Korrelation der anti-Xa-Spiegel mit den anti-IIa-Spiegeln und der Thrombingenerierung bei Patienten mit Adipositas unter Thrombose-Prophylaxe

Background: The optimal dose of tinzaparin for prophylaxis in obese medical patients is not well defined. Objectives: To evaluate the anti-Xa activity in obese medical patients on tinzaparin prophylaxis adjusted for actual bodyweight. Methods: Patients with a body mass index of ≥30 kg/m2 treated with 50 IU/kg tin- zaparin once daily were prospectively included. Anti-Xa and anti-IIa activity; von Wil- lebrand factor antigen and von Willebrand activity; factor VIII activity; D-dimer, prothrombin fragments; and thrombin generation were measured 4 hours after sub- cutaneous injection between days 1 and 14 after the initiation of tinzaparin prophylaxis. Results: We included 121 plasma samples from 66 patients (48.5% women), with a median weight of 125 kg (range, 82-300 kg) and a median body mass index of 41.9 kg/ m2 (range, 30.1-88.6 kg/m2). The target anti-Xa activity of 0.2 to 0.4 IU/mL was ach- ieved in 80 plasma samples (66.1%); 39 samples (32.2%) were below and 2 samples (1.7%) above the target range. The median anti-Xa activity was 0.25 IU/mL (IQR, 0.19- 0.31 IU/mL), 0.23 IU/mL (IQR, 0.17-0.28 IU/mL), and 0.21 IU/mL (IQR, 0.17-0.25 IU/mL) on days 1 to 3, days 4 to 6, and days 7 to 14, respectively. The anti-Xa activity did not differ among the weight groups (P = .19). Injection into the upper arm compared to the abdomen resulted in a lower endogenous thrombin potential, a lower peak thrombin, and a trend to a higher anti-Xa activity. Conclusion: Dosing of tinzaparin adjusted for actual bodyweight in obese patients achieved anti-Xa activity in the target range for most patients, without accumulation or overdosing. In addition, there is a significant difference in thrombin generation depending on the injection site

A data-driven approach for modelling Karst spring discharge using transfer function noise models

Karst aquifers are important sources of fresh water on a global scale. The hydrological modelling of karst spring discharge, however, still poses a challenge. In this study we apply a transfer function noise (TFN) model in combination with a bucket-type recharge model to simulate karst spring discharge. The application of the noise model for the residual series has the advantage that it is more consistent with assumptions for optimization such as homoscedasticity and independence. In an earlier hydrological modeling study, named Karst Modeling Challenge (KMC; Jeannin et al., J Hydrol 600:126–508, 2021), several modelling approaches were compared for the Milandre Karst System in Switzerland. This serves as a benchmark and we apply the TFN model to KMC data, subsequently comparing the results to other models. Using different data-model-combinations, the most promising data-model-combination is identified in a three-step least-squares calibration. To quantify uncertainty, the Bayesian approach of Markov-chain Monte Carlo (MCMC) sampling is subsequently used with uniform priors for the previously identified best data-model combination. The MCMC maximum likelihood solution is used to simulate spring discharge for a previously unseen testing period, indicating a superior performance compared to all other models in the KMC. It is found that the model gives a physically feasible representation of the system, which is supported by field measurements. While the TFN model simulated rising limbs and flood recession especially well, medium and baseflow conditions were not represented as accurately. The TFN approach poses a well-performing data-driven alternative to other approaches that should be considered in future studies

Critical Investigation of Betaine Hydrochloride-Based Deep Eutectic Solvent for Ionometallurgical Metal Production

The applicability of a deep eutectic solvent (DES) consisting of betainium hydrochloride, urea and glycerol is examined with respect to ionometallurgical metal extraction and compared with the ionic liquid (IL) betainium bis(trifluoromethylsulfonyl)imide ([Hbet][NTf2]). The DES dissolves numerous metal oxides, where not only betaine and chloride act as stabilizing ligands, but also nascent ammonia seems to be essential. From such solutions, cobalt, copper, zinc, tin, lead, and even vanadium can be electrodeposited, demonstrating the feasibility of ionometallurgy. However, repeated recycling of the DES is not conceivable. NMR spectroscopy and mass spectrometry identify numerous decomposition reactions taking place at 60 °C already. The by-products that are formed not only make recycling more difficult, but also pose a toxicity problem. The opportunities and obstacles of DESs and ILs for their application in ionometallurgy are critically discussed. It is shown that a thorough understanding of the underlying chemical processes is critical

