CalScope: methodology and lessons learned for conducting a remote statewide SARS-CoV-2 seroprevalence study in California using an at-home dried blood spot collection kit and online survey.

BACKGROUND: To describe the methodology for conducting the CalScope study, a remote, population-based survey launched by the California Department of Public Health (CDPH) to estimate SARS-CoV-2 seroprevalence and understand COVID-19 disease burden in California. METHODS: Between April 2021 and August 2022, 666,857 randomly selected households were invited by mail to complete an online survey and at-home test kit for up to one adult and one child. A gift card was given for each completed survey and test kit. Multiple customized REDCap databases were used to create a data system which provided task automation and scalable data management through API integrations. Support infrastructure was developed to manage follow-up for participant questions and a communications plan was used for outreach through local partners. RESULTS: Across 3 waves, 32,671 out of 666,857 (4.9%) households registered, 6.3% by phone using an interactive voice response (IVR) system and 95.7% in English. Overall, 25,488 (78.0%) households completed surveys, while 23,396 (71.6%) households returned blood samples for testing. Support requests (n = 5,807) received through the web-based form (36.3%), by email (34.1%), and voicemail (29.7%) were mostly concerned with the test kit (31.6%), test result (26.8%), and gift card (21.3%). CONCLUSIONS: Ensuring a well-integrated and scalable data system, responsive support infrastructure for participant follow-up, and appropriate academic and local health department partnerships for study management and communication allowed for successful rollout of a large population-based survey. Remote data collection utilizing online surveys and at-home test kits can complement routine surveillance data for a state health department.

Observations of Respiratory Syncytial Virus (RSV) Nucleic Acids in Wastewater Solids Across the United States in the 2022-2023 Season: Relationships with RSV Infection Positivity and Hospitalization Rates.

Respiratory syncytial virus (RSV) is a leading cause of respiratory illness and hospitalization, but clinical surveillance detects only a minority of cases. Wastewater surveillance could determine the onset and extent of RSV circulation in the absence of sensitive case detection, but to date, studies of RSV in wastewater are few. We measured RSV RNA concentrations in wastewater solids from 176 sites during the 2022-2023 RSV season and compared those to publicly available RSV infection positivity and hospitalization rates. Concentrations ranged from undetectable to 107 copies per gram. RSV RNA concentration aggregated at state and national levels correlated with infection positivity and hospitalization rates. RSV season onset was determined using both wastewater and clinical positivity rates using independent algorithms for 14 states where both data were available at the start of the RSV season. In 4 of 14 states, wastewater and clinical surveillance identified RSV season onset during the same week; in 3 states, wastewater onset preceded clinical onset, and in 7 states, wastewater onset occurred after clinical onset. Wastewater concentrations generally peaked in the same week as hospitalization rates but after case positivity rates peaked. Differences in onset and peaks in wastewater versus clinical data may reflect inherent differences in the surveillance approaches.

Monthly Sulfadoxine-Pyrimethamine During Pregnancy Prevents Febrile Respiratory Illnesses: A Secondary Analysis of a Malaria Chemoprevention Trial in Uganda.

