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Objective Rituximab (RTX)‐treated patients exhibit suboptimal responses to COVID‐19 vaccines. However, existing research primarily involves patients already receiving RTX when vaccines were introduced, failing to account for the current landscape where patients are vaccinated before initiating RTX. Our objective was to compare the serological response to COVID‐19 vaccines in patients vaccinated before or after RTX initiation. Methods We included 254 RTX‐treated patients with autoimmune inflammatory rheumatic diseases (AIIRDs) and 113 blood donors (BDs) in a retrospective, observational cohort study. Patients were categorized based on the timing of RTX treatment relative to primary COVID‐19 vaccination. Serological vaccine responses were assessed using three immunoassays, and logistic regression analysis was used to identify predictors of serological response. Results Patients vaccinated before initiating RTX treatment had significantly higher seroconversion rates of SARS‐CoV‐2 immunoglobulin G (87%) and neutralizing antibodies (91%) compared with those receiving RTX before and after vaccination (n = 132) (61% and 65%, respectively). In the logistic regression analysis, a positive serological response was associated with the number of vaccines administered >9 months after the last RTX treatment. Patients receiving the highest number of vaccines with >9 months after RTX showed a response comparable to that of the BDs. Conclusion Vaccinating before RTX initiation yields a robust serological response in patients with AIIRDs. Furthermore, we highlight the reversibility of antibody impairment after RTX treatment cessation, provided that adequate vaccinations occur within a minimum of 9 months after RTX. Our findings offer essential insights for clinical decision‐making regarding COVID‐19 vaccination and RTX treatment, alleviating concerns about future RTX use.

Accurate streamflow estimation is essential for effective water resource management and adapting to extreme events in the face of changing climate conditions. Hydrological models have been the conventional approach for streamflow interpolation and extrapolation in time and space for the past few decades. However, their large-scale applications have encountered challenges, including issues related to efficiency, complex parameterization, and constrained performance. Deep learning methods, such as long short-term memory (LSTM) networks, have emerged as a promising and efficient approach for large-scale streamflow estimation. In this study, we have conducted a series of experiments to identify optimal hybrid modeling schemes to consolidate physically based models with LSTM aimed at enhancing streamflow estimation in Denmark. The results show that the hybrid modeling schemes outperformed the Danish National Water Resources Model (DKM) in both gauged and ungauged basins. While the standalone LSTM rainfall–runoff model outperformed DKM in many basins, it faced challenges when predicting the streamflow in groundwater-dependent catchments. A serial hybrid modeling scheme (LSTM-q), which used DKM outputs and climate forcings as dynamic inputs for LSTM training, demonstrated higher performance. LSTM-q improved the mean Nash–Sutcliffe efficiency (NSE) by 0.22 in gauged basins and 0.12 in ungauged basins compared to DKM. Similar accuracy improvements were achieved with alternative hybrid schemes, i.e., by predicting the residuals between DKM-simulated streamflow and observations using LSTM. Moreover, the developed hybrid models enhanced the accuracy of extreme events, which encourages the integration of hybrid models within an operational forecasting framework. This study highlights the advantages of synergizing existing physically based hydrological models (PBMs) with LSTM models, and the proposed hybrid schemes hold the potential to achieve high-quality large-scale streamflow estimations.

Denmark's complete reliance on groundwater for water supply presents a unique case study in management of natural resources, urban planning, and water resilience in the face of climate change. This paper examines the groundwater management strategies in Denmark in general, focusing on Denmark's four largest cities—Copenhagen, Aarhus, Odense, and Aalborg— each facing distinct challenges due to their demographic, geographical, hydrogeological, and economic characteristics. Through analysis of these cities' approaches to groundwater management, this research contributes to the global discourse on sustainable urban water supply systems. As coastal groundwater cities (CGC), these urban areas must navigate the complexities of sustaining growing populations, mitigating climate change impacts, and coastal processes while ensuring the long-term viability of their groundwater resources. Copenhagen and Aalborg, built atop semi-confined fractured and locally karstic carbonate rocks, highlights the specific challenges associated with karstic groundwater systems, while, Aarhus, and Odense built on glaciofluvial aquifers faces different issues. The different groundwater challenges in these cities underscores the importance of integrating urban development with water resource management and environmental sustainability, offering valuable insights and lessons learned for other regions facing similar challenges. This study, thus not only sheds light on Denmark's groundwater management practices, but also emphasizes the need for innovative solutions to ensure the resilience of urban water supply systems in a changing climate and increasing pressures of emerging organic contaminants and elevated concentrations of geogenic elements induced by water abstraction and fluctuating water tables. Advanced Danish monitoring and modelling tools applied to support decision-making and innovation within the water sector are continuously developed and improved to support resilient and sustainable management of the available water resources.

