The association between prolonged SARS-CoV-2 symptoms and work outcomes (preprint)

While the early effects of the COVID-19 pandemic on the United States labor market are well-established, less is known about the long-term impact of SARS-CoV-2 infection and Long COVID on employment. To address this gap, we analyzed self-reported data from a prospective, national cohort study to estimate the effects of SARS-CoV-2 symptoms at three months post-infection on missed workdays and return to work. The analysis included 2,939 adults in the Innovative Support for Patients with SARS-CoV-2 Infections Registry (INSPIRE) study who tested positive for their initial SARS-CoV-2 infection at the time of enrollment, were employed before the pandemic, and completed a baseline and three-month electronic survey. At three months post-infection, 40.8% of participants reported at least one SARS-CoV-2 symptom and 9.6% of participants reported five or more SARS-CoV-2 symptoms. When asked about missed work due to their SARS-CoV-2 infection at three months, 7.1% of participants reported missing >=10 workdays and 13.9% of participants reported not returning to work since their infection. At three months, participants with >=5 symptoms had a higher adjusted odds ratio (aOR) of missing >=10 workdays (2.96, 95% CI 1.81-4.83) and not returning to work (2.44, 95% CI 1.58-3.76) compared to those with no symptoms. Prolonged SARS-CoV-2 symptoms were common, affecting 4-in-10 participants at three-months post-infection, and were associated with increased odds of work loss, most pronounced among adults with >=5 symptoms at three months. Despite the end of the Federal COVID-19 Public Health Emergency and efforts to "return to normal", policymakers must consider the clinical and economic implications of the COVID-19 pandemic on peoples employment status and work absenteeism, particularly as data characterizing the numerous health and well-being impacts of Long COVID continue to emerge. Improved understanding of risk factors for lost work time may guide efforts to support people in returning to work.

Doubly regularized generalized linear models for spatial observations with high-dimensional covariates (preprint)

A discrete spatial lattice can be cast as a network structure over which spatially-correlated outcomes are observed. A second network structure may also capture similarities among measured features, when such information is available. Incorporating the network structures when analyzing such doubly-structured data can improve predictive power, and lead to better identification of important features in the data-generating process. Motivated by applications in spatial disease mapping, we develop a new doubly regularized regression framework to incorporate these network structures for analyzing high-dimensional datasets. Our estimators can easily be implemented with standard convex optimization algorithms. In addition, we describe a procedure to obtain asymptotically valid confidence intervals and hypothesis tests for our model parameters. We show empirically that our framework provides improved predictive accuracy and inferential power compared to existing high-dimensional spatial methods. These advantages hold given fully accurate network information, and also with networks which are partially misspecified or uninformative. The application of the proposed method to modeling COVID-19 mortality data suggests that it can improve prediction of deaths beyond standard spatial models, and that it selects relevant covariates more often.

Dynamic gene expression analysis reveals distinct severity phases of immune and cellular dysregulation in COVID-19 (preprint)

BackgroundCOVID-19 patients experience dynamic changes in immune and cellular function over time with potential clinical implications. However, there is insufficient research investigating, on a gene expression level, the mechanisms that become activated or suppressed over time as patients deteriorate or recover, which can inform use of repurposed and novel drugs as therapies. ObjectiveTo investigate longitudinal changes in gene expression profiles throughout the COVID-19 disease timeline. MethodsThree-hundred whole blood samples from 128 adult patients were collected during hospitalization from COVID-19, with up to five samples per patient. Transcriptome sequencing (RNA-Seq), differential gene expression analysis and pathway enrichment was performed. Drug-gene set enrichment analysis was used to identify FDA-approved medications that could inhibit critical genes and proteins at each disease phase. Prognostic gene-expression signatures were generated using machine learning to distinguish 3 disease stages. ResultsSamples were longitudinally grouped by clinical criteria and gene expression into six disease phases: Mild, Moderate, Severe, Critical, Recovery, and Discharge. Distinct mechanisms with differing trajectories during COVID-19 hospitalization were apparent. Antiviral responses peaked early in COVID-19, while heme metabolism pathways became active much later during disease. Adaptive immune dysfunction, inflammation, and metabolic derangements were most pronounced during phases with higher disease severity, while hemostatic abnormalities were elevated early and persisted throughout the disease course. Drug-gene set enrichment analysis predicted repurposed medications for potential use, including platelet inhibitors in early disease, antidiabetic medications for patients with increased disease severity, and dasatinib throughout the disease course. Disease phases could be categorized using specific gene signatures for prognosis and treatment selection. Disease phases were also highly correlated to previously developed sepsis endotypes, indicating that severity and disease timing were significant contributors to heterogeneity observed in sepsis and COVID-19. ConclusionsHigher temporal resolution of longitudinal mechanisms in COVID-19 revealed multiple immune and cellular changes that were activated at different phases of COVID-19. Understanding how a patients gene expression profile changes over time can permit more accurate risk stratification of patients and provide time-dependent personalized treatments with repurposed medications. This creates an opportunity for timely intervention before patients transition to a more severe phase, potentially accelerating patients to recovery.

