Internet of medical things for abnormality detection in infants using mobile phone app with cry signal analysis

Detecting the baby's cry sounds is significant and is the first step that enables effective diagnosis in the branch of pediatrics. Despite the complexity in the analysis of the baby's cry signal, an automated cry signal segmentation system can be introduced for the diagnosis of earache, colic pain, cold, diaper rashes, or due to hunger. This is a challenging task as this type of automated cry sound segmentation algorithm is dependent on the wavelet coefficients extracted from the cry signal. These coefficients are the inputs to train the cry signal-oriented diagnostic system. A completely computerized segmentation algorithm is designed to extract the details and approximation coefficients of the cry signal during the expiration and inspiration process. These coefficients are used to train the convolutional neural networks (CNN). The prime focus of this work is to devise a smartphone-based app that will record the baby's cry signal, segment it using the wavelet transform, and classify them using CNN based on the diagnosis made to identify the earache, colic pain, cold, diaper rashes, fever, respiratory problem or hunger. This indigenous smartphone app will enable the young mothers to identify the problem existing with their infants and facilitate an easy nurturing of the newborn. This non-contact type of diagnosis finds a lot of importance in the present scenario, where the COVID-19 social distancing is followed enabling the physician, infant, and mother to be devoid of the fear of this pandemic situation. The main objective of this proposal is to design a cry signal based infant diagnostic system which focuses on scrutinizing the neonatal pathologies by extracting the features present in the signal of the baby's cry in a realistic clinical environment. This mobile app once developed, will be a part of the internet of medical things © 2023 Elsevier Inc. All rights reserved.

Pragmatic Assessment of Influenza Vaccine Effectiveness in the Department of Defense (PAIVED): Updates from Year 4 of a Multi-site Trial

Background. The effectiveness of the influenza vaccine is varies with circulating strain concordance and timing of influenza spread in a community. The Pragmatic Assessment of Influenza Vaccine Effectiveness in the DoD (PAIVED) study is a multiyear, randomized clinical trial of three FDA-licensed vaccine types (egg-based, cellbased, and recombinant), designed to determine which influenza vaccine platform is most effective among adults in a military setting. Methods. Participants in the fourth year of PAIVED (2021-22 influenza season) were enrolled from September 2021 through January 2022 at 9 military facilities. Participants were asked each week about influenza-like illness (ILI) symptoms. If the participants reported ILI symptoms, research staff scheduled an acute and convalescent ILI visit. Additional details about the study are included in Figure 1. Results. In year 4, 4,688 participants were enrolled, among whom 63.8% were male, 56.5% were white, and the average age was 34 years (Tables 1 and 2). As of early April, 1,297 ILIs had been reported. Most participants reported a single ILI (987 (87%)), while 140 participants reported two ILIs and 10 reported three ILIs. The mean duration of the reported ILIs was 11 days, with a mean 5 days of limited activity. Three participants were hospitalized. Among the samples processed to date, influenza has been identified in four participants. Themost common pathogens in year 4 were SARS-CoV-2 and rhino/enterovirus (Figure 2). During all four years of PAIVED, we enrolled 15,449 participants, among whom 188 episodes of influenza have been identified so far (1.2%). Conclusion. The fourth year of PAIVED was characterized by early (preenrollment) spread of influenza in some areas, as well the nationwide spread of the SARS-CoV-2 Omicron variant in December. As the swabs are processed and participants? military health records are reviewed, we expect to identify more influenza cases;however, transmission patterns were far lower than historical averages due to pandemic precautions, making this surveillance data from identified strains more valuable. Comparative influenza vaccine effectiveness calculations will be performed to inform future vaccine purchasing decisions and we will compare serological response to the different vaccines. (Figure Presented).

Pathogen Co-infections and Trends in Influenza-like Illness in PAIVED

Background. The Pragmatic Assessment of Influenza Vaccine Effectiveness in the Department of Defense (DoD) (PAIVED) is a multicenter, multiservice study assessing influenza vaccine effectiveness in active-duty service members, retirees, and dependents. In its fourth season (2021/22), PAIVED offers a unique opportunity to examine influenza-like illness (ILI) trends prior to and during the COVID-19 pandemic in a prospectively followed, well-defined cohort. Methods. Over the past 4 influenza seasons, PAIVED has enrolled DoD beneficiaries who were randomized to receive egg-based, cell-based, or recombinant-derived influenza vaccine. Participants provided some basic demographic information and were then sent a weekly text or email that inquired about ILI symptoms, defined as 1) having cough or sore throat, plus 2) feeling feverish/having chills or having body aches/fatigue. Participants with ILI completed a daily symptom diary for one week and submitted a nasal swab for PCR-based pathogen detection. Results. Over the 4 seasons, 15,449 participants were followed for ILI (Table 1) with 3,407 participants reporting a total of 3,985 ILIs. For the 2021/22 season, ILI reports peaked in January (Figure 1). Overall, 4.7% of episodes had more than one pathogen identified (Table 2). Among the 122 coinfections identified to date, most were coinfections with rhinoviruses (91/122, 75%), including rhinovirus coinfections with seasonal coronaviruses (29, 24%), metapneumovirus (18, 15%), SARS-CoV-2 (17, 14%), and influenza (14, 11%). SARS-CoV-2 and influenza were found together in one sample. The lab data will continue to be processed for the current season (2021/22). Conclusion. ILI rates were lowest during the third year (2020/21), consistent with national influenza surveillance reports of influenza and outpatient ILI activity, suggesting that measures taken to reduce transmission of SARS-CoV-2 reduced the spread of other respiratory viruses. The emergence of the SARS-CoV-2 omicron variant in December 2021 was associated with higher ILI rates. Among those individuals for whom a sample was collected, coinfections were highest in 2018/19. Data collection and specimen analysis are ongoing for 2021/22. (Figure Presented).

