Atomically accurate de novo design of single-domain antibodies (preprint)

Despite the central role that antibodies play in modern medicine, there is currently no way to rationally design novel antibodies to bind a specific epitope on a target. Instead, antibody discovery currently involves time-consuming immunization of an animal or library screening approaches. Here we demonstrate that a fine-tuned RFdiffusion network is capable of designing de novo antibody variable heavy chains (VHH's) that bind user-specified epitopes. We experimentally confirm binders to four disease-relevant epitopes, and the cryo-EM structure of a designed VHH bound to influenza hemagglutinin is nearly identical to the design model both in the configuration of the CDR loops and the overall binding pose.

Routes of importation and spatial dynamics of SARS-CoV-2 variants during localised interventions in Chile (preprint)

South America suffered large SARS-CoV-2 epidemics between 2020 and 2022 caused by multiple variants of interest and concern, some causing substantial morbidity and mortality. However, their transmission dynamics are poorly characterised. The epidemic situation in Chile enables us to investigate differences in the distribution and spread of variants Alpha, Gamma, Lambda, Mu and Delta. Chile implemented non-pharmaceutical interventions and an integrated genomic and epidemiological surveillance system that included airport and community surveillance to track SARS-CoV-2 variants. Here we combine viral genomic data and anonymised human mobility data from mobile phones to characterise the routes of importation of different variants into Chile, the relative contributions of airport-based importations to viral diversity versus land border crossings and test the impact of the mobility network on the diffusion of viral lineages within the country. We find that Alpha, Lambda and Mu were identified in Chile via airport surveillance six, four and five weeks ahead of their detection via community surveillance, respectively. Further, some variants that originated in South America were imported into Chile via land rather than international air travel, most notably Gamma. Different variants exhibited similar trends of viral dissemination throughout the country following their importation, and we show that the mobility network predicts the time of arrival of imported lineages to different Chilean comunas. Higher stringency of local NPIs was also associated with fewer domestic viral importations. Our results show how genomic surveillance combined with high resolution mobility data can help predict the multi-scale geographic expansion of emerging infectious diseases. Significance statementGlobal preparedness for pandemic threats requires an understanding of the global variations of spatiotemporal transmission dynamics. Regional differences are important because the local context sets the conditions for the unfolding of local epidemics, which in turn affect transmission dynamics at a broader scale. Knowledge gaps from the SARS-CoV-2 pandemic remain for regions like South America, where distinct sets of viral variants emerged and spread from late 2020 onwards, and where changes in human behaviour resulted in epidemics which differed from those observed in other regions. Our interdisciplinary analysis of the SARS-CoV-2 epidemic in Chile provides insights into the spatiotemporal trends of viral diffusion in the region which shed light on the drivers that can influence future epidemic waves and pandemics.

COVID-19 pandemic re-shaped the global dispersal of seasonal influenza viruses (preprint)

Understanding how the global dispersal patterns of seasonal influenza viruses were perturbed during and after the COVID-19 pandemic is needed to inform influenza intervention and vaccination strategies in the post-pandemic period. Although global human mobility has been identified as a key driver of influenza dispersal1, alongside climatic and evolutionary factors2,3, the impact of international travel restrictions on global influenza transmission and recovery remains unknown. Here we combine molecular, epidemiological, climatic, and international travel data within a phylodynamic framework to show that, despite human mobility remaining the principal driver of global influenza virus dissemination, the pandemics onset led to a shift in the international population structure and migration network of seasonal influenza lineages. We find that South Asia and Africa played important roles as exporters and phylogenetic trunk locations of influenza in 2020 and 2021, and we highlight the association between population movement, antigenic drift and persistence during the intensive non-pharmaceutical interventions (NPIs) phase. The influenza B/Yamagata lineage disappeared in a context of reduced relative genetic diversity, moderate lineage turnover, and lower positive selection pressure. Our results demonstrate that mobility perturbations reshaped the global dispersal dynamics of influenza viruses, with potential implications for vaccine design and genomic surveillance programmes. As the risk of future pandemics persists, our study provides an opportunity to assess the impact of NPIs during the pandemic on respiratory infectious diseases beyond the interplay between SARS-CoV-2 and influenza viruses.

