Transformation of Sports Practices in Pandemic and Post-pandemic Times

This study explores how sports activities and practices have transformed during the pandemic. These social phenomena have impacted society, organizations, interactions, and individuals (Luhmann, 2003). We conducted desk research and expert interviews in Italy and Romania. The data collected will demonstrate that the two countries represent different patterns of pandemic development. Italy was the most affected European country in the first wave of SARS-CoV-2 (hereafter COVID-19) in 2020. On the opposite side, Romania managed to control the situation well at the beginning of the pandemic. However, it was stronger affected in 2021, while Italy managed to control the situation much better. The desk research consisted of reviewing available official sources and literature (De Nunes, 2020;Pleyers, 2020) related to measures and policies taken to control the effects of Covid on sports activities. Qualitative data were obtained from expert interviews and a critical theoretical framework was applied to assess the countries' restrictions. Our research aims to help to understand how social capabilities could be used to support sports activities in crisis times, as the COVID-19 pandemic was and is – first and foremost – a social phenomenon. Not surprisingly, COVID-19 has spread thanks to the multiple relationships – cultural, economic, political, etc. – that the world's population has forged over a definitively global space, with differential impacts across places (Bailey et al., 2021) that pose sociology to face to understand these new complex scenarios, the main issues we had to face, the successes, the criticalities and the lessons learned. © This is an open access, peer reviewed article published under the Creative Commons License (CC BY 3.0).

Long-COVID syndrome in hospitalized patients after 2 years of follow-up

Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic affected many leading to higher mortality and morbidity worldwide. The post-COVID syndrome (PCS) is characterized by heterogeneous group of clinical manifestations which can frequently lead to a significant worsening in everyday life, working and social conditions. Methods: We prospectively examined in a cohort of patients discharged from our hospital "Saint Andrea”, Vercelli, Italy, from 10th March 2020 to 15th January 2021, with COVID-19 diagnosis during the first wave of pandemic the prevalence and characteristics of PCS after 2 years of follow-up. Results: Overall included patients were 306;prevalence of PCS after 2 years was 43.8%;the fatigue assessment scale (FAS) evidenced that only 8.5% of patients suffered from a severe fatigue with important limitations. Most frequently observed symptoms/conditions were: fatigue (38.2%), breathlessness (19.3%), "brain fog” (29.7%), sleeping disorders (28.8%), post-traumatic stress disorder (29.4%), anxiety (39.9%);only 7.2% of patients resumed the work without limitations or rest period. In multivariate analysis intensive care unit (ICU) admission [odds ratio (OR) =3.950;95% confidence interval (CI): 2.466–8.112;P=0.002], length of hospitalization (OR =1.855;95% CI: 1.248–5.223;P=0.004) and nosocomial infections (OR =2.556;95% CI: 1.443–5.292;P<0.001) were predictive of PCS at 2 years in the study population. Conclusions: After 2 years of follow-up, the 43.8% of enrolled subjects suffered from the PCS, but only the 8.5% with severe limitations in everyday life. We expect these data to highlight the importance of clinical and non-clinical aspect following the PCS in hospitalized patients. © Journal of Public Health and Emergency. All rights reserved.

Undergraduate Research and Education in Quantum Machine Learning

This Work-In-Progress paper describes a program in quantum machine learning launched in the academic year of 2021-22. The program engaged undergraduate students from STEM areas with faculty and industry mentors. Because of the COVID-19 conditions, this undergraduate engagement was offered in a virtual format. In 2022, some face-to-face meetings with presentations were also held. The program included: a) training in machine learning with quantum simulators, b) weekly presentations, and c) semester end presentations. The assessment of the program included surveys, interviews, and presentation observations. Challenges and opportunities from virtual engagement were also part of the assessment. © 2022 IEEE.

SOCIAL PLACES: SHIFTING NEIGHBORHOOD PERCEPTIONS AMONG AGING AMERICANS DURING COVID-19

This study examines individual and community factors related to older adults' perceived losses in places to socialize with people of similar and different ages in their neighborhoods during COVID-19. In the 11-month wave of the COVID-19 Coping Study from March-April 2021, responses to perceived availability were "Less,” "About the Same,” or "More.” Most respondents reported less availability in places to socialize with those of similar (68.0%) or different (68.4%) ages. Ordinal logistic regressions showed respondents who lived alone perceived less availability in places to socialize with those of similar or different ages than those living with others (ORs 0.67, 95% CI 0.47, 0.97). Those living in metropolitan compared to non-metropolitan areas also perceived less availability in places to socialize with those of similar ages (OR 0.62, 95% CI 0.39, 0.98). These findings enhance our understanding of COVID-19-related losses in community resources that facilitate healthy aging in place.