Exploring Embeddings for Measuring Text Relatedness: Unveiling Sentiments and Relationships in Online Comments (preprint)

After the COVID-19 pandemic caused internet usage to grow by 70%, there has been an increased number of people all across the world using social media. Applications like Twitter, Meta Threads, YouTube, and Reddit have become increasingly pervasive, leaving almost no digital space where public opinion is not expressed. This paper investigates sentiment and semantic relationships among comments across various social media platforms, as well as discusses the importance of shared opinions across these different media platforms, using word embeddings to analyze components in sentences and documents. It allows researchers, politicians, and business representatives to trace a path of shared sentiment among users across the world. This research paper presents multiple approaches that measure the relatedness of text extracted from user comments on these popular online platforms. By leveraging embeddings, which capture semantic relationships between words and help analyze sentiments across the web, we can uncover connections regarding public opinion as a whole. The study utilizes pre-existing datasets from YouTube, Reddit, Twitter, and more. We made use of popular natural language processing models like Bidirectional Encoder Representations from Transformers (BERT) to analyze sentiments and explore relationships between comment embeddings. Additionally, we aim to utilize clustering and Kl-divergence to find semantic relationships within these comment embeddings across various social media platforms. Our analysis will enable a deeper understanding of the interconnectedness of online comments and will investigate the notion of the internet functioning as a large interconnected brain.

Administration of vaccine-boosted COVID-19 convalescent plasma to SARS-CoV-2 infected hamsters decreases virus replication in lungs and hastens resolution of the infection despite transiently enhancing disease and lung pathology. (preprint)

The utility of COVID-19 convalescent plasma (CCP) for treatment of immunocompromised patients who are not able to mount a protective antibody response against SARS-CoV-2 and who have contraindications or adverse effects from currently available antivirals remains unclear. To better understand the mechanism of protection in CCP, we studied viral replication and disease progression in SARS-CoV-2 infected hamsters treated with CCP plasma obtained from recovered COVID patients that had also been vaccinated with an mRNA vaccine, hereafter referred to as Vaxplas. We found that Vaxplas dramatically reduced virus replication in the lungs and improved infection outcome in SARS-CoV-2 infected hamsters. However, we also found that Vaxplas transiently enhanced disease severity and lung pathology in treated animals likely due to the deposition of immune complexes, activation of complement and recruitment of increased numbers of macrophages with an M1 proinflammatory phenotype into the lung parenchyma.

Design of SARS-CoV-2 protease inhibitors with improved affinity and reduced sensitivity to mutations (preprint)

Inhibitors of the SARS-CoV-2 main protease (Mpro) such as nirmatrelvir (NTV) and ensitrelvir (ETV) have proven effective in reducing the severity of COVID-19, but the presence of resistance-conferring mutations in sequenced viral genomes raises concerns about future drug resistance. Second-generation oral drugs that retain function on these mutants are thus urgently needed. We hypothesized that the covalent HCV protease inhibitor boceprevir (BPV) could serve as the basis for orally bioavailable drugs that inhibit SARS-CoV-2 Mpro more tightly than existing drugs. Performing structure-guided modifications of BPV, we developed a picomolar-affinity inhibitor, ML2006a4, with antiviral activity, oral pharmacokinetics, and therapeutic efficacy similar or superior to NTV. A crucial feature of ML2006a4 is a novel derivatization of the ketoamide reactive group that improves cell permeability and oral bioavailability. Finally, ML2006a4 is less sensitive to several mutations that cause resistance to NTV or ETV and occur in the natural SARS-CoV-2 population. Thus, anticipatory drug design can preemptively address potential resistance mechanisms.