Background: Trials evaluating antimalarials for intermittent preventive treatment in pregnancy (IPTp) have shown that dihydroartemisinin-piperaquine (DP) is a more efficacious antimalarial than sulfadoxine-pyrimethamine (SP); however, SP is associated with higher birthweight, suggesting that SP demonstrates "nonmalarial" effects. Chemoprevention of nonmalarial febrile illnesses (NMFIs) was explored as a possible mechanism. Methods: In this secondary analysis, we leveraged data from 654 pregnant Ugandan women without HIV infection who participated in a randomized controlled trial comparing monthly IPTp-SP with IPTp-DP. Women were enrolled between 12 and 20 gestational weeks and followed through delivery. NMFIs were measured by active and passive surveillance and defined by the absence of malaria parasitemia. We quantified associations among IPTp regimens, incident NMFIs, antibiotic prescriptions, and birthweight. Results: Mean "birthweight for gestational age" Z scores were 0.189 points (95% CI, .045-.333) higher in women randomized to IPTp-SP vs IPTp-DP. Women randomized to IPTp-SP had fewer incident NMFIs (incidence rate ratio, 0.74; 95% CI, .58-.95), mainly respiratory NMFIs (incidence rate ratio, 0.69; 95% CI, .48-1.00), vs IPTp-DP. Counterintuitively, respiratory NMFI incidence was positively correlated with birthweight in multigravidae. In total 75% of respiratory NMFIs were treated with antibiotics. Although overall antibiotic prescriptions were similar between arms, for each antibiotic prescribed, "birthweight for gestational age" Z scores increased by 0.038 points (95% CI, .001-.074). Conclusions: Monthly IPTp-SP was associated with reduced respiratory NMFI incidence, revealing a potential nonmalarial mechanism of SP and supporting current World Health Organization recommendations for IPTp-SP, even in areas with high-grade SP resistance. While maternal respiratory NMFIs are known risk factors of lower birthweight, most women in our study were presumptively treated with antibiotics, masking the potential benefit of SP on birthweight mediated through preventing respiratory NMFIs.

A modified Michaelis-Menten equation estimates growth from birth to 3 years in healthy babies in the USA.

BACKGROUND: Standard pediatric growth curves cannot be used to impute missing height or weight measurements in individual children. The Michaelis-Menten equation, used for characterizing substrate-enzyme saturation curves, has been shown to model growth in many organisms including nonhuman vertebrates. We investigated whether this equation could be used to interpolate missing growth data in children in the first three years of life and compared this interpolation to several common interpolation methods and pediatric growth models. METHODS: We developed a modified Michaelis-Menten equation and compared expected to actual growth, first in a local birth cohort (N = 97) then in a large, outpatient, pediatric sample (N = 14,695). RESULTS: The modified Michaelis-Menten equation showed excellent fit for both infant weight (median RMSE: boys: 0.22 kg [IQR:0.19; 90% < 0.43]; girls: 0.20 kg [IQR:0.17; 90% < 0.39]) and height (median RMSE: boys: 0.93 cm [IQR:0.53; 90% < 1.0]; girls: 0.91 cm [IQR:0.50;90% < 1.0]). Growth data were modeled accurately with as few as four values from routine well-baby visits in year 1 and seven values in years 1-3; birth weight or length was essential for best fit. Interpolation with this equation had comparable (for weight) or lower (for height) mean RMSE compared to the best performing alternative models. CONCLUSIONS: A modified Michaelis-Menten equation accurately describes growth in healthy babies aged 0-36 months, allowing interpolation of missing weight and height values in individual longitudinal measurement series. The growth pattern in healthy babies in resource-rich environments mirrors an enzymatic saturation curve.

Comparison of US emergency departments by HIV priority jurisdiction designation: A case for geographically targeted screening in teaching hospitals.

The Ending the HIV Epidemic (EHE) Initiative targets a subset of United States (US) priority jurisdictions hardest hit by HIV. It remains unclear which emergency departments (EDs) are the most appropriate targets for EHE-related efforts. To explore this, we used the 2001-2019 National Emergency Department Inventories (NEDI)-USA as a framework to characterize all US EDs, focusing on those in priority jurisdictions and those affiliated with a teaching hospital. We then incorporate multivariable regression to explore the association between ED characteristics and location in an HIV priority jurisdiction. Further, to provide context on the communities these EDs serve, demographic and socioeconomic information and sexually transmitted infection case rate data were included. This reflected 2019 US Census Bureau data on age, race, ethnicity, and proportion uninsured and living in poverty along with 2001-2019 Centers for Disease Control and Prevention case rate data on chlamydia, gonorrhea, and syphilis. We found that EDs in priority jurisdictions (compared to EDs not in priority jurisdictions) more often served populations emphasized in HIV-related efforts (i.e., Black or African American or Hispanic or Latino populations), communities with higher proportions uninsured and living in poverty, and counties with higher rates of chlamydia, gonorrhea, and syphilis. Further, of the groups studied, EDs with teaching hospital affiliations had the highest visit volumes and had steady visit volume growth. In regression, ED annual visit volume was associated with an increased odds of an ED being located in a priority jurisdiction. Our results suggest that geographically targeted screening for HIV in a subset of US priority jurisdiction EDs with a teaching hospital affiliation could be an efficient means to reach vulnerable populations and reduce the burden of undiagnosed HIV in the US.

