3D cell culture models replicate tissue complexity and aim to study cellular interactions and responses in a more physiologically relevant environment compared to traditional 2D cultures. However, the spherical structure of these models makes it difficult to extract meaningful data, necessitating advanced techniques for proper analysis. In silico simulations enhance research by predicting cellular behaviors and therapeutic responses, providing a powerful tool to complement experimental approaches. Despite their potential, these simulations often require advanced computational skills and significant resources, which creates a barrier for many researchers. To address these challenges, we developed an accessible pipeline using open-source software to facilitate virtual tissue simulations. Our approach employs the Cellular Potts Model, a versatile framework for simulating cellular behaviors in tissues. The simulations are constructed from real world 3D image stacks of cancer spheroids, ensuring that the virtual models are rooted in experimental data. By introducing a new metric for parameter optimization, we enable the creation of realistic simulations without requiring extensive computational expertise. This pipeline benefits researchers wanting to incorporate computational biology into their methods, even if they do not possess extensive expertise in this area. By reducing the technical barriers associated with advanced computational modeling, our pipeline enables more researchers to utilize these powerful tools. Our approach aims to foster a broader use of in silico methods in disease research, contributing to a deeper understanding of disease biology and the refinement of therapeutic interventions.

In the field of quality analysis of food and flavoring products, gas chromatography–quadrupole mass spectrometry–ion mobility spectrometry (GC-QMS-IMS) is a powerful technique for the simultaneous detection of volatile organic compounds (VOCs) by both QMS and IMS. GC is an established technique for the separation of complex VOC-rich food products. While subsequent detection by IMS features soft ionization of fragile compounds (e.g., terpenes) with characteristic drift times, MS provides analytes’ m/z values for database substance identification. A limitation of the prominently used static-headspace-based GC-QMS-IMS systems is the substantially higher sensitivity of IMS in comparison to full-scan QMS. The present study describes a new prototypic trapped-headspace (THS)-GC-QMS-IMS setup using mango purees. This approach ultimately allows the combination of soft ionization with m/z values obtained from database-searchable electron ionization (EI) spectra. The new setup features aligned retention times for IMS and MS and sufficient signal intensities for QMS and IMS. The results demonstrate that THS-GC-QMS-IMS allows for the classification of mango purees from different cultivars and that it could be a promising alternative method for authenticity control of food, flavors, and beverages.

In the field of quality analysis of food and flavoring products, gas chromatography – quadrupole mass spectrometry – ion mobility spectrometry (GC-QMS-IMS) is a powerful technique for the simultaneous detection of volatile organic compounds (VOC) by both QMS and IMS. GC is an established technique for the separation of complex VOC-rich foods such as beverages, dairy and fruit products as well as flavorings in general. While the subsequent detection by IMS features soft ionization of fragile compounds (e.g., terpenes) with characteristic drift times, MS provides the analytes’ m/z-values for database substance identification. A limitation of the prominently used static headspace-based GC-QMS-IMS systems is the substantially higher sensitivity of IMS in comparison to full scan QMS, which results in the detection of relevant VOC only in IMS, but not in the QMS analyzer. The present study describes a new prototypic trapped-headspace (THS)-GC-QMS-IMS setup, with optimized active flow regulation, able to gain valuable data of both detectors. This ultimately allows the combination of a soft ionization with m/z values obtained from database-searchable electron ionization (EI) spectra and as such, offers a substantial advantage in fields of quality control. The new setup features aligned retention times for IMS and MS and sufficient signal intensities for QMS and IMS. The analytical setup was evaluated with a set of mango puree samples from various provenances and the data was evaluated by suitable machine learning approaches. The results demonstrate that THS-GC-QMS-IMS allows for the classification of mango purees from different cultivars and can be a promising alternative method for authenticity control of food, flavors, and beverages.

