followers: 11)
Research in Social Stratification and Mobility     Hybrid Journal   (Followers: 12)
Research on Economic Inequality     Hybrid Journal   (Followers: 11)
Research on Language and Social Interaction     Hybrid Journal   (Followers: 20)
Research on Social Work Practice     Hybrid Journal   (Followers: 43)
Review of Social Economy     Hybrid Journal   (Followers: 3)
Revista Brasileira de Tecnologias Sociais     Open Access  
Revista Internacional De Seguridad Social     Hybrid Journal  
Revista Katálysis     Open Access  
Revista Serviço Social em Perspectiva     Open Access  
Safer Communities     Hybrid Journal   (Followers: 63)
Science and Public Policy     Hybrid Journal   (Followers: 30)
Self and Identity     Hybrid Journal   (Followers: 17)
SER Social     Open Access  
Service social     Full-text available via subscription   (Followers: 7)
Serviço Social & Sociedade     Open Access   (Followers: 1)
Sexual Abuse in Australia and New Zealand     Full-text available via subscription   (Followers: 11)
Sexualidad, Salud y Sociedad (Rio de Janeiro)     Open Access   (Followers: 2)
Skriftserien Socialt Arbejde     Open Access  
Social Action : The Journal for Social Action in Counseling and Psychology     Free   (Followers: 2)
Social and Personality Psychology Compass     Hybrid Journal   (Followers: 20)
Social Behavior and Personality : An International Journal     Full-text available via subscription   (Followers: 13)
Social Choice and Welfare     Hybrid Journal   (Followers: 11)
Social Cognition     Full-text available via subscription   (Followers: 20)
Social Compass     Hybrid Journal   (Followers: 5)
Social Influence     Hybrid Journal   (Followers: 7)
Social Justice Research     Hybrid Journal   (Followers: 21)
Social Philosophy and Policy     Full-text available via subscription   (Followers: 25)
Social Policy & Administration     Hybrid Journal   (Followers: 28)
Social Policy and Society     Hybrid Journal   (Followers: 217)
Social Science Japan Journal     Hybrid Journal   (Followers: 14)
Social Semiotics     Hybrid Journal   (Followers: 10)
Social Work     Hybrid Journal   (Followers: 38)
Social Work & Social Sciences Review     Open Access   (Followers: 24)
Social Work / Maatskaplike Werk     Open Access  

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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  [343 journals]
  • Pattern and In-Hospital Mortality of Thoracoabdominal Injuries Associated
           with Motor Vehicle Accident-Related Spinal Injury: A Retrospective
           Single-Center Study

    • Abstract: Background. Motor vehicle accident (MVA) is a global health hazard that results in spinal, thoracic, and abdominal injuries. Detailed studies on the association between MVA-related traumatic spinal injury (TSI) and thoracoabdominal injuries are lacking. This study aims to elucidate the prevalence, pattern of association between these injuries, and related outcomes in terms of in-hospital mortality. Methods. This is a retrospective single-center study of MVA-related TSI with thoracoabdominal associated injuries. Descriptive analysis was performed for gender, age, spinal injury level, thoracoabdominal injury region, admission day, hospital stay duration, and discharge category. The association between TSI and thoracoabdominal injury was analyzed, and the chi-square test was used to test the significance of differences. A statistically significant difference was considered at values less than 0.05. Results. The cohort had a mean age of 33.6 ± 17.7 years with predominantly more males (85.1%). Thoracoabdominal injuries were present in 10.5% of MVA-related TSIs, and 9.2% of victims died during their hospital stay. There is a significant () association between the level of the spinal and the region of thoracoabdominal injuries. The presence of TSI-associated thoracic injury significantly () correlated with increased in-hospital mortality more than abdominal injury. Conclusion. Thoracoabdominal injuries concomitant with MVA-related TSI cause considerable mortality. A pattern of association exists between the level of spinal and region of thoracoabdominal injury. Knowledge of this pattern is helpful in the routine practice of trauma health partitioners.
      PubDate: Thu, 21 Oct 2021 12:35:01 +000
       
  • Deep Learning Algorithms-Based CT Images in Glucocorticoid Therapy in
           Asthma Children with Small Airway Obstruction

    • Abstract: CT image information data under deep learning algorithms was adopted to evaluate small airway function and analyze the clinical efficacy of different glucocorticoid administration ways in asthmatic children with small airway obstruction. The Res-NET in the deep learning algorithm was used to perform feature extraction, summary classification, and other reconstruction of CT images. A deep learning network model Mask-R-CNN was constructed to enhance the ability of image reconstruction. A total of 118 children hospitalized with acute exacerbation of asthma in the hospital were recruited. After acute exacerbation treatment, 96 children with asthma were screened out for small airway obstruction, which were divided into glucocorticoid aerosol inhalation group (group A, 32 cases), glucocorticoid combined with bronchodilator aerosol inhalation group (group B, 32 cases), and oral hormone therapy group (group C, 32 cases). Asthmatic children with small airway obstruction were screened after acute exacerbation treatment and were rolled into glucocorticoid aerosol inhalation group (group A), glucocorticoid combined with bronchodilators aerosol inhalation group (group B), and oral hormone therapy group (group C). Lung function indicators (maximal mid-expiratory flow (MMEF75 and 25), 50% forced expiratory flow (FEF50), and 75% forced expiratory flow (FEF75)), FeNO level, and airway inflammation indicators (IL-6, IL-35, and eosinophilic (EOS)) were compared before and one month after treatment. The ratio of airway wall thickness to outer diameter (T/D) and the percentage of airway wall area to total airway area (WA%) were measured by e-Health high-resolution CT (HRCT). The constructed network model was used to measure the patient's coronary artery plaque and blood vessel volume, and the image was reconstructed on the Res-Net network. It was found that the MSE value of the Res-Net network was the lowest, and the efficiency was very high during the training process. T/D and WA (%) of asthmatic children with small airway obstruction after treatment were significantly lower than those before treatment (). After treatment, MMEF75/25 and FEF75 were significantly higher than those before treatment (). Lung function-related indicator FEF50 was significantly higher than that before treatment (). FeNO level after treatment was remarkably lower than that before treatment (). In addition, lung function-related indicators, airway inflammation indicators, and FeNO level improved the most in group C, followed by group B, and those improvements in group A were the least obvious, with great differences among groups (). In summary, the Res-Net model proposed was of certain feasibility and effectiveness for CT image segmentation and can effectively improve the clinical evaluation of patient CT image information. Glucocorticoids could improve small airway function and airway inflammation in asthmatic children with small airway obstruction, and oral corticosteroids were more effective than aerosol inhalation therapy.
      PubDate: Thu, 21 Oct 2021 06:05:02 +000
       
