Electronic Clinical Decision Support Tools: Strategies to Improve the Management of Lower Respiratory Tract Infections in Low-Resource Settings.

Lower respiratory tract infection (LRTI) is a common reason for hospitalization and antibacterial use globally. There is considerable overlap in the clinical presentation of bacterial and viral LRTIs. Low- or middle-income countries (LMICs) face the dual challenge of appropriately targeting antibacterials for bacterial LRTI while reducing inappropriate antibacterials for viral LRTI. We propose a framework by which an electronic clinical decision support tool (eCDST) for diagnosing LRTI and reducing unnecessary antibacterial use may be developed, validated, and prospectively evaluated in an LMIC. The developed tool would be data driven, low-cost, feasible in the local setting, adaptable based on resource availability, and updated in real time, with prospective assessment to identify its clinical impact. We draw upon our team's recent experience developing an eCDST for LRTI management in Sri Lanka. Publicly sharing such processes and data is valuable, such that we can collectively improve clinical care in LMICs and other settings.

Integrated epigenomic exposure signature discovery.

Aim: The epigenome influences gene regulation and phenotypes in response to exposures. Epigenome assessment can determine exposure history aiding in diagnosis.Materials & methods: Here we developed and implemented a machine learning algorithm, the exposure signature discovery algorithm (ESDA), to identify the most important features present in multiple epigenomic and transcriptomic datasets to produce an integrated exposure signature (ES).Results: Signatures were developed for seven exposures including Staphylococcus aureus, human immunodeficiency virus, SARS-CoV-2, influenza A (H3N2) virus and Bacillus anthracis vaccinations. ESs differed in the assays and features selected and predictive value.Conclusion: Integrated ESs can potentially be utilized for diagnosis or forensic attribution. The ESDA identifies the most distinguishing features enabling diagnostic panel development for future precision health deployment.

This article introduces ESDA, a new analytic tool for integrating multiple data types to identify the most distinguishing features following an exposure. Using the ESDA, we were able to identify signatures of infectious diseases. The results of the study indicate that integration of multiple types of large datasets can be used to identify distinguishing features for infectious diseases. Understanding the changes from different exposures will enable development of diagnostic tests for infectious diseases that target responses from the patient. Using the ESDA, we will be able to build a database of human response signatures to different infections and simplify diagnostic testing in the future.

Host response to influenza infections in human blood: association of influenza severity with host genetics and transcriptomic response.

Introduction: Influenza virus infections are a major global health problem. Influenza can result in mild/moderate disease or progress to more severe disease, leading to high morbidity and mortality. Severity is thought to be primarily driven by immunopathology, but predicting which individuals are at a higher risk of being hospitalized warrants investigation into host genetics and the molecular signatures of the host response during influenza infections. Methods: Here, we performed transcriptome and genotype analysis in healthy controls and patients exhibiting mild/moderate or severe influenza (ICU patients). A unique aspect of our study was the genotyping of all participants, which allowed us to assign ethnicities based on genetic variation and assess whether the variation was correlated with expression levels. Results: We identified 169 differentially expressed genes and related molecular pathways between patients in the ICU and those who were not in the ICU. The transcriptome/genotype association analysis identified 871 genes associated to a genetic variant and 39 genes distinct between African-Americans and Caucasians. We also investigated the effects of age and sex and found only a few discernible gene effects in our cohort. Discussion: Together, our results highlight select risk factors that may contribute to an increased risk of ICU admission for influenza-infected patients. This should help to develop better diagnostic tools based on molecular signatures, in addition to a better understanding of the biological processes in the host response to influenza.

Predictive signature of murine and human host response to typical and atypical pneumonia.

