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Objectives: To describe and compare real-world outcomes for patients with mild-to-moderate COVID-19 at high risk for progression to severe COVID-19, treated with sotrovimab versus untreated. Method(s): Electronic health records from the National COVID Cohort Collaborative were used to identify US patients (aged >=12 years) diagnosed with COVID-19 (positive test or ICD-10: U07.1) in an ambulatory setting (26 May 2021-30 April 2022) who met Emergency Use Authorization high-risk criteria. Patients receiving the monoclonal antibody (mAb) sotrovimab within 10 days of diagnosis were assigned to the sotrovimab cohort with an index date on the day of infusion. Untreated patients (no evidence of early mAb treatment or prophylaxis mAb or oral antiviral treatment) were assigned to the untreated cohort with an imputed index date based on the time distribution between diagnosis and sotrovimab infusion for the sotrovimab cohort. The primary endpoint was hospitalization or death (both all-cause) within 29 days of index, reported as descriptive rates and adjusted (via inverse-probability-of-treatment weighting [IPTW]) odds ratios (OR) and 95% confidence intervals (CI). Result(s): Of nearly 2.9 million patients diagnosed with COVID-19 during the analysis time period, 4,992 met the criteria for the sotrovimab cohort and 541,325 were included in the untreated cohort. Patients in the sotrovimab cohort were older (60 versus 54 years), more likely to be male (40% versus 38%) and White (85% versus 75%), and met more EUA criteria (3 versus 2) versus the untreated cohort. The 29-day hospitalization or mortality rates were 3.5% (176/4,992) and 4.5% (24,163/541,325) in the sotrovimab and untreated cohorts respectively (unadjusted OR [95% CI]: 0.77 [0.67,0.90];p=0.001;IPTW-adjusted OR [95% CI]: 0.74 [0.61,0.91];p=0.004). Conclusion(s): Sotrovimab demonstrated clinical effectiveness in preventing severe outcomes (hospitalization, mortality) between 26 May 2021-30 April 2022, which included the Delta variant and early surge of Omicron BA.1/BA.2. Funding(s): GSK (Study 219020)Copyright © 2023
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Based on the international concern about COVID-19 and pulmonary diseases, the number of cases of lung injury caused by COVID-19 pneumonia and its complications has increased dramatically in recent years, and the complexity of the situation makes the combination of single drugs a major problem in respiratory diseases. Therefore, it would be feasible to replace single drug combinations with compounded formulations for the treatment of a diverse array of pulmonary diseases. At the same time, the visualization of the chemical composition of herbal formulations and the study of molecular interactions to reveal the mechanism of action will be of practical significance. This paper introduced the therapeutic mechanisms of the traditional Chinese medicine formula Three Agents White Powder. Three major pulmonary diseases, COVID-19, pulmonary fibrosis, and lung abscess, were investigated using molecular docking and network pharmacology approaches. Mainly using TCMSP, DAVID, STRING, Genecard database screening, and autodock molecular docking methods, this paper verified that this drug applies its healing benefits to pulmonary diseases through multiple components, multiple targets, and multiple pathways, and illustrated the effectiveness of this formula in the adjuvant treatment of extensive pulmonary diseases. © 2023 SPIE.
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Recently, mounting evidence documented an increased morbidity and mortality of COVID-19 in individuals with pre-existing cardiovascular diseases (CVDs). To better understand the pathogenesis of COVID-19 and its impact in CVDs, we designed a text-mining analysis project to evaluate the molecular interfaces between COVID-19 and several known CVDs. We assembled our data corpus from publicly accessible databases and applied text mining to COVID-19 symptoms, comorbidities, and human proteins impacted by COVID-19. Our exploration includes a statistical overview of unstructured text datasets with associated biomedical entities where the information extraction was assisted by data indexing and entity search methodologies. Using 333 human COVID-19-interacting proteins as entities and 8 CVDs classified by MeSH as categories, we examined and computed their Context Aware Semantic Analytic Processing (CaseOLAP) scores. Using this dataset, we determined associations of COVID-19 symptoms with a variety of major and minor comorbidities. Then, we further explored proteins at the interface of COVID-19 and 8 categories of CVD, evaluating relationships between the proteins and CVD categories to determine the proteins' significance in each disease. We then performed pathway analyses on those proteins of significance and their presence in each of the CVD categories. For the first time, our cluster analyses determined which COVID-19-interacting proteins are most relevant for each CVD category. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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Nutrition plays a critical role in preventing, managing, and treating noncommunicable diseases;thus it is essential physicians be able to appropriately refer to and collaborate with interprofessional (IP) care team members, like registered dietitian nutritionists (RDNs). This study evaluated the effectiveness of the Nutrition Clinical Experience (NCE) to increase first-year medical students' (M1s′) understanding of RDNs' roles on the IP healthcare team, common reasons to consult a RDN, and interventions RDNs implement. M1s participated in a 1.5 or 2-h IP clinical observation experience with an outpatient or inpatient RDN. Due to the COVID-19 pandemic, virtual, in-person, and hybrid experiences were offered. Before the experience, students were provided a handout with an overview of a RDN's role, description of medical nutrition therapy, and example cases. During the NCE students shadowed a RDN as they conducted outpatient encounters or inpatient rounds, and were encouraged to discuss real or sample patient cases. Students and RDNs answered post-experience 5-point Likert surveys and free-response questions to evaluate experience effectiveness. Of responding students, 96% agreed or strongly agreed the experience helped them learn about RDNs' roles and 99% agreed or strongly agreed they were more likely to involve a RDN in patient care following the experience. Additionally, after the experience, 78% of participating students identified at least one common reason to consult a RDN and 70% described at least three interventions that RDNs implement. All responding RDNs agreed or strongly agreed the experience is valuable to students and 85% agreed or strongly agreed the NCE allowed them to communicate their roles to students. The ability to deliver the experience virtually makes it a useful curricular program for schools without on-site RDNs to engage preclinical students in IP experiences. This report includes materials required for the experience. © 2023 Elsevier Inc.