Objectives To investigate lectin pathway proteins (LPPs) as biomarkers for axial spondyloarthritis (axSpA) in a cross-sectional cohort with a suspicion of axSpA, comprising newly diagnosed axSpA and chronic low back pain (cLBP) individuals.Methods Serum samples from 515 participants within the OptiRef cohort, including 151 axSpA patients and 364 cLBP patients, were measured using immunoassays for LPPs (mannan-binding lectin (MBL), collectin liver-1 (CL-L1), M-ficolin, H-ficolin and L-ficolin, MBL-associated serine proteases (MASP)−1, –2 and –3, MBL-associated proteins (MAp19 and MAp44) and the complement activation product C3dg).Results Serum levels of L-ficolin, MASP-2 and C3dg were elevated in axSpA patients, whereas levels of MASP-3 and CL-L1 were decreased, and this remained significant for C3dg and MASP-3 after adjustment for C reactive protein (CRP). A univariate regression analysis showed serum levels of CL-L1, MASP-2, MASP-3 and C3dg to predict the diagnosis of axSpA, and MASP-3 and C3dg remained significant in a multivariate logistic regression analysis. Assessment of the diagnostic potential showed that a combination of human leukocyte antigen B27 (HLA-B27) and measurements of L-ficolin, MASP-3 and C3dg increased the diagnostic specificity for axSpA, however, with a concomitant loss of sensitivity.Conclusions Serum levels of complement activation, that is, C3dg, and MASP-3 differed significantly between axSpA and cLBP patients after adjustment for CRP. Although combining HLA-B27 with measurements of L-ficolin, MASP-3 and C3dg increased the diagnostic specificity for axSpA, this seems unjustified due to the concomitant loss of sensitivity. However, both C3dg and MASP-3 were associated with axSpA diagnosis in multivariate logistic regression, suggesting an involvement of complement in the inflammatory processes and possibly pathogenesis in axSpA.

In this study, we upscale and simplify hydrostratigraphic information from a detailed model for Denmark to a pan-European scale. This is part of a larger project to develop a harmonised overview of the volume and depth of groundwater resources in a quasi-3D European groundwater resource model. A 10 km grid and a maximum of c. 10 hydrostratigraphic layers were chosen as the common scale for the European database. The Danish information is based on the national water resources model (the DK-model), where the information is significantly more detailed (100 m grid and up to 26 layers). Information was transferred from the DK-model to the quasi-3D model by a method involving computations of mean volumes and expert assessment to reduce layers in each cell. In this process, detailed hydrostratigraphic information is lost, which could otherwise be used for local groundwater flow modelling in Denmark. However, the strength of the quasi-3D model is that it still contains the volumes of all hydrostratigraphic units, both the saturated and unsaturated parts. Hence, the upscaled model can contribute to a relatively precise calculation of European groundwater resources for the quantitative assessment of groundwater status across Europe at a 10 × 10 km scale.

Nanotechnology enables investigations of single biomacromolecules, but technical challenges have limited the application in liquid biopsies, for example, blood plasma. Nonetheless, tools to characterize single molecular species in such samples represent a significant unmet need with the increasing appreciation of the physiological importance of protein structural changes at nanometer scale. Mannose-binding lectin (MBL) is an oligomeric plasma protein and part of the innate immune system through its ability to activate complement. MBL also serves a role as a scavenger for cellular debris, especially DNA. This may link functions of MBL with several inflammatory diseases in which cell-free DNA now appears to play a role, but mechanistic insight has been lacking. By making nanoparticle tracking analysis possible in human plasma, we now show that superoligomeric structures of MBL form nanoparticles with DNA. These oligomers correlate with disease activity in systemic lupus erythematosus patients. With the direct quantification of the hydrodynamic radius, calculations following the principles of Taylor dispersion in the blood stream connect the size of these complexes to endothelial inflammation, which is among the most important morbidities in lupus. Mechanistic insight from an animal model of lupus supported that DNA-stabilized superoligomers stimulate the formation of germinal center B cells and drive loss of immunological tolerance. The formation involves an inverse relationship between the concentration of MBL superoligomers and antibodies to double-stranded DNA. Our approach implicates the structure of DNA-protein nanoparticulates in the pathobiology of autoimmune diseases.