BACKGROUND: Pneumonia due to typical bacterial, atypical bacterial and viral pathogens can be difficult to clinically differentiate. Host response-based diagnostics are emerging as a complementary diagnostic strategy to pathogen detection. METHODS: We used murine models of typical bacterial, atypical bacterial and viral pneumonia to develop diagnostic signatures and understand the host's response to these types of infections. Mice were intranasally inoculated with Streptococcus pneumoniae, Mycoplasma pneumoniae, influenza or saline as a control. Peripheral blood gene expression analysis was performed at multiple time points. Differentially expressed genes were used to perform gene set enrichment analysis and generate diagnostic signatures. These murine-derived signatures were externally validated in silico using human gene expression data. The response to S. pneumoniae was the most rapid and robust. RESULTS: Mice infected with M. pneumoniae had a delayed response more similar to influenza-infected animals. Diagnostic signatures for the three types of infection had 0.94-1.00 area under the receiver operator curve (auROC). Validation in five human gene expression datasets revealed auROC of 0.82-0.96. DISCUSSION: This study identified discrete host responses to typical bacterial, atypical bacterial and viral aetiologies of pneumonia in mice. These signatures validated well in humans, highlighting the conserved nature of the host response to these pathogen classes.

Prevalence and predictors of antibiotic prescription among patients hospitalized with viral lower respiratory tract infections in Southern Province, Sri Lanka.

BACKGROUND: Antimicrobial overprescription is common for lower respiratory tract infections (LRTI), as viral and bacterial infections generally present with similar clinical features. Overprescription is associated with downstream antimicrobial resistance. This study aims to identify the prevalence and predictors of antibiotic prescription among patients hospitalized with viral LRTI. METHODS: A prospective cohort study was conducted among patients aged ≥1 year hospitalized with viral LRTI in a tertiary care hospital in Southern Province, Sri Lanka from 2018-2021. Demographic, clinical, and laboratory data were recorded. Nasopharyngeal and blood samples were collected for multiplex polymerase chain reaction testing for 21 respiratory pathogens and procalcitonin (PCT) detection, respectively. Demographic and clinical features associated with antibiotic prescription were identified using Chi Square and t-tests; significant variables (p<0.05) were further included in multivariable logistic regression models. The potential impact of biomarker testing on antibiotic prescription was simulated using standard c-reactive protein (CRP) and PCT cut-offs. RESULTS: Of 1217 patients enrolled, 438 (36.0%) had ≥1 respiratory virus detected, with 48.4% of these patients being male and 30.8% children. Influenza A (39.3%) and human rhinovirus/ enterovirus (28.3%) were most commonly detected. A total of 114 (84.4%) children and 266 (87.8%) adults with respiratory viruses were treated with antibiotics. Among children, neutrophil percentage (median 63.6% vs 47.6%, p = 0.04) was positively associated with antibiotic prescription. Among adults, headache (60.6% vs 35.1%, p = 0.003), crepitations/crackles (55.3% vs 21.6%, p<0.001), rhonchi/wheezing (42.9% vs 18.9%, p = 0.005), and chest x-ray opacities (27.4% vs 8.1%, p = 0.01) were associated with antibiotic prescription. Access to CRP and procalcitonin test results could have potentially decreased inappropriate antibiotic prescription in this study by 89.5% and 83.3%, respectively. CONCLUSIONS: High proportions of viral detection and antibiotic prescription were observed among a large inpatient cohort with LRTI. Increased access to point-of-care biomarker testing may improve antimicrobial prescription.

Sepsis endotypes identified by host gene expression across global cohorts.

BACKGROUND: Sepsis from infection is a global health priority and clinical trials have failed to deliver effective therapeutic interventions. To address complicating heterogeneity in sepsis pathobiology, and improve outcomes, promising precision medicine approaches are helping identify disease endotypes, however, they require a more complete definition of sepsis subgroups. METHODS: Here, we use RNA sequencing from peripheral blood to interrogate the host response to sepsis from participants in a global observational study carried out in West Africa, Southeast Asia, and North America (N = 494). RESULTS: We identify four sepsis subtypes differentiated by 28-day mortality. A low mortality immunocompetent group is specified by features that describe the adaptive immune system. In contrast, the three high mortality groups show elevated clinical severity consistent with multiple organ dysfunction. The immunosuppressed group members show signs of a dysfunctional immune response, the acute-inflammation group is set apart by molecular features of the innate immune response, while the immunometabolic group is characterized by metabolic pathways such as heme biosynthesis. CONCLUSIONS: Our analysis reveals details of molecular endotypes in sepsis that support immunotherapeutic interventions and identifies biomarkers that predict outcomes in these groups.

