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  Subjects -> SOCIAL SERVICES AND WELFARE (Total: 224 journals)
Showing 1 - 135 of 135 Journals sorted alphabetically
Aboriginal and Islander Health Worker Journal     Full-text available via subscription   (Followers: 8)
ACOSS Papers     Full-text available via subscription   (Followers: 4)
Adoption & Fostering     Hybrid Journal   (Followers: 17)
Advances in Neurodevelopmental Disorders     Hybrid Journal   (Followers: 3)
Advances in Social Work     Open Access   (Followers: 31)
African Journal of Social Work     Open Access   (Followers: 2)
African Safety Promotion     Full-text available via subscription   (Followers: 4)
African Security     Hybrid Journal   (Followers: 6)
Annals of the American Academy of Political and Social Science     Hybrid Journal   (Followers: 44)
Argumentum     Open Access  
Asia Pacific Journal of Social Work and Development     Hybrid Journal   (Followers: 9)
Asian Journal of Social Science     Hybrid Journal   (Followers: 18)
Asian Social Work and Policy Review     Hybrid Journal   (Followers: 8)
Australasian Journal of Human Security     Full-text available via subscription   (Followers: 2)
Australasian Policing     Full-text available via subscription   (Followers: 7)
Australian Ageing Agenda     Full-text available via subscription   (Followers: 5)
Australian Journal of Emergency Management     Full-text available via subscription   (Followers: 20)
Australian Journal of Social Issues     Hybrid Journal   (Followers: 6)
Australian Journal on Volunteering     Full-text available via subscription   (Followers: 1)
Australian Social Work     Hybrid Journal   (Followers: 13)
AZARBE : Revista Internacional de Trabajo Social y Bienestar     Open Access  
Bakti Budaya     Open Access  
Basic and Applied Social Psychology     Hybrid Journal   (Followers: 43)
British Journal of Social Work     Hybrid Journal   (Followers: 73)
Campbell Systematic Reviews     Open Access   (Followers: 6)
Canadian Social Work Review     Full-text available via subscription   (Followers: 13)
Care Management Journals     Hybrid Journal   (Followers: 5)
Clinical Social Work Journal     Hybrid Journal   (Followers: 27)
Columbia Social Work Review     Open Access  
Communities, Children and Families Australia     Full-text available via subscription   (Followers: 4)
Community Development     Hybrid Journal   (Followers: 20)
Community, Work & Family     Hybrid Journal   (Followers: 24)
ConCienciaSocial     Open Access  
Contemporary Rural Social Work     Open Access   (Followers: 12)
Counseling Outcome Research and Evaluation     Hybrid Journal   (Followers: 12)
Counseling Psychology and Psychotherapy     Open Access   (Followers: 20)
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: 47)
Cuadernos de Trabajo Social     Open Access  
Death Studies     Hybrid Journal   (Followers: 21)
Developing Practice : The Child, Youth and Family Work Journal     Full-text available via subscription   (Followers: 15)
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: 22)
European Journal of Social Psychology     Hybrid Journal   (Followers: 43)
European Journal of Social Security     Full-text available via subscription   (Followers: 6)
European Journal of Social Work     Hybrid Journal   (Followers: 33)
European Journal of Work and Organizational Psychology     Hybrid Journal   (Followers: 34)
European Review of Social Psychology     Hybrid Journal   (Followers: 15)
Families in Society : The Journal of Contemporary Social Services     Full-text available via subscription   (Followers: 11)
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: 6)
Grief Matters : The Australian Journal of Grief and Bereavement     Full-text available via subscription   (Followers: 13)
Groupwork     Full-text available via subscription   (Followers: 1)
Health & Social Care In the Community     Hybrid Journal   (Followers: 48)
Health and Social Care Chaplaincy     Hybrid Journal   (Followers: 9)
Health and Social Work     Hybrid Journal   (Followers: 63)
HOLISTICA ? Journal of Business and Public Administration     Open Access   (Followers: 1)
Hong Kong Journal of Social Work, The     Hybrid Journal   (Followers: 3)
Housing Policy Debate     Hybrid Journal   (Followers: 20)
Human Service Organizations Management, Leadership and Governance     Hybrid Journal   (Followers: 24)
Indonesian Journal of Guidance and Counseling     Open Access  
International Journal of Ageing and Later Life     Open Access   (Followers: 1)
International Journal of Care and Caring     Hybrid Journal  
International Journal of Disability Management Research     Full-text available via subscription   (Followers: 2)
International Journal of East Asian Studies     Open Access   (Followers: 2)
International Journal of School Social Work     Open Access   (Followers: 2)
International Journal of Social Research Methodology     Hybrid Journal   (Followers: 63)
International Journal of Social Welfare     Hybrid Journal   (Followers: 18)
International Journal of Social Work     Open Access   (Followers: 20)
International Journal of Sociology and Social Policy     Hybrid Journal   (Followers: 67)
International Journal on Child Maltreatment : Research, Policy and Practice     Hybrid Journal   (Followers: 2)
International Social Science Journal     Hybrid Journal   (Followers: 22)
International Social Security Review     Hybrid Journal   (Followers: 9)
International Social Work     Hybrid Journal   (Followers: 20)
Islamic Counseling : Jurnal Bimbingan Konseling Islam     Open Access  
Janus Sosiaalipolitiikan ja sosiaalityön tutkimuksen aikakauslehti     Open Access  
Journal for Specialists in Group Work     Hybrid Journal   (Followers: 1)
Journal of Accessibility and Design for All     Open Access   (Followers: 12)
Journal of Applied Social Psychology     Hybrid Journal   (Followers: 59)
Journal of Benefit-Cost Analysis     Hybrid Journal   (Followers: 2)
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: 11)
Journal of Comparative Social Welfare     Hybrid Journal   (Followers: 16)
Journal of Comparative Social Work     Open Access   (Followers: 3)
Journal of Danubian Studies and Research     Open Access  
Journal of Ethnic & Cultural Diversity in Social Work     Hybrid Journal   (Followers: 18)
Journal of European Social Policy     Hybrid Journal   (Followers: 37)
Journal of Evidence-Based Social Work     Hybrid Journal   (Followers: 28)
Journal of Evidence-Informed Social Work     Hybrid Journal   (Followers: 7)
