TH Köln - University of Applied Sciences

Oligonucleotides, both RNA and DNA, are fundamental to life despite being composed of a limited set of simple molecular building blocks. Chemists have long strived to add additional components, especially orthogonal, unnatural base pairs (UBPs). These increase the informational content of nucleic acids and provide site-specific anchors for labelling, enabling applications in aptamer enhancement, RNA structure elucidation, pathway tracing, sequencing, and the construction of semi-synthetic organisms. For this, suitable enzymes and techniques are required to incorporate and later analyse expanded alphabet genetic material. In this review we aim to outline some challenges, achievements, and possibilities that this field encompasses.

There is a great need to categorize cancer types for early cancer detection and treatment. RNA-Seq data is essential for getting insight into the differentially expressed genes. Due to its high dimensionality and complexity, performing an analysis on RNA-Seq data is quite challenging. In the past, RNA-Seq data were analyzed for a single cancer type as a two-class problem (either positive or negative) and did not contain information from other classes of cancer types. To classify different cancer types and discover the most promising genes, RNA-Seq data for different types of cancer should be examined. Multiple repositories offer RNA-Seq-based cancer types data. The present study incorporates a dataset from the Mendeley repository for classification. RNA-Seq values are then converted to their respective 2D images using some transformations. The classification problem is handled by five Transfer Learning (TL) algorithms (VGG16, VGG19, Resnet50, Resnet101, and Resnet152). Four different splitting strategies are applied for each classifier presented in the results and discussion section. A comparative analysis is also carried out with and without data augmentation. Results show that classifiers perform best at a split of 70-30. VGG16 attained the best position on overall results by achieving an accuracy of 95%. Hence, VGG16 is the leading TL algorithm for classification among all the accessible models and is not difficult to execute and easy to comprehend.

Among the icy satellites, Ganymede is unique in likely harboring a subsurface ocean while also possessing an intrinsic magnetic field strong enough to generate its own magnetosphere. Magnetic field measurements offer the potential to constrain ocean properties through electromagnetic induction studies. However, this requires an accurate separation of the various magnetic field contributions in Ganymede's environment, as well as a reliable model of its intrinsic field.Using magnetic field data from the Galileo and Juno flybys, we investigate the challenges and uncertainties associated with modeling and disentangling variable external magnetic field contributions. We assess uncertainties in estimating Jupiter's large-scale magnetospheric field during the flybys and examine limitations of current MHD models of Ganymede's magnetosphere and their impact on inversions of Ganymede's intrinsic magnetic field. Our results highlight significant uncertainties in the external field and magnetospheric modeling, leading to non-negligible ambiguities in inferred intrinsic field parameters and in the interpretation of future magnetic field observations with JUICE.

In disaster risk assessments, spatial population data are fundamental for determining exposure and social vulnerability. Traditionally, these analyses rely on static datasets, such as census records or population density maps. While accurate for representing nighttime distribution, these static models fail to capture the high levels of human mobility during the day. This can lead to significant under- or overestimations of the affected population, particularly in the event of sudden-onset disasters (e.g., flash floods, earthquakes, or critical infrastructure failure).To address this, high-resolution temporal data are required. However, existing approaches often rely on real-world measurements, such as cell phone data or GPS positions. Such data is usually purchased from data companies, limiting its utility for both research and emergency management.This study presents a spatio-temporal population model, initially developed for Germany's rich open data landscape, but with potential to be transferred to similar regions in the future. Using the city of Cologne as a primary test site, the model generates population maps for different time intervals throughout a typical weekday. The methodology employs iterative dasymetric mapping, integrating publicly available socio-demographic data, detailed building footprints, and the "Mobility in Germany" study. This approach ensures high transferability to other German metropolitan areas without requiring proprietary data.The model estimates the number of individuals in each building across seven distinct time intervals (e.g., 8 am – 10 am, 10 am – 1 pm) and further disaggregates the population into socioeconomic groups (e.g., students, elderly). The results were validated against three independent datasets: emergency call volumes, the ENACT POP dataset, and mobile phone positioning data.The model results enable refined disaster risk analysis by incorporating the temporal component of hazards and the corresponding population exposure. In the case of Cologne, this results in areas, such as the inner City at midday, with up to 5 times more exposed citizens than exposure analyses that rely on static data. Following the principles of Open Science, both the model code and the resulting datasets will be made publicly accessible to facilitate dynamic population assessments in other contexts.

Proximal femur fractures are the most common fractures in older people. Despite existing guidelines, post-operative care remains challenging. The integration of an app into rehabilitation presents a promising solution for older patients recovering from such fractures. This study aimed to develop an app-assisted rehabilitation concept for older adults after a proximal femur fracture, define the requirements for the app, and identify the prerequisites for the implementation of the concept. The study was conducted in two phases. In the first phase, online surveys and a focus group explored key aspects of the concept’s development, structured by the Consolidated Framework for Implementation Research (CFIR). In the second phase, a think-aloud approach assessed user perspectives and interactions, with data analysed using the framework approach. This study included patients (n = 11) with and without prior experience using health apps, an informal caregiver (n = 1), as well as physical therapists (n = 9) and physicians with expertise in managing patients after a proximal femur fracture (n = 5). The proposed concept builds on inpatient rehabilitation and integrates an app offering educational content, personalized training programs, questionnaires, and tests to assess relevant treatment factors, supplemented by personal therapy sessions. Autonomy for patients in deciding whether and to what extent informal caregivers are involved in their rehabilitation was emphasized. Training for both patients and stakeholders to use the app effectively is essential. The concept primarily aims to promote patients’ independence. The app requirements include adaptability to geriatric patients’ physical abilities, user-friendly navigation, motivational design, compatibility with various operating systems, and credibility of content. Prerequisites for implementation include the cognitive and physical abilities of patients and the willingness of both patients and stakeholders to acquire skills needed for the adoption of an app-assisted concept. Organizational structures must support the concept, accompanied by a feasible remuneration framework. The study resulted in the development of the PROGRES(S) concept. Successful design and implementation of this app-assisted rehabilitation concept require consideration of all CFIR dimensions. If proven feasible, acceptable, and effective, PROGRES(S) could complement existing rehabilitation programs and support patients who otherwise lack or have limited access to rehabilitation services.