Understanding COVID-19 testing behaviour in England through a sociodemographic lens (preprint)

BackgroundUnderstanding underlying mechanisms of heterogeneity in test-seeking and reporting behaviour can help to protect the vulnerable and guide equity-driven interventions. Using COVID-19 testing data for England and data from community prevalence surveillance surveys (REACT-1 and ONS-CIS) from October 2020 to March 2022, we investigated the relationship between sociodemographic factors and testing behaviours in England. MethodsWe used mass testing data for lateral flow device (LFD; data for 290 million tests performed and reported) and polymerase chain reaction (PCR) (data for 107 million tests performed and returned from the laboratory) tests made available for the general public, provided by date, self-reported age and ethnicity at lower tier local authority (LTLA) level. Using a mechanistic causal model to debias the PCR testing data, we obtained estimates of weekly SARS-CoV-2 prevalence by self-reported ethnic groups and age groups for LTLAs in England. This approach to debiasing the PCR (or LFD) testing data also estimated a testing bias parameter defined as the odds of testing in infected versus not infected individuals, which would be close to zero if the likelihood of test seeking (or seeking and reporting) was the same regardless of infection status. Using confirmatory PCR data, we estimated false positivity rates, sensitivity, specificity, and the rate of decline in detection probability by PCR by sociodemographic groups. We also estimated the daily incidence allowing us to determine the fraction of cases captured by the testing programme. FindingsFrom March 2021 onwards, individuals in the most deprived regions reported approximately half as many LFD tests per-capita than those in the least deprived areas (Median ratio [Inter quartile range, IQR]: 0{middle dot}50 [0{middle dot}44, 0{middle dot}54]). During October 2020 - June 2021, PCR testing patterns were in the opposite direction (Median ratio [IQR]: 1{middle dot}8 [1{middle dot}7, 1{middle dot}9]). Infection prevalences in Asian or Asian British communities were considerably higher than those of other ethnic groups during the Alpha and Omicron BA.1 waves. Our estimates indicate that the England COVID-19 testing program detected 26% - 40% of all cases (including asymptomatic cases) over the study period with no consistent differences by deprivation levels or ethnic groups. PCR testing biases were generally higher than for LFDs, which was in line with the general policy of symptomatic and asymptomatic use of these tests. During the invasion phases of the Delta and Omicron variants of concern, the PCR testing bias in the most deprived populations was roughly double (ratio: 2{middle dot}2 and 2{middle dot}7 respectively) that in the least. We also determined that ethnic minorities and older individuals were less likely to use confirmatory PCR tests through most of the pandemic and that there was possibly a longer delay in reporting a positive LFD test in the Black populations. InterpretationDifferences in testing behaviours across sociodemographic groups may be reflective of the relatively higher costs of self-isolation to vulnerable populations, differences in test accessibility, digital literacy, and differing perception about the utility of tests and risks posed by infection. Our work shows how mass testing data can be used in conjunction with surveillance surveys to identify gaps in the uptake of public health interventions at fine scale levels and by sociodemographic groups. It provides a framework for monitoring local interventions and yields valuable lessons for policy makers in ensuring an equitable response to future pandemics. FundingUK Health Security Agency.

Dispersal Patterns and Influence of Air Travel During the Global Expansion of SARS-CoV-2 Variants of Concern

The Alpha, Beta and Gamma SARS-CoV-2 Variants of Concern (VOCs) co-circulated globally during 2020-21, fueling waves of infections. They were displaced by Delta during a third wave worldwide in 2021, in turn displaced by Omicron in late 2021. In this study, we use phylogenetic and phylogeographic methods to reconstruct the dispersal patterns of VOCs worldwide. We find that source-sink dynamics varied substantially by VOC, and identify countries that acted as global and regional hubs of dissemination. We demonstrate a declining role of presumed origin countries of VOCs to their global dispersal, estimating that India contributed <15% of Delta exports and South Africa <1-2% of Omicron dispersal. We estimate that >80 countries had received introductions of Omicron within 100 days of emergence, associated with accelerating passenger air travel and higher transmissibility. Our study highlights the rapid dispersal of highly transmissible variants with implications for genomic surveillance along the hierarchical airline network. Graphical Data analysis clarifies that dispersal of SARS-CoV-2 variants from their sites of initial detection was related to the amount of global air travel at the time of the variant's emergence, and that travel volume through "hub” sites distinct from the site of emergence was a key driver of variant spread.