Controllable Generative Modelling and Diversity Image Restoration for Biomedical Image data

This PhD thesis presents research on fluorescence microscopy employing computer vision techniques and has been submitted to the Faculty of Computer Science at TU Dresden. Fluorescence microscopy is a vital tool in life sciences, enabling visualization of cellular and sub-cellular structures using fluorescent markers. However, technical constraints limit the number of structures that can be imaged simultaneously. This thesis addresses this limitation by proposing methods to decompose superimposed fluorescence images—where multiple structures are captured in a single channel—into distinct channels through supervised image decomposition and unsupervised denoising. First, we present μSplit, a GPU-efficient approach for image decomposition that leverages large contextual information. Next, we introduce denoiSplit, a method combining image unmixing with unsupervised denoising, improving decomposition quality in noisy images through noise modeling and KL-divergence optimization. denoiSplit also enables posterior sampling, which is then used for pixelwise uncertainty estimation. Building on these, we develop MicroSplit, merging μSplit’s context efficiency with denoiSplit’s denoising capabilities, and extending support for 3D inputs. Evaluated across nine datasets, MicroSplit demonstrates robustness under varying noise levels and superposition asymmetry. To address evaluation challenges, we develop MicroSSIM, a microscopy-adapted Structural Similarity Index that overcomes several issues of traditional SSIM with microscopy data, providing a reliable metric for denoising and decomposition tasks. Finally, we present indiSplit, a novel method handling unknown mixing ratios in superimposed inputs. By predicting mixing ratios and applying ratio-specific normalization, indiSplit achieves robust performance across varying conditions, addressing both image splitting and bleed-through removal with a single network. This thesis advances fluorescence microscopy by enabling efficient multi-structure imaging, improving decomposition accuracy, and providing robust evaluation tools.:Contents Acknowledgements List of Publications xii 1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 Fluorophores and Their Role in Fluorescence Microscopy . . . . . . 1 1.1.2 Single-color Imaging Process . . . . . . . . . . . . . . . . . . . . . . 2 1.1.3 Multi-color Imaging Process . . . . . . . . . . . . . . . . . . . . . . . 3 1.1.4 Challenges in Fluorescence Microscopy . . . . . . . . . . . . . . . . . 3 1.2 Our approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.3.1 Linear Unmixing and Independent Component Analysis (ICA) . . . 6 1.3.2 Virtual Staining Methods . . . . . . . . . . . . . . . . . . . . . . . . 8 1.3.3 Connections to Computer Vision on Natural Images . . . . . . . . . 8 1.4 Organization of this thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 μSplit: image decomposition for fluorescence microscopy 12 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.3 Our Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.4 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.5 Experiments and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3 denoiSplit: a method for joint microscopy image splitting and unsuper- vised denoising 28 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.2.1 Image Denoising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.2.2 Image Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.2.3 Uncertainty Calibration . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.3 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.4 Our Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.4.1 Network Architecture and Training Objective . . . . . . . . . . . . . 32 3.4.2 Hierarchical KL Loss Weighing for Variational Training . . . . . . . 33 3.4.3 Adding Suitable Pixel Noise Models . . . . . . . . . . . . . . . . . . 34 3.4.4 Computing Calibrated Data Uncertainties . . . . . . . . . . . . . . . 35 3.5 Experiments and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.5.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 BioSR dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Hagen et al. Actin-Mitochondria Dataset . . . . . . . . . . . 36 Synthetic Noise Levels . . . . . . . . . . . . . . . . . . . . . . 38 3.5.2 Baselines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.5.3 Qualitative and Quantitative Evaluation of Results . . . . . . . . . . 40 3.6 Performance on more splitting tasks . . . . . . . . . . . . . . . . . . . . . . 42 Hagen et al. Actin-Mitochondria Dataset . . . . . . . . . . . 44 PaviaATN dataset . . . . . . . . . . . . . . . . . . . . . . . . 44 3.7 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4 MicroSSIM: Improved Structural Similarity for Comparing Microscopy Data 46 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.2 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.3 Our approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3.1 Saturation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.3.2 Handling background and large pixel intensities . . . . . . . . . . . . 