Impact of COVID-19 Pandemic on Influenza-like Illness (ILI) Experience among Healthcare Workers in Military Treatment Facilities

Background. Healthcare workers (HCWs) are at heightened risk of exposure to respiratory pathogens, and occupy an important epidemiologic position in the COVID-19 pandemic. PAIVED, a multicenter, multiservice study assessing influenza vaccine effectiveness in the Department of Defense over four consecutive influenza seasons (2018-22), provides an opportunity to describe influenza like illness (ILI) experience and assess the impact of SARS-CoV-2 in HCWs compared to non-HCWs. Methods. PAIVED participants were randomized to receive either egg-based, cell-based, or recombinant-derived influenza vaccine and then surveyed weekly for ILI. At enrollment, participants provided key demographic data including whether they were HCWs with direct patient contact. ILI was defined a priori as 1) having cough or sore throat plus 2) feeling feverish/having chills or having body aches/fatigue. Participants with ILI completed a symptom diary for seven days and submitted a nasal swab for pathogen detection. Study recruitment was conducted from September-January over four consecutive years. Results. Of 13188 eligible participants enrolled, 4819 (36%) were HCWs. Overall, HCWs were more likely to be female (43% vs 31%), active duty military (86% vs 69%), and to identify as white (61% vs 56%). HCWs more commonly reported ILI than non-HCWs (25% vs 21%, p< 0.01). Of those experiencing ILI, SARS-CoV-2 was identified in a higher proportion of HCWs than non-HCWs (17% vs 12%, p< 0.01). Influenza was isolated in similar proportion of HCWs and non-HCWs (5% vs 4%). Each group reported similar ILI duration and severity (p< 0.01). Conclusion. In a prior analysis of the 2019-20 PAIVED season, HCWs were more likely than non-HCWs to report ILI, have shorter illness duration, and isolate influenza A (H1N1). The propensity for HCWs to report ILI persisted over the four years. While SARS-CoV-2 emerged as a major pathogen in both groups, HCWs were more likely to have it identified as a cause of ILI, suggesting increased risk of symptomatic SARS-CoV-2 in our HCW population. Influenza incidence was lower than that of SARS-COV-2, and did not differ between HCWs and non-HCWs. Mean duration of illness did not differ between groups over four years;this equalization may relate to the higher incidence of SARS-CoV-2 in HCWs.

The Host Response to SARS-CoV-2 Infection Differs by Age

Background. Infection with SARS-CoV-2 and the resulting host immune response has been primarily characterized in middle and older aged populations due to a higher incidence of symptoms in these age groups. Due to reduced severity of disease, children were poorly studied and assumed to be less frequently infected compared to older age groups. We measured the viral load and adaptive immune response across the age-spectrum to define the age-dependent viral and host responses. Methods. From March 2020-March 2022, we enrolled individuals across the age spectrum who presented to U.S. military medical treatment facilities with COVID-19-like symptoms. In this longitudinal cohort study, demographic and clinical data were collected in addition to nasopharyngeal swabs and peripheral blood. Magnitude of viral RNA was measured by quantitative PCR (qPCR) from nasopharyngeal samples and SARS-CoV-2-specific IgG antibodies were measured from blood with multiplex microsphere immunoassays. Results. 4,768 SARS-CoV-2 positive participants were enrolled, among whom 42, 64, 89, 380, 948 and 245 individuals were in age brackets 0-4y, 5-11y, 12-17y, 18-44, 45-64y, and >65y, respectively. Viral load as measured by qPCR was determined to be similar across age groups within the first week post symptom onset. The magnitude of the IgG antibody response against the spike protein was also compared across age groups at early and convalescent time points and was higher in those over the age of 65 years. Conclusion. Early viral load during acute infection did not correlate with age in individuals who experienced COVID-19. These findings diverge from other respiratory viruses, such as respiratory syncytial virus and influenza where children tend to have higher viral loads. In contrast, the magnitude of the antibody response against the spike protein correlated with older age at acute and convalescent time points. Together our data suggest that the host response against SAR-CoV-2 differs with age and is not associated with the acute viral load. Defining age-dependent immunity against SARS-CoV-2 has the potential to identify key immunologic responses that can be used to optimize treatment and vaccine strategies.

