Privacy-Preserving Fast Three-Factor Authentication and Key Agreement for IoT-Based E-Health Systems

Electronic healthcare (e-health) systems have received renewed interest, particularly in the current COVID-19 pandemic (e.g., lockdowns and changes in hospital policies due to the pandemic). However, ensuring security of both data-at-rest and data-in-transit remains challenging to achieve, particularly since data is collected and sent from less insecure devices (e.g., patients' wearable or home devices). While there have been a number of authentication schemes, such as those based on three-factor authentication, to provide authentication and privacy protection, a number of limitations associated with these schemes remain (e.g., (in)security or computationally expensive). In this study, we present a privacy-preserving three-factor authenticated key agreement scheme that is sufficiently lightweight for resource-constrained e-health systems. The proposed scheme enables both mutual authentication and session key negotiation in addition to privacy protection, with minimal computational cost. The security of the proposed scheme is demonstrated in the Real-or-Random model. Experiments using Raspberry Pi show that the proposed scheme achieves reduced computational cost (of up to 89.9% in comparison to three other related schemes).

[Effects of vaccines on the viral negative conversion of children with COVID-19].

Objective: To explore the effect of vaccination on viral negative conversion of children with COVID-19. Methods: A retrospective cohort study was conducted. A cohort of 189 children aged 3-14 years with COVID-19 admitted to Renji Hospital (South branch) of Shanghai Jiao Tong University School of Medicine from April 7th to May 19th 2022 was enrolled in the study. According to the vaccination status, the infected children were divided into an unvaccinated group and a vaccinated group. Age, gender, severity, clinical manifestations, and laboratory tests, etc. were compared between groups, by rank sum test or chi-square test. The effects of vaccination on viral negative conversion were analyzed by a Cox mixed-effects regression model. Additionally, a questionnaire survey was conducted among the parents of unvaccinated children to analyze the reasons for not being vaccinated. Results: A total of 189 children aged 3-14 years were enrolled, including 95 males (50.3%) and 94 females (49.7%), aged 5.7 (4.1,8.6) years. There were 117 cases (61.9%) in the unvaccinated group and 72 cases (38.1%) in the vaccinated group. The age of the vaccinated group was higher than that of the unvaccinated group (8.8 (6.8, 10.6) vs. 4.5 (3.6, 5.9) years, Z=9.45, P<0.001). No significant differences were found in clinical manifestations, disease severity, and laboratory results between groups (all P>0.05), except for the occurrence rate of cough symptoms, which was significantly higher in the vaccinated group than in the non-vaccinated group (68.1% (49/72) vs. 50.4% (59/117),χ2=5.67, P=0.017). The Kaplan-Meier survival curve and Cox mixed-effects regression model showed that the time to the viral negative conversion was significantly shorter in the vaccinated group compared with the unvaccinated group (8 (7, 10) vs. 11 (9, 12) d, Z=5.20, P<0.001; adjusted HR=2.19 (95%CI 1.62-2.97)). For questionnaire survey on the reasons for not receiving a vaccination, 115 questionnaires were distributed and 112 valid questionnaires (97.4%) were collected. The main reasons for not being vaccinated were that parents thought that their children were not in the range of appropriate age for vaccination (51 cases, 45.5%) and children were in special physical conditions (47 cases, 42.0%). Conclusion: Vaccination can effectively shorten the negative conversion time of children with COVID-19 and targeted programs should be developed to increase eligible children's vaccination rate for SARS-CoV-2 vaccination.

A study on CO VID-19 awareness, psychological status and stress reduction among health care workers after “traffic control” in Dali Bai Autonomous Prefecture

Objective To understand the awareness, psychological status and stress reduction of health care workers involved in the emergency response Corona Virus Disease 2019 (COVID-19) outbreak since the “traffic control” in Dali Bai Autonomous Prefecture on 26 January 2020, in order to inform the development of relevant measures. To provide a reference basis for the development of related measures. Methods The study participants were invited through the Dali Bai Autonomous Prefecture medical and nursing exchange group by snowball sampling method based on WeChat from February 4 to February 5, and the invited participants filled out the questionnaires online(Questionnaire Star). The invailed questionnaires were strictly eliminated according to the quality control conditions, and the questionnaires that fit the research study were selected for collation, statistical analysis was performed. Results Onerall high awareness of COVID-19 among health care workers after “traffic control” in Dali Bai Autonomous Prefecture, with the highest knowledge of the source of infection was 95.95% and the lowest genotype knowledge rate of 64. 86%. The differences between the different psychological profiles of anxiety and stress, loneliness and depression among health care workers were statistically significant (x2 = 25. 439, P < 0. 01), and the highest percentage of anxiety among health care workers was 79. 73% and the lowest percentage of depression was 50. 85%;health care workers mainly reduced stress by watching TV and surfing the Internet, and the composition ratios of the two main forms of reducing stress were 68.92% and 60. 81%, respectively. Conclusion Different types of mental health problems existed among health care workers of different genders, occupations, titles and marital status after the “traffic control” in Dali Bai Autonomous Prefecture. Therefore, targeted mental health guidance and interventions for different health care workers. © 2022, Editorial Department of Medical Pest Control. All rights reserved.

Dual Encoder Fusion U-Net (DEFU-Net) for Cross-manufacturer Chest X-ray Segmentation

A number of methods based on deep learning have been applied to medical image segmentation and have achieved state-of-the-art performance. Due to the importance of chest x-ray data in studying COVID-19, there is a demand for state-of-the-art models capable of precisely segmenting soft tissue on the chest x-rays. The dataset for exploring best segmentation model is from Montgomery and Shenzhen hospital which had opened in 2014. The most famous technique is U-Net which has been used to many medical datasets including the Chest X-rays. However, most variant U-Nets mainly focus on extraction of contextual information and skip connections. There is still a large space for improving extraction of spatial features. In this paper, we propose a dual encoder fusion U-Net framework for Chest X-rays based on Inception Convolutional Neural Network with dilation, Densely Connected Recurrent Convolutional Neural Network, which is named DEFU-Net. The densely connected recurrent path extends the network deeper for facilitating contextual feature extraction. In order to increase the width of network and enrich representation of features, the inception blocks with dilation are adopted. The inception blocks can capture globally and locally spatial information from various receptive fields. At the same time, the two paths are fused by summing features, thus preserving the contextual and spatial information for decoding part. This multi-learning-scale model is benefiting in Chest X-ray dataset from two different manufacturers (Montgomery and Shenzhen hospital). The DEFU-Net achieves the better performance than basic U-Net, residual U-Net, BCDU-Net, R2U-Net and attention R2U-Net. This model has proved the feasibility for mixed dataset and approaches state-of-the-art. The source code for this proposed framework is public https://github.com/uceclz0/DEFU-Net.