Austrian Center for Medical Innovation and Technology

Circulating cell-free microRNAs (miRNAs) are emerging as promising biomarkers with broad potential for clinical applications. However, pre-analytical variability significantly affects miRNA quantification and hampers reproducibility across studies. This work aims to define the Minimum Preanalytical Information required for the publication of studies on circulating cell-free miRNA-based biomarkers. We review key pre-analytical factors that influence circulating miRNA levels quantified using RT-qPCR. Critical variables include blood collection timing, sample type, centrifugation protocols, transport and storage conditions, hemolysis, lipemia, medication, physical activity and pathogen inactivation methods. We introduce a standardized checklist to promote methodological transparency and inter-study comparability. The final aim is to enhance the reliability of miRNA-based biomarker research and support its successful translation into clinical practice.

Background: Maxillary sinus floor augmentation (MSFA) is commonly used to increase posterior maxillary bone volume prior to implant placement. Although generally successful, late complications can impact long-term outcomes. The purpose of the study was to estimate the incidence and timing of atypical late complications following (MSFA) using bovine xenohybrid bone grafts. The study also aimed to evaluate whether preoperative bone volume is associated with the risk of complications. Methods: This retrospective cohort study was conducted at the Center of Oral and Maxillofacial Surgery, Danube Private University, Krems-Stein, Austria, and included patients who underwent MSFA with bovine xenohybrid bone grafts and either simultaneous or staged implant placement between January 2020 and December 2023. Preoperative bone volume of the posterior maxilla measured via cone beam computed tomography (CBCT) in the planned implant insertion position. The primary endpoint was the time (days) from MSFA to the occurrence of a graft-related complication (defined as atypical if occurring more than 6 months after MSFA and not related to peri-implantitis) The covariates included subjects’ age, sex, the quantity of graft used for MSFA, timing of dental implant insertion (simultaneous vs. staged) and implant dimensions. Kaplan–Meier analysis and Cox proportional hazards regression were used to evaluate time-to-event data. Only one graft site per patient was analyzed. Results: Atypical complications occurred in 9 out of 47 patients (19.1%), with an average time to onset of 645 days. In a multivariable analysis, a lower preoperative bone volume was found to be an independent predictor of an increased risk of complications (hazard ratio [HR]: 0.972; 95% confidence interval [CI]: 0.925–1.021; p = 0.252). However, the quantity of graft used for MSFA was not found to be a predictor (p = 0.46). Conclusions: Within the limitations of a retrospective study, reduced native bone volume appears to increase the risk of atypical late complications following MSFA with bovine xenohybrid grafts. This makes closer clinical and radiologic follow-up of patients over a longer period very necessary.

Vitreoretinal surgery includes a group of highly complex microsurgical procedures that demand precision. Robotic systems can enhance surgical performance, particularly for novice surgeons, while ensuring patient safety through advanced sensing capabilities. Optical Coherence Tomography (OCT), commonly used for eye anatomy imaging, is typically implemented via microscopes or diagnostic devices. This paper introduces the GEYEDANCE system, a bilateral teleoperated microsurgery platform integrating OCT directly at the end-effector of its remote manipulator, offering multi-modal feedback. The system enables intraoperative global eye modelling and surface reconstruction by exploiting a neural network-based tool-to-tissue distance estimation module. Its performance was validated in the operating room using ex-vivo eyes, effectively simulating the surgical steps of various vitreoretinal procedures.

Anatomical models are essential tools for teaching, patient education, or training. Recent developments in 3D printing enabled the production of customised models based on individual imaging data. Although most 3D printing processes can accurately reproduce anatomical structures geometrically, they lack similarity in haptic properties. Therefore, in this study, we investigated the influence of highly viscous silicone oil injections in 3D printed silicone samples on enhancing viscoelastic behaviour. For this, 72 specimens with 3 different infill densities (20 %, 30 %, 40 %) were printed and tested using stress relaxation tests. Afterwards, they were filled using 3 different high viscous silicone oils (1 kPa ⋅ s, 5 kPa ⋅ s, 10 kPa ⋅ s) and retested. The material properties of the silicone infill/silicone oil combination were extracted from the structural properties of the tested samples using an optimisation strategy based on a finite element model to get the material response for the infill only. Alongside the infill density, the storage modulus increases from 28.0 to 52.3 kPa for empty samples. By adding high viscous silicone oil the loss modulus is increased from 3.3–5.6 kPa up to 12.0–20.0 kPa. The resulting loss tangent increases from 0.10–0.12 to 0.28–0.29 for the different infill densities. With this range of possible viscoelastic properties, several different biological soft tissues can be modelled. It could be proven that a silicone oil injection is a promising way to increase the loss moduli of 3D printed silicone samples, greatly increasing the design space of possible printable viscoelastic properties.