Defining Usual Oral Temperature Ranges in Outpatients Using an Unsupervised Learning Algorithm.

Importance: Although oral temperature is commonly assessed in medical examinations, the range of usual or "normal" temperature is poorly defined. Objective: To determine normal oral temperature ranges by age, sex, height, weight, and time of day. Design, Setting, and Participants: This cross-sectional study used clinical visit information from the divisions of Internal Medicine and Family Medicine in a single large medical care system. All adult outpatient encounters that included temperature measurements from April 28, 2008, through June 4, 2017, were eligible for inclusion. The LIMIT (Laboratory Information Mining for Individualized Thresholds) filtering algorithm was applied to iteratively remove encounters with primary diagnoses overrepresented in the tails of the temperature distribution, leaving only those diagnoses unrelated to temperature. Mixed-effects modeling was applied to the remaining temperature measurements to identify independent factors associated with normal oral temperature and to generate individualized normal temperature ranges. Data were analyzed from July 5, 2017, to June 23, 2023. Exposures: Primary diagnoses and medications, age, sex, height, weight, time of day, and month, abstracted from each outpatient encounter. Main Outcomes and Measures: Normal temperature ranges by age, sex, height, weight, and time of day. Results: Of 618 306 patient encounters, 35.92% were removed by LIMIT because they included diagnoses or medications that fell disproportionately in the tails of the temperature distribution. The encounters removed due to overrepresentation in the upper tail were primarily linked to infectious diseases (76.81% of all removed encounters); type 2 diabetes was the only diagnosis removed for overrepresentation in the lower tail (15.71% of all removed encounters). The 396 195 encounters included in the analysis set consisted of 126 705 patients (57.35% women; mean [SD] age, 52.7 [15.9] years). Prior to running LIMIT, the mean (SD) overall oral temperature was 36.71 °C (0.43 °C); following LIMIT, the mean (SD) temperature was 36.64 °C (0.35 °C). Using mixed-effects modeling, age, sex, height, weight, and time of day accounted for 6.86% (overall) and up to 25.52% (per patient) of the observed variability in temperature. Mean normal oral temperature did not reach 37 °C for any subgroup; the upper 99th percentile ranged from 36.81 °C (a tall man with underweight aged 80 years at 8:00 am) to 37.88 °C (a short woman with obesity aged 20 years at 2:00 pm). Conclusions and Relevance: The findings of this cross-sectional study suggest that normal oral temperature varies in an expected manner based on sex, age, height, weight, and time of day, allowing individualized normal temperature ranges to be established. The clinical significance of a value outside of the usual range is an area for future study.

Recruitment into antibody prevalence studies: a randomized trial of postcards vs. letters as invitations.