A simple analytical vortex model is presented and used to study an idealized wind turbine rotor in uniform and sheared inflow, respectively. Our model predicts that 1D momentum theory should be applied locally when modelling a non-uniformly loaded rotor in a sheared inflow. Hence the maximum local power coefficient (expressed with respect to the local, upstream velocity) of an ideal rotor is not affected by the presence of shear. We study the interaction between the wake vorticity generated by the rotor and the wind shear vorticity and find that their mutual interaction results in no net generation of axial vorticity: the wake effects and the shear effects exactly cancel each other out. This means that there are no resulting cross-shear-induced velocities and therefore also no cross-shear deflection of the wake in this model.

Various methods are available for assessing uncertainties in climate impact studies. Among such methods, model weighting by expert elicitation is a practical way to provide a weighted ensemble of models for specific real-world impacts. The aim is to decrease the influence of improbable models in the results and easing the decision-making process. In this study both climate and hydrological models are analysed, and the result of a research experiment is presented using model weighting with the participation of six climate model experts and six hydrological model experts. For the experiment, seven climate models are a priori selected from a larger EURO-CORDEX (Coordinated Regional Downscaling Experiment – European Domain) ensemble of climate models, and three different hydrological models are chosen for each of the three European river basins. The model weighting is based on qualitative evaluation by the experts for each of the selected models based on a training material that describes the overall model structure and literature about climate models and the performance of hydrological models for the present period. The expert elicitation process follows a three-stage approach, with two individual rounds of elicitation of probabilities and a final group consensus, where the experts are separated into two different community groups: a climate and a hydrological modeller group. The dialogue reveals that under the conditions of the study, most climate modellers prefer the equal weighting of ensemble members, whereas hydrological-impact modellers in general are more open for assigning weights to different models in a multi-model ensemble, based on model performance and model structure. Climate experts are more open to exclude models, if obviously flawed, than to put weights on selected models in a relatively small ensemble. The study shows that expert elicitation can be an efficient way to assign weights to different hydrological models and thereby reduce the uncertainty in climate impact. However, for the climate model ensemble, comprising seven models, the elicitation in the format of this study could only re-establish a uniform weight between climate models.

There is an urgent demand for assessments of climate change impacts on the hydrological cycle at high spatial resolutions. In particular, the impacts on shallow groundwater levels, which can lead to both flooding and drought, have major implications for agriculture, adaptation, and urban planning. Predicting such hydrological impacts is typically performed using physically based hydrological models (HMs). However, such models are computationally expensive, especially at high spatial resolutions. This study is based on the Danish national groundwater model, set up as a distributed, integrated surface–subsurface model at a 500 m horizontal resolution. Recently, a version at a higher resolution of 100 m was created, amongst others, to better represent the uppermost groundwater table and to meet end-user demands for water management and climate adaptation. However, the increase in resolution of the hydrological model also increases computational bottleneck. To evaluate climate change impacts, a large ensemble of climate models was run with the 500 m hydrological model, while performing the same ensemble run with the 100 m resolution nationwide model was deemed infeasible. The desired outputs at the 100 m resolution were produced by developing a novel, hybrid downscaling method based on machine learning (ML). Hydrological models for five subcatchments, covering around 9 % of Denmark and selected to represent a range of hydrogeological settings, were run at 100 m resolutions with forcings from a reduced ensemble of climate models. Random forest (RF) algorithms were established using the simulated climate change impacts (future – present) on water table depth at 100 m resolution from those submodels as training data. The trained downscaling algorithms were then applied to create nationwide maps of climate-change-induced impacts on the shallow groundwater table at 100 m resolutions. These downscaled maps were successfully validated against results from a validation submodel at a 100 m resolution excluded from training the algorithms, and compared to the impact signals from the 500 m HM across Denmark. The suggested downscaling algorithm also opens for the spatial downscaling of other model outputs. It has the potential for further applications where, for example, computational limitations inhibit running distributed HMs at fine resolutions.