Neuromodulation Virtual Care Technology Provides Safe and Effective Drg/Scs Remote Programming: E-Poster Viewing

Introduction: About 20.4% of US adults suffer from chronic pain and need consistent management plans which were disrupted in 2020 with the COVID-19 pandemic.1,2,3 Patients who use programmable neuromodulation devices to treat chronic pain typically require follow-up visits to address changes in symptoms. An FDA-approved teleprogramming platform enables real-time remote programming via mobile devices for movement disorder and chronic pain patients who use neuromodulation devices. The platform eases the burden travel imposed on many patients, allowing physicians to quickly resolve patient symptoms. The Remote Optimization, Adjustment, and Measurement for Chronic Pain Therapy (ROAM-CPT) study is a post-market, prospective, non-randomized, multi-center investigation to determine that the telehealth system meets patients' therapeutic needs safely and effectively. Materials / Methods: 62 consented subjects across 4 sites, with an implanted neuromodulation device, participating in the REALITY study (NCT03876054) were enrolled in ROAM-CPT and were provided access to the telehealth software. A questionnaire designed for both patient and physician was available after each remote session. The primary success rate was determined by the ability to establish an audio-video connection, complete remote programming or device check, and provide patient clinical care similar to an in-patient session. Additionally, the physicians' and patients' preferences, satisfaction, and reduction in the burden of care compared to in-person sessions were determined. Result(s): 15 patients initiated and completed an audio-video session. All physicians' confirmed services are akin to in-person sessions. During the study, 53.3% of the sessions were complex programming (change in three or more parameters), 26.7% simple programming (change in 1-2 parameter), and 20.0% device interrogation. Overall, all surveyed providers preferred remote care and 93.3% (14/15) of subjects did not require additional clinical care services. Of the 15 subjects across 4 sites, all but 1 reported rapid resolution (reduction in pain), preferred remote care to in-patient, and would recommend a remote session. Patients also reported getting faster appointment time as well as saving travel time and resources typically spent towards an in-person session. Discussion(s): The remote neuromodulation technology provides secure audio-video chat connectivity, programming changes such as amplitude, systems check, and session reports. Physicians easily provide patients care using this platform while patients' therapeutic needs were quickly resolved from the comfort of their homes using their mobile devices. Conclusion(s): Teleprogramming provides real-time programming capabilities and optimizes therapy for patients with neurostimulation devices. Learning Objectives: 1. Teleprogramming provides real-time, safe programming that equals an in-person session. No safety concerns were recorded for all 15 session 2. Virtual clinic affords clinicians the ability to provide quick patient care, does not increase the need for additional follow-up. All 15 participating patients reported resolved therapy needs. 14/15 did not require additional follow-up. 3. Physicians and patients both prefer Virtual clinic 4/4 surveyed physicians and 14/15 surveyed patients preferred virtual clinic. Keywords: Teleprogramming, Neuromodulation, Neurosphere, Virtual clinic, Remote programming, Telehealth Copyright © 2022

Research proposal on architectural copper-based antimicrobic solutions in high traffic spaces

This contribution investigates research opportunities in the field of architecture and design management focusing on user health in high traffic spaces. The field of application is Airport Passenger Terminals. Looking at the COVID-19 pandemic and anticipating the possibility of events of the same magnitude, it is necessary to approach the problem of the safety in public spaces. Based on the State of the Art about antimicrobial material studies, Science of Architecture could propose innovative solutions that are compliant with health safety and prevention for high-use surfaces. These solutions will combine antimicrobial materials with a digital solution that could manage data about surfaces, allowing the maintenance team to valuate and optimize operations. After few hours the hygiene level of copper-based surfaces is higher than any other material. Copper-based furniture could be paired with sensors that send data to management software. Combining the use of scientifically demonstrated antibacterial surfaces with high-performance management tools could be the best option to achieve health safety and contribute to social sustainability. Airport terminals are the ideal high-traffic buildings to use as test model because they have all the characteristics that could be analysed concerning the safety and the perception of safety of architectural spaces by users.

Quantum Machine Learning for Audio Classification with Applications to Healthcare

Accessible rapid COVID-19 testing continues to be necessary and several studies involving deep neural network (DNN) methods for detection have been published. As part of a sponsored NSF I/UCRC project, our team explored the use of deep learning algorithms for recognizing COVID-19 related cough audio signatures. More specifically, we have worked with several DNN algorithms and cough audio databases and reported results with the VGG-13 architecture. In this paper, we report a study on the use of quantum neural networks for audio signature detection and classification. A hybrid quantum neural network (QNN) model for COVID-19 cough classification is developed. The design of the QNN simulation architecture is described and results are given with and without quantum noise. Comparative results between classical and quantum neural network methods for COVID-19 audio detection are also presented. © 2022 IEEE.