BACKGROUND: In a potential epidemic of an emerging infection, representative population-based serologic studies are required to determine the extent of immunity to the infectious agent, either from natural infection or vaccination. Recruitment strategies need to optimize response rates. METHODS: Within a seroepidemiologic study to determine the true burden of SARS-CoV2 infection in two Bay Area counties, we evaluated whether letter (L) or postcard (P) invitations with reminders were more effective at recruiting participant households. Using geographic, probability-based sampling, 9,999 representative addresses, split between Santa Clara and Solano counties, were randomized to receive an initial invitation (L or P); a randomized reminder mailing sent two weeks later to all non-respondents created four mailing type groups (L/L, L/P, P/L, P/P). Interested households provided contact information via survey to perform blood spot collection at home for testing and then receive SARS-CoV2 serology results. Comparison of demographics among respondents and non-respondents used census tract data. RESULTS: Receiving any reminder mailing increased household response rates from 4.2% to between 8-13% depending on mailing combination. Response rates from two letters were 71% higher than from two postcards (13.2% vs. 7.7%, OR = 1.83 [95% CI: 1.5-2.2]). Respondents were older, more educated and more likely white than non-respondents. Compared to Solano county, Santa Clara county had different demographics and increased household response rates (L/L: 15.7% vs 10.7%; P/P: 9.2% vs. 6.1%; p < 0.0001); the effect of mailing types, however, was the same (L/L vs. P/P: Santa Clara: OR = 1.83 [95% CI: 1.4-2.3]; Solano: OR = 1.84 [95% CI:1.4-2.5]). CONCLUSION: Letters, as both invitations and reminders, are a more effective recruitment tool than postcards and should be considered when seeking a representative population-based sample for serological testing.

Use of Wastewater Metrics to Track COVID-19 in the US.

Importance: Widespread use of at-home COVID-19 tests hampers determination of community COVID-19 incidence. Objective: To examine the association of county-level wastewater metrics with high case and hospitalization rates nationwide both before and after widespread use of at-home tests. Design, Setting, and Participants: This observational cohort study with a time series analysis was conducted from January to September 2022 in 268 US counties in 22 states participating in the US Centers for Disease Control and Prevention's National Wastewater Surveillance System. Participants included the populations of those US counties. Exposures: County level of circulating SARS-CoV-2 as determined by metrics based on viral wastewater concentration relative to the county maximum (ie, wastewater percentile) and 15-day percentage change in SARS-CoV-2 (ie, percentage change). Main Outcomes and Measures: High county incidence of COVID-19 as evidenced by dichotomized reported cases (current cases ≥200 per 100 000 population) and hospitalization (≥10 per 100 000 population lagged by 2 weeks) rates, stratified by calendar quarter. Results: In the first quarter of 2022, use of the wastewater percentile detected high reported case (area under the curve [AUC], 0.95; 95% CI, 0.94-0.96) and hospitalization (AUC, 0.86; 95% CI, 0.84-0.88) rates. The percentage change metric performed poorly, with AUCs ranging from 0.51 (95% CI, 0.50-0.53) to 0.57 (95% CI, 0.55-0.59) for reported new cases, and from 0.50 (95% CI, 0.48-0.52) to 0.55 (95% CI, 0.53-0.57) for hospitalizations across the first 3 quarters of 2022. The Youden index for detecting high case rates was wastewater percentile of 51% (sensitivity, 0.82; 95% CI, 0.80-0.84; specificity, 0.93; 95% CI, 0.92-0.95). A model inclusive of both metrics performed no better than using wastewater percentile alone. The performance of wastewater percentile declined over time for cases in the second quarter (AUC, 0.84; 95% CI, 0.82-0.86) and third quarter (AUC, 0.72; 95% CI, 0.70-0.75) of 2022. Conclusions and Relevance: In this study, nationwide, county wastewater levels relative to the county maximum were associated with high COVID-19 case and hospitalization rates in the first quarter of 2022, but there was increasing dissociation between wastewater and clinical metrics in subsequent quarters, which may reflect increasing underreporting of cases, reduced testing, and possibly lower virulence of infection due to vaccines and treatments. This study offers a strategy to operationalize county wastewater percentile to improve the accurate assessment of community SARS-CoV-2 infection prevalence when reliability of conventional surveillance data is declining.

Evaluation of Acebilustat, a Selective Inhibitor of Leukotriene B4 Biosynthesis, for Treatment of Outpatients With Mild-Moderate Coronavirus Disease 2019: A Randomized, Double-Blind, Placebo-Controlled Phase 2 Trial.

