Nitric oxide controls proliferation of Leishmania major by inhibiting the recruitment of permissive host cells.

Nitric oxide (NO) is an important antimicrobial effector but also prevents unnecessary tissue damage by shutting down the recruitment of monocyte-derived phagocytes. Intracellular pathogens such as Leishmania major can hijack these cells as a niche for replication. Thus, NO might exert containment by restricting the availability of the cellular niche required for efficient pathogen proliferation. However, such indirect modes of action remain to be established. By combining mathematical modeling with intravital 2-photon biosensors of pathogen viability and proliferation, we show that low L. major proliferation results not from direct NO impact on the pathogen but from reduced availability of proliferation-permissive host cells. Although inhibiting NO production increases recruitment of these cells, and thus pathogen proliferation, blocking cell recruitment uncouples the NO effect from pathogen proliferation. Therefore, NO fulfills two distinct functions for L. major containment: permitting direct killing and restricting the supply of proliferation-permissive host cells.

Class-Switch Recombination Occurs Infrequently in Germinal Centers.

Class-switch recombination (CSR) is a DNA recombination process that replaces the immunoglobulin (Ig) constant region for the isotype that can best protect against the pathogen. Dysregulation of CSR can cause self-reactive BCRs and B cell lymphomas; understanding the timing and location of CSR is therefore important. Although CSR commences upon T cell priming, it is generally considered a hallmark of germinal centers (GCs). Here, we have used multiple approaches to show that CSR is triggered prior to differentiation into GC B cells or plasmablasts and is greatly diminished in GCs. Despite finding a small percentage of GC B cells expressing germline transcripts, phylogenetic trees of GC BCRs from secondary lymphoid organs revealed that the vast majority of CSR events occurred prior to the onset of somatic hypermutation. As such, we have demonstrated the existence of IgM-dominated GCs, which are unlikely to occur under the assumption of ongoing switching.

Systematic review to estimate the prevalence of inflammatory rheumatic diseases in Germany.

OBJECTIVE: This study aimed to update the prevalence estimates of inflammatory rheumatic diseases (IRD) in Germany. METHODS: A systematic literature search in PubMed and Web of Science (last search 08 November 2022) identified original articles (regional and nationwide surveys and claims data analyses for arthritides, connective tissue diseases, and vasculitides) on prevalences for the period 2014-2022. Data sources, collection period, case definition, and risk of bias are reported. Prevalences were estimated from available national data, with consideration of international data. RESULTS: Screening by two authors yielded 263 hits, of which 18 claims data analyses and 2 surveys met the inclusion criteria. Prevalences ranged from 0.42 to 1.85% (rheumatoid arthritis), 0.32-0.5% (ankylosing spondylitis), 0.11-0.32% (psoriatic arthritis), 0.037-0.14% (systemic lupus erythematosus), 0.07-0.77% (Sjögren's disease/sicca syndrome), 0.14-0.15% (polymyalgia rheumatica, ≥ 40 years), 0.04-0.05% (giant cell arteritis, ≥ 50 years), and 0.015-0.026% (ANCA-associated vasculitis). The risk of bias was moderate in 13 and high in 7 studies. Based on the results, we estimate the prevalence of IRD in Germany to be 2.2-3.0%, which corresponds to approximately 1.5-2.1 million affected individuals. The prevalence of juvenile idiopathic arthritis was reported to be around 0.10% (0.07-0.10%) of 0-18-year-olds, corresponding to about 14,000 children and adolescents in Germany. CONCLUSION: This systematic review shows an increase in the prevalence of IRD in Germany, which is almost exclusively based on claims data analyses. In the absence of multistage population studies, the available data are, overall, uncertain sources for prevalence estimates, with a moderate to high risk of bias.

Engineering the biological conversion of formate into crotonate in Cupriavidus necator.