Autoantigen profiling reveals a shared post-COVID signature in fully recovered and long COVID patients.

Some individuals do not return to baseline health following SARS-CoV-2 infection, leading to a condition known as long COVID. The underlying pathophysiology of long COVID remains unknown. Given that autoantibodies have been found to play a role in severity of SARS-CoV-2 infection and certain other post-COVID sequelae, their potential role in long COVID is important to investigate. Here, we apply a well-established, unbiased, proteome-wide autoantibody detection technology (T7 phage-display assay with immunoprecipitation and next-generation sequencing, PhIP-Seq) to a robustly phenotyped cohort of 121 individuals with long COVID, 64 individuals with prior COVID-19 who reported full recovery, and 57 pre-COVID controls. While a distinct autoreactive signature was detected that separated individuals with prior SARS-CoV-2 infection from those never exposed to SARS-CoV-2, we did not detect patterns of autoreactivity that separated individuals with long COVID from individuals fully recovered from COVID-19. These data suggest that there are robust alterations in autoreactive antibody profiles due to infection; however, no association of autoreactive antibodies and long COVID was apparent by this assay.

Good or best practice statements: proposal for the operationalisation and implementation of GRADE guidance.

An evidence-based approach is considered the gold standard for health decision-making. Sometimes, a guideline panel might judge the certainty that the desirable effects of an intervention clearly outweigh its undesirable effects as high, but the body of supportive evidence is indirect. In such cases, the application of the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach for grading the strength of recommendations is inappropriate. Instead, the GRADE Working Group has recommended developing ungraded best or good practice statement (GPS) and developed guidance under which circumsances they would be appropriate.Through an evaluation of COVID-1- related recommendations on the eCOVID Recommendation Map (COVID-19.recmap.org), we found that recommendations qualifying a GPS were widespread. However, guideline developers failed to label them as GPS or transparently report justifications for their development. We identified ways to improve and facilitate the operationalisation and implementation of the GRADE guidance for GPS.Herein, we propose a structured process for the development of GPSs that includes applying a sequential order for the GRADE guidance for developing GPS. This operationalisation considers relevant evidence-to-decision criteria when assessing the net consequences of implementing the statement, and reporting information supporting judgments for each criterion. We also propose a standardised table to facilitate the identification of GPS and reporting of their development. This operationalised guidance, if endorsed by guideline developers, may palliate some of the shortcomings identified. Our proposal may also inform future updates of the GRADE guidance for GPS.

SARS-CoV-2 Enters Human Leydig Cells and Affects Testosterone Production In Vitro.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a SARS-like coronavirus, continues to produce mounting infections and fatalities all over the world. Recent data point to SARS-CoV-2 viral infections in the human testis. As low testosterone levels are associated with SARS-CoV-2 viral infections in males and human Leydig cells are the main source of testosterone, we hypothesized that SARS-CoV-2 could infect human Leydig cells and impair their function. We successfully detected SARS-CoV-2 nucleocapsid in testicular Leydig cells of SARS-CoV-2-infected hamsters, providing evidence that Leydig cells can be infected with SARS-CoV-2. We then employed human Leydig-like cells (hLLCs) to show that the SARS-CoV-2 receptor angiotensin-converting enzyme 2 is highly expressed in hLLCs. Using a cell binding assay and a SARS-CoV-2 spike-pseudotyped viral vector (SARS-CoV-2 spike pseudovector), we showed that SARS-CoV-2 could enter hLLCs and increase testosterone production by hLLCs. We further combined the SARS-CoV-2 spike pseudovector system with pseudovector-based inhibition assays to show that SARS-CoV-2 enters hLLCs through pathways distinct from those of monkey kidney Vero E6 cells, a typical model used to study SARS-CoV-2 entry mechanisms. We finally revealed that neuropilin-1 and cathepsin B/L are expressed in hLLCs and human testes, raising the possibility that SARS-CoV-2 may enter hLLCs through these receptors or proteases. In conclusion, our study shows that SARS-CoV-2 can enter hLLCs through a distinct pathway and alter testosterone production.