BACKGROUND: The vast majority of coronavirus disease 2019 (COVID-19) disease occurs in outpatients where treatment is limited to antivirals for high-risk subgroups. Acebilustat, a leukotriene B4 inhibitor, has potential to reduce inflammation and symptom duration. METHODS: In a single-center trial spanning Delta and Omicron variants, outpatients were randomized to 100 mg/d of oral acebilustat or placebo for 28 days. Patients reported daily symptoms via electronic query through day 28 with phone follow-up on day 120 and collected nasal swab samples on days 1-10. The primary outcome was sustained symptom resolution to day 28. Secondary 28-day outcomes included time to first symptom resolution, area under the curve (AUC) for longitudinal daily symptom scores, duration of viral shedding through day 10, and symptoms on day 120. RESULTS: Sixty participants were randomized to each study arm. At enrollment, the median duration was 4 days (interquartile range, 3-5 days), and the median number of symptoms was 9 (7-11). Most patients (90%) were vaccinated, with 73% having neutralizing antibodies. A minority of participants (44%; 35% in the acebilustat arm and 53% in placebo) had sustained symptom resolution at day 28 (hazard ratio, 0.6 [95% confidence interval, .34-1.04]; P = .07 favoring placebo). There was no difference in the mean AUC for symptom scores over 28 days (difference in mean AUC, 9.4 [95% confidence interval, -42.1 to 60.9]; P = .72). Acebilustat did not affect viral shedding or symptoms at day 120. CONCLUSIONS: Sustained symptoms through day 28 were common in this low-risk population. Despite this, leukotriene B4 antagonism with acebilustat did not shorten symptom duration in outpatients with COVID-19. Clinical Trials Registration. NCT04662060.

Use of wastewater metrics to track COVID-19 in the U.S.: a national time-series analysis over the first three quarters of 2022.

Background: Widespread use of at-home COVID-19 tests hampers determination of community COVID-19 incidence. Using nationwide data available through the US National Wastewater Surveillance System, we examined the performance of two wastewater metrics in predicting high case and hospitalizations rates both before and after widespread use of at-home tests. Methods: We performed area under the receiver operating characteristic (ROC) curve analysis (AUC) for two wastewater metrics-viral concentration relative to the peak of January 2022 ("wastewater percentile") and 15-day percent change in SARS-CoV-2 ("percent change"). Dichotomized reported cases (≥ 200 or <200 cases per 100,000) and new hospitalizations (≥ 10 or <10 per 100,000) were our dependent variables, stratified by calendar quarter. Using logistic regression, we assessed the performance of combining wastewater metrics. Results: Among 268 counties across 22 states, wastewater percentile detected high reported case and hospitalizations rates in the first quarter of 2022 (AUC 0.95 and 0.86 respectively) whereas the percent change did not (AUC 0.54 and 0.49 respectively). A wastewater percentile of 51% maximized sensitivity (0.93) and specificity (0.82) for detecting high case rates. A model inclusive of both metrics performed no better than using wastewater percentile alone. The predictive capability of wastewater percentile declined over time (AUC 0.84 and 0.72 for cases for second and third quarters of 2022). Conclusion: Nationwide, county wastewater levels above 51% relative to the historic peak predicted high COVID rates and hospitalization in the first quarter of 2022, but performed less well in subsequent quarters. Decline over time in predictive performance of this metric likely reflects underreporting of cases, reduced testing, and possibly lower virulence of infection due to vaccines and treatments.

Longitudinal comparison of the developing gut virome in infants and their mothers.

The human gut virome and its early life development are poorly understood. Prior studies have captured single-point assessments with the evolution of the infant virome remaining largely unexplored. We performed viral metagenomic sequencing on stool samples collected longitudinally from a cohort of 53 infants from age 2 weeks to 3 years (80.7 billion reads), and from their mothers (9.8 billion reads) to examine and compare viromes. The asymptomatic infant virome consisted of bacteriophages, nonhuman dietary/environmental viruses, and human-host viruses, predominantly picornaviruses. In contrast, human-host viruses were largely absent from the maternal virome. Previously undescribed, sequence-divergent vertebrate viruses were detected in the maternal but not infant virome. As infants aged, the phage component evolved to resemble the maternal virome, but by age 3, the human-host component remained dissimilar from the maternal virome. Thus, early life virome development is determined predominantly by dietary, infectious, and environmental factors rather than direct maternal acquisition.