Precision phenotyping of 'long COVID' through machine learning

Background. Characterizing, diagnosing, and caring for 'long COVID' patients has proven to be challenging due to heterogenous symptoms and broad definitions of these post-acute sequelae. Here, we take a machine learning approach to identify discrete clusters of long COVID symptoms which may define specific long COVID phenotypes. Figure 1: (A) Principal component analysis followed by K-means clustering identified three groups of participants. (B) Heatmap depicting three distinct clusters (high values are in red and low value are in blue);Cluster 1 exhibits sensory symptoms (e.g., loss of smell and/or taste), Cluster 2 exhibits fatigue and difficulty thinking (e.g., changes in ability to think) symptoms, and Cluster 3 exhibits difficulty breathing and exercise intolerance symptoms. (C) Clinical and demographic characteristics of 97 military health system beneficiaries by identified clusters Methods. The Epidemiology, Immunology, and Clinical Characteristics of Emerging Infectious Diseases with Pandemic Potential (EPICC) study is a longitudinal COVID-19 cohort study with data and biospecimens collected from 10 military treatment facilities and online recruitment. Demographic and clinical characteristics were collected using case report forms and surveys completed at enrollment and at 1, 3, 6, 9, and 12 months. For this analysis, we identified those who reported any moderate to severe persistent symptoms on surveys collected 6-months post-COVID-19 symptom onset. Using the survey responses, we applied principal component analysis (PCA) followed by unsupervised machine learning clustering algorithm K-means to identify groups with distinct clusters of symptoms. Results. Of 1299 subjects with 6-month survey responses, 97 (7.47%) reported moderate to severe persistent symptoms. Among these subjects, three clusters were identified using PCA (Figure 1A). Cluster 1 is characterized by sensory symptoms (loss of taste and/or smell), Cluster 2 by fatigue and difficulty thinking, and Cluster 3 by difficulty breathing and exercise intolerance (Figure 1B). More than half of these subjects (57%) were female, 64% were 18-44 years old, and 64% had no comorbidities at enrollment (Figure 1C). Those in the sensory symptom cluster were all outpatients at the time of initial COVID-19 presentation (p < 0.01). The difficulty breathing and exercise intolerance symptom-clusters had a higher proportion of older participants (Age group >= 45-64) with more comorbidities (CCI >= 1-2). Conclusion. We identified three distinct 'long COVID' phenotypes among those with moderate to severe COVID-19 symptoms at 6-months post-symptom onset. With further validation and characterization, this framework may allow more precise classification of long COVID cases, and potentially improve the diagnosis, prognosis, and treatment of post- infectious sequelae.

Correlates of Omicron SARS-CoV-2 viral load: diagnostic and clinical implications

Background. Omicron SARS-CoV-2 infections are associated with less frequent olfactory sensory loss and a predominance of pharyngitis symptoms compared to prior variants, with proposed diagnostic implications. We examined whether such symptomology predicts a higher RNA abundance in the oropharynx. We further investigated how age, symptom-day, vaccination history and clinical severity correlate with viral load to inform clinical prognostication and transmission modeling. Methods. The EPICC study is a longitudinal cohort of COVID-19 cases enrolled through U.S military medical treatment facilities. Demographic and clinical characteristics were measured with interviews and surveys. Nasopharyngeal (NP), oropharyngeal (OP) and nasal swabs (NS) were collected for SARS-CoV-2 qPCR and sequence genotyping. Multivariable linear regression models were fit to estimate the effect of anatomical site on SARS-CoV-2 RNA abundance (a proxy for viral load), adjusting for sampling time, vaccine history and host age. Results. We analyzed 77 sequence-confirmed Omicron cases;no BA.2 cases were detected. The median age was 38.8 years. 81.8% were vaccinated and 15.6% cases were hospitalized. 80.0%, 21.8%, and 65.5% reported nasal congestion, loss of smell or taste, and sore throat, respectively. The median RNA abundance was lowest in OP swabs (p < 0.001) (Fig 1). Linear regression confirmed that OP sampling was associated with lower viral load (p < 0.001). We further noted that greater age and symptom-day were independent correlates of viral load (Table 1). By bivariate analysis there was a trend toward lower RNA abundance in vaccinated subjects (p = 0.35). RNA abundance (at any site) was substantially higher in hospitalized (10634 N2 genome equivalents [GE]/reaction) versus outpatient cases (1419 N1 GE/reaction) but this was not statistically significant (p = 0.26). Conclusion. We noted prevalent sore throat symptoms and infrequent sensory loss in Omicron cases. Despite this, viral load was highest in NP/NS collected swabs as has been noted in prior variants. We note an age correlation with RNA abundance, and provide a viral load decay rate which may be useful for transmission modeling. Vaccination and clinical severity may also correlate with Omicron viral load, as noted with prior SARS-CoV-2 variants.

