Early coronavirus disease 2019 restrictive measures and changes in maternal characteristics, use of assisted reproductive technology, and stillbirth

BACKGROUND

The initial COVID-19 pandemic response-related effects on conceptions following the use of assisted reproductive technologies (ART), and on changes in the maternal characteristics of women who conceived during the early vs. pre-pandemic period, have been understudied.

OBJECTIVES

To examine the effects of ART clinic closures in the United States (US) in March 2020 on the frequency of ART-conceived live births, multiple births and stillbirths; and to describe changes in the characteristics of women who conceived in the early pandemic period.

METHODS

Population-based cohort study including all births in the US from January 2015 to December 2020 (22,907,688 live births; 134,537 stillbirths). Interrupted time series (ITS) methodology was used to estimate rate ratios (RR) of expected versus observed rates in December 2020 (i.e., among births conceived mainly in March 2020). Demographic and clinical characteristics were compared between mothers who conceived in March 2020 versus March 2015-2019.

RESULTS

Overall, 1.1% of live births and 1.7% of stillbirths were conceived by ART. ART-conceived live births decreased by 57.0% in December 2020 (observed vs. expected RR 0.43, 95% confidence interval [CI] 0.40, 0.45), and these declines occurred in all subgroups of women. Multiple births also declined in December 2020. Stillbirth rates increased in December 2020 in ART-conceived births (RR 2.55, 95% CI 1.63, 3.92) but remained unchanged in the non-ART group. Maternal characteristics of women who conceived in the early pandemic versus pre-pandemic period differed and included an increased prevalence of pre-pregnancy obesity class 3 and chronic hypertension.

CONCLUSIONS

The early pandemic closure of ART clinics resulted in a substantial decline in ART-conceived live births and multiple births in December 2020 and an increase in the proportion of stillbirths among ART-conceived births. Women who conceived in the early pandemic period also had an increased prevalence of obesity and chronic hypertension.

Circulating anti-nuclear autoantibodies in COVID-19 survivors predict long COVID symptoms

BACKGROUND

Autoimmunity has been reported in patients with severe coronavirus disease 2019 (COVID-19). We investigated whether anti-nuclear/extractable-nuclear antibodies (ANAs/ENAs) were present up to a year after infection, and if they were associated with the development of clinically relevant post-acute sequalae of COVID-19 (PASC) symptoms.

METHODS

A rapid-assessment line immunoassay was used to measure circulating levels of ANAs/ENAs in 106 convalescent COVID-19 patients with varying acute phase severities at 3, 6 and 12 months post-recovery. Patient-reported fatigue, cough and dyspnoea were recorded at each time point. Multivariable logistic regression model and receiver operating curves were used to test the association of autoantibodies with patient-reported outcomes and pro-inflammatory cytokines.

RESULTS

Compared to age- and sex-matched healthy controls (n=22) and those who had other respiratory infections (n=34), patients with COVID-19 had higher detectable ANAs at 3 months post-recovery (p<0.001). The mean number of ANA autoreactivities per individual decreased between 3 and 12 months (from 3.99 to 1.55) with persistent positive titres associated with fatigue, dyspnoea and cough severity. Antibodies to U1-snRNP and anti-SS-B/La were both positively associated with persistent symptoms of fatigue (p<0.028, area under the curve (AUC) 0.86) and dyspnoea (p<0.003, AUC=0.81). Pro-inflammatory cytokines such as tumour necrosis factor (TNF)-α and C-reactive protein predicted the elevated ANAs at 12 months. TNF-α, D-dimer and interleukin-1β had the strongest association with symptoms at 12 months. Regression analysis showed that TNF-α predicted fatigue (β=4.65, p=0.004) and general symptomaticity (β=2.40, p=0.03) at 12 months.

INTERPRETATION

Persistently positive ANAs at 12 months post-COVID are associated with persisting symptoms and inflammation (TNF-α) in a subset of COVID-19 survivors. This finding indicates the need for further investigation into the role of autoimmunity in PASC.