To advance the sustainability of the biobased economy, our society needs to develop novel bioprocesses based on truly renewable resources. The C1-molecule formate is increasingly proposed as carbon and energy source for microbial fermentations, as it can be efficiently generated electrochemically from CO2 and renewable energy. Yet, its biotechnological conversion into value-added compounds has been limited to a handful of examples. In this work, we engineered the natural formatotrophic bacterium C. necator as cell factory to enable biological conversion of formate into crotonate, a platform short-chain unsaturated carboxylic acid of biotechnological relevance. First, we developed a small-scale (150-mL working volume) cultivation setup for growing C. necator in minimal medium using formate as only carbon and energy source. By using a fed-batch strategy with automatic feeding of formic acid, we could increase final biomass concentrations 15-fold compared to batch cultivations in flasks. Then, we engineered a heterologous crotonate pathway in the bacterium via a modular approach, where each pathway section was assessed using multiple candidates. The best performing modules included a malonyl-CoA bypass for increasing the thermodynamic drive towards the intermediate acetoacetyl-CoA and subsequent conversion to crotonyl-CoA through partial reverse ß-oxidation. This pathway architecture was then tested for formate-based biosynthesis in our fed-batch setup, resulting in a two-fold higher titer, three-fold higher productivity, and five-fold higher yield compared to the strain not harboring the bypass. Eventually, we reached a maximum product titer of 148.0 ± 6.8 mg/L. Altogether, this work consists in a proof-of-principle integrating bioprocess and metabolic engineering approaches for the biological upgrading of formate into a value-added platform chemical.

Appropriate relaxation of non-pharmaceutical interventions minimizes the risk of a resurgence in SARS-CoV-2 infections in spite of the Delta variant.

We analyze the relaxation of non-pharmaceutical interventions (NPIs) under an increasing number of vaccinations in Germany. For the spread of SARS-CoV-2 we employ a SIR-type model that accounts for age-dependence and includes realistic contact patterns between age groups. The implementation of NPIs occurs on changed contact patterns, improved isolation, or reduced infectiousness when, e.g., wearing masks. We account for spatial heterogeneity and commuting activities in between regions in Germany, and the testing of commuters is considered as a further NPI. We include the ongoing vaccination process and analyze the effect of the B.1.617.2 (Delta) variant, which is considered to be 40%-60% more infectious then the currently dominant B.1.1.7 (Alpha) variant. We explore different opening scenarios under the ongoing vaccination process by assuming that local restrictions are either lifted in early July or August with or without continued wearing of masks and testing. Our results indicate that we can counteract the resurgence of SARS-CoV-2 despite the Delta variant with appropriate timing for the relaxation of NPIs. In all cases, however, school children are hit the hardest.

Rate of Immune Complex Cycling in Follicular Dendritic Cells Determines the Extent of Protecting Antigen Integrity and Availability to Germinal Center B Cells.

Follicular dendritic cells (FDCs) retain immune complexes (ICs) for prolonged time periods and are important for germinal center (GC) reactions. ICs undergo periodic cycling in FDCs, a mechanism supporting an extended half-life of Ag. Based on experimental data, we estimated that the average residence time of PE-ICs on FDC surface and interior were 21 and 36 min, respectively. GC simulations show that Ag cycling might impact GC dynamics because of redistribution of Ag on the FDC surface and by protecting Ag from degradation. Ag protection and influence on GC dynamics varied with Ag cycling time and total Ag concentration. Simulations predict that blocking Ag cycling terminates the GC reaction and decreases plasma cell production. Considering that cycling of Ag could be a target for the modulation of GC reactions, our findings highlight the importance of understanding the mechanism and regulation of IC cycling in FDCs.

[Systematic review to estimate the prevalence of inflammatory rheumatic diseases in Germany. German version]. / Systematisches Review zur Schätzung der Prävalenz entzündlich rheumatischer Erkrankungen in Deutschland.