  • Deep Learning-Based Computed Tomography Imaging to Diagnose the Lung
           Nodule and Treatment Effect of Radiofrequency Ablation

    • Abstract: This study aimed to detect and diagnose the lung nodules as early as possible to effectively treat them, thereby reducing the burden on the medical system and patients. A lung computed tomography (CT) image segmentation algorithm was constructed based on the deep learning convolutional neural network (CNN). The clinical data of 69 patients with lung nodules diagnosed by needle biopsy and pathological comprehensive diagnosis at hospital were collected for specific analysis. The CT image segmentation algorithm was used to distinguish the nature and volume of lung nodules and compared with other computer aided design (CAD) software (Philips ISP). 69 patients with lung nodules were treated by radiofrequency ablation (RFA). The results showed that the diagnostic sensitivity of the CT image segmentation algorithm based on the CNN was obviously higher than that of the Philips ISP for solid nodules
      PubDate: Wed, 20 Oct 2021 13:20:01 +000
       
  • Model Construction of Using Physiological Signals to Detect Mental Health
           Status

    • Abstract: Background. Mental health is a direct indicator of human mental activity, and it also affects all aspects of the human body. It plays a very important role in monitoring human mental health. Objectives. To design a mental health state detection model based on physiological signals to detect human mental health. Methods. For the detection of mental health, the sliding window method is used to divide the physiological signal dataset and the corresponding time into several segments and then calculate the physiological signal data in the sliding window for each physiological signal to form a sequence of characteristic values; according to the heart rate variability of the physiological signal, the heart rate variability (HRV) is extracted from the interval spectrum waveform: through the discrete trend analysis in statistics, the change characteristics of the ECG signal are analyzed, and the sequence statistical indicators of the physiological signal are calculated. With the help of a support vector machine used for the significant accuracy with less computation power, the physiological signals of the mental state are classified, and the discriminant function of the mental health state signals is normalized. A mental health state detection model is constructed according to the index system, the optimal solution of the model is obtained through the optimization function, and the mental health state detection is completed. Result. The detection error of the proposed model is less which improves the detection accuracy and is less time consuming. Conclusion. The detection model using physiological signals is proposed to evaluate the mental health status. As compared to the other detection models, its detection time is short and method error is always less than 2% which shows its accuracy and effectiveness.
      PubDate: Wed, 20 Oct 2021 13:20:01 +000
       
  • Impact of HSP90α, CEA, NSE, SCC, and CYFRA21-1 on Lung Cancer
           Patients

    • Abstract: Lung cancer is a lethal disease, and early diagnosis with the aid of biomarkers such as HSP90α protein can certainly assist the doctors to start treatment of patient at the earliest and can save their lives. To analyse the diagnostic value of HSP90α expression in lung cancer patients by collecting data of patients through IoT devices to avoid delay in treatments, a study has been presented in this paper where the significance of HSP90α biomarker is highlighted in early diagnosis of patients suffering from lung cancer. The second objective of the research study is to examine the correlation between the appearance level of HSP90α biomarker and the clinicopathological features of lung cancer. It is also evaluated whether the changes in HSP90α index are indicative or noteworthy before and after surgery of lung cancer patients. An observatory study of 78 patients with lung cancer in Qinhuangdao Hospital is presented in this paper where the samples were collected from June 2018 to March 2020. Their data were collected through IoT devices used in the latest healthcare facilities of the hospital. The ELISA method was utilized to identify the level of plasma HSP90 and to analyse HSP90 levels between the lung cancer group and healthy group of people. The relationship between HSP90 and the clinical pathological features of 78 patients suffering from lung cancer was analysed. An electrochemical luminescence method was used to detect CEA, NSE, SCC, and CYFRA21-1 levels. ROC curve and box plots were used to determine the analytic value of HSP90 and other biomarkers used in lung cancer diagnosis. Forty-two patients with moderate to early stage lung cancer with surgical correction were selected, and paired sample T test was used to analyse HSP90 levels before and after surgery. The plasma HSP90 level of lung cancer patients was quite higher as compared to the group of healthy people as per the values depicted in the research study. Second, HSP90 levels are substantially higher in pathologic type, differentiation degree, stage, and the existence of the lung, liver, and bone metastases (  0.05). The ROC value for HSP90 was 0.599, while the area under the curve of HSP90 combined with other four tumour markers was 0.915 in the presented case study, indicating the presence of lung cancer. Patients with lung cancer had statistically significant differences in HSP90 expression levels before and after surgery ( 
      PubDate: Wed, 20 Oct 2021 13:05:01 +000
       