No One Left Behind: How the Salvation Army's Bridging the Gap Program Successfully Innovated and Adapted Programming During the COVID-19 Pandemic

Bridging the Gap (BTG) is a program created by the Salvation Army in 1996 that provides programming and support services to court-involved youth ages 12-17. The program was designed for in-person delivery and has been done that way since inception. The COVID-19 pandemic forced the program to pivot to a synchronous, online format and also created new challenges for participants and their families. The BTG facilitators were not only able to shift the program to a synchronous, online format in a matter of weeks, but they also remained in close contact with their participants and ensured that they and their families had all needs met. The current article discusses how the BTG program adjusted delivery methods, provided services and support to its participants and their families, and their protocols for a safe reopening. How the BTG program pivoted and were able to successfully facilitate quality programing and support their participants during the pandemic can serve as a roadmap for other programs.

Mitigation of trauma utilizing exercise, meditation, and yoga by individuals working in healthcare settings during the COVID-19 pandemic

Infectious disease outbreaks often place healthcare workers in life-threatening situations. The COVID-19 Pandemic has dramatically impacted healthcare workers psychologically. Post-Traumatic Stress Disorder (PTSD) symptomatology is often reported (Brooks et al., 2020). However, it has been documented (Hall et al., 2020;Lang et al., 2020;Motta, 2020;Van Der Kolk et al., 2014) that exercise, meditation, and yoga can serve to mitigate the level of perceived trauma in individuals exposed to significantly distressing events. The objective of this study was to examine the level of trauma experienced by healthcare workers employed during the COVID-19 Pandemic, and the extent to which protective factors (i.e., exercise, meditation, and yoga) may serve to mitigate their levels of perceived trauma. It was hypothesized that healthcare workers who exercise, meditate, and engage in yoga would report lower levels of PTSD symptomatology on two measures known to assess it, namely the PCL-5 and the IES-R (PCL-5;Weathers et al., 2013;IES-R;Weiss & Marmar, 1997), as per prior research findings. Participants consisted of 166 United States healthcare workers employed since March 2020. Participants were asked to complete an Informed Consent, Demographic Questionnaire, and the two PTSD measures. Results suggested that healthcare workers who engaged in exercise demonstrated significantly higher scores on the PCL-5. Participants who engaged in meditation and yoga demonstrated significantly higher scores on both the PCL-5 and the IES-R. Additionally, the frequency of engagement in meditation and yoga was associated with significantly higher scores on both the PCL-5 and the IES-R. Further, engagement in more than one of these activities was associated with significantly higher scores on both measures. These results are contrary to previous research findings. Possible explanations for these discrepancies were offered. Additional analyses suggested that participants currently in psychotherapy had significantly higher scores on both the PCL-5 and the IES-R. Those having more exposure to COVID-19 patients demonstrated significantly higher IES-R scores. Those who contracted COVID-19 had significantly higher PCL-5 and IES-R scores. Those who personally knew someone hospitalized with COVID-19 had significantly higher scores on both measures. This study's strengths and limitations were discussed. Recommendations for future research were offered. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Factors associated with resistance to SARS-CoV-2 infection discovered using large-scale medical record data and machine learning.

