How Do Multiple Actors Conduct Science Communication About Omicron on Weibo: A Mixed‐Method Study

This article explores science communication about Omicron on Weibo by eight actors from November 2021 to June 2022. Regarding the themes of vaccines, symptoms, and medicines, we examined the actors' communication with content analysis, presented the interactions of different actors using social network analysis, and assessed the impact of weibos on public sentiment using SnowNLP and descriptive statistics. The results showed that scientists are still the most important actors, focusing on science issues and using contrasting and contextual frames. Central‐level media play an essential mediating role, relaying scientific knowledge. Science communication on Weibo had a positive impact on public sentiment. © 2023 by the author(s);licensee Cogitatio Press (Lisbon, Portugal). This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY).

AI Based Directory Discovery Attack and Prevention of the Medical Systems

The medical system has been targeted by the cyber attackers, who aim to bring down the health security critical infrastructure. This research is motivated by the recent cyber-attacks happened during COVID 19 pandemics which resulted in the compromise of the diagnosis results. This study was carried to demonstrate how the medical systems can be penetrated using AI-based Directory Discovery Attack and present security solutions to counteract such attacks. We then followed the NIST (National Institute of Standards and Technology) ethical hacking methodology to launch the AI-based Directory Discovery Attack. We were able to successfully penetrate the system and gain access to the core of the medical directories. We then proposed a series of security solutions to prevent such cyber-attacks. © 2022 Creative Commons.

WashRing: An Energy-Efficient and Highly Accurate Handwashing Monitoring System Via Smart Ring

The outbreak of COVID-19 has greatly changed everyone's lifestyle all over the world. One of the best ways to prevent the spread of infections is by washing hands properly. Although a number of hand hygiene monitoring systems have been proposed, they either cannot achieve high accuracy in practice or work only in limited environments such as hospitals. Therefore, a ubiquitous, energy-efficient and highly accurate hand hygiene monitoring system is still lacking. In this paper, we present WashRing—the first smart ring-based handwashing monitoring system. In WashRing, we design a Partially Observable Markov Decision Process (POMDP) based adaptive sampling approach to achieve high energy efficiency. Then, we design an automatic feature extraction scheme based on wavelet scattering and a CNN-LSTM neural network to achieve fine-grained gesture recognition. Finally, we model the handwashing gesture classification as a few-shot learning problem to mitigate the burden of collecting extensive data from five fingers. We collect data from 25 subjects over 2 months and evaluate the system performance on both commercial OURA ring and customized ring. Evaluation results show that WashRing achieves 97.8%accuracy which is 10.2%–15.9%higher than state-of-the-arts. Our adaptive sampling approach reduces energy consumption by 64.2%compared to fixed duty cycle sampling strategies. IEEE

Optimizing the supply of essential goods under closed-off management: A case study of COVID-19

The COVID-19 pandemic is threatening people's health and economic development around the world. Many areas and cities are opting for actions such as lockdown or closed-off management to prevent rapid spread of the virus. Under these actions, it is necessary and important to guarantee the supply of essential goods. In this paper, we develop a multi-objective optimization model to optimize the supply of essential goods under closed-off management. A mixed binary integer programming model is built to help identify the optimal safety stock level as well as the supply network so that the demands for essential goods could be guaranteed. The effectiveness of our method is demonstrated by an example. We also perform sensitivity analysis for key parameters. Our work provides a reasonable solution for the supply of essential goods under closed-off management. Copyright (C) 2022 The Authors.

Coronavirus disease 2019 in dermatology practice: Perspective of three levels of prevention on public health

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, has become a major public exigency of international concern. The COVID-19 epidemic has spread rapidly around the world, profoundly impacting people's lives. Patients are among those most affected by the pandemic. COVID-19 has adversely affected health-care systems, and the effects are long-lasting and devastating. Most medical institutions in the impacted countries and regions have been imbued with COVID-19 cases, both confirmed and suspected, leading to an overburdened health-care workforce like never before. While most of the critical situations involved internal medicine departments, such as infectious diseases, and intensive care units, other specialties, including dermatology, have also been profoundly affected by this pandemic. Dermatoepidemiology, the application of epidemiological methods to dermatology practice, is an important emerging discipline in dermatology. In this review, we discussed the influence of the COVID-19 epidemic on dermatology practice, as well as the application of public health strategies in dermatology. These findings from genetic epidemiological research, clinical trial networks, and pharmacovigilance research suggested that further research in dermatology requires collaborative studies across different fields, institutions, and countries. To solve the highly complex unsolved problems that we face, dermatologists and epidemiologists should be dynamic team members with multiple approach skills.