  • Clinical and Imaging Study of Repetitive Transcranial Magnetic Stimulation
           

    • Abstract: Morphine is tolerable after long-term use. After long-term use, it will have a great impact on the human body, and the treatment effect is not good. In recent years, the continuous development of repetitive transcranial magnetic stimulation (rTMS) treatment technology has made a treatment. Drug-resistant morphine dependence has a breakthrough. In this article, to study the effect of repeated transcranial magnetic stimulation in the treatment of morphine dependence through mGluR5/TDP43/NR2B pathway, experiments were carried out on rats to compare the changes in the images of rats after different periods of morphine use and their effects on morphine withdrawal. During the period, the performance of rats provides a reference for repeated transcranial stimulation to treat morphine dependence. According to the experimental results, after stopping morphine, withdrawal from the rats, irritable acts, and patience diminished. This is a decrease of more than 50% in comparison with the one of the normal group. There was a different degree of variability in the treatment images of mGluR5/TDP43 and so on after rTMS treatment, and the changes were large. These reductions in detoxification responses in rodents suggest that rTMS serves an instrumental role in the prevention and treatment of phosphorylation related to morphine dependence.
      PubDate: Wed, 20 Oct 2021 11:20:01 +000
       
  • Tumor Region Location and Classification Based on Fuzzy Logic and Region
           Merging Image Segmentation Algorithm

    • Abstract: Early diagnosis of tumor plays an important role in the improvement of treatment and survival rate of patients. However, breast tumors are difficult to be diagnosed by invasive examination, so medical imaging has become the most intuitive auxiliary method for breast tumor diagnosis. Although there is no universal perfect method for image segmentation so far, the consensus on the general law of image segmentation has produced considerable research results and methods. In this context, this paper focuses on the breast tumor image segmentation method based on CNN and proposes an improved DCNN method combined with CRF. This method can obtain the information of multiscale and pixels better. The experimental results show that, compared with DCNN without these methods, the segmentation accuracy is significantly improved.
      PubDate: Wed, 20 Oct 2021 11:20:01 +000
       
  • Impact of Factors of Online Deceptive Reviews on Customer Purchase
           Decision Based on Machine Learning

    • Abstract: Online deceptive reviews widely exist in the online shopping environment. Numerous studies have investigated the impact of online product reviews on customer behaviour and sales. However, the existing literature is mainly based on real product reviews; only a few studies have investigated deceptive reviews. Based on the results of deceptive reviews, this article explores the factors that affect customer purchase decision in online review systems, which is flooded by deceptive reviews. Therefore, a deceptive review influence model is proposed based on three influential factors of online review system, sentiment characteristics, review length, and online seller characteristics. Based on them, text mining is used to quantify the indicators of the three influential factors. Through principal component analysis and linear regression, the experimental results of electronic appliances on Tmall show that the three influential factors are positively related to customers' purchase intention and decision making.
      PubDate: Tue, 19 Oct 2021 12:35:01 +000
       
  • Modified Look-Locker Inverse-Recovery (MOLLI) Sequence of Quantitative
           Imaging in Dirty Magnetic Resonance Longitudinal Relaxation Time
           Diagnostic Value of GE Combined with Longitudinal Relaxation Time
           Quantitative Imaging for Myocardial Amyloidosis

    • Abstract: The pathological changes of myocarditis include degeneration and necrosis of myocardial cells and infiltration of inflammatory cells in the myocardial interstitium, accompanied by obvious myocardial fibrosis. Myocardial fibrosis is a determinant of ventricular remodeling and an important indicator of the classification of clinical risk factors and has an important value in evaluating the prognosis of heart disease. Cardiac magnetic resonance (CMR) is the “gold standard” for evaluating the shape and function of the heart, and it can show the characteristic pathological changes of myocardial tissue. The traditional gadolinium imaging agent delays the enhanced sequence images to visually show the extent of the affected myocardial fibrosis, but it cannot effectively identify small focal fibrosis or widespread diffuse fibrosis. The CMR longitudinal relaxation time quantitative technique can directly measure the relaxation time (T1) determined by the myocardial tissue and does not depend on the signal strength of the reference tissue and can quantitatively analyze the affected myocardium. In this study, the initial and enhanced quantitative imaging techniques of CMR were used to measure the magnetic value of the myocardium in patients with myocarditis, to explore the diagnostic value of myocardial fibrosis, and to analyze the correlation between cardiac fibrosis and cardiac function.
      PubDate: Tue, 19 Oct 2021 12:35:01 +000
       
  • The Awareness of the Human Papillomavirus Infection and Oropharyngeal
           Cancer in People to Improve the Health Care System at Al Qunfudhah Region,
           Kingdom of Saudi Arabia

    • Abstract: With 14 million new infections each year, the human papillomavirus (HPV) is the most common sexually transmitted infection (STI) among both men and women in the United States (US). Infections with the human papillomavirus (HPV) are responsible for a considerable portion of the global cancer burden. HPV-related oral malignancies are on the rise around the world, according to epidemiological studies. To provide accurate advice to their patients, dental practitioners require thorough, up-to-date HPV-related knowledge. Methods. In this cross-sectional study, data were collected by the purposely constructed questionnaire. A questionnaire composed of the demographic items and items related to the awareness and knowledge about Human papillomavirus. The questionnaire was constructed after a series of discussions between the panel of experts. This panel was composed of a subject specialist, researcher, and language expert. The Cronbach alpha of the questionnaire was calculated. The study will be conducted in the Al Qunfudhah. Results. The Cronbach alpha of the questionnaire was 0.72. Out of a total of 550 respondents, with a mean (SD) age of 47.5 (11.5), the female respondents were 167 (30.4%) while male were 383 (69.6%). 20.5% of the respondents (out of 550) were having awareness of HPV. Implications. Knowledge of HPV-related oral cancer is critical, and it is advised to be taught as part of dental students’ basic curriculum and clinical training. This problem can be solved by better educational training programs. Knowledge of HPV-related oral cancer is critical, and it is advised to be taught as part of dental students’ basic curriculum and clinical training.
      PubDate: Tue, 19 Oct 2021 11:05:02 +000
       