There have been over 621 million cases of COVID-19 worldwide with over 6.5 million deaths. Despite the high secondary attack rate of COVID-19 in shared households, some exposed individuals do not contract the virus. In addition, little is known about whether the occurrence of COVID-19 resistance differs among people by health characteristics as stored in the electronic health records (EHR). In this retrospective analysis, we develop a statistical model to predict COVID-19 resistance in 8,536 individuals with prior COVID-19 exposure using demographics, diagnostic codes, outpatient medication orders, and count of Elixhauser comorbidities in EHR data from the COVID-19 Precision Medicine Platform Registry. Cluster analyses identified 5 patterns of diagnostic codes that distinguished resistant from non-resistant patients in our study population. In addition, our models showed modest performance in predicting COVID-19 resistance (best performing model AUROC = 0.61). Monte Carlo simulations conducted indicated that the AUROC results are statistically significant (p < 0.001) for the testing set. We hope to validate the features found to be associated with resistance/non-resistance through more advanced association studies.

Tracking COVID-19 in the United States With Surveillance of Aggregate Cases and Deaths.

Early during the COVID-19 pandemic, the Centers for Disease Control and Prevention (CDC) leveraged an existing surveillance system infrastructure to monitor COVID-19 cases and deaths in the United States. Given the time needed to report individual-level (also called line-level) COVID-19 case and death data containing detailed information from individual case reports, CDC designed and implemented a new aggregate case surveillance system to inform emergency response decisions more efficiently, with timelier indicators of emerging areas of concern. We describe the processes implemented by CDC to operationalize this novel, multifaceted aggregate surveillance system for collecting COVID-19 case and death data to track the spread and impact of the SARS-CoV-2 virus at national, state, and county levels. We also review the processes established to acquire, process, and validate the aggregate number of cases and deaths due to COVID-19 in the United States at the county and jurisdiction levels during the pandemic. These processes include time-saving tools and strategies implemented to collect and validate authoritative COVID-19 case and death data from jurisdictions, such as web scraping to automate data collection and algorithms to identify and correct data anomalies. This topical review highlights the need to prepare for future emergencies, such as novel disease outbreaks, by having an event-agnostic aggregate surveillance system infrastructure in place to supplement line-level case reporting for near-real-time situational awareness and timely data.

Clinical Characteristics and Cause of Death Among Hospitalized Decedents With Cancer and COVID-19.

There is scant information on the clinical progression, end-of-life decisions, and cause of death of patients with cancer diagnosed with COVID-19. Therefore, we conducted a case series of patients admitted to a comprehensive cancer center who did not survive their hospitalization. To determine the cause of death, 3 board-certified intensivists reviewed the electronic medical records. Concordance regarding cause of death was calculated. Discrepancies were resolved through a joint case-by-case review and discussion among the 3 reviewers. During the study period, 551 patients with cancer and COVID-19 were admitted to a dedicated specialty unit; among them, 61 (11.6%) were nonsurvivors. Among nonsurvivors, 31 (51%) patients had hematologic cancers, and 29 (48%) had undergone cancer-directed chemotherapy within 3 months before admission. The median time to death was 15 days (95% confidence interval [CI], 11.8 to 18.2). There were no differences in time to death by cancer category or cancer treatment intent. The majority of decedents (84%) had full code status at admission; however, 53 (87%) had do-not-resuscitate orders at the time of death. Most deaths were deemed to be COVID-19 related (88.5%). The concordance between the reviewers for the cause of death was 78.7%. In contrast to the belief that COVID-19 decedents die because of their comorbidities, in our study only 1 of every 10 patients died of cancer-related causes. Full-scale interventions were offered to all patients irrespective of oncologic treatment intent. However, most decedents in this population preferred care with nonresuscitative measures rather than full support at the end of life.

A 2-Gene Host Signature for Improved Accuracy of COVID-19 Diagnosis Agnostic to Viral Variants.

