Anahita Ali^*, Santosh Kumar

International Journal of Mental Health Promotion, Vol.25, No.7, pp. 833-845, 2023, DOI:10.32604/ijmhp.2023.028799 - 01 June 2023

Abstract Since the coronavirus pandemic, many factors led to the change in the mental well-being of hospital administrators and their staff. The pandemic negatively impacted the availability and capability of health professionals to deliver essential services and meet rising demand. Therefore, this study aimed to understand the perspective of hospital administrators about issues and challenges that negatively impacted their staff’s mental health and hospital administrators’ coping response to mitigate those challenges and issues. An exploratory qualitative study was conducted with 17 hospital administrators (superintendents, deputy superintendents, nursing in charge and hospital in charge) working in a… More > Graphic Abstract

Yoshikiyo Kanada^1,#, Shota Suzumura^1,2,#, Soichiro Koyama¹, Kazuya Takeda¹, Kenta Fujimura¹, Takuma Ii¹, Shigeo Tanabe¹, Hiroaki Sakurai^1,*

International Journal of Mental Health Promotion, Vol.25, No.7, pp. 855-861, 2023, DOI:10.32604/ijmhp.2023.028956 - 01 June 2023

Abstract Mental health difficulties can impact students’ motivation, focus, and ability to communicate with others. Students attending medical universities are more likely to experience anxiety, depression, and other mood changes for the first time. However, no study has examined their prevalence among Japanese rehabilitation students. This study investigated the prevalence of anxiety among Japanese rehabilitation students and aimed to identify its predictors. A cross-sectional study was conducted among 148 first-year physical and occupational therapy students at a private medical university in Japan in June 2022. Data on sociodemographic and personal characteristics, such as gender, age, subject… More >

L. Amaadour, I. Lahrch, O. Siyouri, K. Oualla, Z. Benbrahim, S. Arifi, C. Aarab, S. El Fakir, N. Mellas

Psycho-Oncologie, Vol.17, No.1, pp. 38-43, 2023, DOI:10.3166/pson-2022-0221

Abstract Introduction: La Covid-19 constitue une cause de plusieurs affections mentales, notamment chez les patients atteints de cancer qui sont déjà considérés comme une population vulnérable. Ainsi, l’objectif de la présente étude était d’évaluer les troubles psychologiques des patients suivis pour une maladie tumorale maligne, sous traitement médical systémique, ayant eu une infection au SARS-CoV2 ; ainsi que les conséquences que ces troubles psychologiques peuvent avoir sur l’adhésion aux soins oncologiques.
Méthode: Il s’agit d’une étude transversale sur une période de quatre mois (pic de la pandémie) menée au département d’oncologie médicale du centre hospitalier universitaire Hassan-II de… More >

Aoli Li^1,#, Yan You^1,2,#, Kunli Wu³, Huibin Shan⁴, Younglee Kim⁵, Qilian He^1,*

International Journal of Mental Health Promotion, Vol.25, No.6, pp. 783-797, 2023, DOI:10.32604/ijmhp.2023.026956 - 06 May 2023

Abstract Objective: To explore the double psychosocial threats of the COVID-19 pandemic, targeted behavior toward Chinese Americans, and the correlates to their mental health. Methods: A quantitative, cross-sectional, and descriptive design was utilized by using a purposive convenience sample of 301 Chinese Americans over the age of 18 residing in the United States. Online data collection was conducted through the social media platform WeChat from April 8–21, 2021. Descriptive statistical analysis was used for the participants’ demographic characteristics, Multidimensional Scale of Perceived Social Support (MSPSS), Double Threat Situations, COVID-19 Racial Discrimination, and General Anxiety Disorder-7 (GAD-7). Stepwise logistic… More > Graphic Abstract

Asma Qaiser¹, Saman Hina¹, Abdul Karim Kazi^1,*, Saad Ahmed², Raheela Asif³

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 73-90, 2023, DOI:10.32604/iasc.2023.036784 - 29 April 2023