  • Artificial Intelligence-based MRI Images for Brain in Prediction of
           Alzheimer's Disease

    • Abstract: The study aimed to explore the accuracy and stability of Deep metric learning (DML) algorithm in Magnetic Resonance Imaging (MRI) examination of Alzheimer's Disease (AD) patients. In this study, MRI data of patients obtained were from Alzheimer's Disease Neuroimaging Initiative (ADNI) database (A total of 180 AD cases, 88 women, 92 men; 188 samples in healthy conditions (HC), including 90 females and 98 males. 210 samples of mild cognitive impairment (MCI), 104 females and 106 males). On the basis of deep learning, an early AD diagnosis system was constructed using CNN (Convolutional Neural Network) and DML algorithms. Then, the system was used to classify AD, HC, and MCI, and the two algorithms were compared for the accuracy and stability of in classification of MRI images. It was found that in the classification of AD and HC, the classification accuracy and sensitivity of the deep measurement learning model are both 0.83, superior to the CNN model; in terms of specificity, the classification specificity of the DML model was 0.82, slightly lower than that of the CNN model; and that in the classification of MCI and HC, the classification accuracy and sensitivity of the DML model was 0.65, superior to the CNN model; and in terms of specificity, the classification specificity of the DML model was 0.66, slightly lower than that of the CNN model. It suggested that the DML model demonstrated better classification effects on early AD patients. The loss curve analysis results showed that, for classification of AD and HC or MCI and HC, the DML algorithm can improve the convergence speed of the AD early prediction model. Therefore, the DML algorithm can significantly improve the clarity and quality of MRI images, elevate the classification accuracy and stability of early AD patients, and accelerate the convergence of the model, providing a new way for early prediction of AD.
      PubDate: Tue, 19 Oct 2021 11:05:02 +000
       
  • Analysing a Novel RNA-Binding-Protein-Related Prognostic Signature Highly
           Expressed in Breast Cancer

    • Abstract: Background. Breast cancer (BRCA) is one of the most common cancers and the leading cause of cancer-related death in women. RNA-binding proteins (RBPs) play an important role in the emergence and pathogenesis of tumors. The target RNAs of RBPs are very diverse; in addition to binding to mRNA, RBPs also bind to noncoding RNA. Noncoding RNA can cause secondary structures that can bind to RBPs and regulate multiple processes such as splicing, RNA modification, protein localization, and chromosomes remodeling, which can lead to tumor initiation, progression, and invasion. Methods. (1) BRCA data were downloaded from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases and were used as training and testing datasets, respectively. (2) The prognostic RBPs-related genes were screened according to the overlapping differentially expressed genes (DEGs) from the TCGA database. (3) Univariate Cox proportional hazard regression was performed to identify the genes with significant prognostic value. (4) Further, we used the LASSO regression to construct a prognostic signature and validated the signature in the TCGA and ICGC cohort. (5) Besides, we also performed prognostic analysis, expression level verification, immune cell correlation analysis, and drug correlation analysis of the genes in the model. Results. Four genes (MRPL13, IGF2BP1, BRCA1, and MAEL) were identified as prognostic gene signatures. The prognostic model has been validated in the TCGA and ICGC cohorts. The risk score calculated with four genes signatures could largely predict overall survival for 1, 3, and 5 years in patients with BRCA. The calibration plot demonstrated outstanding consistency between the prediction and actual observation. The findings of online database verification revealed that these four genes were significantly highly expressed in tumors. Also, we observed their significant correlations with some immune cells and also potential correlations with some drugs. Conclusion. We constructed a 4-RBPs-based prognostic signature to predict the prognosis of BRCA patients, and it has the potential for treating and diagnosing BRCA.
      PubDate: Mon, 18 Oct 2021 10:20:02 +000
       
  • The Efficacy of Hydroxychloroquine Combined with Huangqi Tablets in the
           Treatment of Diabetic Nephropathy

    • Abstract: Objective. This study aimed to analyze the effect of hydroxychloroquine combined with Huangqi tablets in the treatment of diabetic nephropathy (DN). Methods. Eighty patients with DN were enrolled and divided into two groups by a random number table. 27 patients received routine treatment + hydroxychloroquine (group A), while 27 patients received routine treatment + hydroxychloroquine + Huangqi tablets (group B) and 26 patients received routine treatment (group C). Results. FPG, 2h PG, and HbA1c levels as well as TC and TG levels were lower in group B than in groups A and C at the end of 3 months of treatment and were lower in group A than in group C ( 
      PubDate: Mon, 18 Oct 2021 08:50:01 +000
       
  • A Computer-Aided Method for Digestive System Abnormality Detection in WCE
           Images

    • Abstract: Wireless capsule endoscopy (WCE) is a powerful tool for the diagnosis of gastrointestinal diseases. The output of this tool is in video with a length of about eight hours, containing about 8000 frames. It is a difficult task for a physician to review all of the video frames. In this paper, a new abnormality detection system for WCE images is proposed. The proposed system has four main steps: (1) preprocessing, (2) region of interest (ROI) extraction, (3) feature extraction, and (4) classification. In ROI extraction, at first, distinct areas are highlighted and nondistinct areas are faded by using the joint normal distribution; then, distinct areas are extracted as an ROI segment by considering a threshold. The main idea is to extract abnormal areas in each frame. Therefore, it can be used to extract various lesions in WCE images. In the feature extraction step, three different types of features (color, texture, and shape) are employed. Finally, the features are classified using the support vector machine. The proposed system was tested on the Kvasir-Capsule dataset. The proposed system can detect multiple lesions from WCE frames with high accuracy.
      PubDate: Mon, 18 Oct 2021 08:50:01 +000
       