Journal of Family Issues     Hybrid Journal   (Followers: 21)
Journal of Forensic Social Work     Hybrid Journal   (Followers: 12)
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: 7)
Journal of Human Rights and Social Work     Hybrid Journal   (Followers: 3)
Journal of Integrated Care     Hybrid Journal   (Followers: 18)
Journal of International and Comparative Social Policy     Hybrid Journal   (Followers: 3)
Journal of Investigative Psychology and Offender Profiling     Hybrid Journal   (Followers: 11)
Journal of Language and Social Psychology     Hybrid Journal   (Followers: 19)
Journal of Occupational Science     Hybrid Journal   (Followers: 27)
Journal of Personality and Social Psychology     Full-text available via subscription   (Followers: 320)
Journal of Policy Practice     Hybrid Journal   (Followers: 8)
Journal of Policy Practice and Research     Hybrid Journal   (Followers: 2)
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: 145)
Journal of Public Mental Health     Hybrid Journal   (Followers: 14)
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: 3)
Journal of Social Issues     Hybrid Journal   (Followers: 17)
Journal of Social Philosophy     Hybrid Journal   (Followers: 27)
Journal of Social Policy     Hybrid Journal   (Followers: 42)
Journal of Social Service Research     Hybrid Journal   (Followers: 11)
Journal of Social Work     Hybrid Journal   (Followers: 83)
Journal of Social Work Education     Hybrid Journal   (Followers: 14)
Journal of Social Work in Disability & Rehabilitation     Hybrid Journal   (Followers: 14)
Journal of Social Work in the Global Community     Open Access   (Followers: 1)
Journal of Social Work Practice in the Addictions     Hybrid Journal   (Followers: 11)
Journal of the Society for Social Work and Research     Full-text available via subscription   (Followers: 11)
Jurnal Karya Abdi Masyarakat     Open Access  
Just Policy: A Journal of Australian Social Policy     Full-text available via subscription   (Followers: 6)
Kontext : Zeitschrift für Systemische Therapie und Familientherapie     Hybrid Journal  
L'Orientation scolaire et professionnelle     Open Access  
Learning in Health and Social Care     Hybrid Journal   (Followers: 11)
Leidfaden : Fachmagazin für Krisen, Leid, Trauer     Hybrid Journal  
Links to Health and Social Care     Open Access  
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: 36)
Mental Health and Substance Use: dual diagnosis     Hybrid Journal   (Followers: 24)
Merrill-Palmer Quarterly     Full-text available via subscription   (Followers: 1)
Mortality: Promoting the interdisciplinary study of death and dying     Hybrid Journal   (Followers: 9)
Mundos do Trabalho     Open Access   (Followers: 1)
National Emergency Response     Full-text available via subscription   (Followers: 2)
New Zealand Journal of Occupational Therapy     Full-text available via subscription   (Followers: 69)
Nordic Social Work Research     Hybrid Journal   (Followers: 6)
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  
Parity     Full-text available via subscription   (Followers: 2)
Partner Abuse     Hybrid Journal   (Followers: 9)
Pedagogia i Treball Social : Revista de Cičncies Socials Aplicades     Open Access  
Personality and Social Psychology Bulletin     Hybrid Journal   (Followers: 170)
Personality and Social Psychology Review     Hybrid Journal   (Followers: 51)
Philosophy & Social Criticism     Hybrid Journal   (Followers: 22)
Policy Sciences     Hybrid Journal   (Followers: 14)
Practice: Social Work in Action     Hybrid Journal   (Followers: 16)
Prospectiva : Revista de Trabajo Social e Intervención Social     Open Access  
Psychoanalytic Social Work     Hybrid Journal   (Followers: 12)
Public Policy and Aging Report     Hybrid Journal   (Followers: 3)
Qualitative Research     Hybrid Journal   (Followers: 35)
Qualitative Social Work     Hybrid Journal   (Followers: 21)
Quality in Ageing and Older Adults     Hybrid Journal   (Followers: 44)
Race and Social Problems     Hybrid Journal   (Followers: 11)
Research in Social Stratification and Mobility     Hybrid Journal   (Followers: 13)
Research on Economic Inequality     Hybrid Journal   (Followers: 10)
Research on Language and Social Interaction     Hybrid Journal   (Followers: 19)
Research on Social Work Practice     Hybrid Journal   (Followers: 30)
Review of Social Economy     Hybrid Journal   (Followers: 4)
Revista Internacional De Seguridad Social     Hybrid Journal  
Revista Serviço Social em Perspectiva     Open Access  
Safer Communities     Hybrid Journal   (Followers: 50)
Science and Public Policy     Hybrid Journal   (Followers: 25)
Self and Identity     Hybrid Journal   (Followers: 21)
Service social     Full-text available via subscription   (Followers: 9)
Sexual Abuse in Australia and New Zealand     Full-text available via subscription   (Followers: 9)
Skriftserien Socialt Arbejde     Open Access   (Followers: 1)
Social Action : The Journal for Social Action in Counseling and Psychology     Free   (Followers: 2)
Social and Personality Psychology Compass     Hybrid Journal   (Followers: 17)
Social Behavior and Personality : An International Journal     Full-text available via subscription   (Followers: 13)
Social Choice and Welfare     Hybrid Journal   (Followers: 12)
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: 24)
Social Philosophy and Policy     Full-text available via subscription   (Followers: 25)
Social Policy & Administration     Hybrid Journal   (Followers: 31)
Social Policy and Society     Hybrid Journal   (Followers: 142)
Social Science Japan Journal     Hybrid Journal   (Followers: 11)
Social Semiotics     Hybrid Journal   (Followers: 7)
Social Work     Hybrid Journal   (Followers: 39)
Social Work & Social Sciences Review     Open Access   (Followers: 20)
Social Work / Maatskaplike Werk     Open Access  
Social Work and Society     Open Access   (Followers: 2)
Social Work Education: The International Journal     Hybrid Journal   (Followers: 14)
Social Work Research     Hybrid Journal   (Followers: 24)
Social Work Review     Full-text available via subscription   (Followers: 16)
Social Work With Groups     Hybrid Journal   (Followers: 7)
Socialinė teorija, empirija, politika ir praktika     Open Access  
Socialmedicinsk Tidskrift     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  [339 journals]
  • Comparing Conventional and Deep Feature Models for Classifying Fundus
           Photography of Hemorrhages