Characteristics of California Emergency Departments in Centers for Disease Control and Prevention-Designated HIV Priority Counties.

BACKGROUND: Refocused national HIV testing initiatives include a geographic focus. OBJECTIVE: Using a geographic focus, we sought to identify which emergency departments (EDs) might be the most efficient targets for future HIV testing efforts, using California as an example. METHODS: Retrospective analysis of California EDs, emergency physicians, and patients served, along with county-level estimates of HIV prevalence and proportion of the population living in poverty. Emphasis was placed on characterizing EDs affiliated with teaching hospitals and those located in Centers for Disease Control (CDC) and Prevention HIV priority counties. RESULTS: Of the 320 EDs studied, 178 were in priority counties, 29 were affiliated with teaching hospitals, and 24 had both characteristics. Of the 12,869,889 ED visits included, 61.8% occurred in priority counties, 14.7% in EDs affiliated with teaching hospitals, and 12.0% in EDs with both characteristics. The subset of EDs in priority counties with teaching hospital affiliations (compared with priority and nonpriority county ED groups without a teaching hospital affiliation) had higher overall median visit volumes and higher proportions of visits by at-risk and CDC-targeted populations (e.g., individuals who were homeless, those who identified as Black or African American race, and those who identified as Hispanic or Latino ethnicity, all p < 0.01). CONCLUSIONS: EDs in priority counties affiliated with teaching hospitals are major sources of health care in California. These EDs more often serve populations disproportionately impacted by HIV. These departments are efficient targets to direct testing efforts. Increasing testing in these EDs could reduce the burden of undiagnosed HIV in California.

Gestational diabetes is driven by microbiota-induced inflammation months before diagnosis.

OBJECTIVE: Gestational diabetes mellitus (GDM) is a condition in which women without diabetes are diagnosed with glucose intolerance during pregnancy, typically in the second or third trimester. Early diagnosis, along with a better understanding of its pathophysiology during the first trimester of pregnancy, may be effective in reducing incidence and associated short-term and long-term morbidities. DESIGN: We comprehensively profiled the gut microbiome, metabolome, inflammatory cytokines, nutrition and clinical records of 394 women during the first trimester of pregnancy, before GDM diagnosis. We then built a model that can predict GDM onset weeks before it is typically diagnosed. Further, we demonstrated the role of the microbiome in disease using faecal microbiota transplant (FMT) of first trimester samples from pregnant women across three unique cohorts. RESULTS: We found elevated levels of proinflammatory cytokines in women who later developed GDM, decreased faecal short-chain fatty acids and altered microbiome. We next confirmed that differences in GDM-associated microbial composition during the first trimester drove inflammation and insulin resistance more than 10 weeks prior to GDM diagnosis using FMT experiments. Following these observations, we used a machine learning approach to predict GDM based on first trimester clinical, microbial and inflammatory markers with high accuracy. CONCLUSION: GDM onset can be identified in the first trimester of pregnancy, earlier than currently accepted. Furthermore, the gut microbiome appears to play a role in inflammation-induced GDM pathogenesis, with interleukin-6 as a potential contributor to pathogenesis. Potential GDM markers, including microbiota, can serve as targets for early diagnostics and therapeutic intervention leading to prevention.

A modified Michaelis-Menten equation estimates growth from birth to 3 years in healthy babies in the US.