Ethical Dilemma in Global Vaccine Cooperation and Its Countermeasures: Taking the Global Eradication of Smallpox as an Example

Vaccine cooperation is an important means to deal with global infectious diseases. However, the cooperation cannot be achieved overnight. Ethical dilemma is one of the obstacles that hinders vaccine cooperation. Reviewing the history, the most successful vaccine collaboration to date has been the global smallpox eradication program. In the process of eradicating smallpox, there were also many ethical dilemmas, including the international pattern of the US-Soviet hegemony, which impacted the mutual help between countries, the ethical disputes of the vaccine itself hindering solidarity and cooperation among actors, and the vaccine coercion adopted to overcome vaccine hesitancy undermining the principle of proportionality among the freedom, equality and efficacy. The ethical dilemmas of vaccine cooperation were resolved by shaping professional and scientific consensus among medical professional groups, reaching consensus on cooperation between leading countries and developing countries, and integrating local culture to improve vaccination methods. Finally, in 1980, the world successfully eradicated smallpox. The case of smallpox eradication provides us lessons for vaccine cooperation against COVID-19 and the construction of a community of common health for mankind today. © 2022, Editorial department of Chinese Medical Ethics. All rights reserved.

Attacking Pathways of Health Information System (HIS)

The health information system (HIS) has been targeted by the hackers especially during the pandemics of COVID 19. This paper is motivated by the recent cyber incidents happened to healthcare organisations. This study was conducted to demonstrate how the HIS can be hacked and provide some recommendations to protect the HIS. We created a simulated virtual environment by implementing an open-source medical system. We then followed the NIST pen-testing methodology to perform ethical hacking. The hacking was successful, and we have managed to exploit several vulnerabilities of the simulated HIS. We then proposed cyber security recommendations to protect the HIS. Future work will consider demonstrating attacks to a specialized cardiac diagnosis medical system, e.g. the arrhythmia detection and classification in ambulatory ECGs, and explore how the core of its algorithms can be hacked and protected.

Effect of ontological insecurity on vaccination behavior against COVID-19: a hospital-based cross-sectional study.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) has brought great uncertainty to our society and it may have disrupted people's ontological security. Consequently, this hospital-based study concerns the impact of ontological insecurity on vaccination behavior against COVID-19. STUDY DESIGN: This cross-sectional study was conducted among hospital inpatients. METHODS: A questionnaire survey addressing inpatient ontological insecurity and vaccination behavior against COVID-19 was administered in Taizhou, China. A total of 1223 questionnaires were collected; specifically, 1185 of them were credible, for a validity rate of 96.9%. RESULTS: The score of ontological insecurity was 13.27 ± 7.84, which was higher in participants who did not recommend vaccination for others than those who did (12.95 ± 8.25 vs 14.00 ± 6.78, P = 0.022). There was no difference between the vaccinated and unvaccinated groups (13.22 ± 7.96 vs 13.35 ± 7.67, P = 0.779). Lower ontological insecurity (odds ratio [OR] = 1.40, 95% confidence interval [CI]: 1.08-1.81) and being inoculated with COVID-19 vaccines (OR = 2.17, 95% CI: 1.67-2.82) were significantly associated with recommendation of COVID-19 vaccines to others after adjusting for sex, age, education, and occupation. Associations between low ontological insecurity and recommendations for COVID-19 vaccines were observed in men, adults aged 18-59 years, non-farmers, and vaccine recipients. CONCLUSIONS: This study suggests that the ontological insecurity of participants affects their behavior of recommending the COVID-19 vaccination to others rather than getting vaccinated themselves. This promotion of vaccination can be considered from the perspective of improving ontological security in China.