OBJECTIVE: To update the estimated prevalence of inflammatory rheumatic diseases (IRD) in Germany. METHODS: A systematic literature search in PubMed and Web of Science (last search 8 November 2022) identified original articles (regional and nationwide surveys and routine data analyses for arthritides, connective tissue diseases, and vasculitides) on the prevalence for the period 2014-2022. Data sources, collection period, case definition, and risk of bias are reported. The prevalences were estimated from available national data, with consideration of international data. RESULTS: Screening by 2 authors yielded 263 hits, of which 18 routine data analyses and 2 surveys met the inclusion criteria. Prevalence data ranged from 0.42% to 1.85% (rheumatoid arthritis), 0.32-0.5% (ankylosing spondylitis), 0.11-0.32% (psoriatic arthritis), 0.037-0.14% (systemic lupus erythematosus), 0.07-0.77% (Sjoegren's disease/sicca syndrome), 0.14-0.15% (polymyalgia rheumatica, ≥ 40 years), 0.04-0.05% (giant cell arteritis, ≥ 50 years), and 0.015-0.026% (ANCA-associated vasculitis). The risk of bias was moderate in 13 and high in 7 studies. Based on the results, we estimate the prevalence of IRD in Germany to be 2.2-3.0%, which corresponds to approximately 1.5-2.1 million affected individuals. Prevalence data of juvenile idiopathic arthritis was reported to be around 0.10% (0.07-0.10%) of patients 0-18 years old, corresponding to about 14,000 children and adolescents in Germany. CONCLUSION: This systematic review shows an increase in the prevalence of IRD in Germany, which is almost exclusively based on routine data analyses. In the absence of multistage population studies, the available data are overall uncertain sources for prevalence estimates at moderate to high risk of bias.

Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants in Germany.

BACKGROUND: Despite the vaccination process in Germany, a large share of the population is still susceptible to SARS-CoV-2. In addition, we face the spread of novel variants. Until we overcome the pandemic, reasonable mitigation and opening strategies are crucial to balance public health and economic interests. METHODS: We model the spread of SARS-CoV-2 over the German counties by a graph-SIR-type, metapopulation model with particular focus on commuter testing. We account for political interventions by varying contact reduction values in private and public locations such as homes, schools, workplaces, and other. We consider different levels of lockdown strictness, commuter testing strategies, or the delay of intervention implementation. We conduct numerical simulations to assess the effectiveness of the different intervention strategies after one month. The virus dynamics in the regions (German counties) are initialized randomly with incidences between 75 and 150 weekly new cases per 100,000 inhabitants (red zones) or below (green zones) and consider 25 different initial scenarios of randomly distributed red zones (between 2 and 20% of all counties). To account for uncertainty, we consider an ensemble set of 500 Monte Carlo runs for each scenario. RESULTS: We find that the strength of the lockdown in regions with out of control virus dynamics is most important to avoid the spread into neighboring regions. With very strict lockdowns in red zones, commuter testing rates of twice a week can substantially contribute to the safety of adjacent regions. In contrast, the negative effect of less strict interventions can be overcome by high commuter testing rates. A further key contributor is the potential delay of the intervention implementation. In order to keep the spread of the virus under control, strict regional lockdowns with minimum delay and commuter testing of at least twice a week are advisable. If less strict interventions are in favor, substantially increased testing rates are needed to avoid overall higher infection dynamics. CONCLUSIONS: Our results indicate that local containment of outbreaks and maintenance of low overall incidence is possible even in densely populated and highly connected regions such as Germany or Western Europe. While we demonstrate this on data from Germany, similar patterns of mobility likely exist in many countries and our results are, hence, generalizable to a certain extent.

Naive- and Memory-like CD21low B Cell Subsets Share Core Phenotypic and Signaling Characteristics in Systemic Autoimmune Disorders.

An expansion of CD21low B cells has been described in a variety of diseases associated with persistent immune stimulation as in chronic infection, immunodeficiency, or autoimmunity. Different developmental stages of CD21low B cells have been highlighted in specific diseases; however, a systematic comparison of distribution, phenotype, and signaling capacity of these populations has not yet been performed to delineate the pivotal character of this unusual B cell population. Screening of more than 200 patients with autoimmune disease demonstrated that the prevalence of patients with expanded CD21low B cells varies between diseases. The expansion was frequent in patients with systemic lupus erythematosus, in which it correlated to relative B cell lymphopenia and duration of disease. Different proportions of distinct developmental stages of CD21low B cells co-occur in nearly all patients with autoimmune disease. Although in most patients, naive-like and CD27- switched memory B cells were the most prominent CD21low subpopulations, there was no detectable association of the pattern with the underlying disease. Despite their distinct developmental stage, all CD21low B cells share a common core phenotype including the increased expression of inhibitory receptors, associated with an elevated constitutive phosphorylation of proximal signaling molecules downstream of the BCR but impaired Ca2+ mobilization and NF-κB activation after BCR stimulation. Further, this was accompanied by impaired upregulation of CD69, although CD86 upregulation was preserved. Beyond maturation-associated differences, the common core characteristics of all CD21low B cell populations suggests either a common ancestry or a shared sustained imprint by the environment they originated in.

