Exploring Natural Immune Responses to Shigella Exposure Using Multiplex Bead Assays on Dried Blood Spots in High-Burden Countries: Protocol From a Multisite Diarrhea Surveillance Study.

Background: Molecular diagnostics on human fecal samples have identified a larger burden of shigellosis than previously appreciated by culture. Evidence of fold changes in immunoglobulin G (IgG) to conserved and type-specific Shigella antigens could be used to validate the molecular assignment of type-specific Shigella as the etiology of acute diarrhea and support polymerase chain reaction (PCR)-based microbiologic end points for vaccine trials. Methods: We will test dried blood spots collected at enrollment and 4 weeks later using bead-based immunoassays for IgG to invasion plasmid antigen B and type-specific lipopolysaccharide O-antigen for Shigella flexneri 1b, 2a, 3a, and 6 and Shigella sonnei in Shigella-positive cases and age-, site-, and season-matched test-negative controls from all sites in the Enterics for Global Health (EFGH) Shigella surveillance study. Fold antibody responses will be compared between culture-positive, culture-negative but PCR-attributable, and PCR-positive but not attributable cases and test-negative controls. Age- and site-specific seroprevalence distributions will be identified, and the association between baseline antibodies and Shigella attribution will be estimated. Conclusions: The integration of these assays into the EFGH study will help support PCR-based attribution of acute diarrhea to type-specific Shigella, describe the baseline seroprevalence of conserved and type-specific Shigella antibodies, and support correlates of protection for immunity to Shigella diarrhea. These insights can help support the development and evaluation of Shigella vaccine candidates.

SARS-CoV-2 antibody prevalence by industry, workplace characteristics, and workplace infection prevention and control measures, North Carolina, 2021 to 2022.

Background: The COVID-19 pandemic has disproportionately affected workers in certain industries and occupations, and the workplace can be a high risk setting for SARS-CoV-2 transmission. In this study, we measured SARS-CoV-2 antibody prevalence and identified work-related risk factors in a population primarily working at industrial livestock operations. Methods: We used a multiplex salivary SARS-CoV-2 IgG antibody assay to determine infection-induced antibody prevalence among 236 adult (≥18 years) North Carolina residents between February 2021 and August 2022. We used the National Institute for Occupational Safety and Health Industry and Occupation Computerized Coding System (NIOCCS) to classify employed participants' industry and compared infection-induced IgG prevalence by participant industry and with the North Carolina general population. We also combined antibody results with reported SARS-CoV-2 molecular test positivity and vaccination history to identify evidence of prior infection. We used logistic regression to estimate odds ratios of prior infection by potential work-related risk factors, adjusting for industry and date. Results: Most participants (55%) were infection-induced IgG positive, including 71% of animal slaughtering and processing industry workers, which is 1.5 to 4.3 times higher compared to the North Carolina general population, as well as higher than molecularly-confirmed cases and the only other serology study we identified of animal slaughtering and processing workers. Considering questionnaire results in addition to antibodies, the proportion of participants with evidence of prior infection increased slightly, to 61%, including 75% of animal slaughtering and processing workers. Participants with more than 1000 compared to 10 or fewer coworkers at their jobsite had higher odds of prior infection (adjusted odds ratio [aOR] 4.5, 95% confidence interval [CI] 1.0 to 21.0). Conclusions: This study contributes evidence of the severe and disproportionate impacts of COVID-19 on animal processing and essential workers and workers in large congregate settings. We also demonstrate the utility of combining non-invasive biomarker and questionnaire data for the study of workplace exposures.

Application of machine learning models to identify serological predictors of COVID-19 severity and outcomes.

Critically ill people with COVID-19 have greater antibody titers than those with mild to moderate illness, but their association with recovery or death from COVID-19 has not been characterized. In 178 COVID-19 patients, 73 non-hospitalized and 105 hospitalized patients, mucosal swabs and plasma samples were collected at hospital enrollment and up to 3 months post-enrollment (MPE) to measure virus RNA, cytokines/chemokines, binding antibodies, ACE2 binding inhibition, and Fc effector antibody responses against SARS-CoV-2. The association of demographic variables and >20 serological antibody measures with intubation or death due to COVID-19 was determined using machine learning algorithms. Predictive models revealed that IgG binding and ACE2 binding inhibition responses at 1 MPE were positively and C1q complement activity at enrollment was negatively associated with an increased probability of intubation or death from COVID-19 within 3 MPE. Serological antibody measures were more predictive than demographic variables of intubation or death among COVID-19 patients.

Evaluating SARS-CoV-2 Saliva and Dried Blood Spot Surveillance Strategies in a Congregate Population.