Sepsis is a life-threatening multi-organ failure caused by the body's immune response to infection. Clinical symptoms of sepsis vary from one person to another likely due to differences in host factors, infecting pathogen, and comorbidities. This difference in clinical symptoms may contribute to the lack of effective interventions for sepsis. Therefore, approaches tailored to targeting groups of patients who present similarly are of great interest. This study analysed a large group of sepsis patients with diverse symptoms using laboratory markers and mathematical analysis. We report four patient groups that differ by risk of death and immune response profile. Targeting these defined groups with tailored interventions presents an exciting opportunity to improve the health outcomes of patients with sepsis.

Implementation of a Prospective Index-Cluster Sampling Strategy for the Detection of Presymptomatic Viral Respiratory Infection in Undergraduate Students.

Background: Index-cluster studies may help characterize the spread of communicable infections in the presymptomatic state. We describe a prospective index-cluster sampling strategy (ICSS) to detect presymptomatic respiratory viral illness and its implementation in a college population. Methods: We enrolled an annual cohort of first-year undergraduates who completed daily electronic symptom diaries to identify index cases (ICs) with respiratory illness. Investigators then selected 5-10 potentially exposed, asymptomatic close contacts (CCs) who were geographically co-located to follow for infections. Symptoms and nasopharyngeal samples were collected for 5 days. Logistic regression model-based predictions for proportions of self-reported illness were compared graphically for the whole cohort sampling group and the CC group. Results: We enrolled 1379 participants between 2009 and 2015, including 288 ICs and 882 CCs. The median number of CCs per IC was 6 (interquartile range, 3-8). Among the 882 CCs, 111 (13%) developed acute respiratory illnesses. Viral etiology testing in 246 ICs (85%) and 719 CCs (82%) identified a pathogen in 57% of ICs and 15% of CCs. Among those with detectable virus, rhinovirus was the most common (IC: 18%; CC: 6%) followed by coxsackievirus/echovirus (IC: 11%; CC: 4%). Among 106 CCs with a detected virus, only 18% had the same virus as their associated IC. Graphically, CCs did not have a higher frequency of self-reported illness relative to the whole cohort sampling group. Conclusions: Establishing clusters by geographic proximity did not enrich for cases of viral transmission, suggesting that ICSS may be a less effective strategy to detect spread of respiratory infection.

T-cell count and T-cell telomere length in patients with severe COVID-19.

Lymphocyte telomere length (TL) is highly variable and shortens with age. Short telomeres may impede TL-dependent T-cell clonal expansion with viral infection. As SARS-CoV-2 infection can induce prolonged and severe T-cell lymphopenia, infected adults, and particularly older adults with short telomeres, may display severe T-cell lymphopenia. To examine the relationship between T-cell TL parameters and T-cell counts, we studied 40 patients hospitalized with severe COVID-19. T-cells were isolated from lymphocytes, counted using flow cytometry, and their TL parameters were measured using the Telomere Shortest Length Assay. The cohort (median age = 62 years, 27% female) was racially and ethnically diverse (33% White, 35% Black, and 33% Other). On intensive care unit study day 1, T-cell count (mean=1.03 x109/L) was inversely related to age (p=0.007) and higher in females than males (p=0.025). Mean TL was 3.88 kilobases (kb), and 45.3% of telomeres were shorter than 3 kb. Using multiple regression analysis and adjusting for age and sex, T-cell count decreased with increased proportion of T-cell telomeres shorter than 3 kb (p=0.033) and increased with mean TL (p=0.052). Our findings suggest an association between the buildup of short telomeres within T-cells and explain in part reduced peripheral blood T-cell counts in patients with severe COVID-19. Shortened T-cell telomeres may be a risk factor for COVID-19-associated T-cell lymphopenia.