Cross-sectional study for derivation of a cut-off value for identification of an early versus delayed diagnosis of endometriosis based on analytical and descriptive research methods.

BACKGROUND: Endometriosis is a benign, hormone-dependent, chronic inflammatory gynecological disease accompanied by cyclic and acyclic pelvic pain and other complaints. The long lists of research recommendations in the AWMF guideline (Burghaus et al., Geburtshilfe Frauenheilkd 81:422-46, 2021) and ESHRE Endometriosis Guideline (ESHRE Endometriosis Guideline Development Group, Endometriosis: Guideline of European Society of Human Reproduction and Embryology, 2022) show that there is still a great need for research in all aspects of the disease. Diagnostic delay, defined as the mean time between symptom onset and confirmed diagnosis, is a particular problem associated with endometriosis. Some quantitative and qualitative studies have investigated possible reasons for this. A range of physician-related (Dixon et al., Br J Gen Pract 71:e668-e676, 2021; van der Zanden and Nap, Reprod Biomed Online 32:527-31, 2016) and patient-related factors (Sayer-Jones and Sherman, Health Psychol Behav Med 9:456-79, 2021) as well as stigmatization of the topic of menstruation by society have been identified (Kruckenberg, Frauenarzt 59:2-5, 2018; Seear, Soc Sci Med 69:1220-7, 2009). The consequences of the disease being diagnosed late (or too late) on the course of disease, the quality of life and the costs of the disease have already been documented in studies (Sims Int J Environ Res Public Health 18(15):8210, 2021; Surrey Adv Ther 37:1087-99, 2020). However, a systematically derived cut-off value that clearly distinguishes between short and long delay is still lacking. Therefore, the aim of our study was to derive a threshold value for the definition of a target corridor for endometriosis diagnosis based on descriptive and analytical methods. METHODS: Since our review of the rather sparse publications on diagnostic delay did not yield satisfactory results, we used descriptive statistics and location parameters to calculate a cut-off value for German population data from the EndoCost study. Statistical methods were used for correlation analysis of shortDD versus longDD (correlation analysis and logistic regression) and group membership (discriminant analysis). RESULTS: Five years was identified as the cut-off value that significantly differentiated between shortDD and longDD based on various disease-related variables. This suggests that endometriosis should be definitively diagnosed within less than five years to minimize the risk of an unfavorable course of the disease. CONCLUSION: Our findings confirmed that an early onset of endometriosis-related symptoms is the most important risk factor for a long diagnostic delay. Consequently, adolescent females should receive increased attention as an especially vulnerable group. Evidently, there is an urgent need to develop adequate concepts to improve the endometriosis education and care among this target group.

Development of the reproduction number from coronavirus SARS-CoV-2 case data in Germany and implications for political measures.

BACKGROUND: SARS-CoV-2 has induced a worldwide pandemic and subsequent non-pharmaceutical interventions (NPIs) to control the spread of the virus. As in many countries, the SARS-CoV-2 pandemic in Germany has led to a consecutive roll-out of different NPIs. As these NPIs have (largely unknown) adverse effects, targeting them precisely and monitoring their effectiveness are essential. We developed a compartmental infection dynamics model with specific features of SARS-CoV-2 that allows daily estimation of a time-varying reproduction number and published this information openly since the beginning of April 2020. Here, we present the transmission dynamics in Germany over time to understand the effect of NPIs and allow adaptive forecasts of the epidemic progression. METHODS: We used a data-driven estimation of the evolution of the reproduction number for viral spreading in Germany as well as in all its federal states using our model. Using parameter estimates from literature and, alternatively, with parameters derived from a fit to the initial phase of COVID-19 spread in different regions of Italy, the model was optimized to fit data from the Robert Koch Institute. RESULTS: The time-varying reproduction number (Rt) in Germany decreased to <1 in early April 2020, 2-3 weeks after the implementation of NPIs. Partial release of NPIs both nationally and on federal state level correlated with moderate increases in Rt until August 2020. Implications of state-specific Rt on other states and on national level are characterized. Retrospective evaluation of the model shows excellent agreement with the data and usage of inpatient facilities well within the healthcare limit. While short-term predictions may work for a few weeks, long-term projections are complicated by unpredictable structural changes. CONCLUSIONS: The estimated fraction of immunized population by August 2020 warns of a renewed outbreak upon release of measures. A low detection rate prolongs the delay reaching a low case incidence number upon release, showing the importance of an effective testing-quarantine strategy. We show that real-time monitoring of transmission dynamics is important to evaluate the extent of the outbreak, short-term projections for the burden on the healthcare system, and their response to policy changes.