The continued emergence of SARS-CoV-2 variants is one of several factors that may cause false-negative viral PCR test results. Such tests are also susceptible to false-positive results due to trace contamination from high viral titer samples. Host immune response markers provide an orthogonal indication of infection that can mitigate these concerns when combined with direct viral detection. Here, we leverage nasopharyngeal swab RNA-seq data from patients with COVID-19, other viral acute respiratory illnesses, and nonviral conditions (n = 318) to develop support vector machine classifiers that rely on a parsimonious 2-gene host signature to diagnose COVID-19. We find that optimal classifiers include an interferon-stimulated gene that is strongly induced in COVID-19 compared with nonviral conditions, such as IFI6, and a second immune-response gene that is more strongly induced in other viral infections, such as GBP5. The IFI6+GBP5 classifier achieves an area under the receiver operating characteristic curve (AUC) greater than 0.9 when evaluated on an independent RNA-seq cohort (n = 553). We further provide proof-of-concept demonstration that the classifier can be implemented in a clinically relevant RT-qPCR assay. Finally, we show that its performance is robust across common SARS-CoV-2 variants and is unaffected by cross-contamination, demonstrating its utility for improved accuracy of COVID-19 diagnostics. IMPORTANCE In this work, we study upper respiratory tract gene expression to develop and validate a 2-gene host-based COVID-19 diagnostic classifier and then demonstrate its implementation in a clinically practical qPCR assay. We find that the host classifier has utility for mitigating false-negative results, for example due to SARS-CoV-2 variants harboring mutations at primer target sites, and for mitigating false-positive viral PCR results due to laboratory cross-contamination. Both types of error carry serious consequences of either unrecognized viral transmission or unnecessary isolation and contact tracing. This work is directly relevant to the ongoing COVID-19 pandemic given the continued emergence of viral variants and the continued challenges of false-positive PCR assays. It also suggests the feasibility of pan-respiratory virus host-based diagnostics that would have value in congregate settings, such as hospitals and nursing homes, where unrecognized respiratory viral transmission is of particular concern.

Low Prevalence of Interferon-α Autoantibodies in People Experiencing Long COVID Symptoms.

Interferon (IFN)-specific autoantibodies have been implicated in severe COVID-19 and have been proposed as a potential driver of the persistent symptoms characterizing Long COVID, a type of post-acute sequelae of SARS-CoV-2 infection (PASC). We report than only two of 215 SARS-CoV-2 convalescent participants tested over 394 timepoints, including 121 people experiencing Long COVID symptoms, had detectable IFN-α2 antibodies. Both had been hospitalized during the acute phase of the infection. These data suggest that persistent anti-IFN antibodies, although a potential driver of severe COVID-19, are unlikely to contribute to Long COVID symptoms in the post-acute phase of the infection.

Benefits of near-universal vaccination and treatment access to manage COVID-19 burden in the United States (preprint)

Background: As we enter the fourth year of the COVID-19 pandemic, SARS-CoV-2 infections still cause high morbidity and mortality in the United States. During 2020-2022, COVID-19 was one of the leading causes of death in the United States and by far the leading cause among infectious diseases. Vaccination uptake remains low despite this being an effective burden reducing intervention. The development of COVID-19 therapeutics provides hope for mitigating severe clinical outcomes. This modeling study examines combined strategies of vaccination and treatment to reduce the burden of COVID-19 epidemics over the next decade. Methods: We use a validated mathematical model to evaluate the reduction of incident cases, hospitalized cases, and deaths in the United States through 2033 under various levels of vaccination and treatment coverage. We assume that future seasonal transmission patterns for COVID-19 will be similar to those of influenza virus. We account for the waning of infection-induced immunity and vaccine-induced immunity in a future with stable COVID-19 dynamics. Due to uncertainty in the duration of immunity following vaccination or infection, we consider two exponentially-distributed waning rates, with means of 365 days (one year) and 548 days (1.5 years). We also consider treatment failure, including rebound frequency, as a possible treatment outcome. Results: As expected, universal vaccination is projected to eliminate transmission and mortality. Under current treatment coverage (13.7%) and vaccination coverage (49%), averages of 89,000 annual deaths (548-day waning) and 120,000 annual deaths (365-day waning) are expected by the end of this decade. Annual mortality in the United States can be reduced below 50,000 per year with >81% annual vaccination coverage, and below 10,000 annual deaths with >84% annual vaccination coverage. Universal treatment reduces hospitalizations by 88% and deaths by 93% under current vaccination coverage. A reduction in vaccination coverage requires a comparatively larger increase in treatment coverage in order for hospitalization and mortality levels to remain unchanged. Conclusions: Adopting universal vaccination and universal treatment goals in the United States will likely lead to a COVID-19 mortality burden below 50,000 deaths per year, a burden comparable to that of influenza virus.