Epigenetic and transcriptional responses in circulating leukocytes are associated with future decompensation during SARS-CoV-2 infection.

To elucidate host response elements that define impending decompensation during SARS-CoV-2 infection, we enrolled subjects hospitalized with COVID-19 who were matched for disease severity and comorbidities at the time of admission. We performed combined single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) on peripheral blood mononuclear cells (PBMCs) at admission and compared subjects who improved from their moderate disease with those who later clinically decompensated and required invasive mechanical ventilation or died. Chromatin accessibility and transcriptomic immune profiles were markedly altered between the two groups, with strong signals in CD4+ T cells, inflammatory T cells, dendritic cells, and NK cells. Multiomic signature scores at admission were tightly associated with future clinical deterioration (auROC 1.0). Epigenetic and transcriptional changes in PBMCs reveal early, broad immune dysregulation before typical clinical signs of decompensation are apparent and thus may act as biomarkers to predict future severity in COVID-19.

Host-response transcriptional biomarkers accurately discriminate bacterial and viral infections of global relevance.

Diagnostic limitations challenge management of clinically indistinguishable acute infectious illness globally. Gene expression classification models show great promise distinguishing causes of fever. We generated transcriptional data for a 294-participant (USA, Sri Lanka) discovery cohort with adjudicated viral or bacterial infections of diverse etiology or non-infectious disease mimics. We then derived and cross-validated gene expression classifiers including: 1) a single model to distinguish bacterial vs. viral (Global Fever-Bacterial/Viral [GF-B/V]) and 2) a two-model system to discriminate bacterial and viral in the context of noninfection (Global Fever-Bacterial/Viral/Non-infectious [GF-B/V/N]). We then translated to a multiplex RT-PCR assay and independent validation involved 101 participants (USA, Sri Lanka, Australia, Cambodia, Tanzania). The GF-B/V model discriminated bacterial from viral infection in the discovery cohort an area under the receiver operator curve (AUROC) of 0.93. Validation in an independent cohort demonstrated the GF-B/V model had an AUROC of 0.84 (95% CI 0.76-0.90) with overall accuracy of 81.6% (95% CI 72.7-88.5). Performance did not vary with age, demographics, or site. Host transcriptional response diagnostics distinguish bacterial and viral illness across global sites with diverse endemic pathogens.

Evaluation of the Panbio™ COVID-19 IgG rapid test device performance.

Background: The Panbio™ COVID-19 IgG Rapid Test Device ("Panbio™") detects IgG antibodies against the SARS-CoV-2 spike protein from viral infection or vaccination. Objectives: To determine the diagnostic sensitivity and specificity of the Panbio™ professional use test, using fingerstick whole blood and venous plasma. Study design: Fingerstick whole blood and venous plasma from each participant were tested with Panbio™ and compared against the SARS-CoV-2 IgG II assay on the Abbott Architect™ platform (Europe) or the equivalent AdviseDx SARS-CoV-2 IgG II Abbott Alinity i™ platform (US). 447 evaluable participants were enrolled across 6 US and 9 European clinical centers. Results: For unvaccinated participants with PCR-confirmed infection ≥21 days post-symptom onset, the Panbio™ sensitivity with fingerstick whole blood was 92.6 % (95 % CI: 85.9, 96.7), and the specificity was 97.0 % (95 % CI: 93.1, 99.0). For venous plasma, the sensitivity was 90.0 % (95 % CI: 79.5, 96.2) for participants with PCR-confirmed infection and symptom onset 22-180 days ago; the specificity was 96.3 % (92.2, 98.6). For vaccinated participants, the sensitivity was 98.4 % (95 % CI: 91.2, 100.0) for fingerstick whole blood and 96.7 % (95 % CI: 88.7, 99.6) for venous plasma. Conclusion: The Panbio™ test had high sensitivity and specificity for detecting IgG against the SARS-CoV-2 spike protein.