The optimal approach to COVID-19 surveillance in congregate populations remains unclear. Our study at the US Naval Academy in Annapolis, Maryland, USA, assessed the concordance of antibody prevalence in longitudinally collected dried blood spots and saliva in a setting of frequent PCR-based testing. Our findings highlight the utility of salivary-based surveillance.

Anthropometric measures and arsenic methylation among pregnant women in rural northern Bangladesh.

INTRODUCTION: Arsenic methylation converts inorganic arsenic (iAs) to monomethyl (MMA) and dimethyl (DMA) arsenic compounds. Body mass index (BMI) has been positively associated with arsenic methylation efficiency (higher DMA%) in adults, but evidence in pregnancy is inconsistent. We estimated associations between anthropometric measures and arsenic methylation among pregnant women in rural northern Bangladesh. METHODS: We enrolled pregnant women (n = 784) (median [IQR] gestational week: 14 [13, 15]) in Gaibandha District, Bangladesh from 2018 to 2019. Anthropometric measures were BMI, subscapular and triceps skinfold thicknesses, and mid-upper arm circumference (MUAC), fat area (MUAFA), and muscle area (MUAMA). Arsenic methylation measures were urinary iAs, MMA, and DMA divided by their sum and multiplied by 100 (iAs%, MMA%, and DMA%), primary methylation index (MMA/iAs; PMI), and secondary methylation index (DMA/MMA; SMI). In complete cases (n = 765 [97.6%]), we fitted linear, beta, and Dirichlet regression models to estimate cross-sectional differences in iAs%, MMA%, DMA%, PMI, and SMI per IQR-unit difference in each anthropometric measure, adjusting for drinking water arsenic, age, gestational age, education, living standards index, and plasma folate, vitamin B12, and homocysteine. RESULTS: Median (IQR) BMI, subscapular skinfold thickness, triceps skinfold thickness, MUAC, MUAFA, and MUAMA were 21.5 (19.4, 23.8) kg/m2, 17.9 (13.2, 24.2) mm, 14.2 (10.2, 18.7) mm, 25.9 (23.8, 28.0) cm, 15.3 (10.5, 20.3) cm2, and 29.9 (25.6, 34.2) cm2, respectively. Median (IQR) iAs%, MMA%, DMA%, PMI, and SMI were 12.0 (9.3, 15.2)%, 6.6 (5.3, 8.3)%, 81.0 (77.1, 84.6)%, 0.6 (0.4, 0.7), and 12.2 (9.3, 15.7), respectively. In both unadjusted and adjusted linear models, all anthropometric measures were negatively associated with iAs%, MMA%, and PMI and positively associated with DMA% and SMI. For example, fully adjusted mean differences (95% CI) in DMA% per IQR-unit difference in BMI, subscapular skinfolds thickness, triceps skinfold thickness, MUAC, MUAFA, and MUAMA were 1.72 (1.16, 2.28), 1.58 (0.95, 2.21), 1.74 (1.11, 2.37), 1.45 (0.85, 2.06), 1.70 (1.08, 2.31), and 0.70 (0.13, 1.27) pp, respectively. CONCLUSIONS: Anthropometric measures were positively associated with arsenic methylation efficiency among pregnant women in the early second trimester.

A Saliva-Based Serological and Behavioral Analysis of SARS-CoV-2 Antibody Prevalence in Howard County, Maryland.