We developed ordinary least squares regression models to predict uptake of cadmium and lead, two metals that are of public health significance because of their toxicity, in the edible tissues of lettuce. Models were parameterised using data on soil metal concentration, pH, and organic carbon. To assess the impact of physical contamination in form of aerial deposition and soil-splash on the metal concentration in lettuce, separate linear regression models were parameterised for indoor- and outdoor-grown lettuce, assuming the physical contamination to be negligible for indoor conditions. Both Cd models showed high model fit and strong predictive performance, when tested on an independent dataset, suggesting uptake via roots to be dominant. For Pb, the indoor model performed better than the outdoor model, indicating that physical contamination, contributes significantly to metal concentration in lettuce leaves. Our results highlight the importance of the parameterisation data when developing uptake models for predictions and risk assessment. Regression models for predicting Pb concentration in lettuce based on indoor data should not be used for predicting lettuce concentrations cultivated in outdoor conditions unless the contribution of physical contamination is explicitly accounted for.

The power performance of a wind turbine in complex terrain is studied by means of large eddy simulations (LESs). The simulations show that the turbine performance is significantly different compared to what should be expected from the available wind. The reason for this deviation is that the undisturbed flow field behind the turbine is non-homogeneous and therefore results in a very different wake development and induction than seen for a turbine in flat homogeneous terrain.

Background Patients with systemic lupus erythematosus (SLE) have an increased risk of thrombosis even when they do not have antiphospholipid syndrome (APS). Interactions between complement activation and activated platelets have been suggested in SLE and APS and could play a role in the increased thrombosis risk. Objectives To explore factors potentially related to the prothrombotic pathophysiology in patients with SLE, primary APS, and healthy controls, by investigating lectin pathway proteins (LPPs), complement activation, platelet aggregation, and platelet activation. Methods This cross-sectional cohort study included 20 SLE patients, 17 primary APS, and 39 healthy controls. Flow cytometry and light transmission aggregometry were used to assess platelet activation and aggregation. Using time-resolved immunofluorometric assays, the plasma concentrations of 11 LPPs and C3dg, reflecting complement activation, were measured. Results H-ficolin plasma concentrations were higher in SLE and APS patients than in controls (p = 0.01 and p = 0.03). M-ficolin was lower in SLE than in APS (p = 0.01) and controls (p = 0.03). MAp19 was higher in APS patients than in SLE patients (p = 0.01) and controls (p

Blockage effects due to the interaction of five wind turbines in a row are investigated through both Reynolds-averaged Navier–Stokes simulations and site measurements. Since power performance tests are often carried out at sites consisting of several turbines in a row, the objective of this study is to evaluate whether the power performance of the five turbines differs from that of an isolated turbine. A number of simulations are performed, in which we vary the turbine inter-spacing (1.8, 2 and 3 rotor diameters) and the inflow angle between the incoming wind and the orthogonal line to the row (from 0 to 45∘). Different values of the free-stream velocity are considered to cover a broad wind speed range of the power curve. Numerical results show consistent power deviations for all five turbines when compared to the isolated case. The amplitude of these deviations depends on the location of the turbine within the row, the inflow angle, the inter-spacing and the power curve region of operation. We show that the power variations do not cancel out when averaging over a large inflow sector (from −45 to +45∘) and find an increase in the power output of up to +1 % when compared to the isolated case under idealised conditions (neutral atmospheric conditions, no vertical wind shear or ground effects). We simulate power performance “measurements” with both a virtual mast and nacelle-mounted lidar and find a combination of power output increase and upstream velocity reduction, which causes an increase of +4 % in the power coefficient under idealised conditions. We also use measurements from a real site consisting of a row of five wind turbines to validate the numerical results. From the analysis of the measurements, we also show that the power performance is impacted by the neighbouring turbines. Compared to when the inflow is perpendicular to the row, the power output varies by +1.8 % and −1.8 % when the turbine is the most downwind and upwind of the line, respectively.