Engineered immunogens to elicit antibodies against conserved coronavirus epitopes.

Immune responses to SARS-CoV-2 primarily target the receptor binding domain of the spike protein, which continually mutates to escape acquired immunity. Other regions in the spike S2 subunit, such as the stem helix and the segment encompassing residues 815-823 adjacent to the fusion peptide, are highly conserved across sarbecoviruses and are recognized by broadly reactive antibodies, providing hope that vaccines targeting these epitopes could offer protection against both current and emergent viruses. Here we employ computational modeling to design scaffolded immunogens that display the spike 815-823 peptide and the stem helix epitopes without the distracting and immunodominant receptor binding domain. These engineered proteins bind with high affinity and specificity to the mature and germline versions of previously identified broadly protective human antibodies. Epitope scaffolds interact with both sera and isolated monoclonal antibodies with broadly reactivity from individuals with pre-existing SARS-CoV-2 immunity. When used as immunogens, epitope scaffolds elicit sera with broad betacoronavirus reactivity and protect as "boosts" against live virus challenge in mice, illustrating their potential as components of a future pancoronavirus vaccine.

Factors Associated with COVID-19 Vaccination Promptness after Eligibility in a North Carolina Longitudinal Cohort Study.

Many studies identified factors associated with vaccination intention and hesitancy, but factors associated with vaccination promptness and the effect of vaccination intention on vaccination promptness are unknown. This study identified factors associated with COVID-19 vaccination promptness and evaluated the role of vaccination intention on vaccination promptness in 1223 participants in a community-based longitudinal cohort study (June 2020 to December 2021). Participants answered questions regarding COVID-19 vaccination intention, vaccination status, and reasons for not receiving a vaccine. The association of baseline vaccine hesitancy with vaccination was assessed by the Kaplan-Meier survival analysis. Follow-up analyses tested the importance of other variables predicting vaccination using the Cox proportional hazards model. Older age was associated with shorter time to vaccination (HR = 1.76 [1.37-2.25] 85-year-old versus 65-year-old). Lower education levels (HR = 0.80 [0.69-0.92]), household incomes (HR = 0.84 [0.72-0.98]), and baseline vaccination intention of 'No' (HR = 0.16 [0.11-0.23]) were associated with longer times to vaccination. The most common reasons for not being vaccinated (N = 58) were vaccine safety concerns (n = 33), side effects (n = 28), and vaccine effectiveness (n = 25). Vaccination campaigns that target populations prone to hesitancy and address vaccine safety and effectiveness could be helpful in future vaccination rollouts.

Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data.

Resolving chromatin-remodeling-linked gene expression changes at cell-type resolution is important for understanding disease states. Here we describe MAGICAL (Multiome Accessibility Gene Integration Calling and Looping), a hierarchical Bayesian approach that leverages paired single-cell RNA sequencing and single-cell transposase-accessible chromatin sequencing from different conditions to map disease-associated transcription factors, chromatin sites, and genes as regulatory circuits. By simultaneously modeling signal variation across cells and conditions in both omics data types, MAGICAL achieved high accuracy on circuit inference. We applied MAGICAL to study Staphylococcus aureus sepsis from peripheral blood mononuclear single-cell data that we generated from subjects with bloodstream infection and uninfected controls. MAGICAL identified sepsis-associated regulatory circuits predominantly in CD14 monocytes, known to be activated by bacterial sepsis. We addressed the challenging problem of distinguishing host regulatory circuit responses to methicillin-resistant and methicillin-susceptible S. aureus infections. Although differential expression analysis failed to show predictive value, MAGICAL identified epigenetic circuit biomarkers that distinguished methicillin-resistant from methicillin-susceptible S. aureus infections.