Abstract In the past few years, social media and online news platforms have played an essential role in distributing news content rapidly. Consequently. verification of the authenticity of news has become a major challenge. During the COVID-19 outbreak, misinformation and fake news were major sources of confusion and insecurity among the general public. In the first quarter of the year 2020, around 800 people died due to fake news relevant to COVID-19. The major goal of this research was to discover the best learning model for achieving high accuracy and performance. A novel case study of More >

Meilin Wu^1,2, Lianggui Tang^1,2,*, Qingda Zhang^1,2, Ke Yan^1,2

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 179-198, 2023, DOI:10.32604/iasc.2023.036684 - 29 April 2023

Abstract As COVID-19 poses a major threat to people’s health and economy, there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently. In non-stationary time series forecasting jobs, there is frequently a hysteresis in the anticipated values relative to the real values. The multilayer deep-time convolutional network and a feature fusion network are combined in this paper’s proposal of an enhanced Multilayer Deep Time Convolutional Neural Network (MDTCNet) for COVID-19 prediction to address this problem. In particular, it is possible to record the deep features and temporal dependencies in uncertain time series, More >

Shroog Alshomrani^*, Muhammad Arif, Mohammed A. Al Ghamdi

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5717-5742, 2023, DOI:10.32604/cmc.2023.038059 - 29 April 2023

Abstract Coronavirus has infected more than 753 million people, ranging in severity from one person to another, where more than six million infected people died worldwide. Computer-aided diagnostic (CAD) with artificial intelligence (AI) showed outstanding performance in effectively diagnosing this virus in real-time. Computed tomography is a complementary diagnostic tool to clarify the damage of COVID-19 in the lungs even before symptoms appear in patients. This paper conducts a systematic literature review of deep learning methods for classifying the segmentation of COVID-19 infection in the lungs. We used the methodology of systematic reviews and meta-analyses (PRISMA) More >

Sobia Nawaz¹, Sidra Rasheed², Wania Sami³, Lal Hussain^4,5,*, Amjad Aldweesh^6,*, Elsayed Tag eldin⁷, Umair Ahmad Salaria^8,9, Mohammad Shahbaz Khan¹⁰

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5213-5228, 2023, DOI:10.32604/cmc.2023.037543 - 29 April 2023

Abstract This study is designed to develop Artificial Intelligence (AI) based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays (CXRs). The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of CXRs. In this study, AI-based analysis tools were developed that can precisely classify COVID-19 lung infection. Publicly available datasets of COVID-19 (N = 1525), non-COVID-19 normal (N = 1525), viral pneumonia (N = 1342) and bacterial pneumonia (N = 2521) from the Italian Society of Medical and… More >

Shabir Hussain¹, Muhammad Ayoub², Yang Yu¹, Junaid Abdul Wahid¹, Akmal Khan³, Dietmar P. F. Moller⁴, Hou Weiyan^1,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5355-5377, 2023, DOI:10.32604/cmc.2023.036779 - 29 April 2023

Abstract As the COVID-19 pandemic swept the globe, social media platforms became an essential source of information and communication for many. International students, particularly, turned to Twitter to express their struggles and hardships during this difficult time. To better understand the sentiments and experiences of these international students, we developed the Situational Aspect-Based Annotation and Classification (SABAC) text mining framework. This framework uses a three-layer approach, combining baseline Deep Learning (DL) models with Machine Learning (ML) models as meta-classifiers to accurately predict the sentiments and aspects expressed in tweets from our collected Student-COVID-19 dataset. Using the… More >

Kun Yang^1,2, Lei Zhao¹, Xianghui Wang¹, Mingyang Zhang¹, Linyan Xue^1,2, Shuang Liu^1,2, Kun Liu^1,2,3,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5159-5176, 2023, DOI:10.32604/cmc.2023.036401 - 29 April 2023

Abstract The diagnosis of COVID-19 requires chest computed tomography (CT). High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease, so it is of clinical importance to study super-resolution (SR) algorithms applied to CT images to improve the resolution of CT images. However, most of the existing SR algorithms are studied based on natural images, which are not suitable for medical images; and most of these algorithms improve the reconstruction quality by increasing the network depth, which is not suitable for machines with limited resources. To alleviate these issues, we propose… More >