Pesticides are contaminants of priority concern that continue to present a significant risk to drinking water quality. While pollution mitigation in catchment systems is considered a cost-effective alternative to costly drinking water treatment, the effectiveness of pollution mitigation measures is uncertain and needs to be able to consider local biophysical, agronomic, and social aspects. We developed a probabilistic decision support tool (DST) based on spatial Bayesian belief networks (BBNs) that simulates inherent pesticide leaching risk to ground- and surface water quality to inform field-level pesticide mitigation strategies in a small (3.1 km2) drinking water catchment with limited observational data. The DST accounts for the spatial heterogeneity in soil properties, topographic connectivity, and agronomic practices; the temporal variability of climatic and hydrological processes; and uncertainties related to pesticide properties and the effectiveness of management interventions. The rate of pesticide loss via overland flow and leaching to groundwater and the resulting risk of exceeding a regulatory threshold for drinking water was simulated for five active ingredients. Risk factors included climate and hydrology (e.g. temperature, rainfall, evapotranspiration, and overland and subsurface flow), soil properties (e.g. texture, organic matter content, and hydrological properties), topography (e.g. slope and distance to surface water/depth to groundwater), land cover and agronomic practices, and pesticide properties and usage. The effectiveness of mitigation measures such as the delayed timing of pesticide application; a 10 %, 25 %, or 50 % reduction in the application rate; field buffers; and the presence/absence of soil pan on risk reduction were evaluated. Sensitivity analysis identified the month of application, the land use, the presence of buffers, the field slope, and the distance as the most important risk factors, alongside several additional influential variables. The pesticide pollution risk from surface water runoff showed clear spatial variability across the study catchment, whereas the groundwater leaching risk was uniformly low, with the exception of prosulfocarb. Combined interventions of a 50 % reduced pesticide application rate, management of the plough pan, delayed application timing, and field buffer installation notably reduced the probability of a high risk of overland runoff and groundwater leaching, with individual measures having a smaller impact. The graphical nature of BBNs facilitated interactive model development and evaluation with stakeholders to build model credibility, while the ability to integrate diverse data sources allowed a dynamic field-scale assessment of “critical source areas” of pesticide pollution in time and space in a data-scarce catchment, with explicit representation of uncertainties.

Runoff signatures characterize a catchment's response and provide insight into the hydrological processes. These signatures are governed by the co-evolution of catchment properties and climate processes, making them useful for understanding and explaining hydrological responses. However, catchment behaviours can vary significantly across different spatial scales, which complicates the identification of key drivers of hydrologic response. This study represents catchments as networks of variables linked by cause-and-effect relationships. We examine whether the direct causes of runoff signatures can explain these signatures across different environments, with the goal of developing more robust, parsimonious, and physically interpretable predictive models. We compare predictive models that incorporate causal information derived from the relationships between catchment, climate, and runoff characteristics. We use the Peter and Clarck (PC) causal discovery algorithm, along with three prediction models: Bayesian Network (BN), Generalized Additive Model (GAM), and Random Forest (RF). The results indicate that among models, BN exhibits the smallest decline in accuracy between training and test simulations compared to the other models. While RF achieves the highest overall performance, it also demonstrates the most significant drop in accuracy between the training and test phases. When the training sample is small, the accuracy of the causal RF model, which uses causal parents as predictors, is comparable to that of the non-causal RF model, which uses all selected variables as predictors. This study demonstrates the potential of causal inference techniques in representing the interconnected processes in hydrological systems in a more interpretable and effective manner. [ABSTRACT FROM AUTHOR]

Background: Anifrolumab, a monoclonal antibody targeting the type 1 interferon (IFN-I) signaling pathway, holds promise as a therapeutic intervention for systemic lupus erythematosus (SLE). However, its use is associated with an increased risk of infections, particularly viral infections like herpes zoster (HZ). Results from the clinical trials on anifrolumab show yearly rates of upper respiratory tract infections of 34% and HZ of 6.1%. An increased frequency of other specific viral infections, including herpes simplex virus (HSV), was not reported. Case Reports: Here, we present 2 cases of patients with SLE treated with anifrolumab, both experiencing severe adverse reactions in the form of disseminated herpesvirus infections, specifically disseminated HSV-2 and varicella zoster virus (VZV, HZ encephalitis). To the best of our knowledge, no previous reports of severe disseminated HSV-2 or HZ have been published in anifrolumab-treated patients. The patient in case 1 experienced a primary HSV-2 infection following anifrolumab treatment, potentially explaining the severity of the infection. The patient in case 2 had a history of previous HZ skin infections, which may have increased her risk of disseminated infection. Both patients recovered from the infections with minor sequelae, but they still require prophylactic antiviral treatment. These cases highlight the critical role of IFN-I immunity in protecting against herpesvirus infections. Conclusions: Thorough risk assessment before anifrolumab initiation, considering the patient's viral infection history, vaccination status, and potential exposure risks, is essential. Administration of recombinant zoster vaccine before anifrolumab therapy may benefit susceptible individuals. [ABSTRACT FROM AUTHOR]