Single cell susceptibility to SARS-CoV-2 infection is driven by variable cell states (preprint)

The ability of a virus to infect a cell type is at least in part determined by the presence of host factors required for the viral life cycle. However, even within cell types that express known factors needed for infection, not every cell is equally susceptible, suggesting that our knowledge of the full spectrum of factors that promote infection is incomplete. Profiling the most susceptible subsets of cells within a population may reveal additional factors that promote infection. However, because viral infection dramatically alters the state of the cell, new approaches are needed to reveal the state of these cells prior to infection with virus. Here, we used single-cell clone tracing to retrospectively identify and characterize lung epithelial cells that are highly susceptible to infection with SARS-CoV-2. The transcriptional state of these highly susceptible cells includes markers of retinoic acid signaling and epithelial differentiation. Loss of candidate factors identified by our approach revealed that many of these factors play roles in viral entry. Moreover, a subset of these factors exert control over the infectable cell state itself, regulating the expression of key factors associated with viral infection and entry. Analysis of patient samples revealed the heterogeneous expression of these factors across both cells and patients in vivo. Further, the expression of these factors is upregulated in particular inflammatory pathologies. Altogether, our results show that the variable expression of intrinsic cell states is a major determinant of whether a cell can be infected by SARS-CoV-2.

A Systematic Review on the Relationship Between Socioeconomic Conditions and Emotional Disorder Symptoms During Covid-19: Unearthing the Potential Role of Economic Concerns and Financial Strain (preprint)

Background Covid-19 has disrupted the lives of many and resulted in high prevalence rates of mental disorders. Despite a vast amount of research into the social determinants of mental health during Covid-19, little is known about whether the results are consistent with the social gradient in mental health. Here we report a systematic review of studies that investigated how SEC indicators, such as education and income, predict emotional health (depression and anxiety) risk during the pandemic. Furthermore, we examined which classes of SEC indicators would best predict symptoms of emotional disorders.Methods Following PRISMA guidelines, we conducted search over six databases, including Scopus, PubMed, etc., between November 4, 2021 and November 11, 2021 for studies that investigated how SEC indicators predict emotional health risks during Covid-19, after obtaining approval from PROSPERO (ID: CRD42021288508). Using Covidence as the platform, 362 articles (324 cross-sectional/repeated cross-sectional and 38 longitudinal) were included in this review according to the eligibility criteria. We categorized SEC indicators into ‘actual versus perceived’ and ‘static versus fluid’ classes to explore their differential effects on emotional health.Results Out of the 1479 SEC indicators used in these 362 studies, our results showed that 43.68% of the SEC indicators showed ‘expected’ results (i.e., higher SEC predicting better emotional health outcomes); 51.86% reported non-significant results and 4.46% reported the reverse. Economic concerns (67.16% expected results) and financial strains (64.16%) emerged as the best predictors while education (26.85%) and living conditions (30.14%) were the worst.Conclusions This review summarizes how different SEC indicators influenced emotional health risks across 98 countries, with a total of 5,677,007 participants, ranging from high to low-income countries. Our findings showed that not all SEC indicators were strongly predictive of emotional health risks. In fact, over half of the SEC indicators studied showed a null effect. We found that perceived and fluid SEC indicators, particularly economic concerns and financial strain could best predict depressive and anxiety symptoms. These findings have implications for policymakers to further understand how different SEC classes affect mental health during a pandemic in order to tackle associated social issues effectively.

Immune Response to COVID-19 in India through Vaccination and Natural Infection.