Evaluation of SARS-CoV-2 identification methods through surveillance of companion animals in SARS-CoV-2-positive homes in North Carolina, March to December 2020.

We collected oral and/or rectal swabs and serum from dogs and cats living in homes with SARS-CoV-2-PCR-positive persons for SARS-CoV-2 PCR and serology testing. Pre-COVID-19 serum samples from dogs and cats were used as negative controls, and samples were tested in duplicate at different timepoints. Raw ELISA results scrutinized relative to known negative samples suggested that cut-offs for IgG seropositivity may require adjustment relative to previously proposed values, while proposed cut-offs for IgM require more extensive validation. A small number of pet dogs (2/43, 4.7%) and one cat (1/21, 4.8%) were positive for SARS-CoV-2 RNA, and 28.6 and 37.5% of cats and dogs were positive for anti-SARS-CoV-2 IgG, respectively.

Access to COVID-19 testing by individuals with housing insecurity during the early days of the COVID-19 pandemic in the United States: a scoping review.

Introduction: The COVID-19 pandemic focused attention on healthcare disparities and inequities faced by individuals within marginalized and structurally disadvantaged groups in the United States. These individuals bore the heaviest burden across this pandemic as they faced increased risk of infection and difficulty in accessing testing and medical care. Individuals experiencing housing insecurity are a particularly vulnerable population given the additional barriers they face. In this scoping review, we identify some of the barriers this high-risk group experienced during the early days of the pandemic and assess novel solutions to overcome these barriers. Methods: A scoping review was performed following PRISMA-Sc guidelines looking for studies focusing on COVID-19 testing among individuals experiencing housing insecurity. Barriers as well as solutions to barriers were identified as applicable and summarized using qualitative methods, highlighting particular ways that proved effective in facilitating access to testing access and delivery. Results: Ultimately, 42 studies were included in the scoping review, with 143 barriers grouped into four categories: lack of cultural understanding, systemic racism, and stigma; medical care cost, insurance, and logistics; immigration policies, language, and fear of deportation; and other. Out of these 42 studies, 30 of these studies also suggested solutions to address them. Conclusion: A paucity of studies have analyzed COVID-19 testing barriers among those experiencing housing insecurity, and this is even more pronounced in terms of solutions to address those barriers. Expanding resources and supporting investigators within this space is necessary to ensure equitable healthcare delivery.

Mast cell activation in lungs during SARS-CoV-2 infection associated with lung pathology and severe COVID-19.

Lung inflammation is a hallmark of Coronavirus disease 2019 (COVID-19) in patients who are severely ill, and the pathophysiology of disease is thought to be immune mediated. Mast cells (MCs) are polyfunctional immune cells present in the airways, where they respond to certain viruses and allergens and often promote inflammation. We observed widespread degranulation of MCs during acute and unresolved airway inflammation in SARS-CoV-2-infected mice and nonhuman primates. Using a mouse model of MC deficiency, MC-dependent interstitial pneumonitis, hemorrhaging, and edema in the lung were observed during SARS-CoV-2 infection. In humans, transcriptional changes in patients requiring oxygen supplementation also implicated cells with a MC phenotype in severe disease. MC activation in humans was confirmed through detection of MC-specific proteases, including chymase, the levels of which were significantly correlated with disease severity and with biomarkers of vascular dysregulation. These results support the involvement of MCs in lung tissue damage during SARS-CoV-2 infection in animal models and the association of MC activation with severe COVID-19 in humans, suggesting potential strategies for intervention.

Differential host responses within the upper respiratory tract and peripheral blood of children and adults with SARS-CoV-2 infection.

