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Using the epidemiologic triangle framework (i.e., agent, host, environment), this early-semester activity was designed to help students both understand the multifaceted nature of COVID-19 and relax their tendency to compartmentalize knowledge. Specifically, the activity asked students to categorize the titles of 24 COVID-related research papers into the three categories (agent, host, environment) and 12 arbitrary subcategories. Instead of giving lectures and making students read research papers, we engaged them in reviewing and categorizing the titles to better understand the epidemiologic triangle. Students' content knowledge about COVID-related jargon was significantly improved and their attitude toward reading research papers was improved in some cases. Their responses toward the activity were overwhelmingly positive. Therefore, in this paper, we also provide additional suggestions for applications. [ABSTRACT FROM AUTHOR]

Patients with cancer can be immunocompromised because of their disease and/or due to anticancer therapy. In this population, severe influenza virus infections are associated with an elevated risk of morbidity and mortality. Influenza vaccination is therefore highly recommended in cancer patients, including those receiving anticancer therapy. However, vaccination coverage remains far below the recommended target for vulnerable subjects. Six specialists in oncology, hematology, immunology, and public health/vaccinology convened with the objective of developing strategies, based on evidence and clinical experience, for improving influenza vaccination coverage in cancer patients. This viewpoint provides an overview of current influenza vaccination recommendations in cancer patients, discusses barriers to vaccination coverage, and presents strategies for overcoming said barriers. New immunization issues raised by the COVID-19 pandemic are also addressed. Future directions include improving public education on influenza vaccination, providing the media with accurate information, improving knowledge among healthcare professionals, improving access to vaccines for cancer patients, co-administration of the influenza and COVID-19 vaccines, increased collaboration between oncologists and other health professionals, increased accessibility of digital vaccination registries to specialists, shared information platforms, and promoting immunization campaigns by healthcare systems with the support of scientific societies. [ABSTRACT FROM AUTHOR]

Objectives: This review aimed to determine the form, results, and barriers to exercise-based rehabilitation programs for severe COVID-19 survivors. Methods: Searching for the article was carried out from August to October 2021. We used the PubMed database as a search media with the keywords (['COVID-19' OR 'COVID 19' OR 'SARS CoV-2'... [ABSTRACT FROM AUTHOR]

Pyrolysis of medical waste components combined via a novel systematic combination approach (sequentially binary, ternary, and quaternary copyrolysis) was conducted at 400°C to investigate the synergy between medical waste components in improving chemical characteristics and yields of pyrolytic oil. Pyrolysis of hydrocarbon-polymer-containing materials such as medical gloves and rubber bands produced more than 30% of liquid products with substantial compositions of saturated aliphatic hydrocarbon polymers. On the other hand, moisture- and carbonyl-rich pyrolytic liquid products with low selectivity were obtained from pyrolysis of lignocellulosic biomass waste such as HVS paper (houtvrij schrijfpapier, meaning "writing paper made from wood pulp") and garden waste. Binary copyrolysis of lignocellulosic biomass and medical gloves exhibited improvement on pyrolytic liquid yield and selectivity toward saturated aliphatic hydrocarbon polymers due to hydrogen donor as the medical glove fraction became dominant. The addition of rubber band to the mixture of HVS paper and medical face masks enhanced the pyrolytic liquid yield. The pyrolysis of the mixture of HVS paper, medical face masks, medical gloves, and either rubber bands or cotton fabrics with mixture ratio of 60:20:10:10 yielded the most optimum pyrolytic liquid yield with significant distribution of alkanes in the pyrolytic liquid products. [ABSTRACT FROM AUTHOR]