In India, COVID-19 (Corona Virus Disease-2019) continues to this day, although with subdued intensity, following two major waves of viral infection. Despite ongoing vaccination drives to curb the spread of COVID-19, the relative potential of the administered vaccines to render immune protection to the general population and their advantage over natural infection remain undocumented. In this study, we examined the humoral and cell-mediated immune responses induced by the two vaccines Covishield and Covaxin, in individuals living in and around Kolkata, India. We also compared the immune responses induced separately by vaccination and natural infection. Our results indicate that although Covishield generates a better humoral immune response toward SARS-CoV-2, both vaccines are almost equivalent in terms of cell-mediated immune response to the virus. Both Covishield and Covaxin, however, are more effective toward the wild-type virus than the Delta variant. Additionally, the overall immune response resulting from natural infection in and around Kolkata is not only similar to that generated by vaccination but the cell-mediated immune response to SARS-CoV-2 also lasts for at least ten months in some individuals after the viral infection.

Female Representation in Pediatric Ophthalmology at the American Academy of Ophthalmology Annual Meeting From 2018 to 2022.

PURPOSE: To identify trends in female pediatric ophthalmologist authorship and representation at the American Academy of Ophthalmology (AAO) Annual Meeting from 2018 to 2022. METHODS: Participant data from 2018 to 2022 were collected from the AAO website, organized by conference activity (papers, posters, instruction courses, videos, symposia, subspecialty day, and awards), and analyzed by sex using an online tool. Chi-squared and odds ratio analyses were performed to determine trends in authorship sex and associations between the sex of paper and poster authors in each category. RESULTS: Of 923 pediatric ophthalmology presentations from 2018 to 2022, 46.2% (426 of 923) of presenters and 46.6% (281 of 603) of unique individual participants were women. Overall, 48% (174 of 362) of first and senior authors of papers and posters were women. No significant difference or association between female first and female senior authors was observed (52% vs 44%, P = .14; odds ratio 1.59, P = .13). There was no significant change in the proportion of total female presenters from 2018 to 2019 (-3.09%, P = .53), 2019 to 2020 (0.76%, P = .88), 2020 to 2021 (9.09%, P = .09), 2021 to 2022 (-5.68%, P = .30), or 2018 to 2022 (1.08%, P = .84). CONCLUSIONS: Since 2018, female representation at the AAO Annual Meeting has remained consistent and nears 50%. The lack of a significant difference between the proportion of female first and senior authors suggests that junior female pediatric ophthalmologists are climbing the ranks and more broadly engaging in mentorship roles. Considering the increasing proportion of female pediatric ophthalmologists, the absence of corollary, statistically significant increases in female participation may be of concern. [J Pediatr Ophthalmol Strabismus. 20XX;X(X):XX-XX.].

Facilitating systems-level analyses of all-cause and Covid-mediated sepsis through SeptiSearch, a manually-curated compendium of dysregulated gene sets.

Background: Sepsis is a dysfunctional host response to infection. The syndrome leads to millions of deaths annually (19.7% of all deaths in 2017) and is the cause of most deaths from severe Covid infections. High throughput sequencing or 'omics' experiments in molecular and clinical sepsis research have been widely utilized to identify new diagnostics and therapies. Transcriptomics, quantifying gene expression, has dominated these studies, due to the efficiency of measuring gene expression in tissues and the technical accuracy of technologies like RNA-Seq. Objective: Most of these studies seek to uncover novel mechanistic insights into sepsis pathogenesis and diagnostic gene signatures by identifying genes differentially expressed between two or more relevant conditions. However, little effort has been made, to date, to aggregate this knowledge from such studies. In this study we sought to build a compendium of previously described gene sets that combines knowledge gained from sepsis-associated studies. This would enable the identification of genes most associated with sepsis pathogenesis, and the description of the molecular pathways commonly associated with sepsis. Methods: PubMed was searched for studies using transcriptomics to characterize acute infection/sepsis and severe sepsis (i.e., sepsis combined with organ failure). Several studies were identified that used transcriptomics to identify differentially expressed (DE) genes, predictive/prognostic signatures, and underlying molecular responses and pathways. The molecules included in each gene set were collected, in addition to the relevant study metadata (e.g., patient groups used for comparison, sample collection time point, tissue type, etc.). Results: After performing extensive literature curation of 74 sepsis-related publications involving transcriptomics, 103 unique gene sets (comprising 20,899 unique genes) from thousands of patients were collated together with associated metadata. Frequently described genes included in gene sets as well as the molecular mechanisms they were involved in were identified. These mechanisms included neutrophil degranulation, generation of second messenger molecules, IL-4 and -13 signaling, and IL-10 signaling among many others. The database, which we named SeptiSearch, is made available in a web application created using the Shiny framework in R, (available at https://septisearch.ca). Conclusions: SeptiSearch provides members of the sepsis community the bioinformatic tools needed to leverage and explore the gene sets contained in the database. This will allow the gene sets to be further scrutinized and analyzed for their enrichment in user-submitted gene expression data and used for validation of in-house gene sets/signatures.