Emergence of the SARS-CoV-2 Omicron Variant in the Pragmatic Assessment of Influenza Vaccine Effectiveness in the Department of Defense (PAIVED) Study

Background. Novel SARS-CoV-2 (SCV2) variants may differ in epidemiology and clinical impact. PAIVED, a randomized trial comparing the efficacy of 3 different platforms of inactivated influenza vaccines in adult military health system beneficiaries, actively surveils participants for influenza-like illness (ILI), including COVID-19, and conducts targeted investigations among those who develop ILI. The current season (2021/22) offered an opportunity to assess symptomatology associated with emerging SCV2 variants in this prospective cohort. Methods. Following receipt of influenza vaccine, PAIVED participants receive a weekly email or text message querying for ILI symptoms. Those who reported ILI completed a validated symptom diary (FLU-PRO Plus) daily for 7 days and collected a nasal swab. Nasal specimens underwent multiplex PCR testing, followed by SCV2 genome sequencing as applicable. PAIVED study participants from the 2021-22 influenza season who reported an ILI, had confirmed infection with SCV2 for which sequence data is available, and completed at least one FLU-PRO Plus survey were included in this analysis. Results. To date, 293 participants (7% of active cohort;22.5% reporting ILI) tested positive for SCV2;sequencing has identified 23 Delta and 200 Omicron variants (199 BA.1, 1 BA.2). Among the 212 participants with sequenced SCV2 and symptom data, 55% were male, 57% were white, and 85% were active-duty military (Table 1). Overall, peak symptom severity was classified as mild to moderate in 79.3% of cases, fever duration averaged 2.5+/-2.2 days, and there were activity limitations for a mean of 5.2+/-3.8 days. No differences in maximum symptom scores (total or by domain) were detected for participants infected with Omicron compared to Delta. Figure 1 depicts variation in mean symptom scores by day of ILI, grouped by variant Conclusion. Omicron emerged as the predominant SCV2 variant causing ILI in our cohort this season, typically manifesting with mild symptoms. Further exploration of potential differences in ILI experience between SCV2 variants and other ILI causes, plus the impact and timing of vaccination, will add insight into the relative contribution of such factors on symptomatology.

SARS-CoV-2 infection is associated with decreased reported physical fitness in a US military longitudinal cohort

Background. COVID-19 may have deleterious effects on the fitness of active duty US military service members. We seek to understand the long-term functional consequences of SARS-CoV-2 infection in this critical population, and in other military healthcare beneficiaries. Methods. The Epidemiology, Immunology, and Clinical Characteristics of Emerging Infectious Diseases with Pandemic Potential (EPICC) study is a longitudinal cohort study to describe the outcomes of SARS-CoV-2 infection in US Military Health System beneficiaries. Subjects provided information about difficulties experienced with daily activities, exercise, and physical fitness performance via electronic surveys. Subjects completed surveys at enrollment and at 1, 3, 6, 9, and 12 months. Results. 5,910 subjects completed survey fitness questions, 3,244 (55%) of whom tested SARS-CoV-2 positive at least once during the period of observation. Over 75% of subjects were young adults and over half were male (Table 1). 1,093 (34.3%) of SARS-CoV-2-positive subjects reported new or increased difficulty exercising compared to 393 (14.8%) SARS-CoV-2 negative subjects (p < 0.01) (Table 2). The most commonly reported symptoms related to problems with exercise and activities were dyspnea and fatigue.Among the active-duty members who answered the question about their service-mandated physical fitness test scores, 43.2% of SARS-CoV-2-positive participants reported that their scores had worsened in the study period, compared with 24.3%of SARS-CoV-2 negative participants.Among SARS-CoV-2-positive subjects, reports of difficulty exercising and performing daily activities were highest within one month of the first positive test, decreasing in prevalence among the cohort only slightly to 24% and 18%, respectively, at 12 months (Figure 1). Conclusion. A substantial proportion of military service-members in this cohort have reported impairment of their service-mandated physical fitness scores after COVID-19;this proportion is significantly higher than those who are SARS-CoV-2 negative and persists to 12 months in many;similar complaints were reported among non-active duty. Further objective evaluation of post-COVID fitness impairment in this population is warranted. (Figure Presented).