    • Abstract: Diabetic retinopathy is an eye-related pathology creating abnormalities and causing visual impairment, proper treatment of which requires identifying irregularities. This research uses a hemorrhage detection method and compares the classification of conventional and deep features. Especially, the method identifies hemorrhage connected with blood vessels or residing at the retinal border and was reported challenging. Initially, adaptive brightness adjustment and contrast enhancement rectify degraded images. Prospective locations of hemorrhages are estimated by a Gaussian matched filter, entropy thresholding, and morphological operation. Hemorrhages are segmented by a novel technique based on the regional variance of intensities. Features are then extracted by conventional methods and deep models for training support vector machines and the results are evaluated. Evaluation metrics for each model are promising, but findings suggest that comparatively, deep models are more effective than conventional features.
      PubDate: Sat, 19 Nov 2022 05:05:00 +000
       
  • EEG-Based Spectral Dynamic in Characterization of Poststroke Patients with
           Cognitive Impairment for Early Detection of Vascular Dementia

    • Abstract: One common type of vascular dementia (VaD) is poststroke dementia (PSD). Vascular dementia can occur in one-third of stroke patients. The worsening of cognitive function can occur quickly if not detected and treated early. One of the potential medical modalities for observing this disorder by considering costs and safety factors is electroencephalogram (EEG). It is thought that there are differences in the spectral dynamics of the EEG signal between the normal group and stroke patients with cognitive impairment so that it can be used in detection. Therefore, this study proposes an EEG signal characterization method using EEG spectral power complexity measurements to obtain features of poststroke patients with cognitive impairment and normal subjects. Working memory EEGs were collected and analyzed from forty-two participants, consisting of sixteen normal subjects, fifteen poststroke patients with mild cognitive impairment, and eleven poststroke patients with dementia. From the analysis results, it was found that there were differences in the dynamics of the power spectral in each group, where the spectral power of the cognitively impaired group was more regular than the normal group. Notably, (1) significant differences in spectral entropy (SpecEn) with a value
      PubDate: Sat, 19 Nov 2022 04:05:00 +000
       
  • An Ensemble of Deep Learning Enabled Brain Stroke Classification Model in
           Magnetic Resonance Images

    • Abstract: Brain stroke is a major cause of global death and it necessitates earlier identification process to reduce the mortality rate. Magnetic resonance imaging (MRI) techniques is a commonly available imaging modality used to diagnose brain stroke. Presently, machine learning (ML) and deep learning (DL) models can be extremely utilized for disease detection and classification processes. Amongst the available approaches, the convolutional neural network (CNN) models have been widely used for computer vision and image processing issues such as ImageNet, facial detection, and digit classification. In this article, a novel computer aided diagnosis (CAD) based brain stroke detection and classification (CAD-BSDC) model has been developed for MRI images. The proposed CAD-BSDC technique aims in classifying the provided MR brain image as normal or abnormal. The CAD-BSDC technique involves different subprocesses such as preprocessing, feature extraction, and classification. Firstly, the input image undergoes preprocessing using adaptive thresholding (AT) technique for improving the image quality. Followed by, an ensemble of feature extractors such as MobileNet, CapsuleNet, and EfficientNet models are used. Besides, the hyperparameter tuning of the deep learning models takes place using the improved dragonfly optimization (IDFO) algorithm. Moreover, satin bowerbird optimization (SBO) based stacked autoencoder (SAE) is used for the classification of brain stroke. The design of optimal SAE using the SBO algorithm shows the novelty of the work. The performance of the presented technique was validated utilizing benchmark dataset which includes T2-weighted MR brain image collected from the axial axis with size of 256 × 256. The simulation outcomes indicated the promising efficiency of the proposed CAD-BSDC technique over the latest state of art approaches in terms of various performance measures.
      PubDate: Fri, 18 Nov 2022 08:20:01 +000
       
  • DPP-4 Inhibitor Improved the Cognitive Function in Diabetic Rats

    • Abstract: Diabetes-associated cognitive dysfunction is a major problem of the international community. Dipeptidyl peptidase-4 (DPP-4) inhibitors are drugs with hypoglycemic effect widely used in diabetic treatment in clinic. In this article, we studied the effect of the DPP-4 inhibitor saxagliptin on cognitive function in diabetic rats. Firstly, to observe cognitive dysfunction caused by diabetes, we built the diabetic rat model. Subsequently, the effect of diabetes on cognitive function was evaluated by Morris Water Maze Task. Thirdly, the mechanism of the alleviation effect of DPP-4 inhibitor on cognitive dysfunction was investigated. Specifically, (1) the anti-inflammation mechanism was revealed by quantifying the accumulation of the inflammatory factor interleukin-1β (IL-1β) in the hippocampus area by western blotting and the glial fibrillary acidic protein (GFAP) by immunohistochemistry; (2) the anti-tau phosphorylation mechanism was revealed by quantifying phosphorylated tau by western blotting. This work represents the first study demonstrating the alleviation effect of DPP-4 inhibitor on cognitive dysfunction caused by diabetes. Results obtained here could be useful to seeking for a medical solution with high efficacy to the diabetes-associated cognitive dysfunction.
      PubDate: Fri, 18 Nov 2022 06:35:00 +000
       
  • Prenatal Monitoring of Perinatal Pregnant Women and Fetus Based on a Smart
           Electronic Fetal Monitoring System

    • Abstract: The aim of the study is to study the prenatal monitoring of perinatal pregnant women based on a smart electronic fetal monitoring system. Through the comparative analysis of 230 pregnant women in maternal and child health care hospital who received fetal heart monitoring during the perinatal period and those who did not receive fetal heart monitoring during the perinatal period, cases of fetal distress, neonatal asphyxia, and cesarean section were observed in both groups. Results show that the incidences of fetal complications and cesarean sections in the experimental group were 16.36% and 36.82%, which was significantly higher than 4.50% and 17.50% in the control group( 
      PubDate: Fri, 18 Nov 2022 06:35:00 +000
       
  • Retracted: Clinical and Imaging Analysis of Cerebral Infarction Caused by
           Spontaneous Cerebral Artery Dissection Based on Augmented Reality
           Technology

    • PubDate: Thu, 17 Nov 2022 15:05:00 +000
       
  • Retracted: Value Analysis of Using Urinary Microalbumin in Artificial
           Intelligence Medical Institutions to Detect Early Renal Damage in Diabetes
           

    • PubDate: Thu, 17 Nov 2022 06:35:00 +000
       
  • The Immediate Effect of Backward Walking on External Knee Adduction Moment
           in Healthy Individuals