The objective of the study was to estimate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence in the Howard County, Maryland, general population and demographic subpopulations attributable to natural infection or coronavirus disease 2019 (COVID-19) vaccination and to identify self-reported social behaviors that may affect the likelihood of recent or past SARS-CoV-2 infection. A cross-sectional, saliva-based serological study of 2,880 residents of Howard County, Maryland, was carried out from July through September 2021. Natural SARS-CoV-2 infection prevalence was estimated by inferring infections among individuals according to anti-nucleocapsid immunoglobin G levels and calculating averages weighted by sample proportions of various demographics. Antibody levels between BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna) recipients were compared. Antibody decay rate was calculated by fitting exponential decay curves to cross-sectional indirect immunoassay data. Regression analysis was carried out to identify demographic factors, social behaviors, and attitudes that may be linked to an increased likelihood of natural infection. The estimated overall prevalence of natural infection in Howard County, Maryland, was 11.9% (95% confidence interval, 9.2% to 15.1%), compared with 7% reported COVID-19 cases. Antibody prevalence indicating natural infection was highest among Hispanic and non-Hispanic Black participants and lowest among non-Hispanic White and non-Hispanic Asian participants. Participants from census tracts with lower average household income also had higher natural infection rates. After accounting for multiple comparisons and correlations between participants, none of the behavior or attitude factors had significant effects on natural infection. At the same time, recipients of the mRNA-1273 vaccine had higher antibody levels than those of BNT162b2 vaccine recipients. Older study participants had overall lower antibody levels compared with younger study participants. The true prevalence of SARS-CoV-2 infection is higher than the number of reported COVID-19 cases in Howard County, Maryland. A disproportionate impact of infection-induced SARS-CoV-2 positivity was observed across different ethnic/racial subpopulations and incomes, and differences in antibody levels across different demographics were identified. Taken together, this information may inform public health policy to protect vulnerable populations. IMPORTANCE We employed a highly innovative noninvasive multiplex oral fluid SARS-CoV-2 IgG assay to ascertain our seroprevalence estimates. This laboratory-developed test has been applied in NCI's SeroNet consortium, possesses high sensitivity and specificity according to FDA Emergency Use Authorization guidelines, correlates strongly with SARS-CoV-2 neutralizing antibody responses, and is Clinical Laboratory Improvement Amendments-approved by the Johns Hopkins Hospital Department of Pathology. It represents a broadly scalable public health tool to improve understanding of recent and past SARS-CoV-2 exposure and infection without drawing any blood. To our knowledge, this is the first application of a high-performance salivary SARS-CoV-2 IgG assay to estimate population-level seroprevalence, including identifying COVID-19 disparities. We also are the first to report differences in SARS-CoV-2 IgG responses by COVID-19 vaccine manufacturers (BNT162b2 [Pfizer-BioNTech] and mRNA-1273 [Moderna]). Our findings demonstrate remarkable consistency with those of blood-based SARS-CoV-2 IgG assays in terms of differences in the magnitude of SARS-CoV-2 IgG responses between COVID-19 vaccines.

Longitudinal Analysis of Humoral and Cellular Immune Response Following SARS-CoV-2 Vaccination Supports Utilizing Point-Of-Care Tests to Enhance COVID-19 Booster Uptake.

Individuals with weaker neutralizing responses show reduced protection with SARS-CoV-2 variants. Booster vaccines are recommended for vaccinated individuals, but the uptake is low. We present the feasibility of utilizing point-of-care tests (POCT) to support evidence-based decision-making around COVID-19 booster vaccinations. Using infectious virus neutralization, ACE2 blocking, spike binding, and TCR sequencing assays, we investigated the dynamics of changes in the breadth and depth of blood and salivary antibodies as well as T-cell clonal response following mRNA vaccination in a cohort of healthcare providers. We evaluated the accuracy of two POCTs utilizing either blood or saliva to identify those in whom humoral immunity was inadequate. >4 months after two doses of mRNA vaccine, SARS-CoV-2 binding and neutralizing Abs (nAbs) and T-cell clones declined 40-80%, and 2/3rd lacked Omicron nAbs. After the third mRNA booster, binding and neutralizing Abs increased overall in the systemic compartment; notably, individuals with previously weak nAbs gained sharply. The third dose failed to stimulate secretory IgA, but salivary IgG closely tracked systemic IgG levels. Vaccine boosting increased Ab breadth against a divergent bat sarbecovirus, SHC014, although the TCR-beta sequence breadth was unchanged. Post 3rd booster dose, Ab avidity increased for the Wuhan and Delta strains, while avidity against Omicron and SHC014 increased to levels seen for Wuhan after the second dose. Negative results on POCTs strongly correlated with a lack of functional humoral immunity. The third booster dose helps vaccinees gain depth and breadth of systemic Abs against evolving SARS-CoV-2 and related viruses. Our findings show that POCTs are useful and easy-to-access tools to inform inadequate humoral immunity accurately. POCTs designed to match the circulating variants can help individuals with booster vaccine decisions and could serve as a population-level screening platform to preserve herd immunity. One Sentence Summary: SARS-CoV-2 point-of-care antibody tests are valuable and easy-to-access tools to inform inadequate humoral immunity and to support informed decision-making regarding the current and future booster vaccination.