A study of relationship between lipid profile and severity of SARS-CoV-2

COVID-19 is a severe acute respiratory disease caused by coronavirus 2. While many biochemical alterations have been studied in patients with COVID-19, only a few studies were available to explore the relationship between serum lipid profile values and the severity of SARS COVID-19 infection. A cross-sectional study was conducted at Chettinad Hospital and Research Institute on 128 patients infected with SARS COVID-19 from March 2020 to September 2020. It was an age and sex-matched study. Patients were categorized into mild and severe based on the signs and symptoms. A fasting serum lipid profile and IL-6 levels were measured and Pearson's correlation analysis was done. There was a highly significant decrease in the median and IQR levels of TC, HDL, and LDL in severe cases as compared to mild cases [TC - mild: (256,64), severe (125,44), HDL - mild (46,11), severe (25,13), and LDL - mild (170,48), severe (76,36)]. TGL showed a significant decrease [mild: (170,67), severe:(110,69)]. IL-6 showed a significant increase in severe cases when compared to mild cases [mild:(20,37), severe:(62,105)]. Pearson's correlation analysis showed a significant inverse relationship between the levels of TC, HDL, and IL-6. However, TGL and LDL showed inverse but no significant relationship with IL-6. As the severity of COVID-19 increases, lipid profile levels start decreasing. Hypolipidemia is a pathognomic finding in severe SARS COVID-19 infection. Copyright © 2022 The Authors.

COVID-19 CHRONIC IMPAIRMENT WITH PULMONARY SYMPTOMS

SESSION TITLE: Long COVID: It Can Take Your Breath Away SESSION TYPE: Original Investigations PRESENTED ON: 10/16/2022 10:30 am - 11:30 am PURPOSE: As the novel coronavirus SARS-CoV-2 swept the globe causing COVID-19 infection, a syndrome now known as “long COVID” has been well described in 10-30% of those who have experienced COVID-19. This study hoped to characterize changes in anatomical structure and physiology that may explain the ongoing dyspnea experienced by some individuals affected by the COVID-19 pandemic. METHODS: Patients with a history of symptomatic COVID-19 confirmed by positive PCR or antibody testing, between the age of 18-65, without pre-existing significant cardiopulmonary disease, and currently experiencing ongoing exertional or respiratory symptoms at least 3 months after onset of initial COVID symptoms were enrolled into this study. Each participant underwent standardized testing for underlying cardiopulmonary pathology by performance of a high-resolution chest CT, transthoracic echocardiography, electrocardiogram, full pulmonary function testing with lung volumes and diffusing capacity, impulse oscillometry, and a six minute walk test. RESULTS: To date, 63 patients have enrolled in the study with ongoing completion of study procedures. Of the current patients enrolled, 29 have had a high resolution chest CT completed;16 or 55% had radiographic evidence of pulmonary pathology. Most common were a nodular pattern (38%), mosaic attenuation (34%), residual ground glass opacities (28%), septal thickening (14%). Thirty-six participants performed the six minute walk test with an average walk distance of 1338.9 feet ± 520.4 feet with no participants desaturating below 90%. Pulmonary function testing has been completed in 36 participants with normal mean values. Impulse oscillometry testing performed on 30 individuals revealed mixed results with resistance at 5 Hz (R5) showing no substantive change to bronchodilator with a -14% ± 5%, however the area of reactance showed a potentially significant bronchodilator response with bronchodilator change of -43% ± 41%. CONCLUSIONS: In this interim analysis, we evaluated the radiographic and physiologic changes seen in a group of patients at least three months after symptomatic infection with COVID-19. There were radiographic changes in 50% of patients with a reticulonodular pattern as the most often reported finding. However, this finding did not correlate with PFT or exercise findings in the cohort;few showed significant PFT changes and the 6MWT did not show desaturations or limitation in walking distance. Pulmonary function testing and impulse oscillometry showed no statistically substantive physiologic derangements that might explain the ongoing symptoms of the group evaluated. CLINICAL IMPLICATIONS: Other than radiographic findings, there were no unified findings that could shed further light on the effects of COVID-19 that would predispose an individual to ongoing symptoms. DISCLOSURES: No relevant relationships by Brian Agan no disclosure on file for Timothy Burgess;no disclosure on file for Anuradha Ganesan;No relevant relationships by Stephen Goertzen No relevant relationships by Travis Harrell no disclosure on file for Nikhil Huprikar;No relevant relationships by David Lindholm No relevant relationships by Katrin Mende Speaker/Speaker's Bureau relationship with Janssen Please note: $1001 - $5000 by Michael Morris, value=Honoraria Speaker/Speaker's Bureau relationship with GSK Please note: $1001 - $5000 by Michael Morris, value=Honoraria Removed 03/29/2022 by Michael Morris no disclosure on file for Simon Pollett;no disclosure on file for Julia Rozman;No relevant relationships by Mark Simons No relevant relationships by David Tribble No relevant relationships by Robert Walter

Indigenous Non-invasive Detection of Malnutrition-Induced Anemia from Patient-Sourced Photos Using Smart Phone App