Predictors of Mortality Among Post-COVID-19 Discharged Patients in Northern India: A Case-Control Study.

Introduction The post-discharge all-cause mortality of COVID-19 disease is known, but predictors for the same have not been studied as much. The objective of this study was to develop an understanding of predictors of mortality to guide in prioritizing patient care and preventive approaches. Methods This current research is a single-center unmatched case-control study conducted at a tertiary care center in northern India, between April and September 2022. The data were extracted retrospectively from the hospital's electronic medical records of patients with the assistance of trained physicians using a standardized data extraction sheet. Results A total of 184 patients were enrolled and were segregated into two groups, cases and control, with 92 in each. The mean age of patients was 49.3 ± 17.53 years. The mortality group had a higher mean age (53.24 ± 18.53 yrs) as compared to the control group (45.37 ± 15.58 yrs, p=0.002). Bivariate analysis revealed a significant difference in the two groups with respect to O2 saturation at the time of admission (case - 91.12 ± 12.49 %, control - 95.46 ± 5.01 %, p=0.003); maximum O2 flow rate (L/min) (case - 11.01 ± 22.2, control - 6.41 ± 13.31, p=0.04); ICU need (p=0.005), cancer (p=0.001), O2 requirement at discharge (p=0.001) and acute kidney injury (AKI; p=0.007). On multiple regression analysis, cancer (adjusted odds ratio (aOR) - 2.469; 95% CI 1.183-5.150, p=0.016), ICU admission (aOR - 2.446; 95% CI 1.212-4.938, p=0.013), oxygen at discharge (aOR - 2.340; 95% CI 0.971-5.640, p=0.0586) and AKI (aOR - 5.6; 95% CI 2.351- 13.370, p=0.00) only found to be significant. Conclusion Among the patients released from the hospital post-COVID-19 treatment, the following aspects oxygen requirement (2.3 times), malignancy (2.4 times), ICU admission (2.4 times), and AKI (5.6 times) are risk factors of mortality. The presence of these variables would warrant a close follow-up for these patients in order to decrease post-COVID mortality.

Neurophenotypes of COVID-19: Risk factors and recovery outcomes.

Coronavirus disease 2019 (COVID-19) infection is associated with risk of persistent neurocognitive and neuropsychiatric complications. It is unclear whether the neuropsychological manifestations of COVID-19 present as a uniform syndrome or as distinct neurophenotypes with differing risk factors and recovery outcomes. We examined post-acute neuropsychological profiles following SARS-CoV-2 infection in 205 patients recruited from inpatient and outpatient populations, using an unsupervised machine learning cluster analysis, with objective and subjective measures as input features. This resulted in three distinct post-COVID clusters. In the largest cluster (69%), cognitive functions were within normal limits, although mild subjective attention and memory complaints were reported. Vaccination was associated with membership in this "normal cognition" phenotype. Cognitive impairment was present in the remaining 31% of the sample but clustered into two differentially impaired groups. In 16% of participants, memory deficits, slowed processing speed, and fatigue were predominant. Risk factors for membership in the "memory-speed impaired" neurophenotype included anosmia and more severe COVID-19 infection. In the remaining 15% of participants, executive dysfunction was predominant. Risk factors for membership in this milder "dysexecutive" neurophenotype included disease-nonspecific factors such as neighborhood deprivation and obesity. Recovery outcomes at 6-month follow-up differed across neurophenotypes, with the normal cognition group showing improvement in verbal memory and psychomotor speed, the dysexecutive group showing improvement in cognitive flexibility, and the memory-speed impaired group showing no objective improvement and relatively worse functional outcomes compared to the other two clusters. These results indicate that there are multiple post-acute neurophenotypes of COVID-19, with different etiological pathways and recovery outcomes. This information may inform phenotype-specific approaches to treatment.