Modeling in higher dimensions to improve diagnostic testing accuracy: Theory and examples for multiplex saliva-based SARS-CoV-2 antibody assays.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has emphasized the importance and challenges of correctly interpreting antibody test results. Identification of positive and negative samples requires a classification strategy with low error rates, which is hard to achieve when the corresponding measurement values overlap. Additional uncertainty arises when classification schemes fail to account for complicated structure in data. We address these problems through a mathematical framework that combines high dimensional data modeling and optimal decision theory. Specifically, we show that appropriately increasing the dimension of data better separates positive and negative populations and reveals nuanced structure that can be described in terms of mathematical models. We combine these models with optimal decision theory to yield a classification scheme that better separates positive and negative samples relative to traditional methods such as confidence intervals (CIs) and receiver operating characteristics. We validate the usefulness of this approach in the context of a multiplex salivary SARS-CoV-2 immunoglobulin G assay dataset. This example illustrates how our analysis: (i) improves the assay accuracy, (e.g. lowers classification errors by up to 42% compared to CI methods); (ii) reduces the number of indeterminate samples when an inconclusive class is permissible, (e.g. by 40% compared to the original analysis of the example multiplex dataset) and (iii) decreases the number of antigens needed to classify samples. Our work showcases the power of mathematical modeling in diagnostic classification and highlights a method that can be adopted broadly in public health and clinical settings.

Methodological approaches to optimize multiplex oral fluid SARS-CoV-2 IgG assay performance and correlation with serologic and neutralizing antibody responses.

BACKGROUND: Oral fluid (hereafter, saliva) is a non-invasive and attractive alternative to blood for SARS-CoV-2 IgG testing; however, the heterogeneity of saliva as a matrix poses challenges for immunoassay performance. OBJECTIVES: To optimize performance of a magnetic microparticle-based multiplex immunoassay (MIA) for SARS-CoV-2 IgG measurement in saliva, with consideration of: i) threshold setting and validation across different MIA bead batches; ii) sample qualification based on salivary total IgG concentration; iii) calibration to U.S. SARS-CoV-2 serological standard binding antibody units (BAU); and iv) correlations with blood-based SARS-CoV-2 serological and neutralizing antibody (nAb) assays. METHODS: The salivary SARS-CoV-2 IgG MIA included 2 nucleocapsid (N), 3 receptor-binding domain (RBD), and 2 spike protein (S) antigens. Gingival crevicular fluid (GCF) swab saliva samples were collected before December 2019 (n = 555) and after molecular test-confirmed SARS-CoV-2 infection from 113 individuals (providing up to 5 repeated-measures; n = 398) and used to optimize and validate MIA performance (total n = 953). Combinations of IgG responses to N, RBD and S and total salivary IgG concentration (µg/mL) as a qualifier of nonreactive samples were optimized and validated, calibrated to the U.S. SARS-CoV-2 serological standard, and correlated with blood-based SARS-CoV-2 IgG ELISA and nAb assays. RESULTS: The sum of signal to cutoff (S/Co) to all seven MIA SARS-CoV-2 antigens and disqualification of nonreactive saliva samples with ≤15 µg/mL total IgG led to correct classification of 62/62 positives (sensitivity [Se] = 100.0%; 95% confidence interval [CI] = 94.8%, 100.0%) and 108/109 negatives (specificity [Sp] = 99.1%; 95% CI = 97.3%, 100.0%) at 8-million beads coupling scale and 80/81 positives (Se = 98.8%; 95% CI = 93.3%, 100.0%] and 127/127 negatives (Sp = 100%; 95% CI = 97.1%, 100.0%) at 20-million beads coupling scale. Salivary SARS-CoV-2 IgG crossed the MIA cutoff of 0.1 BAU/mL on average 9 days post-COVID-19 symptom onset and peaked around day 30. Among n = 30 matched saliva and plasma samples, salivary SARS-CoV-2 MIA IgG levels correlated with corresponding-antigen plasma ELISA IgG (N: ρ = 0.76, RBD: ρ = 0.83, S: ρ = 0.82; all p < 0.001). Correlations of plasma SARS-CoV-2 nAb assay area under the curve (AUC) with salivary MIA IgG (N: ρ = 0.68, RBD: ρ = 0.78, S: ρ = 0.79; all p < 0.001) and with plasma ELISA IgG (N: ρ = 0.76, RBD: ρ = 0.79, S: ρ = 0.76; p < 0.001) were similar. CONCLUSIONS: A salivary SARS-CoV-2 IgG MIA produced consistently high Se (> 98.8%) and Sp (> 99.1%) across two bead coupling scales and correlations with nAb responses that were similar to blood-based SARS-CoV-2 IgG ELISA data. This non-invasive salivary SARS-CoV-2 IgG MIA could increase engagement of vulnerable populations and improve broad understanding of humoral immunity (kinetics and gaps) within the evolving context of booster vaccination, viral variants and waning immunity.

The Pregnancy, Arsenic, and Immune Response (PAIR) Study in rural northern Bangladesh.