Exploring the E-Learning Adoption Intentions of College Students Amidst the COVID-19 Epidemic Outbreak in China

This research paper investigated the adoption behavior of college students toward the e-learning system amidst the current COVID-19. The model was developed and validated based on the Technology Acceptance Model (TAM) and the partial least squares-structural equation modeling (PLS-SEM) was used to analyze the data. The data was generated from 316 Chinese college students based on the convenient sampling technique. The research outcomes indicate that the perceived ease of use is a significant predictor of the intention to use and perceived usefulness of an e-learning system. To our surprise, perceived usefulness does not determine the intention to use an e-learning system. Computer self-efficacy and technical support respectively were significant determinants of the perceived usefulness and the perceived ease of use of an e-learning system. Interestingly, the study showed that internet experience does not influence students’ sensitivity to the usefulness and ease of use associated with an e-learning system. The theoretical and managerial implications of these results findings are thoroughly interrogated. © The Author(s) 2022.

The effects of regularisation on RNN models for Covid-19 time series forecasting

Many time series forecasting models applied to the COVID-19 pandemic data have been limited to the amount of locations that they operate on. To improve the efficiency of a model it is desirable to have one model produce outputs for as many different locations as possible. Another drawback of previous models is that most operate on large amounts of data. However, during the initial states of the spread of the disease, before the epidemic became a pandemic, there was not enough data for the models therefore the proposed model not only has to produce forecasts for multiple locations at once, but they must also be accurate based on small amounts of data. This work proposes a multi-output recurrent neural network capable of producing forecasts for 187 different locations even when trained on only 28 days of time series data for each location. Regularisation methods were used to reduce the noise in the model during training. Applying regularisers helped the model better generalise its predictions for the multiple locations the results show that the model using the Long-Short Term Memory network combined with 20% Dropout performed, on average, 3% better than its baseline without the regularisers the improvement was measured using the Root Mean Squared Error. Previously proposed models were not capable of producing forecasts on a global scale without training multiple versions of the same model. This work proposes one model capable of making predictions on a global scale after only the first four weeks of the epidemic. © 2021 IEEE.

Acupoint selection rules for acupoint application in lung diseases in the post-epidemic era

Objective: To summarize the rules of acupoint selection of acupoint application to prevent and treat lung diseases under the background the post-epidemic era using data-mining technology. Method: The CNKI, Wanfang database, and VIP database were searched for clinical study articles on lung diseases treated by acupoint application published in the past 5 years. Data-eligible papers were extracted to establish a database of acupoint application for lung disease using Microsoft Excel 2019, with the goal of analyzing the frequency of acupoints, acupoint-meridian association, acupoint-location association, specific acupoint frequency, and cluster analysis. Association rules, consisting of acupoints with an application frequency of ≥ 10, were devised by the Apriori algorithm to explore the correlation between acupoint groups and to analyze the rules of the compatibility of acupoint prescriptions. Results: A total of 229 eligible papers met our inclusion criteria. Forty-seven acupoints were applied, for a total frequency of acupoints of 1,035 times. Among these, acupoints for lung diseases were primarily distributed in the back-and-waist and chest-and-abdomen areas. From the analysis of the association rules, we obtained four groups of acupoint association rules based on acupoint clusters with a frequency ≥ 10 and found that Feishu (BL 13), Tiantu (CV 22), Dazhui (GV 14), Dingchuan (EX-B1), and Danzhong (CV 17) constitute the core acupoints of prescriptions for clinical acupoint application to prevent and treat lung diseases. Conclusion: It is clearly shown that the core acupoints are relatively concentrated and that the selected acupoints were mainly locally selected, which could be a matching reference for the long-term prevention and treatment of lung diseases, including COVID-19.

[Epidemiology, clinical characteristics and treatment in 278 patients with coronavirus disease 2019 in Guangzhou].