Background and Objectives: Standard pediatric growth curves cannot be used to impute missing height or weight measurements in individual children. The Michaelis-Menten equation, used for characterizing substrate-enzyme saturation curves, has been shown to model growth in many organisms including nonhuman vertebrates. We investigated this equation could be used to interpolate missing growth data in children in the first three years of life. Methods: We developed a modified Michaelis-Menten equation and compared expected to actual growth, first in a local birth cohort (N=97) then in a large, outpatient, pediatric sample (N=14,695). Results: The modified Michaelis-Menten equation showed excellent fit for both infant weight (median RMSE: boys: 0.22kg [IQR:0.19; 90%<0.43]; girls: 0.20kg [IQR:0.17; 90%<0.39]) and height (median RMSE: boys: 0.93cm [IQR:0.53; 90%<1.0]; girls: 0.91cm [IQR:0.50;90%<1.0]). Growth data were modeled accurately with as few as four values from routine well-baby visits in year 1 and seven values in years 1-3; birth weight or length was essential for best fit. Conclusions: A modified Michaelis-Menten equation accurately describes growth in healthy babies aged 0-36 months, allowing interpolation of missing weight and height values in individual longitudinal measurement series. The growth pattern in healthy babies in resource-rich environments mirrors an enzymatic saturation curve.

Microbiota-mediated reactivation of triclosan oxidative metabolites in colon tissues.

Triclosan (TCS) is a widespread antimicrobial agent that is associated with many adverse health outcomes. Its gut toxicity has been attributed to the molecular modifications mediated by commensal microbes, but microbial transformations of TCS derivatives in the gut lumen are still largely unknown. Aromatic hydroxylation is the predominant oxidative metabolism of TCS that linked to its toxicological effects in host tissues. Here, we aimed to reveal the biological fates of hydroxyl-TCS (OH-TCS) in the colon, where intestinal microbes mainly reside. Unlike the profiles generated via host metabolism, OH-TCS species remain unconjugated in human stools from a cohort study. Through tracking molecular compositions in mouse intestinal tract, elevated abundance of free-form OH-TCS while reduced abundance of conjugated forms was observed in the colon digesta and mucosa. Using antibiotic-treated and germ-free mice, as well as in vitro approaches, we demonstrate that gut microbiota-encoded enzymes efficiently convert glucuronide/sulfate-conjugated OH-TCS, which are generated from host metabolism, back to their bioactive free-forms in colon tissues. Thus, host-gut microbiota metabolic interactions of TCS derivatives were proposed. These results shed light on the crucial roles of microbial metabolism in TCS toxicity, and highlight the importance of incorporating gut microbial transformations in health risk assessment of environmental chemicals.

Early immune markers of clinical, virological, and immunological outcomes in patients with COVID-19: a multi-omics study.

Background: The great majority of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infections are mild and uncomplicated, but some individuals with initially mild COVID-19 progressively develop more severe symptoms. Furthermore, there is substantial heterogeneity in SARS-CoV-2-specific memory immune responses following infection. There remains a critical need to identify host immune biomarkers predictive of clinical and immunological outcomes in SARS-CoV-2-infected patients. Methods: Leveraging longitudinal samples and data from a clinical trial (N=108) in SARS-CoV-2-infected outpatients, we used host proteomics and transcriptomics to characterize the trajectory of the immune response in COVID-19 patients. We characterized the association between early immune markers and subsequent disease progression, control of viral shedding, and SARS-CoV-2-specific T cell and antibody responses measured up to 7 months after enrollment. We further compared associations between early immune markers and subsequent T cell and antibody responses following natural infection with those following mRNA vaccination. We developed machine-learning models to predict patient outcomes and validated the predictive model using data from 54 individuals enrolled in an independent clinical trial. Results: We identify early immune signatures, including plasma RIG-I levels, early IFN signaling, and related cytokines (CXCL10, MCP1, MCP-2, and MCP-3) associated with subsequent disease progression, control of viral shedding, and the SARS-CoV-2-specific T cell and antibody response measured up to 7 months after enrollment. We found that several biomarkers for immunological outcomes are shared between individuals receiving BNT162b2 (Pfizer-BioNTech) vaccine and COVID-19 patients. Finally, we demonstrate that machine-learning models using 2-7 plasma protein markers measured early within the course of infection are able to accurately predict disease progression, T cell memory, and the antibody response post-infection in a second, independent dataset. Conclusions: Early immune signatures following infection can accurately predict clinical and immunological outcomes in outpatients with COVID-19 using validated machine-learning models. Funding: Support for the study was provided from National Institute of Health/National Institute of Allergy and Infectious Diseases (NIH/NIAID) (U01 AI150741-01S1 and T32-AI052073), the Stanford's Innovative Medicines Accelerator, National Institutes of Health/National Institute on Drug Abuse (NIH/NIDA) DP1DA046089, and anonymous donors to Stanford University. Peginterferon lambda provided by Eiger BioPharmaceuticals.