Serial infection with SARS-CoV-2 Omicron BA.1 and BA.2 following three-dose COVID-19 vaccination

SARS-CoV-2 Omicron infections are common among individuals who are vaccinated or have recovered from prior variant infection, but few reports have immunologically assessed serial Omicron infections. We characterized SARS-CoV-2 humoral responses in an individual who acquired laboratory-confirmed Omicron BA.1.15 ten weeks after a third dose of BNT162b2, and BA.2 thirteen weeks later. Responses were compared to 124 COVID-19-naive vaccinees. One month post-second and -third vaccine doses, the participant's wild-type and BA.1-specific IgG, ACE2-displacement and virus neutralization activities were average for a COVID-19-naive triple-vaccinated individual. BA.1 infection boosted the participant's responses to the cohort ≥95th percentile, but even this strong "hybrid" immunity failed to protect against BA.2. Reinfection increased BA.1 and BA.2-specific responses only modestly. Though vaccines clearly protect against severe disease, results highlight the continued importance of maintaining additional protective measures to counteract the immune-evasive Omicron variant, particularly as vaccine-induced immune responses naturally decline over time.

Therapeutic hypothermia attenuates physiologic, histologic, and metabolomic markers of injury in a porcine model of acute respiratory distress syndrome

Acute respiratory distress syndrome (ARDS) is a lung injury characterized by noncardiogenic pulmonary edema and hypoxic respiratory failure. The purpose of this study was to investigate the effects of therapeutic hypothermia on short-term experimental ARDS. Twenty adult female Yorkshire pigs were divided into four groups (n = 5 each): normothermic control (C), normothermic injured (I), hypothermic control (HC), and hypothermic injured (HI). Acute respiratory distress syndrome was induced experimentally via intrapulmonary injection of oleic acid. Target core temperature was achieved in the HI group within 1 h of injury induction. Cardiorespiratory, histologic, cytokine, and metabolomic data were collected on all animals prior to and following injury/sham. All data were collected for approximately 12 h from the beginning of the study until euthanasia. Therapeutic hypothermia reduced injury in the HI compared to the I group (histological injury score = 0.51 ± 0.18 vs. 0.76 ± 0.06; p = 0.02) with no change in gas exchange. All groups expressed distinct phenotypes, with a reduction in pro-inflammatory metabolites, an increase in anti-inflammatory metabolites, and a reduction in inflammatory cytokines observed in the HI group compared to the I group. Changes to respiratory system mechanics in the injured groups were due to increases in lung elastance (E) and resistance (R) (ΔE from pre-injury = 46 ± 14 cmH₂ O L^-1 , p < 0.0001; ΔR from pre-injury: 3 ± 2 cmH₂ O L^-1  s^- , p = 0.30) rather than changes to the chest wall (ΔE from pre-injury: 0.7 ± 1.6 cmH₂ O L^-1 , p = 0.99; ΔR from pre-injury: 0.6 ± 0.1 cmH₂ O L^-1  s^- , p = 0.01). Both control groups had no change in respiratory mechanics. In conclusion, therapeutic hypothermia can reduce markers of injury and inflammation associated with experimentally induced short-term ARDS.

Age-Associated Seroprevalence of Coronavirus Antibodies: Population-Based Serosurveys in 2013 and 2020, British Columbia, Canada

Background

Older adults have been disproportionately affected during the SARS-CoV-2 pandemic, including higher risk of severe disease and long-COVID. Prior exposure to endemic human coronaviruses (HCoV) may modulate the response to SARS-CoV-2 infection and contribute to age-related observations. We hypothesized that cross-reactive antibodies to SARS-CoV-2 are associated with antibodies to HCoV and that both increase with age.

Methods

To assess SARS-CoV-2 unexposed individuals, we drew upon archived anonymized residual sero-surveys conducted in British Columbia (BC), Canada, including before SARS-CoV-2 emergence (May, 2013) and before widespread community circulation in BC (May, 2020). Fifty sera, sex-balanced per ten-year age band, were sought among individuals ≤10 to ≥80 years old, supplemented as indicated by sera from March and September 2020. Sera were tested on the Meso Scale Diagnostics (MSD) electrochemiluminescent multiplex immunoassay to quantify IgG antibody against the Spike proteins of HCoV, including alpha (HCoV-229E, HCoV-NL63) and beta (HCoV-HKU1, HCoV-OC43) viruses, and the 2003 epidemic beta coronavirus, SARS-CoV-1. Cross-reactive antibodies to Spike, Nucleocapsid, and the Receptor Binding Domain (RBD) of SARS-CoV-2 were similarly measured, with SARS-CoV-2 sero-positivity overall defined by positivity on ≥2 targets.