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In this paper, the theory of " Fear injury kidney " in traditional Chinese medicine is systematically reviewed, and it is found that long-term or excessive psychological changes of fear are likely to damage kidney qi and kidney essence. On this basis, the psychological studies of patients, medical staff and the public during the COVID-19 epidemic in China were analyzed, and fear psychology was found to be prevalent among all kinds of people. Modern researches on "Fear injury kidney" have also found that long-term or excessive fear could cause changes in the neuro-endocrine-immune system, which can induce diseases or susceptibility to some diseases. Therefore, during or after the prevalence of COVID-19, different groups of people may have emotional reactions such as stress and fear, which should be paid long-term attention, and the influence of fear on the body cannot be ignored. According to the change rule of psychological state under stress reaction, we should actively respond to and take psychological crisis intervention measures in time to reduce the harm of psychological stress to the body.
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The COVID-19 pandemic has hit the transportation sector hard;particularly air transportation, as a major mode of long-distance transportation, has been affected tremendously. Since the dawn of COVID-19, politicians and policy makers have discussed the idea of introducing travel bubbles between countries (or counties), to allow for a continued exchange of people and goods. The eponymous Trans-Tasmanian travel bubble is a major example, involving quarantine-free travel between Australia and New Zealand. While both countries have tried to form a travel bubble various times, recurring setbacks and difficulties were faced. In October 2021, this ambitious project presumably has come to an end, with both countries announcing the essential capitulation of their COVIDZero strategies and a planned opening towards broader international travel. In this study, we perform a close investigation of the history behind the Trans-Tasmanian travel bubble as an on-off relationship, identifying a set of drivers for the serious problems involving a sustainable setup and operation. We develop a framework which represents important factors for successful travel bubbles and believe that the satisfaction of all factors at once is extremely challenging. Our results and insights are not specific to the Trans-Tasmanian case only, although it is taken as a running example, but can be generalized to various scales and environments. We hope that our study contributes to the literature by improving our understanding of the highly buzzed travel bubble concept, while providing empirical evidence for the troubles that inherently make such bubbles a tightrope walk. © 2022 The Authors
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As the COVID-19 outbreak continues to change crucial aspects of daily life, many suspect that the virus has also had a considerable impact on mental health. This study uses natural language processing (NLP) and machine learning on comments from the website Reddit to determine the effects of the COVID-19 pandemic on 5 mental health communities: r/anxiety, r/depression, r/SuicideWatch, r/mentalhealth, and r/COVID19_support. By applying a support vector machine, we extracted features from the data to determine the issues that these subreddits were struggling with the most during the COVID-19 pandemic. We then used a long short-term memory (LSTM) recurrent neural network to study the change in sentiment of each subreddit over the course of the pandemic. Results indicated that, out of the potential factors studied, feelings of isolation had the most impact on mental health during COVID-19. Additionally, the average sentiment of users from r/COVID19_support has an inverse relationship with the number of new COVID-19 cases per month in the United States. Through this research, we revealed the effectiveness of support vector machines and LSTM neural networks in analyzing mental health in social media comments related to COVID-19. As the COVID-19 pandemic progresses and more data becomes available, processes like the one presented in this research can provide insight into the mental health communities that are most influenced by COVID-19 and the effects of the pandemic that cause the most mental health issues. These findings may produce valuable information for policymakers and mental health physicians. © 2022 IEEE.