    • Abstract: Backward walking (BW) has been recommended as a rehabilitation intervention to prevent, manage, or improve diseases. However, previous studies showed that BW significantly increased the first vertical ground reaction force (GRF) during gait, which might lead to higher loading at the knee. Published reports have not examined the effects of BW on medial compartment knee loading. The objective of this study was to investigate the effects of BW on external knee adduction moment (EKAM). Twenty-seven healthy adults participated in the present study. A sixteen-camera three-dimensional VICON gait analysis system, with two force platforms, was used to collect the EKAM, KAAI, and other biomechanical data during BW and forward walking (FW). The first () and second () EKAM peaks and KAAI () were significantly decreased during BW when compared with FW. The BW significantly decreased the lever arm length at the first EKAM peak () when compared with FW. In conclusion, BW was found to be a useful strategy for reducing the medial compartment knee loading even though the first peak ground reaction force was significantly increased.
      PubDate: Fri, 11 Nov 2022 10:20:00 +000
       
  • Comparative Evaluation of the Multilayer Perceptron Approach with
           Conventional ARIMA in Modeling and Prediction of COVID-19 Daily Death
           Cases

    • Abstract: COVID-19 continues to pose a dangerous global health threat, as cases grow rapidly and deaths increase day by day. This increasing phenomenon does not only affect economic policy but also international policy around the world. In this paper, Pakistan daily death cases of COVID-19, from February 25, 2020, to March 23, 2022, have been modeled using the long-established autoregressive-integrated moving average (ARIMA) model and the machine learning multilayer perceptron (MLP) model. The most befitting model is selected based on the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). Values of the key performance indicator (KPI) showed that the MLP model outperformed the ARIMA model. The MLP model with 20 hidden layers, which emerged as the overall most apt model, was used to predict future daily COVID-19 deaths in Pakistan to enable policymakers and health professionals to put in place systematic measures to reduce death cases. We encourage the Government of Pakistan to intensify its vaccination campaign and encourage everyone to get vaccinated.
      PubDate: Wed, 09 Nov 2022 10:35:00 +000
       
  • A Method for Expanding the Training Set of White Blood Cell Images

    • Abstract: In medicine, the count of different types of white blood cells can be used as the basis for diagnosing certain diseases or evaluating the treatment effects of diseases. The recognition and counting of white blood cells have important clinical significance. But the effect of recognition based on machine learning is affected by the size of the training set. At present, researchers mainly rely on image rotation and cropping to expand the dataset. These methods either add features to the white blood cell image or require manual intervention and are inefficient. In this paper, a method for expanding the training set of white blood cell images is proposed. After rotating the image at any angle, Canny is used to extract the edge of the black area caused by the rotation and then fill the black area to achieve the purpose of expanding the training set. The experimental results show that after using the method proposed in this paper to expand the training set to train the three models of ResNet, MobileNet, and ShuffleNet, and comparing the original dataset and the method trained by the simple rotated image expanded dataset, the recognition accuracy of the three models is obviously improved without manual intervention.
      PubDate: Wed, 09 Nov 2022 08:05:00 +000
       
  • Multi-Chaos-Based Lightweight Image Encryption-Compression for Secure
           Occupancy Monitoring

    • Abstract: With the advancement of camera and wireless technologies, surveillance camera-based occupancy has received ample attention from the research community. However, camera-based occupancy monitoring and wireless channels, especially Wi-Fi hotspot, pose serious privacy concerns and cybersecurity threats. Eavesdroppers can easily access confidential multimedia information and the privacy of individuals can be compromised. As a solution, novel encryption techniques for the multimedia data concealing have been proposed by the cryptographers. Due to the bandwidth limitations and computational complexity, traditional encryption methods are not applicable to multimedia data. In traditional encryption methods such as Advanced Encryption Standard (AES) and Data Encryption Standard (DES), once multimedia data are compressed during encryption, correct decryption is a challenging task. In order to utilize the available bandwidth in an efficient way, a novel secure video occupancy monitoring method in conjunction with encryption-compression has been developed and reported in this paper. The interesting properties of Chebyshev map, intertwining map, logistic map, and orthogonal matrix are exploited during block permutation, substitution, and diffusion processes, respectively. Real-time simulation and performance results of the proposed system show that the proposed scheme is highly sensitive to the initial seed parameters. In comparison to other traditional schemes, the proposed encryption system is secure, efficient, and robust for data encryption. Security parameters such as correlation coefficient, entropy, contrast, energy, and higher key space prove the robustness and efficiency of the proposed solution.
      PubDate: Tue, 08 Nov 2022 17:35:00 +000
       
  • Next-Day Medical Activities Recommendation Model with Double Attention
           Mechanism Using Generative Adversarial Network

    • Abstract: Medical activities recommendation is a key aspect of an intelligent healthcare system, which can assist doctors with little clinical experience in clinical decision making. Medical activities recommendation can be seen as a kind of temporal set prediction. Previous studies about them are based on Recurrent Neural Network (RNN), which does not incorporate personalized medical history or differentiate between the impact of medical activities. To address the above-given issues, this paper proposes a Next-Day Medical Activities Recommendation (NDMARec) model. Specifically, our model firstly proposes an inpatient day embedding method based on soft-attention which balances the impact of different medical activities to get a joint representation of medical activities that occurred within the same day. Then, a fusion module is designed to combine features of inpatient day and medical history to achieve personalization. These features are learned by the self-attention mechanism that solves the long-term dependency problem of RNNs. Last, adversarial training is introduced to improve the generalization ability of our model. Extensive experiments on a real dataset from a hospital are conducted to show that NDMARec outperformed both classical and state-of-the-art methods.
      PubDate: Mon, 07 Nov 2022 15:20:00 +000
       
  • An Ultrasonic-Based Sensor System for Elderly Fall Monitoring in a Smart
           Room

    • Abstract: To reduce the risk of elderly people falling in a private room without relying on a closed-circuit television system that results in serious privacy and trust concerns, a fall monitoring system that protects the privacy and does not monitor a person’s activities is needed. An ultrasonic-based sensor system for elderly fall monitoring in a smart room is proposed in this study. An array of ultrasonic sensors, whose ranges are designed to cover the room space, are initially installed on a wall of the room, and the sensors are rotated to transmit and receive ultrasonic signals to measure the distances to a moving object while preventing ultrasonic signal interference. Distance changes measured by ultrasonic sensors are used as time-independent patterns to recognize when an elderly person falls. To evaluate the performance of the proposed system, a sensor system prototype using long short-term memory was constructed, and experiments with 25 participants were performed. An accuracy of approximately 98% was achieved in this experiment using the proposed method, which was a slight improvement over that of the conventional method.
      PubDate: Mon, 07 Nov 2022 15:05:00 +000
       
  • Personalized Intelligent Syndrome Differentiation Guided By TCM
           Consultation Philosophy

