Danube Private University is a private university located in Krems an der Donau, Austria. The university was founded on 13 August 2009. The university was established in 2009.
Accurate compensation for intra-operative brain shift remains a fundamental challenge in neurosurgical procedures, as misalignment between pre-operative and intra-operative images can lead to suboptimal surgical guidance. In this work, we propose a bidirectional, unsupervised 3D brain shift compensation framework for non-rigid MRI registration, designed to directly estimate dense deformation fields between pre-operative and intra-operative volumes. The proposed approach employs dual image-translation pathways coupled with a dedicated deformation field predictor, enabling robust alignment without requiring paired training data. A key component of the framework is the integration of bidirectional consistency constraints to enforce anatomical correspondence across domains, while deformation field estimation is explicitly optimized for registration accuracy. In addition, we incorporate a Haar wavelet-based perceptual similarity (HaarPSI) loss to improve the preservation of fine-scale anatomical structures during registration. The proposed method is evaluated against classical and state-of-the-art baselines, including Elastix, Voxelmorph, and several adversarial and GAN-based learning approaches adapted with comparable registration modules. Qualitative analyses of deformation fields and error maps further demonstrate improved alignment in regions most affected by brain shift. These results highlight the effectiveness of bidirectional deformation modeling combined with adversarial and perceptual constraints for accurate, physically plausible, and unsupervised 3D non-rigid registration in neurosurgical applications.
Large language models (LLMs) represent a rapidly advancing subset of artificial intelligence with significant potential to augment clinical practice. These tools can assist in literature synthesis, documentation, clinical reasoning, and patient communication. Despite demonstrated capabilities, adoption in healthcare remains limited due to barriers such as limited AI literacy, trust concerns, integration challenges, and generational disparities in digital proficiency. This review critically examines the role of LLMs as cognitive adjuncts in medicine, emphasizing the need for calibrated trust, human oversight, structured implementation strategies, and robust regulatory governance. Evidence suggests that stepwise integration of LLMs, beginning with low-risk tasks, may enhance physician efficiency, reduce cognitive burden, and may support high-quality care. Key limitations, including hallucinations, data currency, environmental impact, and data security, have to be addressed, alongside the need for continuous model refinement. Rigorous clinical trials are needed to establish the efficacy of LLMs. This includes the recognition that LLMs trained on vast web data may contain personal health information, creating a significant privacy risk. Responsible adoption of LLMs is essential to meet the demands of modern medicine while preserving clinical accountability and patient-centered care.
Cefoxitin (CEF), classified as a semisynthetic beta-lactam second-generation cephalosporin antibiotic, can be used against many aerobic and anaerobic bacterial infections. Elevated aspartate aminotransferase levels and a higher prevalence of eosinophilia have been linked to higher CEF dosages, which alleviates the demand for ultra-sensitive determination and monitoring of its concentration level. Sulfur and nitrogen doped carbon quantum dots (N,S-CQDs) were synthesized by microwave techniques using lemon zest as a natural biomass source for green modified CQDs synthesis. N,S-CQDs used as a highly sensitive fluorescent probe, green and economic, for the determination of CEF owing to its excellent photoluminescence (PL) characters and exhibited higher quantum yield (QY) similar to 37.65 % with high aqueous stability. The quenching mechanism is meticulously demonstrated to be a static quenching process through the utilization of fluorescence spectroscopy and Stern-Volmer analysis across various temperature conditions. This work developed a new method for rapid determination of CEF residues by static quenching mechanism with a detection limit (LOD) value of 0.023 mu M within a linearity of 0.1-15.0 mu M. Moreover, the proposed fluorescence probe has potential practical application for CEF determination in pharmaceutical dosage forms and biological fluids. Furthermore, four green evaluation tools, the Complex Modified Green Analytical Procedure Index (ComplexMoGAPI), the blue applicability grade index (BAGI), the carbon footprint reduction index (CaFRI), and a Modified Green Star Area (MoGSA), were evaluated to assess the proposed analytical method. The Evaluation and Performance of Practicality Index (EPPI, 2025) assessment demonstrated that the developed green N,S-CQDs-based fluorometric method for CEF determination is a more sustainable, efficient, and practical analytical approach compared to the reported derivatization method.
This work introduces a new efficient iterative solver for the reconstruction of real-time cone-beam computed tomography, which is based on the prior image constrained compressed sensing (PICCS) regularization and leverages the efficiency of Krylov subspace methods. In particular, we focus on the setting where a sequence of under-sampled computed tomography (CT) scans are taken on the same object with only local changes (e.g. changes in a tumour size or the introduction of a surgical tool). This is very common, for example, in image-guided surgery, where the amount of measurements is limited to ensure the safety of the patient. In this case, we can also typically assume that a (good) initial reconstruction for the solution exists, coming from a previously over-sampled scan, so we can use this information to aid the subsequent reconstructions. The effectiveness of this method is demonstrated in both a synthetic scan and using real CT data, where it can be observed that the PICCS framework is very effective for the reduction of artefacts, and that the new method is faster than other common alternatives used in the same setting.
Automated histopathological image analysis plays a vital role in computer-aided diagnosis of various diseases. Among developed algorithms, deep learning-based approaches have demonstrated excellent performance in multiple tasks, including semantic tissue segmentation in histological images. In this study, we propose a novel approach based on attention-driven feature fusion of convolutional neural networks (CNNs) and vision transformers (ViTs) within a unified dual-encoder model to improve semantic segmentation performance. Evaluation on two publicly available datasets showed that our model achieved μIoU/μDice scores of 76.79