Deep Phenotyping of the Lipidomic Response in COVID and non-COVID Sepsis (preprint)

Lipids may influence cellular penetrance by pathogens and the immune response that they evoke. Here we find broad based lipidomic storm driven predominantly by secretory (s) phospholipase A2 (sPLA2) dependent eicosanoid production occurs in patients with sepsis of viral and bacterial origin and relates to disease severity in COVID-19. Elevations in the cyclooxygenase (COX) products of arachidonic acid (AA), PGD2 and PGI2, and the AA lipoxygenase (LOX) product, 12-HETE, and a reduction in the high abundance lipids, ChoE 18:3, LPC O-16:0 and PC-O-30:0 exhibit relative specificity for COVID-19 amongst such patients, correlate with the inflammatory response and link to disease severity. Linoleic acid (LA) binds directly to SARS-CoV-2 and both LA and its di-HOME products reflect disease severity in COVID-19. AA and LA metabolites and LPC-O-16:0 linked variably to the immune response. These studies yield prognostic biomarkers and therapeutic targets for patients with sepsis, including COVID-19. An interactive purpose built interactive network analysis tool was developed, allowing the community to interrogate connections across these multiomic data and generate novel hypotheses.

Utility of the Brixia CXR score, C-Reactive Protein and Absolute Neutrophil Count in Predicting the Need for Invasive Mechanical Ventilation, Length of Hospital Stay, and Mortality in Moderate to Severe COVID-19 Patients

Background: The Brixia Chest X-ray (CXR) score, C-reactive protein (CRP), and the absolute neutrophil count (ANC) have been useful to predict outcomes in Coronavirus disease 2019 (COVID-19 patients). We studied the utility of the Brixia CXR score, CRP, and ANC in predicting the outcomes in terms of the need for invasive mechanical ventilation, length of stay, and mortality in moderate-severe COVID-19 patients. Material(s) and Method(s): This was a single-centre, retrospective, study on 122 COVID-19 patients. Brixia CXR score, CRP, and ANC on admission to the hospital and the fifth day of hospital stay were noted along with the need for invasive mechanical ventilation (IMV), prolonged length of stay (LOS) >= 14 days, and mortality. Result(s): 122 patients were included for analysis. The median and interquartile range (IQR) for baseline CRP was 81.50 (39-151) mg/L and 11.0 (4-30) mg/L (p < 0.001) on the fifth day. The median and IQR for baseline Brixia score was 10.0 (7-13), and on the fifth day was 7 (4-11) (p <0.001). The receiver operating characteristic curve (ROC) showed that the baseline CRP >= 52.5mg/L predicted both the need for IMV, with an area under the curve (AUC) of 0.628, and prolonged LOS with an AUC of 0.608. The ROC curve depicted that the baseline ANC >8500/muL predicted IMV requirement with an AUC of 0.657. The fifth day CRP >= 32 mg/L, ANC >= 11,000/ muL and Brixia CXR score >= 7 predicted a higher mortality in hospitalized patients. Conclusion(s): Baseline CRP (> 52.5mg/L) predicts the need for IMV and a prolonged LOS, but not mortality. Baseline ANC (> 8500/muL) predicted the need for IMV. CRP, Brixia CXR score, and ANC on the fifth day were not useful to predict LOS or mortality, though there was a significant reduction in CRP and Brixia CXR score on the fifth day compared to baseline after treatment. The fifth day CRP >= 32 mg/L, ANC >= 11,000/ muL and Brixia CXR score >= 7 predicted a higher mortality.Copyright © 2023, College of Anaesthesiologists of Sri Lanka. All rights reserved.

Exploratory Descriptive Analysis of Smart Speaker Utilization in the Emergency Department During the COVID-19 Pandemic.

