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Drug-induced rhabdomyolysis has become increasingly prevalent due to the rising use of medications such as statins, antidepressants, and antipsychotics. These can lead to muscle cell destruction and the release of myoglobin, potentially causing kidney damage. Recent advancements include the use of CytoSorb hemoadsorption as a promising therapy to remove myoglobin and other potentially toxic substances from the bloodstream. A 47-year-old male with a complex medical history presented with weakness, pain, and dizziness. Lab results indicated severe rhabdomyolysis, most likely of medication-induced etiology. He developed acute kidney injury (AKI) and underwent continuous venovenous hemodialysis (CVVHD) combined with CytoSorb hemoadsorption. Despite initial stabilization, rhabdomyolysis parameters surged, necessitating the use of an additional high-flux filter with enhanced middle molecule clearance. CytoSorb therapy was administered for nine consecutive sessions, resulting in decreased creatine kinase (CK) and myoglobin levels. Due to persistent kidney injury, the patient required permanent dialysis and was transferred to a kidney disease center. This case highlights the complexity and severity of drug-induced rhabdomyolysis with hemoadsorption playing a pivotal role in reducing myoglobin levels and improving the patient’s condition. Combining hemoadsorption and filters with enhanced middle molecule clearance holds even more promise for improved myoglobin removal.

Urbanization is progressing at an unprecedented rate in many places around the world. The Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 MultiSpectral Instrument (MSI) missions, combined with deep learning, offer new opportunities to accurately monitor urbanization at a global scale. Although the joint use of SAR and optical data has recently been investigated for urban change detection, existing data fusion methods rely heavily on the availability of sufficient training labels. Meanwhile, change detection methods addressing label scarcity are typically designed for single-sensor optical data. To overcome these limitations, we propose a semi-supervised urban change detection method that exploits unlabeled Sentinel-1 SAR and Sentinel-2 MSI data. Using bitemporal SAR and optical image pairs as inputs, the proposed multi-modal Siamese network predicts urban changes and performs built-up area segmentation for both timestamps. Additionally, we introduce a consistency loss, which penalizes inconsistent built-up area segmentation across sensor modalities on unlabeled data, leading to more robust features. To demonstrate the effectiveness of the proposed method, the SpaceNet 7 dataset, comprising multi-temporal building annotations from rapidly urbanizing areas across the globe, was enriched with Sentinel-1 SAR and Sentinel-2 MSI data. Subsequently, network performance was analyzed under label-scarce conditions by training the network on different fractions of the labeled training set. The proposed method achieved an F1 score of 0.555 when using all available training labels, and produced reasonable change detection results (F1 score of 0.491) even with as little as 10% of the labeled training data. In contrast, multi-modal supervised methods and semi-supervised methods using optical data failed to exceed an F1 score of 0.402 under this condition. Code and data are made publicly available.

Urban population distribution maps are vital elements for monitoring the Sustainable Development Goals, appropriately allocating resources such as vaccination campaigns, and facilitating evidence-based decision making. Typically, population distribution maps are derived from census data from the region of interest. Nevertheless, in several low- and middle-income countries, census information may be unreliable, outdated or unsuitable for spatial analysis at the intra-urban level, which poses severe limitations in the development of urban population maps of adequate quality. To address these shortcomings, we deploy a novel framework utilizing multisource Earth Observation (EO) information such as Sentinel-2 and very-high-resolution Pleiades imagery, openly available building footprint datasets, and deep learning (DL) architectures, providing end-to-end solutions to the production of high quality intra-urban population distribution maps in data scarce contexts. Using several case studies in Sub-Saharan Africa, namely Dakar (Senegal), Nairobi (Kenya) and Dar es Salaam (Tanzania), our results emphasize that the combination of DL and EO data is very potent and can successfully capture relationships between the retrieved image features and population counts at fine spatial resolutions (100 meter). Moreover, for the first time, we used state-of-the-art domain adaptation methods to predict population distributions in Dar es Salaam and Nairobi (R2 = 0.39, 0.60) that did not require national census or survey data from Kenya or Tanzania, but only a sample of training locations from Dakar. The DL architecture is based on a modified ResNet-18 model with dual-streams to analyze multi-modal data. Our findings have strong implications for the development of a new generation of urban population products that are an output of end-to-end solutions, can be updated frequently and rely completely on open data.