  • Doppler Ultrasound under Image Denoising Algorithm in the Diagnosis and
           Treatment of Fetal Growth Restriction Using Aspirin Combined with
           Low-Molecular-Weight Heparin

    • Abstract: Objective. This study explored the clinical application value of image denoising algorithm combined with Doppler ultrasound imaging in evaluation of aspirin combined with low-molecular-weight heparin (LMWH) on fetal growth restriction (FGR). Method. A two-stage image denoising by principal component analysis (PCA) with local pixel grouping (LPG-PCA) denoising algorithm was constructed in this study. Eighty FGR pregnant women were included in the study, and they were rolled into an experimental group (aspirin enteric-coated tablets + LMWH calcium injection) and a control group (LMWH calcium injection) according to the different treatment plans, with 40 cases in each group. All patients were performed with Doppler ultrasound imaging. The blood flow parameters (BFPs) were recorded and compared before and after the treatment in two groups, including power index (PI), resistance index (RI), high systolic blood flow velocity (S), high diastolic blood flow velocity (D), S/D value, and peak systolic velocity (PSV). In addition, the middle cerebral artery (MCA) BFPs, cerebral placental rate (CPR), amniotic fluid index (AFI) and perinatal outcome (PO) of the two groups were compared. Results. The total effective rate of treatment in group A (87.5%) was greatly higher than that in group B (62.5%), showing statistical difference (P 
      PubDate: Sat, 16 Oct 2021 09:35:00 +000
       
  • Early Physical Linear Growth of Small-for-Gestational-Age Infants Based on
           Computer Analysis Method

    • Abstract: This article proposes that machine learning can break through the technical limitations of the linear growth test for the early physique of infants smaller than gestational age and can accurately calculate and predict the consequences of the disease. For testing the linear growth of the early physique of infants smaller than gestational age, the data collection and judgment are carried out according to the computer analysis method. Experimental results show that 47.3% of infants younger than gestational age may have suffocation. The experimental subjects designed in this study are small-for-gestational-age infants who were hospitalized in the neonatal intensive care unit from January 2020 to January 2021. According to the relationship between gestational age and birth weight, the survey subjects were divided into two groups: early group and late group. Male and female small-for-gestational-age infants accounted for 68% and 32%, respectively. Among them, the proportion of early gestational age was the most, with more boys than girls, and sick singleton was more than twins. In the early group, the incidence was 52.1% for neonatal asphyxia, 22.5% for feeding intolerance, 14.8% for intracranial hemorrhage, 6.3% for scleredema, 24.7% for neonatal hyperbilirubinemia, 24.6% for hypoglycemia, 1.1% for apnea, and 3.2% for respiratory distress syndrome. Infants develop differently at different stages of corrected gestational age. The incidence of low body weight (6%) after correction for 3 months was significantly reduced compared with correction for gestational age, and the difference was statistically significant ( 
      PubDate: Sat, 16 Oct 2021 08:20:01 +000
       
  • Smartphone-Based mHealth and Internet of Things for Diabetes Control and
           Self-Management

    • Abstract: In patients with chronic diseases condition, mobile health monitoring facility proves to play a significant role in providing significant assistance toward personal management. This research examined the use of smartphones by diabetes patients and their intentions to apply them for self-care and monitoring as well as management. This cross-sectional survey-based study was conducted in Jul-Aug 2021 with 200 diabetic patients (especially type 2) who were visiting specialized clinics and hospitals of Gujrat state, India. A validated questionnaire survey was designed to collect data, which included questions about demographics, information pertaining to other, use of cellphones, the Internet, and the intention to implement smartphones for diabetes monitoring, self-care, and self-management. A highest number of studied participants have mobile phone (97.5%) and smartphones (87%) and access the Internet on daily basis (83.5%). Younger participants were more inclined to use smartphone apps and have also shown more interest for continuous use in the future (). The majority of participants used apps for nutritional planning (85.5%), to monitor glucose control (76.5%), and for scheduling of diabetes appointments on the calendar (90.5%). Recommendations to use mobile app by doctors or healthcare profession were reported by 20.5% of the participants and attitude and future intention to use mobile apps were reported by the majority of participants. The majority of type 2 diabetes patients choose to use their cellphones and the internet or mobile phone reminder system for medication as well as to plan their diets, monitor their blood sugar levels, and communicate with their doctors. The findings of this research can be used to develop strategies and implement mHealth-based therapies to assist patients with type 2 diabetes to efficiently manage their health and might contribute to reducing patients’ out-of-pocket expenditure as well as reducing disability-adjusted life years (DAILY) attributed by DM.
      PubDate: Sat, 16 Oct 2021 08:05:01 +000
       
  • Risk Factors of Enterostomy Infection Caused by Bacterial Infection
           through Mathematical Modelling-Based Information Data Analysis

    • Abstract: Objective. The study aimed to explore the risk factors of infections after enterostomy through the information data analysis method based on a mathematical model. Methods. 156 cases of enterostomy patients admitted to the hospital were retrospectively selected as the study subjects and were divided into the infection group (17 cases) and normal group (139 cases) according to whether they were complicated with infections. Then, the factors of infection and related indexes before and after surgery were analyzed, and the data of the whole hospital were estimated by mathematical modelling. Results. The length of hospital stay in the infection group was 21 ± 11.2 days, which is longer than 10.1 ± 7.1 days in the normal group ( 
      PubDate: Sat, 16 Oct 2021 07:50:01 +000
       
  • Dynamic Electrocardiogram under P Wave Detection Algorithm Combined with
           Low-Dose Betaloc in Diagnosis and Treatment of Patients with Arrhythmia
           after Hepatocarcinoma Resection