BACKGROUND: Arsenic exposure and micronutrient deficiencies may alter immune reactivity to influenza vaccination in pregnant women, transplacental transfer of maternal antibodies to the foetus, and maternal and infant acute morbidity. OBJECTIVES: The Pregnancy, Arsenic, and Immune Response (PAIR) Study was designed to assess whether arsenic exposure and micronutrient deficiencies alter maternal and newborn immunity and acute morbidity following maternal seasonal influenza vaccination during pregnancy. POPULATION: The PAIR Study recruited pregnant women across a large rural study area in Gaibandha District, northern Bangladesh, 2018-2019. DESIGN: Prospective, longitudinal pregnancy and birth cohort. METHODS: We conducted home visits to enrol pregnant women in the late first or early second trimester (11-17 weeks of gestational age). Women received a quadrivalent seasonal inactivated influenza vaccine at enrolment. Follow-up included up to 13 visits between enrolment and 3 months postpartum. Arsenic was measured in drinking water and maternal urine. Micronutrient deficiencies were assessed using plasma biomarkers. Vaccine-specific antibody titres were measured in maternal and infant serum. Weekly telephone surveillance ascertained acute morbidity symptoms in women and infants. PRELIMINARY RESULTS: We enrolled 784 pregnant women between October 2018 and March 2019. Of 784 women who enrolled, 736 (93.9%) delivered live births and 551 (70.3%) completed follow-up visits to 3 months postpartum. Arsenic was detected (≥0.02 µg/L) in 99.7% of water specimens collected from participants at enrolment. The medians (interquartile ranges) of water and urinary arsenic at enrolment were 5.1 (0.5, 25.1) µg/L and 33.1 (19.6, 56.5) µg/L, respectively. Water and urinary arsenic were strongly correlated (Spearman's ⍴ = 0.72) among women with water arsenic ≥ median but weakly correlated (⍴ = 0.17) among women with water arsenic < median. CONCLUSIONS: The PAIR Study is well positioned to examine the effects of low-moderate arsenic exposure and micronutrient deficiencies on immune outcomes in women and infants. REGISTRATION: NCT03930017.

Reconsideration of Antinucleocapsid IgG Antibody as a Marker of SARS-CoV-2 Infection Postvaccination for Mild COVID-19 Patients.

Antinucleocapsid (anti-N) immunoglobulin G antibody responses were lower in plasma and oral fluid after severe acute respiratory syndrome coronavirus 2 infection in vaccinated patients compared with patients infected before vaccination or infected without vaccination. This raises questions about the long-term use of anti-N antibodies as a marker for natural infection for surveillance.

SARS-CoV-2 Antibody Prevalence among Industrial Livestock Operation Workers and Nearby Community Residents, North Carolina, 2021 to 2022.

Industrial livestock operations (ILOs), particularly processing facilities, emerged as centers of coronavirus disease 2019 (COVID-19) outbreaks in spring 2020. Confirmed cases of COVID-19 underestimate true prevalence. To investigate the prevalence of antibodies against SARS-CoV-2, we enrolled 279 participants in North Carolina from February 2021 to July 2022: 90 from households with at least one ILO worker (ILO), 97 from high-ILO intensity areas (ILO neighbors [ILON]), and 92 from metropolitan areas (metro). More metro (55.4%) compared to ILO (51.6%) and ILON participants (48.4%) completed the COVID-19 primary vaccination series; the median completion date was more than 4 months later for ILO compared to ILON and metro participants, although neither difference was statistically significant. Participants provided a saliva swab we analyzed for SARS-CoV-2 IgG using a multiplex immunoassay. The prevalence of infection-induced IgG (positive for nucleocapsid and receptor binding domain) was higher among ILO (63%) than ILON (42.9%) and metro (48.7%) participants (prevalence ratio [PR], 1.38; 95% confidence interval [CI], 1.06 to 1.80; reference category ILON and metro combined). The prevalence of infection-induced IgG was also higher among ILO participants than among an Atlanta health care worker cohort (PR, 2.45; 95% CI, 1.80 to 3.33) and a general population cohort in North Carolina (PRs, 6.37 to 10.67). The infection-induced IgG prevalence increased over the study period. Participants reporting not masking in public in the past 2 weeks had higher infection-induced IgG prevalence (78.6%) than participants reporting masking (49.3%) (PR, 1.59; 95% CI, 1.19 to 2.13). Lower education, more people per bedroom, Hispanic/Latino ethnicity, and more contact with people outside the home were also associated with higher infection-induced IgG prevalence. IMPORTANCE Few studies have measured COVID-19 seroprevalence in North Carolina, especially among rural, Black, and Hispanic/Latino communities that have been heavily affected. Antibody results show high rates of COVID-19 among industrial livestock operation workers and their household members. Antibody results add to evidence of health disparities related to COVID-19 by socioeconomic status and ethnicity. Associations between masking and physical distancing with antibody results also add to evidence of the effectiveness of these prevention strategies. Delays in the timing of receipt of COVID-19 vaccination reinforce the importance of dismantling vaccination barriers, especially for industrial livestock operation workers and their household members.