Abstract Linguistic bias in online news and social media is widespread but difficult to measure. Yet, its identification and quantification remain difficult due to subjectivity, context dependence, and the scarcity of high-quality gold-label datasets. We aim to reduce annotation effort by leveraging pairwise comparison for bias annotation. To overcome the costliness of the approach, we evaluate more efficient implementations of pairwise comparison-based rating. We achieve this by investigating the effects of various rating techniques and the parameters of three cost-aware alternatives in a simulation environment. Since the approach can in principle be applied to both human and large language model annotation, our work provides a basis for creating high-quality benchmark datasets and for quantifying biases and other subjective linguistic aspects. The controlled simulations include latent severity distributions, distance-calibrated noise, and synthetic annotator bias to probe robustness and cost-quality trade-offs. In applying the approach to human-labeled bias benchmark datasets, we then evaluate the most promising setups and compare them to direct assessment by large language models and unmodified pairwise comparison labels as baselines. Our findings support the use of pairwise comparison as a practical foundation for quantifying subjective linguistic aspects, enabling reproducible bias analysis. We contribute an optimization of comparison and matchmaking components, an end-to-end evaluation including simulation and real-data application, and an implementation blueprint for cost-aware large-scale annotation.

In this open-label single-arm study, we tested the preliminary therapeutic effects of a four-week mindfullness-based intervention (MBI) tailored for fibromyalgia, including weekly online group meetings and daily mindfulness practices. Forty-six patients completed the intervention. The primary outcome was pain intensity scored on a numerical rating scale (NRS). Secondary outcomes included verbal fluency and self-reports of affective pain, quality of life, sleep quality, mood, emotion regulation, and psychological impairment. Transcranial magnetic stimulation was used to measure mindfulness-induced cortical excitability changes. Participants reported statistically reduced pain intensity post-training, with only 15.3% of the patients demonstrating clinically meaningful pain relief. A moderate improvement in quality of life, with only 45.6% clinical responders, were noted. Small-to-medium improvements in affective pain level, mindfulness level, and resilience as well as increase in long-interval intracortical inhibition-a measure of GABABergic inhibition-were observed. The study introduces a novel MBI tailored for fibromyalgia. Nonetheless, given the current study design and the lack of clinical significance of the findings despite statistical significance, the results are insufficient to draw firm conclusions regarding its potential therapeutic efficacy. Future sham-controlled randomized clinical trials are necessary to validate and expand upon these results.

Zusammenfassung Der Völkermord hat trotz seiner tiefgreifenden Auswirkungen bislang die Kriminologie nur unzureichend beschäftigt und stellt ein Forschungsdesiderat dar. Um dieses Defizit aufzuarbeiten, wurde untersucht, ob und welche Informationen internationale Urteile zu Genoziden liefern. Der Beitrag untersucht folglich, welche Handlungsmuster und Erklärungen in drei wegweisenden Urteilen zum Völkermord erkennbar sind. Die Ergebnisse zeigen, dass in allen Fällen vor und während der Gewalttaten eine gezielte Entmenschlichung der Opfer stattfand, kombiniert mit einer konstruierten Bedrohungsassoziation, die Taten in Regimen ohne wirksame Kontrollinstanzen begangen wurden und dass der Genozid koordiniert-arbeitsteilig organisiert war. Diese Befunde verdeutlichen, dass Genozide auf mehreren Ursachen beruhen und systematisch ablaufen. Der Beitrag diskutiert die Aussagekraft und Grenzen juristischer Urteilsdokumente als Datenquelle der Genozidforschung und plädiert dafür, dass gerichtliche Urteilstexte eine fundierte Grundlage bieten kann, um den Genozid als komplexes Phänomen besser zu verstehen. Damit trägt die Arbeit zu einem vertieften phänomenologischen Verständnis des Völkermords bei und skizziert neue konzeptionelle Perspektiven.

Social medicine, as practiced in the Federal Republic of Germany, is based on an interdisciplinary foundation. Within the multifaceted range of tasks assigned to it by the social insurance system, it implements both participatory and transformative processes through multiprofessional cooperation and co-creation. A symposium organized by the DGSMP's "Practical Social Medicine and Rehabilitation" department with significant participation of its Working Group on Nursing at the 60th annual conference of the German Society for Social Medicine and Rehabilitation (DGSMP) on September 19, 2025, at the Charité hospital, was dedicated to the theoretical and practical complexity of social medicine. It was conceived as a catalyst for defining a contemporary profile for social medicine and for fostering constructive discourse in the future. Six individual presentations addressed key concepts constituting social medicine and social legislation for categorizing social medicine's multiprofessional practice. The interdisciplinary agenda included selected topics from epistemological, historical, legal, health science, nursing science, and medical perspectives.