Effects of aging on influenza virus infection dynamics.

UNLABELLED: The consequences of influenza virus infection are generally more severe in individuals over 65 years of age (the elderly). Immunosenescence enhances the susceptibility to viral infections and renders vaccination less effective. Understanding age-related changes in the immune system is crucial in order to design prophylactic and immunomodulatory strategies to reduce morbidity and mortality in the elderly. Here, we propose different mathematical models to provide a quantitative understanding of the immune strategies in the course of influenza virus infection using experimental data from young and aged mice. Simulation results suggested a central role of CD8(+) T cells for adequate viral clearance kinetics in young and aged mice. Adding the removal of infected cells by natural killer cells did not improve the model fit in either young or aged animals. We separately examined the infection-resistant state of cells promoted by the cytokines alpha/beta interferon (IFN-α/ß), IFN-γ, and tumor necrosis factor alpha (TNF-α). The combination of activated CD8(+) T cells with any of the cytokines provided the best fits in young and aged animals. During the first 3 days after infection, the basic reproductive number for aged mice was 1.5-fold lower than that for young mice (P < 0.05). IMPORTANCE: The fits of our models to the experimental data suggest that the increased levels of IFN-α/ß, IFN-γ, and TNF-α (the "inflammaging" state) promote slower viral growth in aged mice, which consequently limits the stimulation of immune cells and contributes to the reported impaired responses in the elderly. A quantitative understanding of influenza virus pathogenesis and its shift in the elderly is the key contribution of this work.

The common interests of health protection and the economy: evidence from scenario calculations of COVID-19 containment policies.

We develop a novel approach integrating epidemiological and economic models that allows data-based simulations during a pandemic. We examine the economically optimal opening strategy that can be reconciled with the containment of a pandemic. The empirical evidence is based on data from Germany during the SARS-CoV-2 pandemic. Our empirical findings reject the view that there is necessarily a conflict between health protection and economic interests and suggest a non-linear U-shape relationship: it is in the interest of public health and the economy to balance non-pharmaceutical interventions in a manner that further reduces the incidence of infections. Our simulations suggest that a prudent strategy that leads to a reproduction number of around 0.75 is economically optimal. Too restrictive policies cause massive economic costs. Conversely, policies that are too loose lead to higher death tolls and higher economic costs in the long run. We suggest this finding as a guide for policy-makers in balancing interests of public health and the economy during a pandemic.

Investigating the Mechanism of Germinal Center Shutdown.

Germinal centers (GCs) are transient structures where affinity maturation of B cells gives rise to high affinity plasma and memory cells. The mechanism of GC shutdown is unclear, despite being an important phenomenon maintaining immune homeostasis. In this study, we used a mathematical model to identify mechanisms that can independently promote contraction of GCs leading to shutdown. We show that GC shutdown can be promoted by antigen consumption by B cells, antigen masking by soluble antibodies, alterations in follicular dendritic cell (FDC) network area, modulation of immune complex cycling rate constants, alterations in T follicular helper signaling, increased terminal differentiation and reduced B cell division capacity. Proposed mechanisms promoted GC contraction by ultimately decreasing the number of B cell divisions and recycling cells. Based on the in-silico predictions, we suggest a combination of experiments that can be potentially employed by future studies to unravel the mechanistic basis of GC shutdown such as measurements of the density of pMHC presentation of B cells, FDC network size per B cell, fraction of cells expressing differentiation markers. We also show that the identified mechanisms differentially affect the efficiency of GC reaction estimated based on the quantity and quality of resulting antibodies.

Antibody Mediated Intercommunication of Germinal Centers.