    • Abstract: This work aimed to study the diagnostic value of dynamic electrocardiogram (ECG) based on P wave detection algorithm for arrhythmia after hepatectomy in patients with primary liver cancer, and to compare the therapeutic effect of different doses of Betaloc. P wave detection algorithm was introduced for ECG automatic detection and analysis, which can be used for early diagnosis of arrhythmia. Sixty patients with arrhythmia after hepatectomy for primary liver cancer were selected as the research objects. They were randomly divided into control group, SD group, MD group, and HD group, with 15 cases in each group. No Betaloc, low-dose (≤47.5 mg), medium-dose (47.5–95 mg), and high-dose (142.5–190 mg) Betaloc were used for treatment. As a result, P wave detection algorithms can mark P waves that may be submerged in strong interference. P waves from arrhythmia database were used to verify the performance of the proposed algorithm. The prediction precision (Pp) of ventricular arrhythmia and atrial arrhythmia was 98.53% and 98.76%, respectively. Systolic blood pressure (117.35 ± 7.33, 126.44 ± 9.38, and 116.02 ± 8.2) mmHg in SD group, MD group, and HD group was significantly lower than that in control group (140.3 ± 7.21) mmHg after two weeks of treatment. Moreover, those of SD group and HD group were significantly lower than MD group (). The effective rate of cardiac function improvement in SD group (72.35 ± 1.21%) was significantly higher than that in control group, MD group, and HD group (38.2 ± 0.98%, 65.12 ± 1.33%, and 60.43 ± 1.25%; ). In short, dynamic ECG based on P wave detection algorithm had high diagnostic value for arrhythmia after hepatectomy in patients with primary liver cancer. It was safe and effective for patients to choose small dose of Betaloc.
      PubDate: Sat, 16 Oct 2021 06:50:00 +000
       
  • Computed Tomography Image Texture under Feature Extraction Algorithm in
           the Diagnosis of Effect of Specific Nursing Intervention on Mycoplasma
           Pneumonia in Children

    • Abstract: To evaluate the effect of specific nursing intervention in children with mycoplasma pneumonia (MP), a feature extraction algorithm based on gray level co-occurrence matrix (GLCM) was proposed and combined with computed tomography (CT) image texture features. Then, 98 children with MP were rolled into the observation group with 49 cases (specific nursing) and the control group with 49 cases (routine nursing). CT images based on feature extraction algorithm of optimized GLCM were used to examine the children before and after nursing intervention, and the recovery of the two groups of children was discussed. The results showed that the proportion of lung texture increase, rope shadow, ground glass shadow, atelectasis, and pleural effusion in the observation group (24.11%, 3.86%, 8.53%, 15.03%, and 3.74%) was significantly lower than that in the control group (28.53%, 10.23%, 13.34%, 21.15%, and 8.13%) after nursing (). There were no significant differences in the proportion of small patchy shadows, large patchy consolidation shadows, and bronchiectasis between the observation group and the control group (). In the course of nursing intervention, in the observation group, the disappearance time of cough, normal temperature, disappearance time of lung rales, and absorption time of lung shadow (2.15 ± 0.86 days, 4.81 ± 1.14 days, 3.64 ± 0.55 days, and 5.96 ± 0.62 days) were significantly shorter than those in the control group (2.87 ± 0.95 days, 3.95 ± 1.06 days, 4.51 ± 1.02 days, and 8.14 ± 1.35 days) (). After nursing intervention, the proportion of satisfaction and total satisfaction in the experimental group (67.08% and 28.66%) was significantly higher than that in the control group (40.21% and 47.39%), while the proportion of dissatisfaction (4.26%) was significantly lower than that in the control group (12.4%) (). To sum up, specific nursing intervention was more beneficial to improve the progress of characterization recovery and the overall recovery effect of children with MP relative to conventional nursing. CT image based on feature extraction algorithm of optimized GLCM was of good adoption value in the diagnosis and treatment of MP in children.
      PubDate: Sat, 16 Oct 2021 06:35:01 +000
       
  • Sports Enterprise Marketing and Financial Risk Management Based on
           Decision Tree and Data Mining

    • Abstract: With the development of modern economy, traditional sports industry enterprises have also been further developed in the financial business. How to ensure information security and financial risk management is the problem faced by sports companies. Risk assessment is the use of mathematical models to calculate the risk factors established in the previous step to predict possible risks. In response to the above problems, we developed a sports enterprise marketing and financial risk management model based on decision trees and data mining. First, we have established a relevant evaluation index system and data samples through in-depth understanding of the actual marketing and financial problems of sports companies. Second, we use the decision tree algorithm to mine and explore related data samples and conduct risk assessment through related indicators. By using the model to calculate the probability of occurrence of the risk, analyze the degree of damage. Finally, the algorithm of this paper is analyzed and discussed through simulation experiments.
      PubDate: Fri, 15 Oct 2021 11:35:01 +000
       
  • Analysis of the Clinical Effect of Music Combined with Hypnosis on Labor
           Analgesia Based on Data Mining

    • Abstract: The objective of this paper is to study the curative effect of music combined with hypnosis on labor pains during childbirth. Based on the algorithm of data mining, we randomly selected 100 women who delivered babies in obstetric units from October 2020 to June 2021, set the control group and the observation group, obtained the relevant clinical data through comparison, and analyzed the value of music combined with hypnotic analgesia midwifery in obstetrics. The results showed that the number of spontaneous delivery cases in the observation group was higher than that in the control group () and the delivery time in the observation group was better than that in the control group (). It is proved that music combined with hypnosis can effectively improve the rate of natural childbirth and shorten the overall labor time, so as to guarantee the health of mother and child.
      PubDate: Fri, 15 Oct 2021 11:20:01 +000
       