The proposed online-based malnutrition-induced anemia detection smart phone app is built, to remotely measure and monitor the anemia and malnutrition in humans by using a non-invasive method. This painless method enables user-friendly measurements of human blood stream parameters like hemoglobin (Hb), iron, folic acid, and vitamin B12 by embedding intelligent image processing algorithms which will process the photos of the fingernails captured by the camera in the smart phone. This smart phone app extracts the color and shape of the fingernails, will classify the anemic and vitamin B12 deficiencies as onset, medieval, and chronic stage with specific and accurate measurements instantly. On the other dimension, this novel technology will place an end to the challenge involved in the disposal of biomedical waste, thereby offering a contactless measurement system during this pandemic Covid-19 situation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Exploring the Potentials of Phytochemicals for Targeting Coronavirus

Since the initial outbreak in December 2019, the COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to more than 3 million deaths worldwide. There is an urgent need for developing a potential therapy against SARS-CoV-2. Exploring the potentials of phytochemicals towards inhibition of SARS-CoV-2 proteins remains of significant scientific interest. The therapeutic values of phytochemicals in the treatment of diseases, such as viral infections, are known for a long time. In this review, we present a brief overview of the past experimental and computational efforts on evaluating phytochemicals against SARS coronaviruses, an earlier coronavirus strain. We discuss natural metabolites of different structural and chemical scaf-folds, including polyphenols, flavonoids, and phytosterols, which can be promising compounds for screening against the currently evolving SARS-CoV-2 virus.

Online learning: ICT-based Tools for Interaction and Effectiveness

COVID 19 has been a challenge in all sectors and education in particular. During the pandemic, there was an immediate and compulsory shift in conducting classes via online mode. Several colleges and schools have asked their teachers to hold classes online, yet most of them are neither equipped nor in the mindset to adapt to this new teaching methodology. Despite these limitations, Google Meet, Zoom, Microsoft Teams, and other platforms have become integral parts of lecturing and learning. Subjects have been taught using these platforms. Despite the challenges, classes are still held online and teachers are still able to reach students. It is important to note, however, that there are some notable challenges across all sessions that remain unnoticed and unresolved. In an online environment, controlling students' absences, engaging all students in discussions, monitoring their presence, keeping them active, conducting assessments, and nurturing creativity are all questionable. Research is needed to initiate and incorporate different ICT (Information Communication Technology) tools, to facilitate effective teaching and learning. The purpose of this paper is to identify the various ICT tools and resources that can be used to support the above-discussed problems and make online teaching and learning more effective. © The Electrochemical Society

Pragmatic Assessment of Influenza Vaccine Effectiveness in the DoD (PAIVED): Updates from Year 2 of Multi-Site Trial

Despite nearly universal influenza vaccination for active duty military personnel, breakthrough influenza infections occur. We are reporting on the second year of the Pragmatic Assessment of Influenza Vaccine Effectiveness in the DoD (PAIVED), comparing three FDA-licensed influenza vaccine types (egg-based, cell -based, and recombinant) to assess differences in immunogenicity and effectiveness.

Long-term clinical outcomes following SARS-CoV-2 infection include persistent symptoms and cardiovascular disease beyond 3 months post-infection

Background. The long-term health effects after SARS-CoV-2 infection remain poorly understood. We evaluated health and healthcare usage after SARS-CoV-2 infection via surveys and longitudinal electronic medical record (EMR) review within the Military Health System (MHS). Methods. We studied MHS beneficiaries enrolled in the Epidemiology, Immunology, and Clinical Characteristics of Emerging Infectious Diseases with Pandemic Potential (EPICC) cohort from March to December 2020. COVID-19 illness symptom severity and duration were derived from surveys initiated in late 2020. In addition, multi-year healthcare encounter history before and after onset of COVID-19 symptoms was collected from the MHS EMR. Odds of organ-system clinical diagnoses within the 3 months pre- and post-symptom onset were calculated using generalized linear models, controlling for age, sex, and race, and including participant as a random effect. Results. 1,015 participants were included who were SARS-CoV-2 positive, symptomatic, and had 3-month follow-up data available in the EMR (Table 1). 625 of these participants had survey data collected more than 28 days post-symptom onset, among whom 17% and 6% reported persistent symptoms at 28-84 days, and 85+ days, respectively. 9.6% had not resumed normal activities by one month. The most frequently reported symptoms persisting beyond 28 days were dyspnea, loss of smell and/or taste, fatigue, and exercise intolerance (Figure 1A). When compared with the period 61 to 90 days prior to symptom onset, the first month post-symptom onset period was associated with increases of pulmonary (aOR = 57, 95% CI 28-112), renal (aOR = 29, 95% CI 10-84), cardiovascular (aOR = 7, 95% CI 5-11), and neurological diagnoses (aOR = 3, 95% CI 2-4) (Figures 1B and 1C). Cardiovascular disease diagnoses remained elevated through 3 months (aOR = 2, 95% CI 1-3). Fig1A. Symptoms reported by EPICC participants with illnesses longer than 28 days;1B. Percent of participants with organ system specific diagnoses on each day, 90 days pre- and post-symptom onset;1C. Odds of organ system specific diagnoses within each month, +/- 3 months of symptom onset, were calculated using generalized linear models, controlling for age, sex, and race and included participants as a random effect. Odds shown are relative to the earliest period included in the model, 61-90 days before onset. Conclusion. In this MHS cohort, a significant proportion of participants had persistent symptoms and cardiovascular disease diagnoses 3 months after COVID-19 illness onset. These findings emphasize the long-term morbidity of COVID-19 and the importance of mitigating SARS-CoV-2 infections. Further analyses will evaluate demographic, clinical, and biomarker predictors of medium-to-long term organ-specific post-acute sequelae.