Antibody diversification and selection of B cells occur in dynamic structures called germinal centers (GCs). Passively administered soluble antibodies regulate the GC response by masking the antigen displayed on follicular dendritic cells (FDCs). This suggests that GCs might intercommunicate via naturally produced soluble antibodies, but the role of such GC-GC interactions is unknown. In this study, we performed in silico simulations of interacting GCs and predicted that intense interactions by soluble antibodies limit the magnitude and lifetime of GC responses. With asynchronous GC onset, we observed a higher inhibition of late formed GCs compared to early ones. We also predicted that GC-GC interactions can lead to a bias in the epitope recognition even in the presence of equally dominant epitopes due to differences in founder cell composition or initiation timing of GCs. We show that there exists an optimal range for GC-GC interaction strength that facilitates the affinity maturation towards an incoming antigenic variant during an ongoing GC reaction. These findings suggest that GC-GC interactions might be a contributing factor to the unexplained variability seen among individual GCs and a critical factor in the modulation of GC response to antigenic variants during viral infections.

Testing and isolation to prevent overloaded healthcare facilities and reduce death rates in the SARS-CoV-2 pandemic in Italy.

Background: During the first wave of COVID-19, hospital and intensive care unit beds got overwhelmed in Italy leading to an increased death burden. Based on data from Italian regions, we disentangled the impact of various factors contributing to the bottleneck situation of healthcare facilities, not well addressed in classical SEIR-like models. A particular emphasis was set on the undetected fraction (dark figure), on the dynamically changing hospital capacity, and on different testing, contact tracing, quarantine strategies. Methods: We first estimated the dark figure for different Italian regions. Using parameter estimates from literature and, alternatively, with parameters derived from a fit to the initial phase of COVID-19 spread, the model was optimized to fit data (infected, hospitalized, ICU, dead) published by the Italian Civil Protection. Results: We show that testing influenced the infection dynamics by isolation of newly detected cases and subsequent interruption of infection chains. The time-varying reproduction number (R t) in high testing regions decreased to <1 earlier compared to the low testing regions. While an early test and isolate (TI) scenario resulted in up to ~31% peak reduction of hospital occupancy, the late TI scenario resulted in an overwhelmed healthcare system. Conclusions: An early TI strategy would have decreased the overall hospital usage drastically and, hence, death toll (∼34% reduction in Lombardia) and could have mitigated the lack of healthcare facilities in the course of the pandemic, but it would not have kept the hospitalization amount within the pre-pandemic hospital limit.

In Silico Analysis of the Longevity and Timeline of Individual Germinal Center Reactions in a Primary Immune Response.

Germinal centers (GCs) are transient structures in the secondary lymphoid organs, where B cells undergo affinity maturation to produce high affinity memory and plasma cells. The lifetime of GC responses is a critical factor limiting the extent of affinity maturation and efficiency of antibody responses. While the average lifetime of overall GC reactions in a lymphoid organ is determined experimentally, the lifetime of individual GCs has not been monitored due to technical difficulties in longitudinal analysis. In silico analysis of the contraction phase of GC responses towards primary immunization with sheep red blood cells suggested that if individual GCs had similar lifetimes, the data would be consistent only when new GCs were formed until a very late phase after immunization. Alternatively, there could be a large variation in the lifetime of individual GCs suggesting that both long and short-lived GCs might exist in the same lymphoid organ. Simulations predicted that such differences in the lifetime of GCs could arise due to variations in antigen availability and founder cell composition. These findings identify the potential factors limiting GC lifetime and contribute to an understanding of overall GC responses from the perspective of individual GCs in a primary immune response.

Germinal Centre Shutdown.

Germinal Centres (GCs) are transient structures in secondary lymphoid organs, where affinity maturation of B cells takes place following an infection. While GCs are responsible for protective antibody responses, dysregulated GC reactions are associated with autoimmune disease and B cell lymphoma. Typically, 'normal' GCs persist for a limited period of time and eventually undergo shutdown. In this review, we focus on an important but unanswered question - what causes the natural termination of the GC reaction? In murine experiments, lack of antigen, absence or constitutive T cell help leads to premature termination of the GC reaction. Consequently, our present understanding is limited to the idea that GCs are terminated due to a decrease in antigen access or changes in the nature of T cell help. However, there is no direct evidence on which biological signals are primarily responsible for natural termination of GCs and a mechanistic understanding is clearly lacking. We discuss the present understanding of the GC shutdown, from factors impacting GC dynamics to changes in cellular interactions/dynamics during the GC lifetime. We also address potential missing links and remaining questions in GC biology, to facilitate further studies to promote a better understanding of GC shutdown in infection and immune dysregulation.