Abstract Background Up to 70% of septic patients develop a diffuse brain dysfunction named “septic associated encephalopathy” which is often solely based on clinical impressions. However, the diagnosis of septic associated encephalopathy is outcome-relevant due to an increase in mortality in these patients. Neuroinflammation as well as a disturbance of cholinergic transmission are assumed to be the causes of both delirium and septic associated encephalopathy. An alteration in cholinergic activity can be objectified by measuring the erythrocytic acetylcholinesterase-activity. Single-point measurements of acetylcholinesterase-activity are of limited value because individual and dynamic changes over time have to be anticipated. Therefore, the hypothesis should be tested whether a longitudinal analysis of acetylcholinesterase-activity in critically ill patients can help to diagnose a suspected septic-associated encephalopathy and whether acetylcholinesterase-activity differs in comparison to non-septic patients. Methods In this prospective, observational, single-center study, 175 patients (45 with sepsis, 130 without sepsis) were included. All patients were admitted to the surgical Intensive Care Unit of the University hospital Ulm, Germany. Patients were examined daily for the presence of delirium using the CAM-ICU. Daily measurement of the acetylcholinesterase-activity was performed in all patients. The possible time-dependent change in acetylcholinesterase-activity was analyzed with a linear regression model considering repeated measurements. Using a time-adjusted model further factors able to affect AChE-activity were investigated. For nonparametric distributions quantitative data were compared using Wilcoxon matched-pairs test. For analysis of independent samples the Mann-Whitney test was performed. Results About 90% of septic patients with suspected septic associated encephalopathy exhibited a statistically significant time-dependent in- or decrease in acetylcholinesterase-activity over a period of at least 5 consecutive days. Conclusion Longitudinal measurement of acetylcholinesterase-activity over several consecutive days revealed a change from baseline only in septic patients with suspected septic-associated encephalopathy. Therefore, longitudinal measurement of acetylcholinesterase-activity is able to diagnose septic associated encephalopathy in septic patients with delirious symptoms. Trial registration Retrospectively registered at German Clinical Trials Register, registration number DRKS00020542 , date of registration: January 27, 2020.

Background. Polymorphonuclear granulocytes (PMN) play a crucial role in host defense. Physiologically, exposure of PMN to the complement activation product C5a results in a protective response against pathogens, whereas in the case of systemic inflammation, excessive C5a substantially impairs neutrophil functions. To further elucidate the inability of PMN to properly respond to C5a, this study investigates the role of the cellular membrane potential of PMN in response to C5a. Methods. Electrophysiological changes in cellular and mitochondrial membrane potential and intracellular pH of PMN from human healthy volunteers were determined by flow cytometry after exposure to C5a. Furthermore, PMN from male Bretoncelles-Meishan-Willebrand cross-bred pigs before and three hours after severe hemorrhagic shock were analyzed for their electrophysiological response. Results. PMN showed a significant dose- and time-dependent depolarization in response to C5a with a strong response after one minute. The chemotactic peptide fMLP also evoked a significant shift in the membrane potential of PMN. Acidification of the cellular microenvironment significantly enhanced depolarization of PMN. In a clinically relevant model of porcine hemorrhagic shock, the C5a-induced changes in membrane potential of PMN were markedly diminished compared to healthy littermates. Overall, these membrane potential changes may contribute to PMN dysfunction in an inflammatory environment.

Thermodynamically induced length fluctuations of high-reflectivity mirror coatings put a fundamental limit on sensitivity and stability of precision optical interferometers like gravitational-wave detectors and ultrastable lasers. The main contribution—Brownian thermal noise—is related to the mechanical loss of the coating material. Al_{0.92}Ga_{0.08}As/GaAs crystalline mirror coatings are expected to reduce this limit. The first measurements of cryogenic silicon cavities revealed the existence of additional noise contributions exceeding the expected Brownian thermal noise. We describe a novel, nonthermal, photoinduced effect in birefringence that is most likely related to the recently discovered birefringence noise. Our studies of the dynamics and power dependence are an important step toward uncovering the underlying mechanisms. Averaging the anticorrelated birefringent noise results in a residual noise that is shown to be substantially different from Brownian thermal noise. To this end, we develop a new method for analyzing the coating noise in higher-order transverse-cavity modes, which makes it possible for the first time to determine the contribution of Brownian thermal noise to the total cavity noise. The new noise contributions must be considered carefully in precision interferometry experiments using similar coatings based on semiconductor materials.

Microfluidics continues to bring innovation to the life sciences. It stimulates progress by enabling new ways of research in biology, chemistry, and biotechnology. However, when designing a microfluidic device, designers have to conduct many tasks by hand—resulting in labor-intensive processes. In particular, when drawing the design of the device, designers have to handle re-occurring entities. Meander channels are one example, which are frequently used in different platforms but always have to fit the respective application and design rules. This work presents an online tool which is capable of automatically generating user-defined, two-dimensional designs of fluidic meander channels facilitating fluidic hydrodynamic resistances. The tool implements specific design rules as it considers the user’s needs and fabrication requirements. The compliance of the meanders generated by the proposed tool is confirmed by fabricating the generated designs and comparing whether the resulting devices indeed realize the desired specification. To this end, two case studies are considered: first, the realization of dedicated fluidic resistances and, second, the realization of dedicated mixing ratios of fluids. The results demonstrate the versatility of the tool regarding application and technology. Overall, the freely accessible tool with its flexibility and simplicity renders manual drawing of meanders obsolete and, hence, allows for a faster, more straightforward design process.

We have measured the absolute frequency of the optical lattice clock based on ^87 Sr at PTB with an uncertainty of $3.9\times {{10}^{-16}}$ using two caesium fountain clocks. This is close to the accuracy of todayʼs best realizations of the SI second. The absolute frequency of the 5 s ^2 1 S _0 – 5s5p ^3 P _0 transition in ^87 Sr is $429\;228\;004\;229\;873$ .13(17) Hz. Our result is in excellent agreement with recent measurements performed in different laboratories worldwide. We improved the total systematic uncertainty of our Sr frequency standard by a factor of five and reach $3\times {{10}^{-17}}$ , opening new prospects for frequency ratio measurements between optical clocks for fundamental research, geodesy or optical clock evaluation.