Objective: To retrospective analyze the epidemiology, clinical characteristics, treatment and prognosis in patients with coronavirus disease 2019 (COVID-19). Methods: A total of 278 patients with COVID-19 admitted to Guangzhou Eighth People's Hospital from January 20 to February 10, 2020 were selected. The general demographic data, epidemiological data, clinical symptoms, laboratory examinations, lung CT imaging, treatment and prognosis were analyzed. Results: There were 130 male patients (46.8%) and 148 females (53.2%) with age (48.1±17.0) years and 88.8% patients between 20-69 years. Two hundred and thirty-six (84.9%) patients had comorbidities. Two hundred and eleven cases (75.9%) were common type. The in-hospital mortality was 0.4% (1/278). The majority (201, 72.3%) were imported cases mainly from Wuhan (89, 44.3%). The most common clinical manifestations were fever (70.9%) and dry cough (61.5%). In some patients, hemoglobin (10.4%), platelets (12.6%) and albumin (55.4%) were lower than the normal range. Other biochemical tests according to liver and function were normal, while lactic dehydrogenase (LDH) was elevated in 61 patients (21.9%), creatine kinase increased in 26 patients (9.4%). Prolonged activated partial thromboplastin time (APTT) was seen in 52 patients (18.7%), D-dimer higher than normal in 140 patients (50.4%), while 117 patients (42.1%) had elevated high-sensitivity C-reactive protein. Typical CT manifestations included single or multiple ground glass shadows especially in lung periphery in early disease which infiltrated and enlarged during progressive stage. Diffuse consolidation with multiple patchy density in severe/critical cases and even "white lung" presented in a few patients. Two hundred and forty-two patients (87.1%) received one or more antiviral agents, 242 (87.1%) combined with antibacterials, 191 (68.7%) with oxygen therapy. There were 198 patients (71.2%) treated with traditional Chinese medicine. Conclusions: COVID-19 could attack patients in all ages with majority of common type and low mortality rate. Clinical manifestations involve multiple organs or systems. Progression of the disease results in critical status which should be paid much attention.

A Nature-Inspired Algorithm to Adaptively Safe Navigation of a Covid-19 Disinfection Robot

Autonomous mobile robots have been extensively used in medical services. During the Covid-19 pandemic, ultraviolet type-C irradiation (UV-C) disinfection robots and spray disinfection robots have been deployed in hospitals and other public open spaces. However, adaptively safe navigation of disinfection robots and spray disinfection robots have not been adequately studied. In this paper, an adaptively safe navigation model of Covid-19 disinfection robots is proposed using a nature-inspired method, cuckoo search algorithm (CSA). A Covid-19 disinfection robot is adaptively navigated to decelerate in the vicinity of objects and obstacles thus it can sufficiently spray and illuminate around objects, which assures objects to be fully disinfected against SARS-CoV-2. In addition, the path smoothing scheme based on the B -spline curve is integrated with adaptive-speed navigation to generate a safer and smoother trajectory at a reasonable distance from the obstacle. Simulation and comparative studies prove the effectiveness of the proposed model, which can plan a reasonable and short trajectory with obstacle avoidance, and show better performance than other meta-heuristic optimization techniques. © 2021, Springer Nature Switzerland AG.

Patient perspective of the implementation of virtual medicine in a post-kidney transplant clinic

Purpose: COVID-19 dramatically altered the model of health care delivery for transplant recipients, necessitating the routine use of virtual medicine. This resulted in consequences for patients and providers, with potential changes on the quality and cost of care. In this study, we present survey results examining the patient perspective of virtual follow-up care in a post-kidney transplant clinic across a large geographic area in Canada. Methods: Kidney transplant recipients followed in a multidisciplinary, posttransplant clinic in Vancouver, Canada were surveyed from April 21, 2020 - June 6, 2020, 4 weeks after the implementation of virtual medicine follow up. The survey included questions on the quality of instructions, ease of connection, quality of interaction with care provider, impact on their experience of care as well as time and cost required to attend clinic. Results: 46% of the 169 respondents were between the age of 40 and 59, while 34% were over the age of 60. 38% were within the first year following kidney transplant. The majority were satisfied with the virtual follow up model and thought the quality of the care was improved (Fig 1). 70% of respondents reported a transit time of more than 30 minutes to attend clinic, and 34% reported costs of > $30 per visit prior to the implementation of virtual medicine (Fig 2). Conclusions: Kidney transplant recipients were satisfied with the quality of care provided using a virtual medicine platform in this survey. The use of virtual medicine to provide care for patients decreased personal resources required to attend virtual clinics. Further study is required to determine if virtual medicine is an equally effective follow up modality in this patient population. (Table Presented).