Results

Samples included 407 sera from 2013, of which 17 were children ≤10 years. The 2020 samples included 488 sera, of which 88 were children ≤10 years. Anti-Spike antibodies to all four endemic HCoV were acquired by 10 years of age. There were 20/407 (5%) sera in 2013 and 8/488 (2%) in 2020 that were considered sero-positive for SARS-CoV-2 based on MSD testing. Of note, antibody to the single SARS-CoV-2 RBD target was detected in 329/407 (81%) of 2013 sera and 91/488 (19%) of 2020 sera. Among the SARS-CoV-2 overall sero-negative population, age was correlated with anti-HCoV antibody levels and these, notably 229E and HKU1, were correlated with cross-reactive anti-SARS-CoV-2 RBD titres. SARS-CoV-2 overall sero-positive individuals showed higher titres to HCoV more generally.

Conclusion

Most people have an HCoV priming exposure by 10 years of age and IgG levels are stable thereafter. Anti-HCoV antibodies can cross-react with SARS-CoV-2 epitopes. These immunological interactions warrant further investigation with respect to their implications for COVID-19 clinical outcomes.

An overview of the first 5 years of the ENIGMA obsessive-compulsive disorder working group: The power of worldwide collaboration

Neuroimaging has played an important part in advancing our understanding of the neurobiology of obsessive-compulsive disorder (OCD). At the same time, neuroimaging studies of OCD have had notable limitations, including reliance on relatively small samples. International collaborative efforts to increase statistical power by combining samples from across sites have been bolstered by the ENIGMA consortium; this provides specific technical expertise for conducting multi-site analyses, as well as access to a collaborative community of neuroimaging scientists. In this article, we outline the background to, development of, and initial findings from ENIGMA's OCD working group, which currently consists of 47 samples from 34 institutes in 15 countries on 5 continents, with a total sample of 2,323 OCD patients and 2,325 healthy controls. Initial work has focused on studies of cortical thickness and subcortical volumes, structural connectivity, and brain lateralization in children, adolescents and adults with OCD, also including the study on the commonalities and distinctions across different neurodevelopment disorders. Additional work is ongoing, employing machine learning techniques. Findings to date have contributed to the development of neurobiological models of OCD, have provided an important model of global scientific collaboration, and have had a number of clinical implications. Importantly, our work has shed new light on questions about whether structural and functional alterations found in OCD reflect neurodevelopmental changes, effects of the disease process, or medication impacts. We conclude with a summary of ongoing work by ENIGMA-OCD, and a consideration of future directions for neuroimaging research on OCD within and beyond ENIGMA.

Protease-Inhibitor Interaction Predictions: Lessons on the Complexity of Protein-Protein Interactions

Protein interactions shape proteome function and thus biology. Identification of protein interactions is a major goal in molecular biology, but biochemical methods, although improving, remain limited in coverage and accuracy. Whereas computational predictions can guide biochemical experiments, low validation rates of predictions remain a major limitation. Here, we investigated computational methods in the prediction of a specific type of interaction, the inhibitory interactions between proteases and their inhibitors. Proteases generate thousands of proteoforms that dynamically shape the functional state of proteomes. Despite the important regulatory role of proteases, knowledge of their inhibitors remains largely incomplete with the vast majority of proteases lacking an annotated inhibitor. To link inhibitors to their target proteases on a large scale, we applied computational methods to predict inhibitory interactions between proteases and their inhibitors based on complementary data, including coexpression, phylogenetic similarity, structural information, co-annotation, and colocalization, and also surveyed general protein interaction networks for potential inhibitory interactions. In testing nine predicted interactions biochemically, we validated the inhibition of kallikrein 5 by serpin B12. Despite the use of a wide array of complementary data, we found a high false positive rate of computational predictions in biochemical follow-up. Based on a protease-specific definition of true negatives derived from the biochemical classification of proteases and inhibitors, we analyzed prediction accuracy of individual features, thereby we identified feature-specific limitations, which also affected general protein interaction prediction methods. Interestingly, proteases were often not coexpressed with most of their functional inhibitors, contrary to what is commonly assumed and extrapolated predominantly from cell culture experiments. Predictions of inhibitory interactions were indeed more challenging than predictions of nonproteolytic and noninhibitory interactions. In summary, we describe a novel and well-defined but difficult protein interaction prediction task and thereby highlight limitations of computational interaction prediction methods.