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As the COVID-19 outbreak continues to change crucial aspects of daily life, many suspect that the virus has also had a considerable impact on mental health. This study uses natural language processing (NLP) and machine learning on comments from the website Reddit to determine the effects of the COVID-19 pandemic on 5 mental health communities: r/anxiety, r/depression, r/SuicideWatch, r/mentalhealth, and r/COVID19_support. By applying a support vector machine, we extracted features from the data to determine the issues that these subreddits were struggling with the most during the COVID-19 pandemic. We then used a long short-term memory (LSTM) recurrent neural network to study the change in sentiment of each subreddit over the course of the pandemic. Results indicated that, out of the potential factors studied, feelings of isolation had the most impact on mental health during COVID-19. Additionally, the average sentiment of users from r/COVID19_support has an inverse relationship with the number of new COVID-19 cases per month in the United States. Through this research, we revealed the effectiveness of support vector machines and LSTM neural networks in analyzing mental health in social media comments related to COVID-19. As the COVID-19 pandemic progresses and more data becomes available, processes like the one presented in this research can provide insight into the mental health communities that are most influenced by COVID-19 and the effects of the pandemic that cause the most mental health issues. These findings may produce valuable information for policymakers and mental health physicians. © 2022 IEEE.
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Background. Although not validated, cycle threshold (Ct) values from real-time (r)RT-PCR are sometimes used as a proxy for infectiousness to inform public health decision-making. A better understanding of variant-specific viral dynamics, including RNA and infectious virus relationships, is needed to clarify implications for diagnostics and transmission. Methods. Non-hospitalized SARS-CoV-2-infected individuals were recruited <= 5 days post-onset and self-collected nasal swabs daily for two weeks. Sequencing was used to determine variant, an in-house quantitative rRT-PCR targeting N gene was used to produce Ct values and determine RNA load, and cytopathic effect was used to assess the presence or absence of infectious virus (binary outcome). We used a Ct threshold of 30 to define high-Ct (Ct > 30) or low-Ct (Ct <= 30) specimens and assessed the percentage of RNA-positive specimens that had infectious virus;variantspecific percentages were compared by chi2 test. Results. We included 113 and 200 RNA-positive specimens from 18 and 28 Omicron- and Delta-infected participants, respectively;timing of RNA-positive specimen collection was similar in both groups (median = 8d post-onset). Maximum observed RNA levels occurred at median of 5 days post-onset for both variants but were lower for participants with Omicron vs Delta [mean RNA copies/mL = 105.2 vs 107.9]. Despite lower RNA levels, infectious virus was frequently detected for both variants [Omicron: median duration = 4.5d;Delta: median = 6d;p = 0.13]. Omicron specimens with infectious virus had higher Cts vs Delta specimens [mean Ct = 29.9 vs 23.2, p < 0.001]. In high-Ct specimens (Ct > 30;Table), the percentage of specimens with infectious virus was typically higher for Omicron vs Delta, and was significantly higher in adults [27.3% vs 9.5%]. In low-Ct specimens (Ct <= 30), the percentage with infectious virus was similar or higher for Omicron vs Delta, and was significantly higher in children [87.5% vs 53.8%] and in those unvaccinated [94.1% vs 47.4%]. Conclusion. CDC does not recommend the use of Ct values as a proxy for infectiousness. These data further highlight that Ct values may not provide a reliable or consistent proxy for infectiousness across variants.
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Background. The biological determinants of post-acute sequelae of SARS-CoV-2 infection (PASC), defined as the persistence or recurrence of symptoms not explained by an alternative medical diagnosis, are poorly understood. We assessed viral and immunological determinants during acute SARS-CoV-2 infection for an association with PASC at 4 to 8 months. Methods. From September 2020 to February 2022, symptomatic nonhospitalized individuals with laboratory-confirmed SARS-CoV-2 infection were identified within 5 days of symptom onset. We used anterior nasal biospecimens to measure the magnitude and duration of RNA and infectious viral shedding as well as blood samples to measure soluble markers of inflammation during the acute phase (first 28 days post-enrollment). PASC was defined as self-report of 1 or more COVID-19 attributed symptoms between 4 and 8 months after initial illness. We compared virologic and inflammatory markers, GFAP (a marker of neuronal damage) and neutralizing antibody levels from the acute phase between those with and without PASC using Mann-Whitney U tests or repeated measures mixed effects linear models. Results. Among 71 SARS-CoV-2-positive participants with a completed follow-up visit between 4 to 8 months, we included 69 with virologic data and 61 with inflammatory marker data. Median age was 37 (IQR: 29 to 48) Overall, 16/72 (23%) reported at least one qualifying PASC symptom. Report of PASC was associated with >9 days of RNA shedding (p=0.04);all participants stopped RNA shedding by day 20. During acute illness, those with subsequent PASC had increased levels of INF-alpha, INF-gamma, IP-10, IL-10, and MCP-1;these differences were greatest in the early period and normalized over 2 to 3 weeks post-illness onset. Compared to those without PASC, during the acute illness those with PASC had increased levels of GFAP and decreased levels of neutralizing antibodies but these differences were not statistically significant. Conclusion. We found indications that viral and immunological factors during acute illness may be associated with PASC, suggesting acute immunologic response to SARS-CoV-2 may have longer term effects and play a role in PASC. Further understanding of the clinically significance of these observations is needed.