BACKGROUND: In March 2020, the U.S. Department of Health and Human Services Office for Civil Rights stated that they would use discretion when enforcing the Health Insurance Portability and Accountability Act regarding remote communication technologies that promoted telehealth delivery during the COVID-19 pandemic. This was in an effort to protect patients, clinicians, and staff. More recently, smart speakers-voice-activated, hands-free devices-are being proposed as productivity tools within hospitals. OBJECTIVE: We aimed to characterize the novel use of smart speakers in the emergency department (ED). METHODS: A retrospective observational study of Amazon Echo Show® utilization from May 2020 to October 2020 in a large academic Northeast health system ED. Voice commands and queries were classified as either patient care-related or non-patient care-related, and then further subcategorized to explore the content of given commands. RESULTS: Of 1232 commands analyzed, 200 (16.23%) were determined to be patient care-related. Of these commands, 155 (77.5%) were clinical in nature (i.e., "drop in on triage") and 23 (11.5%) were environment-enhancing commands (i.e., "play calming sounds"). Among non-patient care-related commands, 644 (62.4%) were for entertainment. Among all commands, 804 (65.3%) were during night-shift hours, which was statistically significant (p < 0.001). CONCLUSIONS: Smart speakers showed notable engagement, primarily being used for patient communication and entertainment. Future studies should examine content of patient care conversations using these devices, effects on frontline staff wellbeing, productivity, patient satisfaction, and even explore opportunities for "smart" hospital rooms.

COVID-associated rhinocerebral mucormycosis: a retrospective analysis of presentation and outcomes.

OBJECTIVES: To comprehensively analyse the disease presentation and mortality of COVID-associated rhino-orbito-cerebral mucormycosis. METHODS: A retrospective analysis of the demographics, clinical and radiographic findings was performed. A binary logistic regression analysis was performed to examine the survival of patients with mucormycosis from hypothesised predictors. RESULTS: A total of 202 patients were included in this study. Statistical significance was demonstrated in the predilection to the male gender, recent history of SARS-COV-2, history of use of corticosteroid and hyperglycemia in this cohort of CAM. The mortality rate was 18.31%. Advanced age, raised HbA1c and intra-orbital extension were found to be predictors adversely affecting survival. CONCLUSION: Early diagnosis, aggressive surgical therapy, early and appropriate medical therapy can help improve outcomes. LEVEL OF EVIDENCE: Level 4.

Pre-existing autoimmunity is associated with increased severity of COVID-19: A retrospective cohort study using data from the National COVID Cohort Collaborative (N3C).

BACKGROUND: Identifying individuals with a higher risk of developing severe COVID-19 outcomes will inform targeted or more intensive clinical monitoring and management. To date, there is mixed evidence regarding the impact of pre-existing autoimmune disease (AID) diagnosis and/or immunosuppressant (IS) exposure on developing severe COVID-19 outcomes. METHODS: A retrospective cohort of adults diagnosed with COVID-19 was created in the National COVID Cohort Collaborative enclave. Two outcomes, life-threatening disease, and hospitalization were evaluated by using logistic regression models with and without adjustment for demographics and comorbidities. RESULTS: Of the 2,453,799 adults diagnosed with COVID-19, 191,520 (7.81%) had a pre-existing AID diagnosis and 278,095 (11.33%) had a pre-existing IS exposure. Logistic regression models adjusted for demographics and comorbidities demonstrated that individuals with a pre-existing AID (OR = 1.13, 95% CI 1.09 - 1.17; P< 0.001), IS (OR= 1.27, 95% CI 1.24 - 1.30; P< 0.001), or both (OR = 1.35, 95% CI 1.29 - 1.40; P< 0.001) were more likely to have a life-threatening COVID-19 disease. These results were consistent when evaluating hospitalization. A sensitivity analysis evaluating specific IS revealed that TNF inhibitors were protective against life-threatening disease (OR = 0.80, 95% CI 0.66- 0.96; P=0.017) and hospitalization (OR = 0.80, 95% CI 0.73 - 0.89; P< 0.001). CONCLUSIONS: Patients with pre-existing AID, exposure to IS, or both are more likely to have a life-threatening disease or hospitalization. These patients may thus require tailored monitoring and preventative measures to minimize negative consequences of COVID-19.