Autoantigen profiling reveals a shared post-COVID signature in fully recovered and Long COVID patients (preprint)

Some individuals do not return to baseline health following SARS-CoV-2 infection, leading to a condition known as Long COVID. The underlying pathophysiology of Long COVID remains unknown. Given that autoantibodies have been found to play a role in severity of COVID infection and certain other post-COVID sequelae, their potential role in Long COVID is important to investigate. Here we apply a well-established, unbiased, proteome-wide autoantibody detection technology (PhIP-Seq) to a robustly phenotyped cohort of 121 individuals with Long COVID, 64 individuals with prior COVID-19 who reported full recovery, and 57 pre-COVID controls. While a distinct autoreactive signature was detected which separates individuals with prior COVID infection from those never exposed to COVID, we did not detect patterns of autoreactivity that separate individuals with Long COVID relative to individuals fully recovered from SARS-CoV-2 infection. These data suggest that there are robust alterations in autoreactive antibody profiles due to infection; however, no association of autoreactive antibodies and Long COVID was apparent by this assay.

Knowledge mobilization activities to support decision-making by youth, parents, and adults using a systematic and living map of evidence and recommendations on COVID-19: protocol for three randomized controlled trials and qualitative user-experience studies.

INTRODUCTION: The COVID-19 pandemic underlined that guidelines and recommendations must be made more accessible and more understandable to the general public to improve health outcomes. The objective of this study is to evaluate, quantify, and compare the public's understanding, usability, satisfaction, intention to implement, and preference for different ways of presenting COVID-19 health recommendations derived from the COVID-19 Living Map of Recommendations and Gateway to Contextualization (RecMap). METHODS AND ANALYSIS: This is a protocol for a multi-method study. Through an online survey, we will conduct pragmatic allocation-concealed, blinded superiority randomized controlled trials (RCTs) in three populations to test alternative formats of presenting health recommendations: adults, parents, and youth, with at least 240 participants in each population. Prior to initiating the RCT, our interventions will have been refined with relevant stakeholder input. The intervention arm will receive a plain language recommendation (PLR) format while the control arm will receive the corresponding original recommendation format as originally published by the guideline organizations (standard language version). Our primary outcome is understanding, and our secondary outcomes are accessibility and usability, satisfaction, intended behavior, and preference for the recommendation formats. Each population's results will be analyzed separately. However, we are planning a meta-analysis of the results across populations. At the end of each survey, participants will be invited to participate in an optional one-on-one, virtual semi-structured interview to explore their user experience. All interviews will be transcribed and analyzed using the principles of thematic analysis and a hybrid inductive and deductive approach. ETHICS AND DISSEMINATION: Through Clinical Trials Ontario, the Hamilton Integrated Research Ethics Board has reviewed and approved this protocol (Project ID: 3856). The University of Alberta has approved the parent portion of the trial (Project ID:00114894). Findings from this study will be disseminated through open-access publications in peer-reviewed journals and using social media. TRIAL REGISTRATION: Clinicaltrials.gov NCT05358990 . Registered on May 3, 2022.

Prevalence of type-1 interferon autoantibodies in adults with non-COVID-19 acute respiratory failure.

Auto-antibodies (Abs) to type I interferons (IFNs) are found in up to 25% of patients with severe COVID-19, and are implicated in disease pathogenesis. It has remained unknown, however, whether type I IFN auto-Abs are unique to COVID-19, or are also found in other types of severe respiratory illnesses. To address this, we studied a prospective cohort of 284 adults with acute respiratory failure due to causes other than COVID-19. We measured type I IFN auto-Abs by radio ligand binding assay and screened for respiratory viruses using clinical PCR and metagenomic sequencing. Three patients (1.1%) tested positive for type I IFN auto-Abs, and each had a different underlying clinical presentation. Of the 35 patients found to have viral infections, only one patient tested positive for type I IFN auto-Abs. Together, our data suggest that type I IFN auto-Abs are uncommon in critically ill patients with acute respiratory failure due to causes other than COVID-19.