2022 international clinical practice guidelines for the treatment and prophylaxis of venous thromboembolism in patients with cancer, including patients with COVID-19

The International Initiative on Thrombosis and Cancer is an independent academic working group of experts aimed at establishing global consensus for the treatment and prophylaxis of cancer-associated thrombosis. The 2013, 2016, and 2019 International Initiative on Thrombosis and Cancer clinical practice guidelines have been made available through a free, web-based mobile phone application. The 2022 clinical practice guidelines, which are based on a literature review up to Jan 1, 2022, include guidance for patients with cancer and with COVID-19. Key recommendations (grade 1A or 1B) include: (1) low-molecular-weight heparins (LMWHs) for the initial (first 10 days) treatment and maintenance treatment of cancer-associated thrombosis;(2) direct oral anticoagulants for the initial treatment and maintenance treatment of cancer-associated thrombosis in patients who are not at high risk of gastrointestinal or genitourinary bleeding, in the absence of strong drug-drug interactions or of gastrointestinal absorption impairment;(3) LMWHs or direct oral anticoagulants for a minimum of 6 months to treat cancer-associated thrombosis;(4) extended prophylaxis (4 weeks) with LMWHs to prevent postoperative venous thromboembolism after major abdominopelvic surgery in patients not at high risk of bleeding;and (5) primary prophylaxis of venous thromboembolism with LMWHs or direct oral anticoagulants (rivaroxaban or apixaban) in ambulatory patients with locally advanced or metastatic pancreatic cancer who are treated with anticancer therapy and have a low risk of bleeding.

Access and Inclusion of Students with Disabilities in Virtual Learning Environments: Implications for Post-Pandemic Teaching

This article reviews the extant literature showing impacts of theCOVID-19 pandemic on access to inclusive education for students with disabilities. It also explores the disproportionate impacts of distance learning and school closures during the COVID-19 pandemic on the legal rights, social emotional supports, and quality of instruction for special education students and their families. Early data show that educational impacts of COVID-19 have exacerbated long-standing issues of inequity;these impacts may have long-term repercussions for this underserved group of students. The authors introduce frameworks that may inform future instructional practices to successfully teach students with disabilities in virtual learning environments.

Technological Acceleration, Ageing Process and COVID-19. The Role of Scientific Information between Opportunities and Limits. Results of an Empirical Research in Central Italy

In this article, the authors present the results of an empirical research regarding people over 65 years old living in Central Italy, that have contracted the Sars-CoV-2 virus. The choice of such target is strictly dependent on the need to investigate, specifically, how this category, in a risky situation, was able to deal with the illness and cope with the whole condition;both in relation to the management of health and governmental information, and in relation to the use of information channels and technological devices. Through a research carried out with qualitative methodology, a series of analysis emerged around: 1) the idea of promoting and ensuring digital literacy with a view to the social innovation to cope with the risks of ‘infodemic’;2) the urge to reflect on the necessity of keeping active and constant the dialogue between scientific community and user/citizen in the view of a transfer of knowledge and techno-scientific information, especially in periods of health crisis, in order to mitigate the effects due to digital divide;3) the need to analyze the life of older people in a context of indeterminacy and fear in which social habits and consolidated certainties have been subjected to a totalizing tension. © 2022,Italian Sociological Review.All Rights Reserved

COVID-19 detection using cough sound analysis and deep learning algorithms

Reliable and rapid non-invasive testing has become essential for COVID-19 diagnosis and tracking statistics. Recent studies motivate the use of modern machine learning (ML) and deep learning (DL) tools that utilize features of coughing sounds for COVID-19 diagnosis. In this paper, we describe system designs that we developed for COVID-19 cough detection with the long-term objective of embedding them in a testing device. More specifically, we use log-mel spectrogram features extracted from the coughing audio signal and design a series of customized deep learning algorithms to develop fast and automated diagnosis tools for COVID-19 detection. We first explore the use of a deep neural network with fully connected layers. Additionally, we investigate prospects of efficient implementation by examining the impact on the detection performance by pruning the fully connected neural network based on the Lottery Ticket Hypothesis (LTH) optimization process. In general, pruned neural networks have been shown to provide similar performance gains to that of unpruned networks with reduced computational complexity in a variety of signal processing applications. Finally, we investigate the use of convolutional neural network architectures and in particular the VGG-13 architecture which we tune specifically for this application. Our results show that a unique ensembling of the VGG-13 architecture trained using a combination of binary cross entropy and focal losses with data augmentation significantly outperforms the fully connected networks and other recently proposed baselines on the DiCOVA 2021 COVID-19 cough audio dataset. Our customized VGG-13 model achieves an average validation AUROC of 82.23% and a test AUROC of 78.3% at a sensitivity of 80.49%.