Modeling in higher dimensions to improve diagnostic testing accuracy: theory and examples for multiplex saliva-based SARS-CoV-2 antibody assays.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has emphasized the importance and challenges of correctly interpreting antibody test results. Identification of positive and negative samples requires a classification strategy with low error rates, which is hard to achieve when the corresponding measurement values overlap. Additional uncertainty arises when classification schemes fail to account for complicated structure in data. We address these problems through a mathematical framework that combines high dimensional data modeling and optimal decision theory. Specifically, we show that appropriately increasing the dimension of data better separates positive and negative populations and reveals nuanced structure that can be described in terms of mathematical models. We combine these models with optimal decision theory to yield a classification scheme that better separates positive and negative samples relative to traditional methods such as confidence intervals (CIs) and receiver operating characteristics. We validate the usefulness of this approach in the context of a multiplex salivary SARS-CoV-2 immunoglobulin G assay dataset. This example illustrates how our analysis: (i) improves the assay accuracy (e.g. lowers classification errors by up to 42 % compared to CI methods); (ii) reduces the number of indeterminate samples when an inconclusive class is permissible (e.g. by 40 % compared to the original analysis of the example multiplex dataset); and (iii) decreases the number of antigens needed to classify samples. Our work showcases the power of mathematical modeling in diagnostic classification and highlights a method that can be adopted broadly in public health and clinical settings.

The Serological Sciences Network (SeroNet) for COVID-19: Depth and Breadth of Serology Assays and Plans for Assay Harmonization.

In October 2020, the National Cancer Institute (NCI) Serological Sciences Network (SeroNet) was established to study the immune response to COVID-19, and "to develop, validate, improve, and implement serological testing and associated technologies" (https://www.cancer.gov/research/key-initiatives/covid-19/coronavirus-research-initiatives/serological-sciences-network). SeroNet is comprised of 25 participating research institutions partnering with the Frederick National Laboratory for Cancer Research (FNLCR) and the SeroNet Coordinating Center. Since its inception, SeroNet has supported collaborative development and sharing of COVID-19 serological assay procedures and has set forth plans for assay harmonization. To facilitate collaboration and procedure sharing, a detailed survey was sent to collate comprehensive assay details and performance metrics on COVID-19 serological assays within SeroNet. In addition, FNLCR established a protocol to calibrate SeroNet serological assays to reference standards, such as the U.S. severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serology standard reference material and first WHO international standard (IS) for anti-SARS-CoV-2 immunoglobulin (20/136), to facilitate harmonization of assay reporting units and cross-comparison of study data. SeroNet institutions reported development of a total of 27 enzyme-linked immunosorbent assay (ELISA) methods, 13 multiplex assays, and 9 neutralization assays and use of 12 different commercial serological methods. FNLCR developed a standardized protocol for SeroNet institutions to calibrate these diverse serological assays to reference standards. In conclusion, SeroNet institutions have established a diverse array of COVID-19 serological assays to study the immune response to SARS-CoV-2 and vaccines. Calibration of SeroNet serological assays to harmonize results reporting will facilitate future pooled data analyses and study cross-comparisons. IMPORTANCE SeroNet institutions have developed or implemented 61 diverse COVID-19 serological assays and are collaboratively working to harmonize these assays using reference materials to establish standardized reporting units. This will facilitate clinical interpretation of serology results and cross-comparison of research data.

Optimal decision theory for diagnostic testing: Minimizing indeterminate classes with applications to saliva-based SARS-CoV-2 antibody assays.

In diagnostic testing, establishing an indeterminate class is an effective way to identify samples that cannot be accurately classified. However, such approaches also make testing less efficient and must be balanced against overall assay performance. We address this problem by reformulating data classification in terms of a constrained optimization problem that (i) minimizes the probability of labeling samples as indeterminate while (ii) ensuring that the remaining ones are classified with an average target accuracy X. We show that the solution to this problem is expressed in terms of a bathtub-type principle that holds out those samples with the lowest local accuracy up to an X-dependent threshold. To illustrate the usefulness of this analysis, we apply it to a multiplex, saliva-based SARS-CoV-2 antibody assay and demonstrate up to a 30 % reduction in the number of indeterminate samples relative to more traditional approaches.

Assessing Residential Exposure to Microbes from Industrial Hog Operations in Rural North Carolina: Methods and Lessons Learned.