Age is among the strongest risk factors for severe outcomes from SARS-CoV-2 infection. We sought to evaluate associations between age and both mucosal and systemic host responses to SARS-CoV-2 infection. We profiled the upper respiratory tract (URT) and peripheral blood transcriptomes of 201 participants (age range of 1 week to 83 years), including 137 non-hospitalized individuals with mild SARS-CoV-2 infection and 64 uninfected individuals. Among uninfected children and adolescents, young age was associated with upregulation of innate and adaptive immune pathways within the URT, suggesting that young children are primed to mount robust mucosal immune responses to exogeneous respiratory pathogens. SARS-CoV-2 infection was associated with broad induction of innate and adaptive immune responses within the URT of children and adolescents. Peripheral blood responses among SARS-CoV-2-infected children and adolescents were dominated by interferon pathways, while upregulation of myeloid activation, inflammatory, and coagulation pathways was observed only in adults. Systemic symptoms among SARS-CoV-2-infected subjects were associated with blunted innate and adaptive immune responses in the URT and upregulation of many of these same pathways within peripheral blood. Finally, within individuals, robust URT immune responses were correlated with decreased peripheral immune activation, suggesting that effective immune responses in the URT may promote local viral control and limit systemic immune activation and symptoms. These findings demonstrate that there are differences in immune responses to SARS-CoV-2 across the lifespan, including between young children and adolescents, and suggest that these varied host responses contribute to observed differences in the clinical presentation of SARS-CoV-2 infection by age. One Sentence Summary: Age is associated with distinct upper respiratory and peripheral blood transcriptional responses among children and adults with SARS-CoV-2 infection.

Lessons From COVID-19 for Pandemic Preparedness: Proceedings From a Multistakeholder Think Tank.

While the coronavirus disease 2019 (COVID-19) pandemic continues to present global challenges, sufficient time has passed to reflect on lessons learned and use those insights to inform policy and approaches to prepare for the next pandemic. In May 2022, the Duke Clinical Research Institute convened a think tank with thought leaders from academia, clinical practice, the pharmaceutical industry, patient advocacy, the National Institutes of Health, the US Food and Drug Administration, and the Centers for Disease Control and Prevention to share, firsthand, expert knowledge of the insights gained from the COVID-19 pandemic and how this acquired knowledge can help inform the next pandemic response. The think tank focused on pandemic preparedness, therapeutics, vaccines, and challenges related to clinical trial design and scale-up during the early phase of a pandemic. Based on the multi-faceted discussions, we outline 10 key steps to an improved and equitable pandemic response.

RADx-UP Testing Core: Access to COVID-19 Diagnostics in Community-Engaged Research with Underserved Populations.

Research on the COVID-19 pandemic revealed a disproportionate burden of COVID-19 infection and death among underserved populations and exposed low rates of SARS-CoV-2 testing in these communities. A landmark National Institutes of Health (NIH) funding initiative, the Rapid Acceleration of Diagnostics-Underserved Populations (RADx-UP) program, was developed to address the research gap in understanding the adoption of COVID-19 testing in underserved populations. This program is the single largest investment in health disparities and community-engaged research in the history of the NIH. The RADx-UP Testing Core (TC) provides community-based investigators with essential scientific expertise and guidance on COVID-19 diagnostics. This commentary describes the first 2 years of the TC's experience, highlighting the challenges faced and insights gained to safely and effectively deploy large-scale diagnostics for community-initiated research in underserved populations during a pandemic. The success of RADx-UP shows that community-based research to increase access and uptake of testing among underserved populations can be accomplished during a pandemic with tools, resources, and multidisciplinary expertise provided by a centralized testing-specific coordinating center. We developed adaptive tools to support individual testing strategies and frameworks for these diverse studies and ensured continuous monitoring of testing strategies and use of study data. In a rapidly evolving setting of tremendous uncertainty, the TC provided essential and real-time technical expertise to support safe, effective, and adaptive testing. The lessons learned go beyond this pandemic and can serve as a framework for rapid deployment of testing in response to future crises, especially when populations are affected inequitably.