52 4.3.3 Optimization to obtain the scaling factor α . . . . . . . . . . . . . . 53 4.4 Derivation for optimality of α . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.5.1 Dataset and training details . . . . . . . . . . . . . . . . . . . . . . . 56 4.5.2 Baselines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.5.3 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.5.4 Comparison with baselines . . . . . . . . . . . . . . . . . . . . . . . 57 4.5.5 Relevance of background, downscaling and α in Saturation . . . . . 59 4.5.6 Importance of dataset level estimation of β . . . . . . . . . . . . . . 61 4.5.7 Inspecting role of offset (β) on vanilla SSIM . . . . . . . . . . . . . . 61 4.5.8 Inspecting uniqueness of α . . . . . . . . . . . . . . . . . . . . . . . . 61 4.5.9 Extension to other SSIM variants . . . . . . . . . . . . . . . . . . . . 62 4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.7 Limitations and future work . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 5 MicroSplit: Semantic Unmixing of Fluorescent Microscopy Data 64 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 5.2.1 Training Modes and Required Training Data . . . . . . . . . . . . . 67 5.2.2 MicroSplit Yields High-Quality Unmixed Structures . . . . . . . . . 69 5.2.3 Error Estimation, Data Uncertainty, and Calibration . . . . . . . . . 71 5.2.4 Downstream Processing of Unmixed Data . . . . . . . . . . . . . . . 73 5.2.4.1 Segmentation of Unmixed Image Data is of High Quality . 74 5.2.4.2 Removing Unwanted Imaging Artifacts . . . . . . . . . . . 74 5.2.5 Limitations of MicroSplit . . . . . . . . . . . . . . . . . . . . . . . . 75 5.3 Online Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 5.3.1 Used Microscopy Datasets . . . . . . . . . . . . . . . . . . . . . . . . 77 HT-H24 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 HT-P23A and HT-P23B . . . . . . . . . . . . . . . . . . . . . 77 Pavia-P24 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 HT-T24 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 HT-LIF24 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 Chicago-Sch23 . . . . . . . . . . . . . . . . . . . . . . . . . . 78 CBG-Z18 and CBG-N18 . . . . . . . . . . . . . . . . . . . . . 79 HT-H23B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.3.2 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.3.3 Training Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.4 Model Architecture and Training . . . . . . . . . . . . . . . . . . . . . . . . 80 5.4.1 Loss Function used to train MicroSplit . . . . . . . . . . . . . . . . . 81 5.4.2 Hyper-parameters used during Training . . . . . . . . . . . . . . . . 82 5.5 Analyzing Factors that Affect Predictive Performance . . . . . . . . . . . . 83 5.5.1 Pixel-noise (pixel-wise independent noise) . . . . . . . . . . . . . . . 83 5.5.2 Spatial Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 5.5.3 Similarity of Structures to be Unmixed . . . . . . . . . . . . . . . . 84 5.5.4 Unequal Channel Intensities . . . . . . . . . . . . . . . . . . . . . . . 85 Unequal Channel Intensities - Two Extreme Examples . . . . 86 5.5.5 Sufficient Lateral Image Context . . . . . . . . . . . . . . . . . . . . 86 5.6 Additional Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.6.1 MicroSplit vs. PICASSO . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.6.2 Training Mode I vs Training Mode II - How Important are Spatial Correlations? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.6.3 Training Mode I vs. Training Mode III - Summed vs. Acquired Inputs 88 5.7 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 5.8 Further Details on all Experiments (Learning Tasks) . . . . . . . . . . . . . 91 5.8.1 Two-channel unmixing tasks . . . . . . . . . . . . . . . . . . . . . . 91 5.8.2 Three-channel unmixing tasks . . . . . . . . . . . . . . . . . . . . . . 92 5.8.3 Four-channel unmixing tasks . . . . . . . . . . . . . . . . . . . . . . 93 5.9 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 6 indiSplit: Bringing Severity Cognizance to Image Decomposition in Fluorescence Microscopy 123 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 6.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 6.3 Our Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 6.3.1 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 6.3.2 Severity Cognizant Input Normalization . . . . . . . . . . . . . . . . 128 6.3.3 Network Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 6.4 Severity Cognizant Input Normalization . . . . . . . . . . . . . . . . . . . . 131 6.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 6.6 Conclusion and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 7 Conclusions and future work 144 Bibliography 14