Introduction The outbreak of COVID-19 has produced an unprecedented number of trials and articles. Objective To study the impact of the COVID-19 pandemic on otolaryngology-head and neck surgery (ORL-HNS) journal processing times. Methods Original papers search of published in selected ORL-HNS journals in terms of times from submission-to-acceptance (S-A), acceptance-to-first online publication (A-P), and submission-to-online publication (S-P). Papers were divided into those published in the pre-COVID-19 era and those during the COVID-19 era. The latter were further divided into unrelated to COVID-19 and related to COVID-19. Results A total of 487 articles from 5 selected ORL-HNS journals were included, of which 236 (48.5%) were published during the pre-COVID-19 era and 251 (51.5%) were published during the COVID-19 era. Among them, 180 (37%) papers were not related to COVID-19, and 71 (14.5%) were related to COVID-19. The S-A duration of COVID-19-related articles was significantly shorter compared with that of papers submitted in the pre-COVID-19 era and to papers submitted in the COVID-19 era but unrelated to COVID-19 (median 6 to 34 days compared to 65 to 125 and 46 to 127, respectively) in all 5 journals. The most prominent reductions in S-A and S-P times were documented in the laryngology and otology/neurotology disciplines, respectively. Conclusions Processing times of the included papers were significantly shorter in most of the selected ORL-HNS journals during the COVID-19 era compared with the pre-COVID-19 era. COVID-19-related papers were processed more rapidly than non-COVID-19-related papers. These findings testify to the possibility of markedly expediting S-P times and hopefully set a precedent for postpandemic publishing schedules. Level Of Evidence: 5 [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic forced governments, multilateral public health organisations and research institutions to undertake research quickly to inform their responses to the pandemic. Most COVID-19-related studies required swift approval, creating ethical and practical challenges for regulatory authorities and researchers. In this paper, we examine the landscape of ethics review processes in Africa during public health emergencies (PHEs). Methods: We searched four electronic databases (Web of Science, PUBMED, MEDLINE Complete, and CINAHL) to identify articles describing ethics review processes during public health emergencies and/or pandemics. We selected and reviewed those articles that were focused on Africa. We charted the data from the retrieved articles including the authors and year of publication, title, country and disease(s) reference, broad areas of (ethical) consideration, paper type, and approach. Results: Of an initial 4536 records retrieved, we screened the titles and abstracts of 1491 articles, and identified 72 articles for full review. Nine articles were selected for inclusion. Of these nine articles, five referenced West African countries including Liberia, Guinea and Sierra Leone, and experiences linked to the Ebola virus disease. Two articles focused on South Africa and Kenya, while the other two articles discussed more general experiences and pitfalls of ethics review during PHEs in Africa more broadly. We found no articles published on ethics review processes in Africa before the 2014 Ebola outbreak, and only a few before the COVID-19 outbreak. Although guidelines on protocol review and approval processes for PHEs were more frequently discussed after the 2014 Ebola outbreak, these did not focus on Africa specifically. Conclusions: There is a gap in the literature about ethics review processes and preparedness within Africa during PHEs. This paper underscores the importance of these processes to inform practices that facilitate timely, context-relevant research that adequately recognises and reinforces human dignity within the quest to advance scientific knowledge about diseases. This is important to improve fast responses to PHEs, reduce mortality and morbidity, and enhance the quality of care before, during, and after pandemics. [ABSTRACT FROM AUTHOR]

While several papers have explored the application of deep learning for COVID-19 detection in chest X-ray images, the consideration of images taken under emergency conditions remains limited. In this paper, we define emergency conditions as situations where inevitable errors occur during X-ray imaging in severe respiratory conditions of COVID-19 patients. These errors include positioning errors, protocol parameter errors, and motion artifacts. Our research aims to propose an intelligent tool based on deep neural networks and ensemble learning to detect COVID-19 cases from chest X-ray images in both standard and emergency conditions. As comprehensive datasets incorporating emergency images are scarce, we first generated a local dataset by investigating COVID-19 cases in our local hospital. We specifically selected cases with the aforementioned errors caused by emergency conditions. This dataset comprises patients in acute situations, as well as technical radiographic errors resulting from a significant number of people requiring radiography images. We evaluated the performance of recent popular pre-trained deep neural networks on our local dataset, which highlighted the importance of considering such emergency images. Subsequently, we transfer-learned 11 popular deep neural networks using our local dataset. Furthermore, we identified the transferred neural networks with the highest accuracies (exceeding 95% accuracy) and implemented ensemble learning methods such as Majority Voting and Error-Correcting Output Codes (ECOC). Our proposed methods excel in identifying COVID-19 in both emergency and non-emergency chest X-rays with high accuracy. [ABSTRACT FROM AUTHOR]