Pragmatic Assessment of Influenza Vaccine Effectiveness in the DoD (PAIVED): Updates from Year 3 of Multi-Site Trial

Background. The SARS-CoV-2 pandemic has spotlighted respiratory infections and the value of effective vaccines. The SARS-CoV-2 vaccine has been remarkably effective;however, influenza vaccine effectiveness has been reported to be lower among active duty military populations than in the general public (18% vs 36%). The Pragmatic Assessment of Influenza Vaccine Effectiveness in the DoD (PAIVED) study compares 3 FDA-licensed influenza vaccine types (egg-based, cell-based, and recombinant) to assess differences in immunogenicity and effectiveness in adults. Methods. Participants in the 3rd year of PAIVED (2020/21 influenza season) were enrolled from October 2020 through January 2021. Participants received weekly surveys about influenza-like-illnesses (ILI) experienced in the past week;if they reported an ILI, they were queried about symptom duration and severity, and asked to self-collect a nasal swab and dried blood sample. Four weeks later, more information about symptom duration and illness burden was obtained via telephone interview, and the participant collected a second blood sample. Results. PAIVED year 3 enrolled 3,269 participants (Table 1). 278 participants reported 1 ILI , while 60 reported 2 ILIs, and 18 reported 3 ILIs. No pathogen was identified for most processed ILI samples (78%);the most common viruses were SARS-CoV-2 (25, 12%), rhinovirus (24, 12%), and seasonal coronaviruses (4, 2%). No influenza has been identified thus far. Among those participants who had convalescent ILI visits (275), the median duration of the reported ILIs was 9 days (IQR 5, 15), with a median of 4 days (IQR 2, 7) of limited activity, and 2 days (IQR 0, 3) with fever. Three individuals were hospitalized. Conclusion. There have been relatively low rates of ILI identified in this study during this season, with only 11% of the participants reporting an ILI so far, consistent with low rates of non-COVID-19 ILI reported elsewhere during the current pandemic. We anticipate some influenza cases may be identified as more samples are processed. Planned analyses include calculating comparative influenza vaccine effectiveness to inform future vaccine purchasing decisions, as well as comparing serological response to the different vaccines.

SARS-CoV-2 Viral Load Does Not Predict Incident Venous Thromboembolism in COVID-19

Background. The risk factors of venous thromboembolism (VTE) in COVID-19 warrant further study. We leveraged a cohort in the Military Health System (MHS) to identify clinical and virological predictors of incident deep venous thrombosis (DVT), pulmonary embolism (PE), and other VTE within 90-days after COVID-19 onset. Methods. PCR or serologically-confirmed SARS-CoV-2 infected MHS beneficiaries were enrolled via nine military treatment facilities (MTF) through April 2021. Case characteristics were derived from interview and review of the electronic medical record (EMR) through one-year follow-up in outpatients and inpatients. qPCR was performed on upper respiratory swab specimens collected post-enrollment to estimate SARS-CoV-2 viral load. The frequency of incident DVT, PE, or other VTE by 90-days post-COVID-19 onset were ascertained by ICD-10 code. Correlates of 90-day VTE were determined through multivariate logistic regression, including age and sampling-time-adjusted log10-SARS-CoV-2 GE/reaction as a priori predictors in addition to other demographic and clinical covariates which were selected through stepwise regression. Results. 1473 participants with SARS-CoV-2 infection were enrolled through April 2021. 21% of study participants were inpatients;the mean age was 41 years (SD = 17.0 years). The median Charlson Comorbidity Index score was 0 (IQR = 0 -1, range = 0 - 13). 27 (1.8%) had a prior history of VTE. Mean maximum viral load observed was 1.65 x 107 genome equivalents/reaction. 36 (2.4%) of all SARS-CoV-2 cases (including inpatients and outpatients), 29 (9.5%) of COVID-19 inpatients, and 7 (0.6%) of outpatients received an ICD-10 diagnosis of any VTE within 90 days after COVID-19 onset. Logistic regression identified hospitalization (aOR = 11.1, p = 0.003) and prior VTE (aOR = 6.2 , p = 0.009) as independent predictors of VTE within 90 days of symptom onset. Neither age (aOR = 1.0, p = 0.50), other demographic covariates, other comorbidities, nor SARS-CoV-2 viral load (aOR = 1.1, p = 0.60) were associated with 90-day VTE. Conclusion. VTE was relatively frequent in this MHS cohort. SARS-CoV-2 viral load did not increase the odds of 90-day VTE. Rather, being hospitalized for SARS-CoV-2 and prior VTE history remained the strongest predictors of this complication.