Monitoring of outlet glacier calving fronts using deep learning analysis of multispectral satellite imagery

Changes of the ice sheets in Greenland and Antarctica have a significant effect on the global sea level. In order to predict future climate scenarios, it is essential to understand ice sheet processes and therefore to observe current changes. As ice sheet changes are strongly influenced by the dynamics of their outlet glaciers, an accurate and continuous monitoring of these outlet glaciers is of paramount importance. One outlet glacier process that has been identified as particularly important in this context is the change in calving front position. Consequently, calving front change data is of major importance. However, due to manual delineation, most current data sets are limited in either spatial coverage or temporal resolution. This cumulative dissertation addresses this problem by applying deep learning to multispectral Landsat imagery in order to achieve accurate, robust and automated delineation of outlet glacier fronts. Throughout three publications, the developed processing system is presented, assessed and applied. The workflow is based on a convolutional neural network that is trained and tested using manually delineated calving front positions. The mean delineation error ranges from about 45 m to 75 m depending on the test data set. Thus, most automatically extracted glacier fronts are indistinguishable from manually digitised ones. Furthermore, the benefit of multispectral, topographic, and textural input features is assessed using a dropped-variable approach. The resulting feature importance assessment emphasises the utilization of multispectral inputs as they lead to more accurate predictions, especially under challenging situations such as difficult ice mélange conditions. Eventually, this processing system is applied to outlet glaciers in Greenland and on the Antarctic Peninsula. The resulting Greenland data product contains 9243 calving front positions across 23 glaciers for the period 2013 to 2021 and the Antarctic Peninsula data product contains 4817 calving front positions across 42 glaciers for the period 2013 to 2023. Unlike most existing manually delineated products, these data sets reliably combine high temporal resolution with large spatial coverage. Derived time series resolve not only long-term and seasonal signals, but also sub-seasonal patterns. These data products will help to better understand the dynamics of outlet glaciers at intra-annual scales, study ice-ocean interactions in more detail, and constrain glacier models. In future, comparable deep-learning based workflows are likely to become integral to the monitoring of glacier calving fronts. This thesis contributed to establishing a new link between deep learning, remote sensing and glaciology – a link that will persist in the long term.:1 Introduction 2 Outlet glaciers of ice sheets 2.1 Dynamics of outlet glaciers 2.2 Monitoring requirements 2.3 Satellite-based observation methods 3 Machine Learning in remote sensing 3.1 Fundamentals 3.1.1 Training 3.1.2 Models 3.1.3 Performance assessment and model optimisation 3.2 Deep learning: from perceptron to deep neural network 3.2.1 Multi-Layer Perceptron 3.2.2 Convolutional Neural Networks 3.3 Analysing remote sensing imagery using deep learning 4 Monitoring of calving front positions 4.1 Challenges and existing approaches 4.2 Existing data sets 5 Publications 6 Outlook 7 ConclusionsDie Veränderungen der Eisschilde in Grönland und der Antarktis haben einen erheblichen Einfluss auf den globalen Meeresspiegel. Um zukünftige Klimaszenarien genauer vorhersagen zu können, ist ein besseres Verständnis der Eisschildprozesse und damit die Beobachtung der aktuellen Veränderungen unerlässlich. Da die Veränderungen der Eisschilde stark von der Dynamik ihrer Auslassgletscher beeinflusst werden, ist ein genaues und kontinuierliches Monitoring dieser Auslassgletscher von hoher Bedeutung. Ein Prozess, der sich in diesem Zusammenhang als besonders wichtig erwiesen hat, ist die Veränderung der Lage der Gletscherfront. Daten über die Entwicklung dieser Gletscherfrontlagen sind daher von großer Bedeutung. Aufgrund der manuellen Digitalisierung, die für die Erstellung solcher Datensätze oft erforderlich ist, sind die meisten aktuellen Datensätze jedoch in ihrer räumlichen Abdeckung und zeitlichen Auflösung begrenzt. Diese kumulative Dissertation befasst sich mit diesem Problem, indem Deep Learning auf multispektrale Landsat-Aufnahmen angewendet wird, um eine genaue, robuste und automatisierte Erfassung von Gletscherfrontlagen zu erreichen. In drei Publikationen wird das entwickelte Prozessierungssystem vorgestellt, evaluiert und angewendet. Der Arbeitsablauf basiert auf einem convolutional neural network, welches mit manuell digitalisierten Gletscherfronten trainiert und getestet wird. Der mittlere Erfassungsfehler dieses Ansatzes liegt je nach Testdatensatz zwischen etwa 45 m und 75 m. Somit sind die meisten automatisch extrahierten Gletscherfronten nicht von den manuell digitalisierten zu unterscheiden. Zusätzlich wird der Nutzen von multispektralen, topografischen und texturalen Eingangsinformationen mithilfe eines dropped-variable-Ansatzes bewertet. Die daraus resultierenden Ergebnisse unterstreichen die Bedeutung der Integration multispektraler Informationen, die insbesondere unter schwierigen Bedingungen, wie z. B. bei stark ausgeprägter ice mélange, zu genaueren Vorhersagen führen. Dieses Prozessierungssystem wird schließlich auf Auslassgletscher in Grönland und auf der Antarktischen Halbinsel angewendet. Das daraus resultierende Datenprodukt für Grönland enthält 9243 Gletscherfrontlagen über 23 Gletscher für den Zeitraum 2013 bis 2021 und das Datenprodukt für die Antarktische Halbinsel enthält 4817 Gletscherfrontlagen über 42 Gletscher für den Zeitraum 2013 bis 2023. Im Gegensatz zu den meisten existierenden manuell digitalisierten Produkten kombinieren diese Datensätze zuverlässig eine hohe zeitliche Auflösung mit einer großen räumlichen Abdeckung. Die abgeleiteten Zeitreihen lösen nicht nur langfristige und saisonale Signale, sondern auch sub-saisonale Muster auf. Diese Datenprodukte werden zu einem besseren Verständnis der Dynamik von Auslassgletschern, der Eis-Ozean-Wechselwirkung und damit zu einer zuverlässigeren Eismodellierung beitragen. In Zukunft werden vergleichbare, auf Deep Learning basierende Prozessierungsverfahren wahrscheinlich zu einem festen Bestandteil des Monitorings von Gletscherfrontlagen werden. Diese Arbeit hat dazu beigetragen, eine neue Brücke zwischen Deep Learning, Fernerkundung und Glaziologie zu schlagen – eine Brücke, die auch auf lange Sicht bestehen bleiben wird.:1 Introduction 2 Outlet glaciers of ice sheets 2.1 Dynamics of outlet glaciers 2.2 Monitoring requirements 2.3 Satellite-based observation methods 3 Machine Learning in remote sensing 3.1 Fundamentals 3.1.1 Training 3.1.2 Models 3.1.3 Performance assessment and model optimisation 3.2 Deep learning: from perceptron to deep neural network 3.2.1 Multi-Layer Perceptron 3.2.2 Convolutional Neural Networks 3.3 Analysing remote sensing imagery using deep learning 4 Monitoring of calving front positions 4.1 Challenges and existing approaches 4.2 Existing data sets 5 Publications 6 Outlook 7 Conclusion