BACKGROUND: Recent advances in molecular source tracking make answering questions from residents regarding their exposure to microbial contaminants from industrial hog operations (IHOs) possible. Associations between residential distance to IHOs and exposure can be addressed by measuring livestock-associated (Staphylococcus aureus) and pig-specific bacteria in the air, on household surfaces, and in participants' nasal and saliva swabs. OBJECTIVES: Here we assess the mechanics, feasibility, capacity-building, and lessons learned during a pilot study employing this novel technology in community-based participatory research of bacterial exposure and human health. METHODS: Together, our team of academics and community members designed a field- and laboratory-based pilot study. Air samples, surface and human swabs, and questionnaires from households at varying distances from IHOs were collected. Data were assessed for completeness and quality by two independent reviewers. These metrics were defined as: missingness (completeness), incorrect data type (validity), out of range (validity), and outliers (accuracy). LESSONS LEARNED: While critical field equipment was obtained, and knowledge exchange occurred, leading to an increased capacity for future work, after review, 38 of 49 households were deemed eligible for inclusion in the study. Of eligible participants, 98% of required electronic survey questions were complete and 100% were valid; an improvement over prior work which employed paper surveys. While all human microbial and air samples were collected from eligible households (n = 231), (5%) of environmental swabs were reported missing. CONCLUSIONS: Using community-appropriate sampling protocols, a pilot study of residential exposure to bacteria from IHOs was completed. While high-quality data was collected from those eligible, we learned the necessity of early and continual data review.

The Serological Sciences Network (SeroNet) for COVID-19: Depth and Breadth of Serology Assays and Plans for Assay Harmonization.

Background: In October 2020, the National Cancer Institute (NCI) Serological Sciences Network (SeroNet) was established to study the immune response to COVID-19, and "to develop, validate, improve, and implement serological testing and associated technologies." SeroNet is comprised of 25 participating research institutions partnering with the Frederick National Laboratory for Cancer Research (FNLCR) and the SeroNet Coordinating Center. Since its inception, SeroNet has supported collaborative development and sharing of COVID-19 serological assay procedures and has set forth plans for assay harmonization. Methods: To facilitate collaboration and procedure sharing, a detailed survey was sent to collate comprehensive assay details and performance metrics on COVID-19 serological assays within SeroNet. In addition, FNLCR established a protocol to calibrate SeroNet serological assays to reference standards, such as the U.S. SARS-CoV-2 serology standard reference material and First WHO International Standard (IS) for anti-SARS-CoV-2 immunoglobulin (20/136), to facilitate harmonization of assay reporting units and cross-comparison of study data. Results: SeroNet institutions reported development of a total of 27 ELISA methods, 13 multiplex assays, 9 neutralization assays, and use of 12 different commercial serological methods. FNLCR developed a standardized protocol for SeroNet institutions to calibrate these diverse serological assays to reference standards. Conclusions: SeroNet institutions have established a diverse array of COVID-19 serological assays to study the immune response to SARS-CoV-2 virus and vaccines. Calibration of SeroNet serological assays to harmonize results reporting will facilitate future pooled data analyses and study cross-comparisons.

Optimal Decision Theory for Diagnostic Testing: Minimizing Indeterminate Classes with Applications to Saliva-Based SARS-CoV-2 Antibody Assays.

In diagnostic testing, establishing an indeterminate class is an effective way to identify samples that cannot be accurately classified. However, such approaches also make testing less efficient and must be balanced against overall assay performance. We address this problem by reformulating data classification in terms of a constrained optimization problem that (i) minimizes the probability of labeling samples as indeterminate while (ii) ensuring that the remaining ones are classified with an average target accuracy X. We show that the solution to this problem is expressed in terms of a bathtub principle that holds out those samples with the lowest local accuracy up to an X-dependent threshold. To illustrate the usefulness of this analysis, we apply it to a multiplex, saliva-based SARS-CoV-2 antibody assay and demonstrate up to a 30 % reduction in the number of indeterminate samples relative to more traditional approaches.

Evaluating immunity to SARS-CoV-2 in nursing home residents using saliva IgG.

