Open Access

Cells can sense geometrical cues with sizes of several tens of micrometers in their vicinity. Recent in vitro studies show that cells can adapt their shape, align along specific directions, or regulate other cellular functions when grown on surfaces with curvatures larger than their size. Although possible mechanisms for such responses like the alignment along axial cues have been suggested, a detailed understanding of the involved cellular processes remains open. This work addresses this gap by systematically investigating mesenchymal cell and nucleus orientation responses using a low‐cost model surface platform, the CurvChip. Using an array of cylindrically curved topographies with radii of curvatures ranging from tens to hundreds of micrometers, the contact guidance response of cells and nuclei is quantified in dependence on substratum curvature and manipulation of cytoskeletal components. Results suggest a desired perceived curvature for the investigated cells, and a very sensitive and robust curvature perception mechanism, as the effect of pharmacological manipulation of cytoskeletal components is relatively small. Furthermore, a comparison with previously published work strengthens the hypothesis of an involvement of the nucleus in the cell response to three‐dimensional (3D) curvatures.

Objectives: Content-based access (CBA) to medical image archives, i.e. data retrieval by means of image-based numerical features computed automatically, has capabilities to improve diagnostics, research and education. In this study, the applicability of CBA methods in dentomaxillofacial radiology is evaluated. Methods: Recent research has discovered numerical features that were successfully applied for an automatic categorization of radiographs. In our experiments, oral and maxillofacial radiographs were obtained from the day-to-day routine of a university hospital and labelled by an experienced dental radiologist regarding the technique and direction of imaging, as well as the displayed anatomy and biosystem. In total, 2000 radiographs of 71 classes with at least 10 samples per class were analysed. A combination of co-occurrence-based texture features and correlation-based similarity measures was used in leaving-one-out experiments for automatic classification. The impact of automatic detection and separation of multi-field images and automatic separability of biosystems were analysed. Results: Automatic categorization yielded error rates of 23.20%, 7.95% and 4.40% with respect to a correct match within the first, fifth and tenth best returns. These figures improved to 23.05%, 7.00%, 4.20%, and 20.05%, 5.65% and 3.25% if automatic decomposition was applied and the classifier was optimized to the dentomaxillofacial imagery, respectively. The dentulous and implant systems were difficult to distinguish. Experiments on non-dental radiographs (10 000 images of 57 classes) yielded 12.6%, 5.6% and 3.6%. Conclusion: Using the same numerical features as in medical radiology, oral and maxillofacial radiographs can be reliably indexed by global texture features for CBA and data mining.

The pre-, intra- and postoperative determination of the entity and dignity of salivary gland tumors (ST) based solely on histomorphological criteria is not reliably in all cases. The spectra of Raman spectroscopy (RS) contain information about the molecular composition of the examined tissue. The aim of the work was to establish an RS-based measurement setup and a workflow for the differentiation of salivary gland tumor tissue and salivary gland tissue. In addition, the barriers of translating RS in salivary gland diagnostics are discussed. 10 µm thick, native cryo-tissue sections of Warthin tumors (n=5) and pleomorphic adenomas (n=4) were examined using RS in both tumor tissue and healthy salivary gland tissue and the data were evaluated in a multivariate data analysis. All measurements were histomorphologically localized in a corresponding HE section. A "principal component" analysis (PCA) of the RS data and coupled discriminant analysis enabled both a distinction between tumor and non-tumor tissue as well as the differentiation of the various tumor entities (based on the histopathological assessment) with a high level of accuracy (93% ). In summary, it could be shown that the RS measurements could be used to reliably distinguish between ST and healthy salivary gland tissue. Another important result is that tissue processing is possible reliably using standard pathological methods. The high number of different ST entities represents a biostatistical challenge. Approaches to the solution include multi-level statistical models and simultaneous correlation with histomorphological criteria.

Background: Digitalization in disaster medicine holds significant potential to accelerate rescue operations and ultimately save lives. Mass casualty incidents demand rapid and accurate information management to coordinate effective responses. Currently, first responders manually record triage results on patient cards, and brief information is communicated to the command post via radio communication. Although this process is widely used in practice, it involves several time-consuming and error-prone tasks. To address these issues, we designed, implemented, and evaluated an app-based mobile triage system. This system allows users to document responder details, triage categories, injury patterns, GPS locations, and other important information, which can then be transmitted automatically to the incident commanders. Objective: This study aims to design and evaluate an app-based mobile system as a triage and coordination tool for emergency and disaster medicine, comparing its effectiveness with the conventional paper-based system. Methods: A total of 38 emergency medicine personnel participated in a within-subject experimental study, completing 2 triage sessions with 30 patient cards each: one session using the app-based mobile system and the other using the paper-based tool. The accuracy of the triages and the time taken for each session were measured. Additionally, we implemented the User Experience Questionnaire along with other items to assess participants’ subjective ratings of the 2 triage tools. Results: Our 2 (triage tool) × 2 (tool order) mixed multivariate analysis of variance revealed a significant main effect for the triage tool (P<.001). Post hoc analyses indicated that participants were significantly faster (P<.001) and more accurate (P=.005) in assigning patients to the correct triage category when using the app-based mobile system compared with the paper-based tool. Additionally, analyses showed significantly better subjective ratings for the app-based mobile system compared with the paper-based tool, in terms of both school grading (P<.001) and across all 6 scales of the User Experience Questionnaire (all P<.001). Of the 38 participants, 36 (95%) preferred the app-based mobile system. There was no significant main effect for tool order (P=.24) or session order (P=.06) in our model. Conclusions: Our findings demonstrate that the app-based mobile system not only matches the performance of the conventional paper-based tool but may even surpass it in terms of efficiency and usability. This advancement could further enhance the potential of digitalization to optimize processes in disaster medicine, ultimately leading to the possibility of saving more lives.

It was found that the material properties of the multi-material substrates can be specifically influenced by the systematic addition of three photopolymers (Tissue Matrix, Vero, Agilus). Mechanical-dynamic properties very close to those of the biological system can be achieved. Densities of 1100 to 1180 kg/m³, Young’s modulus of 0.1 to 25 MPa and Poisson’s ratio of around 0.3 have been achived. Target values for human ligaments reported in the literature vary, with densities ranging from 1100 to 1200 kg/m³, Young’s modulus from 0.05 to 21 MPa, damping ratio from 0.003 to 0.05, and Poisson’s ratio of around 0.3.