Recent advances in machine learning are transforming medical image analysis, particularly in cancer detection and classification. Techniques such as deep learning, especially convolutional neural networks (CNNs) and vision transformers (ViTs), are now enabling the precise analysis of complex histopathological images, automating detection, and enhancing classification accuracy across various cancer types. This study focuses on osteosarcoma (OS), the most common bone cancer in children and adolescents, which affects the long bones of the arms and legs. Early and accurate detection of OS is essential for improving patient outcomes and reducing mortality. However, the increasing prevalence of cancer and the demand for personalized treatments create challenges in achieving precise diagnoses and customized therapies. We propose a novel hybrid model that combines convolutional neural networks (CNN) and vision transformers (ViT) to improve diagnostic accuracy for OS using hematoxylin and eosin (H&E) stained histopathological images. The CNN model extracts local features, while the ViT captures global patterns from histopathological images. These features are combined and classified using a Multi-Layer Perceptron (MLP) into four categories: non-tumor (NT), non-viable tumor (NVT), viable tumor (VT), and non-viable ratio (NVR). Using the Cancer Imaging Archive (TCIA) dataset, the model achieved an accuracy of 99.08%, precision of 99.10%, recall of 99.28%, and an F1-score of 99.23%. This is the first successful four-class classification using this dataset, setting a new benchmark in OS research and offering promising potential for future diagnostic advancements.

Anatomic models are important in medical education and pre-operative planning as they help students or doctors prepare for real scenarios in a risk-free way. Several experimental anatomic models were made with additive manufacturing techniques to improve geometric, radiological, or mechanical realism. However, reproducing the mechanical behavior of soft tissues remains a challenge. To solve this problem, multi-material structuring of soft and hard materials was proposed in this study, and a three-dimensional (3D) printer was built to make such structuring possible. The printer relies on extrusion to deposit certain thermoplastic and silicone rubber materials. Various objects were successfully printed for testing the feasibility of geometric features such as thin walls, infill structuring, overhangs, and multi-material interfaces. Finally, a small medical image-based ribcage model was printed as a proof of concept for anatomic model printing. The features enabled by this printer offer a promising outlook on mimicking the mechanical properties of various soft tissues.

The prevalence of medical errors during surgical procedures has led to a higher emphasis on improving surgical outcomes, by improving surgical planning and training. Anatomical models have become valuable tools for pre-operative planning and current 3D printed models strive to better match real soft biological tissues. This study aimed to develop novel 3D printed material composites with controllable mechanical properties that mimic soft tissue. Concepts of microstructuring, fiber reinforcement and fluid infill in extrusion-based 3D printing are combined to design tunable materials towards target tissues of porcine muscle and liver. Material characterization was performed in triangular wave cyclic experiments under uniaxial tension with increasing displacements. Hereby, initial EI and final EII elastic moduli were evaluated. Further, the viscous response was characterized by the dissipated energy ratio UD and suture retention strength (SRS) was determined by single tensile pull-out tests Elastic moduli of printed materials were successfully tuned to 510 ± 10 kPa, closely resembling porcine muscle with 580 ± 150 kPa. The dissipated energy ratio UD of the silicone was increased from 0.09 ± 0.01 to 0.46 ± 0.17 by addition of gyroid infill and viscous fluid. Suture retention strength (SRS) for porcine liver tissue was 1.64 ± 0.42 N, while that of 3D printed silicone showed a mean SRS of 5.1 ± 0.6 N. Although the exact properties of porcine muscle and liver tissue require finer tuning, this study established techniques for refinement of 3D printed tissue-mimicking materials, ultimately enabling more accurate models for surgical rehearsal.