    • Abstract: Traditional Chinese Medicine (TCM) is one of the oldest medical systems in the world, and inquiry is an essential part of TCM diagnosis. The development of artificial intelligence has led to the proposal of several computational TCM diagnostic methods. However, there are few research studies among them, and they have the following flaws: (1) insufficient engagement with the patient, (2) barren TCM consultation philosophy, and (3) inadequate validation of the method. As TCM inquiry knowledge is abstract and there are few relevant datasets, we devise a novel knowledge representation technique. The mapping of symptoms and syndromes is constructed based on the diagnostics of traditional Chinese medicine. As a guide, the inquiry knowledge base is constructed utilizing the “Ten Brief Inquiries,” TCM’s domain knowledge. Subsequently, a corresponding assessment approach is proposed for an intelligent consultation model for syndrome differentiation. We establish three criteria: the quality of the generated question-answer pairs, the accuracy of model identification, and the average number of questions. Three TCM specialists are asked to undertake a manual evaluation of the model separately. The results reveal that our approach is capable of pretty accurate syndrome differentiation. Furthermore, the model’s question and answer pairs for simulated consultations are relevant, accurate, and efficient.
      PubDate: Mon, 07 Nov 2022 12:35:00 +000
       
  • Multiresolution Mutual Assistance Network for Cardiac Magnetic Resonance
           Images Segmentation

    • Abstract: The automatic segmentation of cardiac magnetic resonance (MR) images is the basis for the diagnosis of cardiac-related diseases. However, the segmentation of cardiac MR images is a challenging task due to the inhomogeneity of MR images intensity distribution and the unclear boundaries between adjacent tissues. In this paper, we propose a novel multiresolution mutual assistance network (MMA-Net) for cardiac MR images segmentation. It is mainly composed of multibranch input module, multiresolution mutual assistance module, and multilabel deep supervision. First, the multibranch input module helps the network to extract local and global features more pertinently. Then, the multiresolution mutual assistance module implements multiresolution feature interaction and progressively improves semantic features to more completely express the information of the tissue. Finally, the multilabel deep supervision is proposed to generate the final segmentation map. We compare with state-of-the-art medical image segmentation methods on the medical image computing and computer-assisted intervention (MICCAI) automated cardiac diagnosis challenge datasets and the MICCAI atrial segmentation challenge datasets. The mean dice scores of our method in the left atrium, right ventricle, myocardium, and left ventricle are 0.919, 0.920, 0.881, and 0.960, respectively. The analysis of evaluation indicators and segmentation results shows that our method achieves the best performance in cardiac magnetic resonance images segmentation.
      PubDate: Mon, 31 Oct 2022 07:05:00 +000
       
  • Pyroptosis-Related Gene Signature Predicts the Prognosis of ccRCC Using
           TCGA and Single-Cell RNA Seq Database

    • Abstract: Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of renal carcinoma, which is not sensitive to both radiotherapy and chemotherapy. The objective response rate of metastatic renal cancer to targeted drugs and immunotherapy is unsatisfactory. Pyroptosis, proven as an inflammatory form of programmed cell death, could be activated by some inflammasomes, while could create a tumor-suppressing environment by releasing inflammatory factors in the tumor. To explore indicators predicting the prognosis of ccRCC and the effect of antitumor therapy, we constructed a pyroptosis risk model containing 4 genes after 11 pyroptosis-related genes of 516 ccRCC cases in the TCGA database were scanned. Based on the risk score, 516 ccRCC cases were divided into two groups for functional enrichment analysis and immune profile to seek functional pathways and potential therapeutic targets. Besides, those results were verified in GSE29609 and single-cell transcriptomic data. The study suggests that the conducted pyroptosis model could predict the prognosis of ccRCC and reflect the immune microenvironment, which may help in immune checkpoint inhibitor treatment.
      PubDate: Sun, 30 Oct 2022 06:50:00 +000
       
  • Patient-Specific Exercises with the Development of an End-Effector Type
           Upper Limb Rehabilitation Robot

    • Abstract: End-effector type upper limb rehabilitation robots (ULRRs) are connected to patients at one distal point, making them have simple structures and less complex control algorithms, and they can avoid abnormal motion and posture of the target anatomical joints and specific muscles. Given that the end-effector type ULRR focuses more on the rehabilitation of the combined motion of upper limb chain, assisting the patient to perform collaborative tasks, and its intervention has some advantages than the exoskeleton type ULRR, we developed a novel three-degree-of-freedom (DOF) end-effector type ULRR. The advantage of the mechanical design is that the designed end-effector type ULRR can achieve three DOFs by using a four-bar mechanism and a lifting mechanism; we also developed the patient-specific exercises including patient-passive exercise and patient-cooperative exercise, and the advantage of the developed patient-cooperative exercise is that we simplified the human-robot coupling system model into a single spring system instead of the mass-spring-damp system, which efficiently improved the response speed of the control system. In terms of the organization structure of the work, we introduced the end-effector type ULRR’s mechanical design, control system, inverse solution of positions, patient-passive exercise based on the inverse solution of positions and the linear position interpolation of servo drives, and patient-cooperative exercise based on the spring model, in sequence. Experiments with three healthy subjects have been conducted, with results showing good trajectory tracking performance in patient-passive exercise and showing effective, flexible, and good real-time interactive performance in patient-cooperative exercise.
      PubDate: Thu, 27 Oct 2022 11:05:01 +000
       
  • Implementation and Evaluation of a Dynamic Neck Brace Rehabilitation
           Device Prototype

    • Abstract: Rehabilitation assistive devices for head/neck pain treatment cannot allow dynamic changes in position and orientation of the head/neck. Moreover, such devices can neither be used simultaneously nor can they assess the patients’ head/neck conditions. This paper aims at designing and implementing a novel dynamic head/neck brace that provides static and dynamic support and/or traction at symmetric and asymmetric positions. This device also provides assessments of the head/neck stiffness for the purpose of fulfilling diagnoses of the head/neck disorders. The device was used and evaluated for its range of motion and its symmetric traction capability using two control modalities. In addition, it was also evaluated in determining the stiffness of the head/neck throughout a simulating mechanical model involved in a set of springs. The device could apply right/left lateral bending to the head/neck ranged −6.97 ± 0.01° to 7.02 ± 0.01° with accuracies of 99.89% and 99.48%, and flexion/extension ranged −8.10 ± 0.02° to 8.12 ± 0.01° with accuracies of 99.57% and 99.42%, respectively, throughout a traction phase of 20 mm. The practical measurements through the symmetric traction tests showed some deviations as compared to that being calculated. Such deviations were greater in flexion/extension rather than the right/left lateral bending. The mean of the obtained error was less than 0.34° for all situations of tests. The accuracies of stiffness measurement of the mechanical model were 99.78% and 99.96%, respectively, throughout performing stair and step tests. The paper presented a novel design of a dynamic head/neck brace that provides support and/or traction to any head/neck positions and capable of evaluating the head/neck stiffness during cervical traction.
      PubDate: Thu, 27 Oct 2022 10:20:00 +000
       