Biomedical research increasingly relies on 3D cell culture models and AI-based analysis can potentially facilitate a detailed and accurate feature extraction on a single-cell level. However, this requires for a precise segmentation of 3D cell datasets, which in turn demands high-quality ground truth for training. Manual annotation, the gold standard for ground truth data, is too time-consuming and thus not feasible for the generation of large 3D training datasets. To address this, we present a novel framework for generating 3D training data, which integrates biophysical modeling for realistic cell shape and alignment. Our approach allows the in silico generation of coherent membrane and nuclei signals, that enable the training of segmentation models utilizing both channels for improved performance. Furthermore, we present a new GAN training scheme that generates not only image data but also matching labels. Quantitative evaluation shows superior performance of biophysical motivated synthetic training data, even outperforming manual annotation and pretrained models. This underscores the potential of incorporating biophysical modeling for enhancing synthetic training data quality.

3D cell culture models replicate tissue complexity, aiming to study cellular interactions and responses in a more physiologically relevant environment compared to traditional 2D cultures. However, the spherical structure of these models makes it difficult to extract meaningful data, necessitating advanced techniques for proper analysis. In silico simulations enhance research by predicting cellular behaviors and therapeutic responses, providing a powerful tool to complement experimental approaches. Despite their potential, these simulations often require advanced computational skills and significant resources, creating a barrier for many researchers. To address these challenges, we developed an accessible pipeline using open-source software to facilitate virtual tissue simulations. Our approach employs the Cellular Potts Model, a versatile framework for simulating cellular behaviors in tissues. The simulations are constructed from real world 3D image stacks of cancer spheroids, ensuring the virtual models are rooted in experimental data. By introducing a new metric for parameter optimization, we enable the creation of realistic simulations without requiring extensive computational expertise. This pipeline benefits researchers wanting to incorporate computational biology into their methods, even if they do not possess extensive expertise in this area. By reducing the technical barriers associated with advanced computational modeling, our pipeline allows more researchers to utilize these powerful tools. Our approach aims to foster broader use of in silico methods in disease research, contributing to a deeper understanding of disease biology and the refinement of therapeutic interventions.

Background: Digital health interventions are increasingly being integrated into oncological care to support patients in managing treatment-related symptoms and psychological distress. In a randomized controlled pilot trial, we investigated the feasibility and preliminary efficacy of a digital health app (SOFIA) among patients with cancer, including those in palliative care. SOFIA consists of an electronic patient-reported outcome (ePRO) assessment and coaching component. We showed good feasibility and high acceptability of SOFIA in routine clinical care. Objective: This pilot study aimed to evaluate user experiences and behavior with SOFIA. We applied a mixed methods design, combining a qualitative exploration of patients’ experiences and a quantitative analysis of app usage patterns. The integrated findings are intended to inform the refinement and further development of digital health interventions to better address the needs, preferences, and engagement behaviors of this patient population. Methods: Patients randomly assigned to the intervention group participated in semistructured interviews at 2 time points during the 12-week intervention period: midway through the intervention (at week 6) (T1) and postintervention (at week 12) (T2). Qualitative data were analyzed using content analysis. User data were descriptively analyzed with Python and the Pandas library (version 2.2.3). Results: Our qualitative analysis of 29 patients revealed benefits regarding the ePRO assessment (empowerment, support, user-friendliness, and facilitation of patient-physician communication), as well as some criticism (inflexible design and insufficient use by physicians). Benefits of the coaching tool included the availability of helpful information and design aspects such as clarity and user-friendliness. Quantitative data from 32 active users of the app showed that 16 (50%) of patients read articles, 28 (87.5%) started journeys, and 15 (46.9%) completed exercises at any point during the study. Conclusions: The results of this mixed methods study may provide important indications for digital interventions, including ePRO assessment, for patients with cancer. From the patients’ perspective, key features include intuitive design, relevant symptom items, reliable reminder functions, the translation of ePROs into clinically actionable information, clear symptom visualizations, seamless integration into electronic health records, and effective physician engagement. Both our qualitative and quantitative data show the importance of therapy-specific content. These results might increase acceptance and usage, and thereby also the clinical benefit, of future digital interventions for patients with cancer.