BACKGROUND: SARS-CoV-2 circulating variants coupled with waning immunity pose a significant threat to the long-term care (LTC) population. Our objective was to measure salivary IgG antibodies in residents and staff of an LTC facility to (1) evaluate IgG response in saliva post-natural infection and vaccination and (2) assess its feasibility to describe the seroprevalence over time. METHODS: We performed salivary IgG sampling of all residents and staff who agreed to test in a 150-bed skilled nursing facility during three seroprevalence surveys between October 2020 and February 2021. The facility had SARS-CoV-2 outbreaks in May 2020 and November 2020, when 45 of 138 and 37 of 125 residents were infected, respectively; they offered two Federal vaccine clinics in January 2021. We evaluated quantitative IgG in saliva to the Nucleocapsid (N), Spike (S), and Receptor-binding domain (RBD) Antigens of SARS-CoV-2 over time post-infection and post-vaccination. RESULTS: One hundred twenty-four residents and 28 staff underwent saliva serologic testing on one or more survey visits. Over three surveys, the SARS-CoV-2 seroprevalence at the facility was 49%, 64%, and 81%, respectively. IgG to S, RBD, and N Antigens all increased post infection. Post vaccination, the infection naïve group did not have a detectable N IgG level, and N IgG levels for the previously infected did not increase post vaccination (p < 0.001). Fully vaccinated subjects with prior COVID-19 infection had significantly higher RBD and S IgG responses compared with those who were infection-naïve prior to vaccination (p < 0.001 for both). CONCLUSIONS: Positive SARS-COV-2 IgG in saliva was concordant with prior infection (Anti N, S, RBD) and vaccination (Anti S, RBD) and remained above positivity threshold for up to 9 months from infection. Salivary sampling is a non-invasive method of tracking immunity and differentiating between prior infection and vaccination to inform the need for boosters in LTC residents and staff.

Methodological approaches to optimize multiplex oral fluid SARS-CoV-2 IgG assay performance and correlation with serologic and neutralizing antibody responses.

Background: Oral fluid (hereafter, saliva) is a non-invasive and attractive alternative to blood for SARS-CoV-2 IgG testing; however, the heterogeneity of saliva as a matrix poses challenges for immunoassay performance. Objectives: To optimize performance of a magnetic microparticle-based multiplex immunoassay (MIA) for SARS-CoV-2 IgG measurement in saliva, with consideration of: i) threshold setting and validation across different MIA bead batches; ii) sample qualification based on salivary total IgG concentration; iii) calibration to U.S. SARS-CoV-2 serological standard binding antibody units (BAU); and iv) correlations with blood-based SARS-CoV-2 serological and neutralizing antibody (nAb) assays. Methods: The salivary SARS-CoV-2 IgG MIA included 2 nucleocapsid (N), 3 receptor-binding domain (RBD), and 2 spike protein (S) antigens. Gingival crevicular fluid (GCF) swab saliva samples were collected before December, 2019 (n=555) and after molecular test-confirmed SARS-CoV-2 infection from 113 individuals (providing up to 5 repeated-measures; n=398) and used to optimize and validate MIA performance (total n=953). Combinations of IgG responses to N, RBD and S and total salivary IgG concentration (µg/mL) as a qualifier of nonreactive samples were optimized and validated, calibrated to the U.S. SARS-CoV-2 serological standard, and correlated with blood-based SARS-CoV-2 IgG ELISA and nAb assays. Results: The sum of signal to cutoff (S/Co) to all seven MIA SARS-CoV-2 antigens and disqualification of nonreactive saliva samples with ≤15 µg/mL total IgG led to correct classification of 62/62 positives (sensitivity [Se]=100.0%; 95% confidence interval [CI]=94.8%, 100.0%) and 108/109 negatives (specificity [Sp]=99.1%; 95% CI=97.3%, 100.0%) at 8-million beads coupling scale and 80/81 positives (Se=98.8%; 95% CI=93.3%, 100.0%] and 127/127 negatives (Sp=100%; 95% CI=97.1%, 100.0%) at 20-million beads coupling scale. Salivary SARS-CoV-2 IgG crossed the MIA cutoff of 0.1 BAU/mL on average 9 days post-COVID-19 symptom onset and peaked around day 30. Among n=30 matched saliva and plasma samples, salivary SARS-CoV-2 MIA IgG levels correlated with corresponding-antigen plasma ELISA IgG (N: ρ=0.67, RBD: ρ=0.76, S: ρ=0.82; all p <0.0001). Correlations of plasma SARS-CoV-2 nAb assay area under the curve (AUC) with salivary MIA IgG (N: ρ=0.68, RBD: ρ=0.78, S: ρ=0.79; all p <0.0001) and with plasma ELISA IgG (N: ρ=0.76, RBD: ρ=0.79, S: ρ=0.76; p <0.0001) were similar. Conclusions: A salivary SARS-CoV-2 IgG MIA produced consistently high Se (>98.8%) and Sp (>99.1%) across two bead coupling scales and correlations with nAb responses that were similar to blood-based SARS-CoV-2 IgG ELISA data. This non-invasive salivary SARS-CoV-2 IgG MIA could increase engagement of vulnerable populations and improve broad understanding of humoral immunity (kinetics and gaps) within the evolving context of booster vaccination, viral variants and waning immunity.