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Background. Antenatal and neonatal viral exposure may put the developing brain at risk for abnormal neurodevelopment. A clinical follow-up program was created in the Congenital Infection Program at Children's National Hospital to follow infants with in utero or early life exposure to SARS-CoV-2. The aim of this study was to determine if infants with early SARS-CoV-2 exposure have abnormal neurodevelopment in infancy. Methods. We performed a retrospective review of all infants evaluated in the follow-up program between 3/2020 - 11/2021. Demographic details, SARS-CoV-2 infection/ testing data, pregnancy/birth data, and specialty consult and NICU records were extracted from infants' medical charts. Infants were divided into 3 SARS-CoV-2 exposure groups: 1) antenatal exposure to symptomatic mother, 2) antenatal exposure to asymptomatic mother, 3) neonatal infection. All infants received a neurologic exam and developmental screening with the Ages and Stages Questionnaire (ASQ) in 5 domains (Communication, Gross Motor, Fine Motor, Problem Solving, Personal-Social) during their evaluation. The ASQ accounts for prematurity. Outcomes of interest were an abnormal neurologic exam or ASQ scores close to or below suggested cut-offs. Multivariate analysis was used to study correlations between exposure group and neurodevelopmental outcomes. Results. Thirty-five infants were seen for up to 3 outpatient visits. Most infants (83%) were exposed in utero - 16 to symptomatic mothers (Group 1) and 12 to asymptomatic mothers (Group 2);1 chart did not have symptom data. Six were exposed only as a neonate (Group 3). Group 1 had abnormal neurologic exams at mean (SD) age 112 (24) days (Table 1) and ASQ scores close to or below cut-offs for all domains (Fig. 1) more frequently than Groups 2 or 3. Group 1 was more likely to score below any ASQ cutoff compared to Group 2 (P=.04);of the 5 domains, differences were significant for Fine Motor (P=.01) and Personal-Social (P=.02). Conclusion. Early SARS-CoV-2 exposure may impact infant development, especially among those exposed in utero to a symptomatic mother. Vaccination and other precautions to reduce spread and symptoms may protect against early neurodevelopmental delays. Future work should prioritize longitudinal follow-up of children with early SARS-CoV-2 exposure. (Table Presented).
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OBJECTIVES: To summarize current evidence on exposures to infectious pathogens in the emergency medical services (EMS) and 911 workforce, and on practices for preventing, recognizing, and controlling occupationally acquired infectious diseases and related exposures in that workforce.
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COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. The efficiency of COVID-19 diagnosis has become highly significant. As deep learning and convolutional neural network (CNN) has been widely utilized and been verified in analyzing medical images, it has become a powerful tool for computer-assisted diagnosis. However, there are two most significant challenges in medical image classification with the help of deep learning and neural networks, one of them is the difficulty of acquiring enough samples, which may lead to model overfitting. Privacy concerns mainly bring the other challenge since medical-related records are often deemed patients’ private information and protected by laws such as GDPR and HIPPA. Federated learning can ensure the model training is decentralized on different devices and no data is shared among them, which guarantees privacy. However, with data located on different devices, the accessible data of each device could be limited. Since transfer learning has been verified in dealing with limited data with good performance, therefore, in this paper, We made a trial to implement federated learning and transfer learning techniques using CNNs to classify COVID-19 using lung CT scans. We also explored the impact of dataset distribution at the client-side in federated learning and the number of training epochs a model is trained. Finally, we obtained very high performance with federated learning, demonstrating our success in leveraging accuracy and privacy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Miniaturization of biosensors has become an imperative demand because of its great potential in in vivo biomarker detection and disease diagnostics as well as the point-of-care testing for coping with public health crisis, such as the coronavirus disease 2019 pandemic. Here, we present an ultraminiature optical fiber-tip biosensor based on the plasmonic gold nanoparticles (AuNPs) directly printed upon the end face of a standard multimode optical fiber at visible light range. An in-situ precision photoreduction technology is developed to additively print the micropatterns of size-controlled AuNPs. The AuNPs reveal distinct localized surface plasmon resonance, whose peak wavelength provides an ideal spectral signal for label-free biodetection. The fabricated optical fiber-tip plasmonic biosensor can not only detect antibody, but also test SARS-CoV-2 mimetic DNA sequence at the concentration level of 0.8 pM. Such an ultraminiature fiber-tip plasmonic biosensor offers a cost-effective biodetection technology for a myriad of applications ranging from point-of-care testing to in vivo diagnosis of stubborn diseases.