  • Therapeutic Effect Analysis of Plasma Bipolar Intelligent Electrotonic for
           Cystostomy in the Treatment of Senile Prostatic Hyperplasia

    • Abstract: To solve prostatic hyperplasia in the elderly, a method of cystostomy with plasma bipolar resection was proposed. From January 2019 to March 2020, 42 patients with BPH who needed surgical treatment in the urological department were selected. Cystostomy was performed in bipolar TURP. The cystostomy group and robot group were divided into two groups. The surgical safety, surgical efficiency, complications, and nursing time between the two groups were compared. The results showed that the experimental and control groups’ RUV values were significantly lower than those before surgery. In comparison, the Qmax value was considerably higher than that before surgery. The difference was statistically significant (), suggesting that the cystostomy group in bipolar TURP had more substantial improvement of dysuria, better recovery of detrusor function, and better prognosis. It was proved that, for BPH below 80 g, cystostomy could reduce the operation time, bladder irrigation time, catheter indwelling time, and postoperative hospital stay, improve the operation efficiency, and have the same effect on patients’ symptoms improvement, more excellent psychological support, and higher quality of life score. It is proved that plasma bipolar resection combined with cystostomy can effectively improve annual BPH surgery.
      PubDate: Fri, 15 Oct 2021 07:50:00 +000
       
  • Intracavitary Electrocardiogram Guidance Aids Excavation of Rhythm
           Abnormalities in Patients with Occult Heart Disease

    • Abstract: In this paper, the analysis of intracavitary electrocardiograms is used to guide the mining of abnormal cardiac rhythms in patients with hidden heart disease, and the algorithm is improved to address the data imbalance problem existing in the abnormal electrocardiogram signals, and a weight-based automatic classification algorithm for deep convolutional neural network electrocardiogram signals is proposed. By preprocessing the electrocardiogram data from the MIT-BIH arrhythmia database, the experimental dataset training algorithm model is obtained, and the algorithm model is migrated into the project. In terms of system design and implementation, by comparing the advantages and disadvantages of the electrocardiogram monitoring system platform, the overall design of the system was carried out in terms of functional and performance requirements according to the system realization goal, and a mobile platform system capable of classifying common abnormal electrocardiogram signals was developed. The system is capable of long-term monitoring and can invoke the automatic classification algorithm model of electrocardiogram signals for analysis. In this paper, the functional logic test and performance test were conducted on the main functional modules of the system. The test results show that the system can run stably and monitor electrocardiogram signals for a long time and can correctly call the deep convolutional neural network-based automatic electrocardiogram signal classification algorithm to analyze the electrocardiogram signals and achieve the requirements of displaying the electrocardiogram signal waveform, analyzing the heartbeat type, and calculating the average heart rate, which achieves the goal of real-time continuous monitoring and analysis of the electrocardiogram signals.
      PubDate: Fri, 15 Oct 2021 07:20:00 +000
       
  • Stressors of Nursing Interns and Their Influencing Factors: A
           Cross-Sectional Study

    • Abstract: Objective. It is still unknown whether the stress level and stressors in Chinese nursing interns are influenced by teacher-related factors. This research was carried out for better understanding of the stress in nursing interns and distribution of stressors during their clinical practice and targeted measures to unwind the stress of nursing interns. Methods. A questionnaire survey, titled Questionnaire on Stressors of Nursing Interns during Clinical Practice, was conducted on nursing interns at a 3A Grade Hospital in Shandong Province. Characteristics of the nursing interns and stressors of nursing interns were collected. A multiple-linear regression model was used to explore the influencing factors of nursing interns’ scores. Results. A total of 132 nursing interns were investigated in this study, and the overall stress scores were calculated. The stressors during the internship include the nature and content of the job, role orientation, workload, conflict between study and work, practice preparation, and interpersonal relationships. Gender, education level, instructor encouragement, and parents engaged in the medical industry were adjusted in the multiple-linear regression model as covariates. All of these factors had significant impacts on the scores of stressors ( 
      PubDate: Thu, 14 Oct 2021 11:35:01 +000
       
  • An Image Recognition Framework for Oral Cancer Cells

    • Abstract: Oral squamous cell carcinoma (OSCC) is a common type of cancer of the oral cavity. Despite their great impact on mortality, sufficient screening techniques for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate recognition of OSCCs would lead to an improved curative result and a reduction in recurrence rates after surgical treatment. The introduction of image recognition technology into the doctor’s diagnosis process can significantly improve cancer diagnosis, reduce individual differences, and effectively assist doctors in making the correct diagnosis of the disease. The objective of this study was to assess the precision and robustness of a deep learning-based method to automatically identify the extent of cancer on digitized oral images. We present a new method that employs different variants of convolutional neural network (CNN) for detecting cancer in oral cells. Our approach involves training the classifier on different images from the imageNet dataset and then independently validating on different cancer cells. The image is segmented using multiscale morphology methods to prepare for cell feature analysis and extraction. The method of morphological edge detection is used to more accurately extract the target, cell area, perimeter, and other multidimensional features followed by classification through CNN. For all five variants of CNN, namely, VGG16, VGG19, InceptionV3, InceptionResNetV2, and Xception, the train and value losses are less than 6%. Experimental results show that the method can be an effective tool for OSCC diagnosis.
      PubDate: Thu, 14 Oct 2021 09:50:03 +000
       
  • Analysis of Risk Factors of Neurobiological Pipeline Care and
           Investigation of Preventive Measures