"I have recovered from COVID": The voices of the healed. An empirical investigation of social and disease experience in central Italy

The Sars-Cov2 pandemic has demonstrated the strong link between health risk and perception of the disease for various reasons: Because it is an event that is being faced for the first time and because the consequences of the disease are still completely unknown. The objective of the article is to give a voice to people who have experienced the disease. Following a qualitative approach, twenty in-depth interviews were conducted with people recovered from Sars- Cov2 living in southern Lazio, using the R-Text Mining Package software as an analysis tool. From the analysis of the interviews, conducted through text mining, word relationships and sentiment analysis, a series of particularly relevant areas and dimensions emerged in order to assess the impact that the disease has had on people's experiences, both on an individual and social level. Specifically, three main topics emerged: Communication, fear and danger. © 2021 Franco Angeli Edizioni. All rights reserved.

«Ho fatto il Covid»: Le voci dei guariti. Una indagine empirica su vissuto sociale ed esperienza di malattia nell'Italia centrale

The Sars-Cov2 pandemic has demonstrated the strong link between health risk and perception of the disease for various reasons: Because it is an event that is being faced for the first time and because the consequences of the disease are still completely unknown. The objective of the article is to give a voice to people who have experienced the disease. Following a qualitative approach, twenty in-depth interviews were conducted with people recovered from Sars- Cov2 living in southern Lazio, using the R-Text Mining Package software as an analysis tool. From the analysis of the interviews, conducted through text mining, word relationships and sentiment analysis, a series of particularly relevant areas and dimensions emerged in order to assess the impact that the disease has had on people's experiences, both on an individual and social level. Specifically, three main topics emerged: Communication, fear and danger. © 2021 Franco Angeli Edizioni. All rights reserved.

Risk factors and incidence of long-COVID syndrome in hospitalized patients: does remdesivir have a protective effect?

BACKGROUND: The definition of 'long-COVID syndrome' (LCS) is still debated and describes the persistence of symptoms after viral clearance in hospitalized or non-hospitalized patients affected by coronavirus disease 2019 (COVID-19). AIM: In this study, we examined the prevalence and the risk factors of LCS in a cohort of patients with previous COVID-19 and followed for at least 6 months of follow-up. DESIGN: We conducted a prospective study including all hospitalized patients affected by COVID-19 at our center of Infectious Diseases (Vercelli, Italy) admitted between 10 March 2020 and 15 January 2021 for at least 6 months after discharge. Two follow-up visits were performed: after 1 and 6 months after hospital discharge. Clinical, laboratory and radiological data were recorded at each visit. RESULTS: A total of 449 patients were included in the analysis. The LCS was diagnosed in 322 subjects at Visit 1 (71.7%) and in 206 at Visit 2 (45.9); according to the post-COVID-19 functional status scale we observed 147 patients with values 2-3 and 175 with values >3 at Visit 1; at Visit 2, 133 subjects had the score between 2-3 and 73 > 3. In multivariate analysis, intensive care unit (ICU) admission (OR = 2.551; 95% CI = 1.998-6.819; P = 0.019), time of hospitalization (OR = 2.255; 95% CI = 1.018-6.992; P = 0.016) and treatment with remdesivir (OR = 0.641; 95% CI = 0.413-0.782; P < 0.001) were independent predictors of LCS. CONCLUSIONS: Treatment with remdesivir leads to a 35.9% reduction in LCS rate in follow-up. Severity of illness, need of ICU admission and length of hospital stay were factor associated with the persistence of PCS at 6 months of follow-up.

Deep Learning with hyper-parameter tuning for COVID-19 Cough Detection

As the COVID-19 pandemic continues, rapid non-invasive testing has become essential. Recent studies and benchmarks motivates the use of modern artificial intelligence (AI) tools that utilize audio waveform spectral features of coughing for COVID-19 diagnosis. In this paper, we describe the system we developed for COVID-19 cough detection. We utilize features directly extracted from the coughing audio and use deep learning algorithms to develop automated diagnostic tools for COVID-19. In particular, we develop a unique modification of the VGG13 deep learning architecture for audio analysis that uses log-mel spectrograms and a combination of binary cross entropy and focal losses. This unique modification enabled the model to achieve highly robust classification of the DiCOVA 2021 COVID-19 data. We also explore the use of data augmentation and an ensembling strategy to further improve the performance on the validation and the blind test datasets. Our model achieved an average validation AUROC of 82.23% and a test AUROC of 78.3% at a sensitivity of 80.49%. © 2021 IEEE.