Validity of at-home rapid antigen lateral flow assay and artificial intelligence read to detect SARS-CoV-2.

BACKGROUND: The gold standard for COVID-19 diagnosis-reverse-transcriptase polymerase chain reaction (RT-PCR)- is expensive and often slow to yield results whereas lateral flow tests can lack sensitivity. METHODS: We tested a rapid, lateral flow antigen (LFA) assay with artificial intelligence read (LFAIR) in subjects from COVID-19 treatment trials (N = 37; daily tests for 5 days) and from a population-based study (N = 88; single test). LFAIR was compared to RT-PCR from same-day samples. RESULTS: Using each participant's first sample, LFAIR showed 86.2% sensitivity (95% CI 73.6%-98.8) and 94.3% specificity (88.8%-99.7%) compared to RT-PCR. Adjusting for days since symptom onset and repeat testing, sensitivity was 97.8% (89.9%-99.5%) on the first symptomatic day and decreased with each additional day. Sensitivity improved with artificial intelligence (AI) read (86.2%) compared to the human eye (71.4%). CONCLUSION: LFAIR showed improved accuracy compared to LFA alone. particularly early in infection.

SARS-CoV-2 Infection during the Omicron Surge among Patients Receiving Dialysis: The Role of Circulating Receptor-Binding Domain Antibodies and Vaccine Doses.

BACKGROUND: It is unclear whether circulating antibody levels conferred protection against SARS-CoV-2 infection among patients receiving dialysis during the Omicron-dominant period. METHODS: We followed monthly semiquantitative SARS-CoV-2 RBD IgG index values in a randomly selected nationwide cohort of patients receiving dialysis and ascertained SARS-CoV-2 infection during the Omicron-dominant period of December 25, 2021 to January 31, 2022 using electronic health records. We estimated the relative risk for documented SARS-CoV-2 infection by vaccination status and by circulating RBD IgG using a log-binomial model accounting for age, sex, and prior COVID-19. RESULTS: Among 3576 patients receiving dialysis, 901 (25%) received a third mRNA vaccine dose as of December 24, 2021. Early antibody responses to third doses were robust (median peak index IgG value at assay limit of 150). During the Omicron-dominant period, SARS-CoV-2 infection was documented in 340 (7%) patients. Risk for infection was higher among patients without vaccination and with one to two doses (RR, 2.1; 95% CI, 1.6 to 2.8, and RR, 1.3; 95% CI, 1.0 to 1.8 versus three doses, respectively). Irrespective of the number of vaccine doses, risk for infection was higher among patients with circulating RBD IgG <23 (506 BAU/ml) (RR range, 2.1 to 3.2, 95% CI, 1.3 to 3.4 and 95% CI, 2.2 to 4.5, respectively) compared with RBD IgG ≥23. CONCLUSIONS: Among patients receiving dialysis, a third mRNA vaccine dose enhanced protection against SARS-CoV-2 infection during the Omicron-dominant period, but a low circulating RBD antibody response was associated with risk for infection independent of the number of vaccine doses. Measuring circulating antibody levels in this high-risk group could inform optimal timing of vaccination and other measures to reduce risk of SARS-CoV-2 infection.