    • Abstract: During clinical care, most neurosurgical patients are critically ill. They have sudden onset of illness that should be treated on time with proper care. The patients require continuous hospitalization for proper treatment. The recovery of patients may be relatively slow and takes some time. Patients and Methods. To explore where the risks of pipeline care lie and the preventive measures. (1) In this paper, 100 neurosurgical patients were treated in our hospital from September 2018 to March 2020. They were firstly selected and divided into two groups. Group A was implemented with routine pipeline care and group B was implemented with the intervention developed by the pipeline team. (2) The design and SMOTE assume that, during the generation of a new synthetic sample of minority classes, the immediate neighbors of the minority class instances were also all minority classes, regardless of their true distribution characteristics, to analyze risk factors during care and summarize preventive measures. Results. The experimental results showed that the total efficiency of nursing care was higher in group B as compared to group A, ; also, the number of pipeline accidents was lower in group B. Conclusion It is important to be meticulous and thoughtful in pipeline care and to comprehensively analyze the possible risk events and then propose preventive measures so that risk events can be reduced.
      PubDate: Wed, 13 Oct 2021 10:50:00 +000
       
  • Influencing Factors of Gastrointestinal Function Recovery after
           Gastrointestinal Malignant Tumor

    • Abstract: Gastric cancer is a malignant tumor with a high incidence in the world, and the incidence rate only increases every year. Because of the loss of mental property caused by surgery and postoperative recovery treatment, it has become a difficult problem for many families to solve. Exploring the factors affecting the recovery of gastrointestinal function after surgery to accelerate the recovery has become one of the important research topics of current medical experts and scholars. The purpose of this article is to explore the factors affecting the recovery of gastrointestinal function after gastrointestinal malignancies. In this paper, firstly through experimental investigation, the fasting time and operation method of patients undergoing gastrointestinal malignant tumor surgery are used as variables to conduct a controlled experiment, and the first defecation time, exhaust time, and bowel sound recovery of the experimental subjects after surgery are recorded. Changes in time and other indicators are compared to verify whether they affect the recovery of gastrointestinal function. Experimental data showed that the recovery time of bowel sounds was 29.10 ± 11.09 h in patients with fasting time less than or equal to 2 days after operation, the time of first exhaustion was 28.75 ± 27.80 h, and the time of first defecation was 54.70 ± 39.40 h. The recovery time of bowel sounds in patients with fasting time longer than 2 days was 40.47 ± 9.40 h, the first exhaust time was 71.40 ± 17.54 h, and the first defecation time was 98.30 ± 28.16 h. Therefore, resuming diet as soon as possible after operation is beneficial to the recovery of gastrointestinal function in patients with gastrointestinal malignancies.
      PubDate: Wed, 13 Oct 2021 10:20:01 +000
       
  • Intelligent Solutions in Chest Abnormality Detection Based on YOLOv5 and
           ResNet50

    • Abstract: Computer-aided diagnosis (CAD) has nearly fifty years of history and has assisted many clinicians in the diagnosis. With the development of technology, recently, researches use the deep learning method to get high accuracy results in the CAD system. With CAD, the computer output can be used as a second choice for radiologists and contribute to doctors doing the final right decisions. Chest abnormality detection is a classic detection and classification problem; researchers need to classify common thoracic lung diseases and localize critical findings. For the detection problem, there are two deep learning methods: one-stage method and two-stage method. In our paper, we introduce and analyze some representative model, such as RCNN, SSD, and YOLO series. In order to better solve the problem of chest abnormality detection, we proposed a new model based on YOLOv5 and ResNet50. YOLOv5 is the latest YOLO series, which is more flexible than the one-stage detection algorithms before. The function of YOLOv5 in our paper is to localize the abnormality region. On the other hand, we use ResNet, avoiding gradient explosion problems in deep learning for classification. And we filter the result we got from YOLOv5 and ResNet. If ResNet recognizes that the image is not abnormal, the YOLOv5 detection result is discarded. The dataset is collected via VinBigData’s web-based platform, VinLab. We train our model on the dataset using Pytorch frame and use the mAP, precision, and F1-score as the metrics to evaluate our model’s performance. In the progress of experiments, our method achieves superior performance over the other classical approaches on the same dataset. The experiments show that YOLOv5’s mAP is 0.010, 0.020, 0.023 higher than those of YOLOv5, Fast RCNN, and EfficientDet. In addition, in the dimension of precision, our model also performs better than other models. The precision of our model is 0.512, which is 0.018, 0.027, 0.033 higher than YOLOv5, Fast RCNN, and EfficientDet.
      PubDate: Wed, 13 Oct 2021 10:20:01 +000
       
  • Adaptive Diagnosis of Lung Cancer by Deep Learning Classification Using
           Wilcoxon Gain and Generator

    • Abstract: Cancer is a complicated worldwide health issue with an increasing death rate in recent years. With the swift blooming of the high throughput technology and several machine learning methods that have unfolded in recent years, progress in cancer disease diagnosis has been made based on subset features, providing awareness of the efficient and precise disease diagnosis. Hence, progressive machine learning techniques that can, fortunately, differentiate lung cancer patients from healthy persons are of great concern. This paper proposes a novel Wilcoxon Signed-Rank Gain Preprocessing combined with Generative Deep Learning called Wilcoxon Signed Generative Deep Learning (WS-GDL) method for lung cancer disease diagnosis. Firstly, test significance analysis and information gain eliminate redundant and irrelevant attributes and extract many informative and significant attributes. Then, using a generator function, the Generative Deep Learning method is used to learn the deep features. Finally, a minimax game (i.e., minimizing error with maximum accuracy) is proposed to diagnose the disease. Numerical experiments on the Thoracic Surgery Data Set are used to test the WS-GDL method's disease diagnosis performance. The WS-GDL approach may create relevant and significant attributes and adaptively diagnose the disease by selecting optimal learning model parameters. Quantitative experimental results show that the WS-GDL method achieves better diagnosis performance and higher computing efficiency in computational time, computational complexity, and false-positive rate compared to state-of-the-art approaches.
      PubDate: Wed, 13 Oct 2021 06:20:01 +000
       
 
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