Pandemics can result in large morbidity and mortality rates that can cause significant adverse effects on the social and economic situations of communities. Monitoring and predicting the spread of pandemics helps the concerned authorities manage the required resources, formulate preventive measures, and control the spread effectively. In the specific case of COVID-19, the UAE (United Arab Emirates) has undertaken many initiatives, such as surveillance and contact tracing by introducing mobile apps such as Al Hosn, containment of spread by limiting the gathering of people, online schooling and remote work, sanitation drives, and closure of public places. The aim of this paper is to predict the trends occurring in pandemic outbreak, with COVID-19 in the UAE being a specific case study to investigate. In this paper, a predictive modeling approach is proposed to predict the future number of cases based on the recorded history, taking into consideration the enforced policies and provided vaccinations. Machine learning models such as LASSO Regression and Exponential Smoothing, and deep learning models such as LSTM, LSTM-AE, and bi-directional LSTM-AE, are utilized. The dataset used is publicly available from the UAE government, Federal Competitiveness and Statistics Centre (FCSC) and consists of several attributes, such as the numbers of confirmed cases, recovered cases, deaths, tests, and vaccinations. An additional categorical attribute is manually added to the dataset describing whether an event has taken place, such as a national holiday or a sanitization drive, to study the effect of such events on the pandemic trends. Experimental results showed that the Univariate LSTM model with an input of a five-day history of Confirmed Cases achieved the best performance with an RMSE of 275.85, surpassing the current state of the art related to the UAE by over 30%. It was also found that the bi-directional LSTMs performed relatively well. The approach proposed in the paper can be applied to monitor similar infectious disease outbreaks and thus contribute to strengthening the authorities' preparedness for future pandemics. [ABSTRACT FROM AUTHOR]

Background: Particularly at the beginning of the pandemic, adults aged 65 and older were portrayed as a homogeneously vulnerable population due to the elevated health risks associated with contracting the COVID-19 disease. This portrayal, combined with travel restrictions, closures of economic sectors, country-wide lockdowns, and suggestions by governmental authorities to limit social contact, had important implications for the wellbeing of older individuals. However, older adults are a heterogeneous population who relies on different resources to cope with stressful periods, like the COVID-19 pandemic. Simultaneously, countries also employed differentmeasures to contain the virus. Research thus far has focused on the short-termconsequences of the pandemic, but studies have yet to address its long-term consequences. Objectives: We explore older adults' lived experiences nearly 2 years after the pandemic onset. Moreover, we focus on the bordering countries of Switzerland and Italy, who employed contrasting containmentmeasures. This paper analyzes (1) Howthe COVID-19 pandemic impacted the experiences of wellbeing of older adults in these regions and (2) How older adults coped with the stressors brought about by the pandemic, in particular social distancing. Methods: The paper draws on 31 semi-structured interviews with 11 Swiss natives residing in Switzerland, 10 Italian migrants residing in Switzerland, and 10 Italian natives residing in Italy. Interviews were conducted from December 2021 to March 2022. Results: Coping mechanisms of the three groups related to acceptance, hobbies, cognitive reframing, telephone use, vaccine use and social distancing. However, results show heterogeneous experiences of wellbeing, with Swiss natives sharing more positive narratives than the other two groups. Moreover, Italian migrants and Italian natives expressed the long-term negative consequences of the pandemic on their experienced wellbeing. [ABSTRACT FROM AUTHOR]

This paper aimed to explore the potential factors of the coronavirus disease 2019 (COVID-19) pandemic on Chinese undergraduates' psychological distress (PD), as well as the moderating impact of coping styles and social support in this relationship. 5638 Chinese college students in Chengdu were randomly selected for an online survey. 10-item Kessler Psychological Distress Scale (K10), the Simplified Coping Style Questionnaire (SCSQ) and the Social Support Rating Scale (SSRS) were used in this paper. In SPSS 17, one-way ANOVA, multiple linear regression and 95% confidence were used to observe the relationship between sociodemographic characteristics, COVID-19 exposure, coping styles, social support and PD of college students. The PD prevalence rate of our sample during the pandemic was 45.5%. Males, students with a romantic relationship, those spending more time on COVID-19, worrying about getting infected, those whose cohabitants were nervous about COVID-19, higher scores of negative coping style, lower positive coping style and social support scores had a higher risk of experiencing PD. The relationship between the nervousness of cohabitants about COVID-19 and the occurrence of PD was moderated by positive coping. Based on the serious psychological condition of Chinese college students during COVID-19. There is an urgent need to enhance the positive coping styles of college students, and systematic support for their mental health is crucial. This article provides theoretical research guidance for students and schools to further prevent and intervene in PD among college students. [ABSTRACT FROM AUTHOR]