A Machine Learning Approach Identifies Distinct Early-Symptom Cluster Phenotypes Which Correlate with Severe SARS-CoV-2 Outcomes

Background. The novel coronavirus disease 2019 (COVID-19) pandemic remains a global challenge. Accurate COVID-19 prognosis remains an important aspect of clinical management. While many prognostic systems have been proposed, most are derived from analyses of individual symptoms or biomarkers. Here, we take a machine learning approach to first identify discrete clusters of early stage-symptoms which may delineate groups with distinct symptom phenotypes. We then sought to identify whether these groups correlate with subsequent disease severity. Methods. The Epidemiology, Immunology, and Clinical Characteristics of Emerging Infectious Diseases with Pandemic Potential (EPICC) study is a longitudinal cohort study with data and biospecimens collected from nine military treatment facilities over 1 year of follow-up. Demographic and clinical characteristics were measured with interviews and electronic medical record review. Early symptoms by organ-domain were measured by FLU-PRO-plus surveys collected for 14 days post-enrollment, with surveys completed a median 14.5 (Interquartile Range, IQR = 13) days post-symptom onset. Using these FLU-PRO-plus responses, we applied principal component analysis followed by unsupervised machine learning algorithm k-means to identify groups with distinct clusters of symptoms. We then fit multivariate logistic regression models to determine how these early-symptom clusters correlated with hospitalization risk after controlling for age, sex, race, and obesity. Results. Using SARS-CoV-2 positive participants (n = 1137) from the EPICC cohort (Figure 1), we transformed reported symptoms into domains and identified three groups of participants with distinct clusters of symptoms. Logistic regression demonstrated that cluster-2 was associated with an approximately three-fold increased odds [3.01 (95% CI: 2-4.52);P < 0.001] of hospitalization which remained significant after controlling for other factors [2.97 (95% CI: 1.88-4.69);P < 0.001]. (A) Baseline characteristics of SARS-CoV-2 positive participants. (B) Heatmap comparing FLU-PRO response in each participant. (C) Principal component analysis followed by k-means clustering identified three groups of participants. (D) Crude and adjusted association of identified cluster with hospitalization. Conclusion. Our findings have identified three distinct groups with early-symptom phenotypes. With further validation of the clusters' significance, this tool could be used to improve COVID-19 prognosis in a precision medicine framework and may assist in patient triaging and clinical decision-making.

Bamlanivimab (BAM) for SARS-CoV-2 Infection: Rates and Risk Factors for Hospitalization after Monoclonal Antibody Administration in a High-Risk Population

Background. In response to the ongoing COVID-19 pandemic, an emergency use authorization (EUA) was issued for neutralizing antibody therapies including BAM. Licensing trials suggest that use of BAM reduces hospitalizations when compared with placebo (1.6% vs 6.3%). However, the real world impact of BAM is not well-described. In this study, risk factors, outcomes, and hospitalization rates among high-risk outpatients presenting with mild-to-moderate COVID-19 who received BAM were examined. Methods. This is a single center retrospective analysis of all patients who received BAM monotherapy between 11/11/2020 and 3/16/2021. Electronic health records were reviewed for baseline demographics, EUA indications, comorbidities, and outcomes to include infusion reactions, hospitalizations, and deaths occurring within 29 days of BAM administration. Moderate COVID-19 was defined as having any infiltrate on chest imaging prior to BAM administration. Chi-squared or Fisher's exact tests were used to compare categorical values as appropriate, and Mann-Whitney U for continuous variables. Results. Of the 101 patients who received BAM (median age 64 years;21% black;4% Hispanic;55% male), 13 were subsequently admitted. 22 patients (22%) had moderately severe disease as evidenced by abnormal imaging. Severity on presentation, number of indications for therapy, hypertension, stroke, diabetes, and number of co-morbidities were significantly associated with subsequent admission (table 1). No patients had adverse infusion reactions. Of those hospitalized, 8 (61.5%) were for COVID-19, the median duration of hospitalization was 2 days, and 4 received guideline-directed treatment for COVID-19 (table 2). Conclusion. In a high-risk population, hospitalization rates were higher than those observed in clinical trials, with 8% of subjects being admitted for COVID-19. Disease severity on presentation, multiple indications for therapy, and the presence of multiple co-morbidities were all associated with subsequent admission. Reassuringly, BAM was well tolerated, and in those requiring admission, hospitalizations were short, resource utilization was low, and there were no deaths.