  • Biomechanical Effect of Disc Height on the Components of the Lumbar Column
           at the Same Axial Load: A Finite-Element Study

    • Abstract: Intervertebral discs are fibrocartilage structures, which play a role in buffering the compression applied to the vertebral bodies evenly while permitting limited movements. According to several previous studies, degenerative changes in the intervertebral disc could be accelerated by factors, such as aging, the female sex, obesity, and smoking. As degenerative change progresses, the disc height could be reduced due to the dehydration of the nucleus pulposus. This study aimed to quantitatively analyze the pressure that each structure of the spine receives according to the change in the disc height and predict the physiological effect of disc height on the spine. We analyzed the biomechanical effect on spinal structures when the disc height was decreased using a finite-element method investigation of the lumbar spine. Using a 3D FE model, the degree and distribution of von-Mises stress according to the disc height change were measured by applying the load of four different motions to the lumbar spine. The height was changed by dividing the anterior and posterior parts of the disc, and analysis was performed in the following four motions: flexion, extension, lateral bending, and axial rotation. Except for a few circumstances, the stress applied to the structure generally increased as the disc height decreased. Such a phenomenon was more pronounced when the direction in which the force was concentrated coincided with the portion where the disc height decreased. This study demonstrated that the degree of stress applied to the spinal structure generally increases as the disc height decreases. The increase in stress was more prominent when the part where the disc height was decreased and the part where the moment was additionally applied coincided. Disc height reduction could accelerate degenerative changes in the spine. Therefore, eliminating the controllable risk factors that cause disc height reduction may be beneficial for spinal health.
      PubDate: Tue, 25 Oct 2022 11:50:01 +000
       
  • Secure Medical Data Model Using Integrated Transformed Paillier and KLEIN
           Algorithm Encryption Technique with Elephant Herd Optimization for
           Healthcare Applications

    • Abstract: In the healthcare industry, where concerns are frequently and appropriately focused on saving someone’s life, access to interfaces and computer systems storing sensitive data, such as medical records, is crucial to take into account. Medical information has to be secretive and protected by the laws of privacy with restrictions on its access. E-health security is a holistic notion that encompasses available medical data’s integrities and confidentiality which ensures that data are not accessed by unauthorized people and allow doctors to offer proper treatment. The patients’ data need to be secured on servers holding medical data. This work adds new features for ensuring storage and access safety through ITPKLEIN-EHO (integrated transformed Paillier and KLEIN algorithms) that use EHOs (elephant herd optimizations) to provide lightweight features. The key space affects lightweight encryption techniques in general. The EHOs (elephant herd optimizations) optimize key spaces by adjusting iteration rounds. The main goal is to encrypt EEGs (electroencephalographic signals) in healthcare and send it to end users using the proposed ITPKLEIN-EHO approach. This suggested technique utilizes MATLAB for its tests on various EEG data sets for implementation. The simulations of the proposed IRPKLEIN-EHO technique are evaluated with other existing techniques in terms of MSEs, PSNRs, SSIMs, PRDs, and encryption/decryption times.
      PubDate: Tue, 25 Oct 2022 11:50:01 +000
       
  • Kidney Tumor Detection and Classification Based on Deep Learning
           Approaches: A New Dataset in CT Scans

    • Abstract: Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist’s workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models; three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the King Abdullah University Hospital (KAUH) has been collected that consists of 8,400 images of 120 adult patients who have performed CT scans for suspected kidney masses. The dataset was divided into 80% for the training set and 20% for the testing set. The accuracy results for the detection models of 2D CNN-6 and ResNet50 reached 97%, 96%, and 60%, respectively. At the same time, the accuracy results for the classification model of the 2D CNN-4 reached 92%. Our novel models achieved promising results; they enhance the diagnosis of patient conditions with high accuracy, reducing radiologist’s workload and providing them with a tool that can automatically assess the condition of the kidneys, reducing the risk of misdiagnosis. Furthermore, increasing the quality of healthcare service and early detection can change the disease’s track and preserve the patient’s life.
      PubDate: Sat, 22 Oct 2022 07:05:01 +000
       
  • SFA-Net: Scale and Feature Aggregate Network for Retinal Vessel
           Segmentation

    • Abstract: A U-Net-based network has achieved competitive performance in retinal vessel segmentation. Previous work has focused on using multilevel high-level features to improve segmentation accuracy but has ignored the importance of shallow-level features. In addition, multiple upsampling and convolution operations may destroy the semantic feature information contained in the decoder layer. To address these problems, we propose a scale and feature aggregate network (SFA-Net), which can make full use of multiscale high-level feature information and shallow features. In this paper, a residual atrous spatial feature aggregate block (RASF) is embedded at the end of the encoder to learn multiscale information. Furthermore, an attentional feature module (AFF) is proposed to enhance the effective fusion between shallow and high-level features. In addition, we designed the multi-path feature fusion (MPF) block to fuse high-level features of different decoder layers, which aims to learn the relationship between the high-level features of different paths and alleviate the information loss. We apply the network to the three benchmark datasets (DRIVE, STARE, and CHASE_DB1) and compare them with the other current state-of-the-art methods. The experimental results demonstrated that the proposed SFA-Net performs effectively, indicating that the network is suitable for processing some complex medical images.
      PubDate: Fri, 21 Oct 2022 11:35:00 +000
       
  • Bioinformatics Analysis of Common Genetic and Molecular Traits and
           Association of Portal Hypertension with Pulmonary Hypertension