This case report discusses a patient with systemic lupus erythematosus (SLE) treated with low-dose azathioprine who developed progressive multifocal leukoencephalopathy (PML). PML is a rare, severe, demyelinating disease linked to John Cunningham polyomavirus (JCV) reactivation. Treated with pembrolizumab, an immune checkpoint inhibitor, the patient initially improved. However, after the fourth dose, her condition rapidly worsened resulting in treatment discontinuation and death. Similar cases highlight the complex interplay of factors in PML development in SLE patients, including immunosuppression and genetic factors. The use of pembrolizumab in PML and SLE necessitates careful consideration of potential complications. [ABSTRACT FROM AUTHOR]

Objective With a vaccine effectiveness of 95% for preventing coronavirus disease 2019 (COVID‐19), Pfizer‐BioNTech BNT162b2 (BNT162b2) was the first vaccine against severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) to be approved. However, immunosuppressive therapy was an exclusion criterion in the phase 3 trial that led to approval. Thus, extrapolation of the trial results to patients with rheumatic diseases treated with immunosuppressive drugs warrants caution. Methods Patients with systemic lupus erythematosus (SLE; n = 61) and rheumatoid arthritis (RA; n = 73) were included from the COPANARD (Corona Pandemic Autoimmune Rheumatic Disease) cohort, followed since the beginning of the COVID‐19 pandemic. Patients received the BNT162b2 vaccine between December 2020 and April 2021. All patients had total antibodies against SARS‐CoV‐2 measured before vaccination and 1 week after the second vaccination (VITROS Immunodiagnostic Products). Results Of 134 patients (median age, 70 years), 77% were able to mount a detectable serological response to the vaccine. Among patients treated with rituximab, only 24% had detectable anti–SARS‐CoV‐2 antibodies in their serum after vaccination. The time since the last rituximab treatment did not seem to influence the vaccine response. No significant difference was observed between patients with RA or SLE when adjusting for treatment, and no correlation between antibody levels and age was detected (r = −0.12; P = 0.18). Conclusion Antibody measurements against SARS‐CoV‐2 in patients with RA and SLE after two doses of the BNT162b2 vaccine demonstrated that 23% of patients could not mount a detectable serological response to the vaccine. B cell–depleting therapy (BCDT) is of specific concern, and our findings call for particular attention to the patients receiving BCDT.

Background Vacuoles, E1 enzyme, X-linked, autoinflammatory, somatic (VEXAS) is an autoinflammatory condition with overlapping features of rheumatology and haematology caused by somatic mutations in the UBA1 gene. Patients present with highly variable symptoms and their path towards diagnosis are often complicated and characterised by extensive examinations. It is, therefore, pivotal that clinicians become familiar with the clinical presentation of VEXAS to advance identification of patients with the disease.Objectives We aimed to (1) characterise patients diagnosed with VEXAS in a tertiary rheumatology referral centre, (2) identify common rheumatological biomarkers that may distinguish VEXAS from other rheumatic diseases and (3) suggest which clinical findings should motivate genetic testing for VEXAS.Methods Patients were identified and diagnosed at the department of Rheumatology, Aarhus University Hospital (AUH), Denmark. Blood samples were examined for VEXAS-associated UBA1 variants by Sanger sequencing at the department of Clinical Immunology, AUH. Clinical and biochemical data were retrieved from the hospital electronic patient chart.Results Eleven male patients with clinical suspicion of VEXAS underwent sequencing. Five of these carried known VEXAS-associated variants. Median age at diagnosis was 84 (75–87) years. All patients had significantly elevated inflammatory markers with a median C-reactive protein (CRP) of 297 (196–386) mg/L and macrocytic anaemia. None of the patients presented common biomarkers for autoimmunity.Conclusion Danish patients with VEXAS syndrome are men with persistent inflammation, constitutional symptoms and heterogeneous clinical presentations. Shared features for all patients in this study were highly elevated inflammatory markers, macrocytic anaemia and negative autoimmune biomarkers.