Is iron deficiency caused by BMPR2 mutations or dysfunction in pulmonary arterial hypertension patients?

Iron deficiency is common in idiopathic and heritable pulmonary arterial hypertension patients (I/HPAH). A previous report suggested a dysregulation of the iron hormone hepcidin, which is controlled by BMP/SMAD signaling involving the bone morphogenetic protein receptor 2 (BMPR-II). Pathogenic variants in the BMPR2 gene are the most common cause of HPAH. Their effect on patients' hepcidin levels has not been investigated. The aim of this study was to assess whether iron metabolism and regulation of the iron regulatory hormone hepcidin was disturbed in I/HPAH patients with and without a pathogenic variant in the gene BMPR2 compared to healthy controls. In this explorative, cross-sectional study hepcidin serum levels were quantified by enzyme-linked immunosorbent assay. We measured iron status, inflammatory parameters and hepcidin modifying proteins such as IL6, erythropoietin, and BMP2, BMP6 in addition to BMPR-II protein and mRNA levels. Clinical routine parameters were correlated with hepcidin levels. In total 109 I/HPAH patients and controls, separated into three groups, 23 BMPR2 variant-carriers, 56 BMPR2 noncarriers and 30 healthy controls were enrolled. Of these, 84% had iron deficiency requiring iron supplementation. Hepcidin levels were not different between groups and corresponded to the degree of iron deficiency. The levels of IL6, erythropoietin, BMP2, or BMP6 showed no correlation with hepcidin expression. Hence, iron homeostasis and hepcidin regulation was largely independent from these parameters. I/HPAH patients had a physiologically normal iron regulation and no false elevation of hepcidin levels. Iron deficiency was prevalent albeit independent of pathogenic variants in the BMPR2 gene