Phase II Multi-Institutional Study of Low-Dose (2 Gy x 2) Palliative Radiotherapy for Symptomatic Bone Metastases From Multiple Myeloma

Purpose/Objective(s): Painful osteolytic bone lesions are common in patients with multiple myeloma (MM). Radiotherapy (RT) is effective in providing pain relief from MM bone lesions in over 80% of patients. There is no consensus as to the most effective dose or fractionation for palliation. Shorter courses of RT are not only more convenient for patients and their families, but they also have less impact on timing of systemic therapies. There is precedent for using 2 Gy x 2 for palliation of lymphomas, which have similar radiosensitivity to myeloma. The primary objective is to determine whether treatment with 2 Gy x 2 to painful myeloma bone lesions achieves patient-reported pain reduction comparable to historical controls at 4 weeks. Secondary objectives will assess QOL endpoints, use of analgesia and time to pain relief, and duration of pain relief. Materials/Methods: Patients who consent to participation will complete quality of life and pain questionnaires (Brief Pain Index, EORTC QLQ-BM22, and EORTC QLQ-C30) prior to treatment and at 2,4,8 weeks and 6 months following treatment. Pain response, as defined by the international consensus on palliative RT for bone metastases, will be measured based on BPI and daily oral morphine equivalent. Reirradiation at standard dose can be considered at ≥4 weeks following initial treatment for indeterminate pain response or pain progression. Cytogenetics and International Myeloma Working Group risk stratification and response criteria are recorded, when available, but are not required for patient participation. Results: This trial, supported by ILROG, has opened at 7 institutions with one more in process of opening. Prior to COVID, accrual was 1.5 patients per month. Since COVID, enrollment has been at 0.7 patients per month. A total of 18 patients have been accrued. The median age of patients accrued is 65.5 years with 7/18 female patients. Fourteen patients are Caucasian. Twelve patients have an ECOG performance score of 1-2. Thirteen patients had pain response captured at 4 weeks following RT. Of the 5 patients that did not complete questionnaires at 4 weeks post-RT, 2 expired, 1 was lost to follow-up, 1 had a missed evaluation and 1 had pain progression). The most common site of treatment was the shoulder (4/18). Conclusion: This ongoing prospective trial in palliation of multiple myeloma bone lesions is feasible and able to accrue at multiple institutions and will provide valuable information as to the role of low-dose RT in this population.

Multiscale financial risk contagion between international stock markets: Evidence from EMD-Copula-CoVaR analysis

Considering the frequency domain and nonlinear characteristics of financial risks, we measure the multiscale financial risk contagion by constructing EMD-Copula-CoVaR models. Using a sample composed of nine international stock markets from January 4, 1999, to May 13, 2021, the empirical study reveals that: (1) EMD-Copula-CoVaR models can effectively measure the multiscale financial risk contagion, and the financial risk contagion is significant at all time scales;(2) The high-frequency component is the major contributor of financial risk contagion;meanwhile, the low-frequency component is the smallest among all time scale components;(3) The risk export of the US financial market to other markets, except the UK under the original and medium-frequency component, is higher than that it receives;and (4) Even though the magnitude of overall financial risk contagion is similar for the COVID-19 pandemic, Subprime Crises, 9/11 terrorist attack and other crises, the relative importance of different frequency components is heterogeneous. Therefore, the countermeasures of risk contagion should be designed according to its multiscale characteristics. © 2021 Elsevier Inc.

AI-Based Security Attack Pathway for Cardiac Medical Diagnosis Systems (CMDS)

The Cardiac Medical Diagnosis Systems (CMDS) are targeted by the cyber attackers. This paper is motivated by the recent cyber-attacks happened during the COVID 19 that have resulted in the compromise of medical data. This study was carried out to demonstrate how the CMDS can be breached into using an AI-based ethical attack pathway and propose security solutions to prevent such beaches. This study is based on an established simulation platform with an open source medical system, the OpenEMR. The system was fed with the ECGs data from the PhysioNet/ Computing in Cardiology (CinC) Challenge 2017. This paper proposed the AI based hacking pathway following the NIST pen-testing methodology based on our previous identified vulnerability related to authentication. We then presented cyber security recommendations to prevent such AI-based attacks. Future work will consider a realistic CMDS, such as the arrhythmia detection and classification in ambulatory ECGs to find out how the algorithms core can be hacked and protected. © 2020 Creative Commons;the authors hold their copyright.

Body temperature detection and data collection during covid-19

At present, COVID-19 is raging in many countries, which poses a great threat to people's life, health, social and economic development. Body temperature screening is very helpful for early detection of potential infected persons and blocking the spread of the epidemic. The paper mainly introduces the body temperature detection and data acquisition system, and designs a system with the function of non-contact temperature measurement and automatic data collection system. This system is suitable for the company or school where the flow of people is large and the temperature needs to be measured quickly, and upload information in real time. © 2021 EDP Sciences. All rights reserved.