    • Abstract: Portal hypertension (PH) is an important cause of pulmonary arterial hypertension(PAH), but its mechanism is still unclear. We used genetic data analysis to explore the shared genes and molecular mechanisms of PH and PAH. We downloaded the PH and PAH data from the GEO database, and used the weighted gene coexpression network analysis method (WGCNA) to analyze the coexpression modules of idiopathic noncirrhotic portal hypertension (INCPH) and cirrhotic portal hypertension (CPH) and pulmonary hypertension, respectively. Enrichment analysis was performed on the common genes, and differential gene expressions (DEGs) were used for verification. The target genes of INCPH and PAH were obtained by string and cytoscape software, and the miRNAs of target genes were predicted by miRwalk, miRDB, and TargetScan and their biological functions were analyzed; finally, we used PanglaoDB to predict the expression of target genes in cells. In WGCNA, gene modules significantly related to PAH, CPH, and INCPH were identified, and enrichment function analysis showed that the common pathway of PAH and CPH were “P53 signaling pathway,” “synthesis of neutral lipids”; PAH and INCPH are “terminal,” “Maintenance Regulation of Granules,” and “Toxin Transport.” DEGs confirmed the results of WGCNA; the common miRNA functions of PAH and cirrhosis were enriched for “P53 signaling pathway,” “TGF-β signaling pathway,” “TNF signaling pathway,” and “fatty acid metabolism,” and the miRNAs-mRNAs network suggested that hsa-miR-22a-3p regulates MDM2 and hsa-miR-34a-5p regulates PRDX4; the target genes of PAH and INCPH are EIF5B, HSPA4, GNL3, RARS, UTP20, HNRNPA2B1, HSP90B1, METAP2, NARS, SACM1L, and their target miRNA function enrichment showed EIF5B, HNRNPA2B1, HSP90B1, METAP2, NARS, SACM1L, and HSPA4 are associated with telomeres and inflammation, panglaoDB showed that target genes are located in endothelial cells, smooth muscle cells, etc. In conclusion, the mechanism of pulmonary hypertension induced by portal hypertension may be related to telomere dysfunction and P53 overactivation, and lipid metabolism and intestinal inflammation are also involved in this process.
      PubDate: Thu, 20 Oct 2022 10:50:00 +000
       
  • Analysis of Trends in Demographic Distribution of Dental Workforce in the
           Kingdom of Saudi Arabia

    • Abstract: Dental professionals are playing an imperative role in the healthcare system. It is important to distribute the dental workforce across the country. Therefore, this study aimed at analyzing the recent distribution of the dental workforce in the Kingdom of Saudi Arabia (KSA) and determining the current dentist-to-population ratio in the KSA. This is a cross-sectional study focused on the dental workforces working in the KSA between 2015 and 2020. Complete data of dentists working in the KSA with different professional ranks were obtained. The data were stratified by gender, professional rank (Saudi and non-Saudi), area of working (13 provinces in the KSA), and sector of working (public and private). A complete list of all dental universities was obtained to identify the increasing number of dental institutes at this current moment. In addition, the dentist-to-population ratio was also evaluated based on the current inhabitant in the KSA and the total dental surgeons. There are a total of 27181 dental surgeons and 8022 dental auxiliaries registered in different specialties as of 2020. Saudi citizens are holding the majority of the posts in both dentist and dental auxiliary categories. The percentage of males and female is slightly higher in dentists and dental auxiliaries, respectively. It also indicated that where most of the dental personnel work in the private sector, dental auxiliaries work in the public sector. Moreover, the highest number of dental workforces is identified in the Riyadh region among all the 13 provinces. Based on the databases, the current dentist-to-population ratio is 1 : 1288.16. In conclusion, the number of dental professionals is ample; however, rural areas lack specialists. Saudi dentists are progressively replacing foreign dentists in different professional ranks working in the KSA.
      PubDate: Wed, 19 Oct 2022 10:50:00 +000
       
  • Mobile Health (mHealth) Technology in Early Detection and Diagnosis of
           Oral Cancer-A Scoping Review of the Current Scenario and Feasibility

    • Abstract: Objective. Oral cancer is one of the most common types of cancer with dreadful consequences. But it can be detected early without much expensive equipment. Screening and early detection of oral cancer using Mobile health (mHealth) technology are reported due to the availability of the extensive network of mobile phones across populations. Therefore, we aimed to explore the existing literature regarding mHealth feasibility in the early detection of oral cancer. Materials and Method. An extensive search was conducted to explore the literature on the feasibility of mobile health for early oral cancer. Clinical studies reporting kappa agreement between on-site dentists and offsite health care workers/dentists in the early detection of oral cancer were included in this review. Studies describing the development of a diagnostic device, app development, and qualitative interviews among practitioners trained in using mobile health were also included in this review for a broader perspective on mHealth. Results. While most of the studies described various diagnostic accuracies using mHealth for oral cancer early detection, few studies reported the development of mobile applications, novel device designs for mHealth applications, and the feasibility of a few mHealth programs for early oral cancer detection. Community health workers equipped with a mobile phone-based app could identify “abnormal” oral lesions. Overall, many studies reported high sensitivity, specificity, and Kappa value of agreement. Effectiveness, advantages, and barriers in oral cancer screening using mHealth are also described. Conclusion. The overall results show that remote diagnosis for early detection of oral cancer using mHealth was found useful in remote settings.
      PubDate: Wed, 19 Oct 2022 10:50:00 +000
       
  • Efficient Model for Coronary Artery Disease Diagnosis: A Comparative Study
           of Several Machine Learning Algorithms

    • Abstract: Background. In today’s industrialized world, coronary artery disease (CAD) is one of the leading causes of death, and early detection and timely intervention can prevent many of its complications and eliminate or reduce the resulting mortality. Machine learning (ML) methods as one of the cutting-edge technologies can be used as a suitable solution in diagnosing this disease. Methods. In this study, different ML algorithms’ performances were compared for their effectiveness in developing a model for early CAD diagnosis based on clinical examination features. This applied descriptive study was conducted on 303 records and overall 26 features, of which 26 were selected as the target features with the advice of several clinical experts. In order to provide a diagnostic model for CAD, we ran most of the most critical classification algorithms, including Multilayer Perceptron (MLP), Support Vector Machine (SVM), Logistic Regression (LR), J48, Random Forest (RF), K-Nearest Neighborhood (KNN), and Naive Bayes (NB). Seven different classification algorithms with 26 predictive features were tested to cover all feature space and reduce model error, and the most efficient algorithms were identified by comparison of the results. Results. Based on the compared performance metrics, SVM (AUC = 0.88, F-measure = 0.88, ROC = 0.85), and RF (AUC = 0.87, F-measure = 0.87, ROC = 0.91) were the most effective ML algorithms. Among the algorithms, the KNN algorithm had the lowest efficiency (AUC = 0.81, F-measure = 0.81, ROC = 0.77). In the diagnosis of coronary artery disease, machine learning algorithms have played an important role. Proposed ML models can provide practical, cost-effective, and valuable support to doctors in making decisions according to a good prediction. Discussion. It can become the basis for developing clinical decision support systems. SVM and RF algorithms had the highest efficiency and could diagnose CAD based on patient examination data. It is suggested that further studies be performed using these algorithms to diagnose coronary artery disease to obtain more accurate results.
      PubDate: Tue, 18 Oct 2022 11:50:00 +000
       