High SARS-CoV-2 seroprevalence in children and adults in the Austrian ski resort of Ischgl.

BACKGROUND: In early March 2020, a SARS-CoV-2 outbreak in the ski resort Ischgl in Austria initiated the spread of SARS-CoV-2 throughout Austria and Northern Europe. METHODS: Between April 21st and 27th 2020, a cross-sectional epidemiologic study targeting the full population of Ischgl (n = 1867), of which 79% could be included (n = 1473, incl. 214 children), was performed. For each individual, the study involved a SARS-CoV-2 PCR, antibody testing and structured questionnaires. A mathematical model was used to help understand the influence of the determined seroprevalence on virus transmission. RESULTS: The seroprevalence was 42.4% (95% confidence interval (CI) 39.8-44.7). Individuals under 18 showed a significantly lower seroprevalence of 27.1% (95% CI 21.3-33.6) than adults (45%; 95% CI 42.2-47.7; OR of 0.455, 95% CI 0.356-0.682, p < 0.001). Of the seropositive individuals, 83.7% had not been diagnosed to have had SARS-CoV-2 infection previously. The clinical course was generally mild. Over the previous two months, two COVID-19-related deaths had been recorded, corresponding to an infection fatality rate of 0.25% (95% CI 0.03-0.91). Only 8 (0.5 %) individuals were newly diagnosed to be infected with SARS-CoV-2 during this study. CONCLUSIONS: Ischgl was hit early and hard by SARS-CoV-2 leading to a high local seroprevalence of 42.4%, which was lower in individuals below the age of 18 than in adults. Mathematical modeling suggests that a drastic decline of newly infected individuals in Ischgl by the end of April occurred due to the dual impact from the non-pharmacological interventions and a high immunization of the Ischgl population.

Pilot Study Using Machine Learning to Identify Immune Profiles for the Prediction of Early Virological Relapse After Stopping Nucleos(t)ide Analogues in HBeAg-Negative CHB.

Treatment with nucleos(t)ide analogues (NAs) may be stopped after 1-3 years of hepatitis B virus DNA suppression in hepatitis B e antigen (HBeAg)-negative patients according to Asian Pacific Association for the Study of Liver and European Association for the Study of Liver guidelines. However, virological relapse (VR) occurs in most patients. We aimed to analyze soluble immune markers (SIMs) and use machine learning to identify SIM combinations as predictor for early VR after NA discontinuation. A validation cohort was used to verify the predictive power of the SIM combination. In a post hoc analysis of a prospective, multicenter therapeutic vaccination trial (ABX-203, NCT02249988), hepatitis B surface antigen, hepatitis B core antigen, and 47 SIMs were repeatedly determined before NA was stopped. Forty-three HBeAg-negative patients were included. To detect the highest predictive constellation of host and viral markers, a supervised machine learning approach was used. Data were validated in a different cohort of 49 patients treated with entecavir. VR (hepatitis B virus DNA ≥ 2,000 IU/mL) occurred in 27 patients. The predictive value for VR of single SIMs at the time of NA stop was best for interleukin (IL)-2, IL-17, and regulated on activation, normal T cell expressed and secreted (RANTES/CCL5) with a maximum area under the curve of 0.65. Hepatitis B core antigen had a higher predictive power than hepatitis B surface antigen but lower than the SIMs. A supervised machine-learning algorithm allowed a remarkable improvement of early relapse prediction in patients treated with entecavir. The combination of IL-2, monokine induced by interferon γ (MIG)/chemokine (C-C motif) ligand 9 (CCL9), RANTES/CCL5, stem cell factor (SCF), and TNF-related apoptosis-inducing ligand (TRAIL) was reliable in predicting VR (0.89; 95% confidence interval: 0.5-1.0) and showed viable results in the validation cohort (0.63; 0.1-0.99). Host immune markers such as SIMs appear to be underestimated in guiding treatment cessation in HBeAg-negative patients. Machine learning can help find predictive SIM patterns that allow a precise identification of patients particularly suitable for NA cessation.