  • A Nomogram for Predicting Cardiovascular Diseases in Chronic Obstructive
           Pulmonary Disease Patients

    • Abstract: Cardiovascular diseases (CVDs) are the most common comorbidities in the chronic obstructive pulmonary disease (COPD), which increase the risk of hospitalization, length of stay, and death in COPD patients. This study aimed to identify the predictors for CVDs in COPD patients and construct a prediction model based on these predictors. In total, 1022 COPD patients in National Health and Nutrition Examination Surveys (NHANES) were involved in the cross-sectional study. All subjects were randomly divided into the training set (n = 709) and testing set (n = 313). The differences before and after the manipulation of the missing data were compared via sensitivity analysis. Univariate and multivariable analyses were employed to screen the predictors of CVDs in COPD patients. The performance of the prediction model was evaluated via the area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and calibration. Subgroup analysis was performed in patients using different COPD diagnosis methods and patients smoking or not smoking in the testing set. We found that male, older age, a smoking history, overweight, a history of blood transfusion, a history of heart disease in close relatives, higher levels of white blood cell (WBC), and monocyte (MONO) were associated with the increased risk of CVDs in COPD patients. Higher levels of platelets (PLT) and lymphocyte (LYM) were associated with reduced risk of CVDs in COPD patients. A prediction model for the risk of CVDs in COPD patients was established based on predictors including gender, age, a smoking history, BMI, a history of blood transfusion, a history of heart disease in close relatives, WBC, MONO, PLT, and LYM. The AUC value of the prediction model was 0.75 (95% CI: 0.71–0.79) in the training set and 0.79 (95%CI: 0.73–0.85) in the testing set. The prediction model established showed good predictive performance in predicting CVDs in COPD patients.
      PubDate: Tue, 18 Oct 2022 09:20:01 +000
       
  • Optimal Deep Learning-Based Vocal Fold Disorder Detection and
           Classification Model on High-Speed Video Endoscopy

    • Abstract: The use of high-speed video-endoscopy (HSV) in the study of phonatory processes linked to speech needs the precise identification of vocal fold boundaries at the time of vibration. The HSV is a unique laryngeal imaging technology that captures intracycle vocal fold vibrations at a higher frame rate without the need for auditory inputs. The HSV is also effective in identifying the vibrational characteristics of the vocal folds with an increased temporal resolution during retained phonation and flowing speech. Clinically significant vocal fold vibratory characteristics in running speech can be retrieved by creating automated algorithms for extracting HSV-based vocal fold vibration data. The best deep learning-based diagnosis and categorization of vocal fold abnormalities is due to the usage of HSV (ODL-VFDDC). The suggested ODL-VFDDC technique starts with temporal segmentation and motion correction to identify vocalized regions from the HSV recording and gathers the position of movable vocal folds across frames. The attributes gathered are fed into the deep belief network (DBN) model. Furthermore, the agricultural fertility algorithm (AFA) is used to optimize the hyperparameter tuning of the DBN model, which improves classification results. In terms of vocal fold disorder classification, the testing results demonstrated that the ODL-VFDDC technique beats the other existing methodologies. The farmland fertility algorithm (FFA) is then used to accurately determine the glottal limits of vibrating vocal folds. The suggested method has successfully tracked the speech fold boundaries across frames with minimum processing cost and high resilience to picture noise. This method gives a way to look at how the vocal folds move during a connected speech that is completely done by itself.
      PubDate: Mon, 17 Oct 2022 08:20:01 +000
       
  • Identifying and Targeting Prediction of the PI3K-AKT Signaling Pathway in
           Drug-Induced Thrombocytopenia in Infected Patients Receiving Linezolid
           Therapy: A Network Pharmacology-Based Analysis

    • Abstract: The pharmacological mechanisms underlying the adverse effects of linezolid on thrombocytopenia have not been conclusively determined. This network pharmacology study aimed at investigating the potential pharmacological mechanisms of linezolid-induced adverse reactions in thrombocytopenia. In this study, target genes for linezolid and thrombocytopenia were compared and analyzed. Overlapping thrombocytopenia-associated targets and predicted targets of linezolid were imported to establish protein-protein interaction networks. Gene Ontology and the Kyoto Encyclopedia of Genes and Genome pathway enrichment analyses were performed to determine the enriched biological terms and pathways. The mechanisms involved in linezolid-induced thrombocytopenia were established to be associated with various biological processes, including T cell activation, peptidyl serine modification, and peptidyl serine phosphorylation. The top five relevant protein targets were obtained, including ALB, AKT1, EGFR, IL6, and MTOR. Enrichment analysis showed that the targets of linezolid were positively correlated with T cell activation responses. The mechanism of action of linezolid was positively correlated with the PI3K-AKT signaling pathway and negatively correlated with the Ras signaling pathway. We identified the important protein targets and signaling pathways involved in linezolid-induced thrombocytopenia in anti-infection therapy, providing new information for subsequent studies on the pathogenesis of drug-induced thrombocytopenia and potential therapeutic strategies for rational use of linezolid in clinical settings.
      PubDate: Sat, 15 Oct 2022 07:05:00 +000
       
  • Machine Learning-Based Hearing Aid Fitting Personalization Using Clinical
           Fitting Data

    • Abstract: The initial software fitting prescribed by the fitting formula largely depends on the patient’s hearing loss, which may not be the optimal preference for a particular user. Certain criteria must also be readjusted by an audiologist to meet the user-specific requirements. Therefore, this study focuses on the novel application of a neural network (NN) technique to build a suitable fitting algorithm with prescribed hearing loss and the corresponding preferred gain to minimize the gap between optimized fittings. The algorithm intended to learn the hearing preferences of an individual user such that the initial fitting may be optimized. These findings demonstrate the efficiency of the algorithm, with and without additional features. Using the clinical fitting data, the average mean square error (MSE) for the simple NN algorithm was 5.4183%. By adding additional features to the data, the algorithm performed better, and the average MSE was as low as 5.2530%. However, the algorithm outperformed Company A fitting software, as the MSE was the highest at 5.4748%. As the company’s automatic fitting has a noticeable discrepancy with clinical fitting records, the impeccable results from this study can lead to a better path towards fitting satisfaction, thus benefiting the hearing-impaired community to a larger extent.
      PubDate: Sat, 15 Oct 2022 07:05:00 +000
       
 
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