Otto Wagner Hospital

Purpose To compare implant–acromion collision risk between the transdeltoid and anterolateral surgical approaches for straight antegrade humeral nailing (SAHN) using a CT-based simulation, and to evaluate whether the Acromion Index (AI) predicts collision.Methods Sixty-eight anonymized shoulder CTs were segmented to generate scapular and humeral surface models. A straight 8-mm nail was virtually advanced along the medullary axis under two conditions: (I) transdeltoid with 0° glenohumeral extension and (II) anterolateral with 30° extension. Two trained investigators independently repeated all simulations and recorded nail-acromion collisions. Agreement was assessed with Cohen’s κ and intraclass correlation coefficients (ICC). Collision rates were compared with paired McNemar tests. The predictive value of AI was analyzed using logistic regression and receiver-operating-characteristic (ROC) analysis. Results Implant-acromion collision occurred in 32/68 shoulders (47.1%) with the transdeltoid approach, versus 2/68 (2.9%) with the anterolateral approach (absolute risk reduction 44%; number-needed-to-treat 2.3; p< 10⁻⁶). AI independently predicted transdeltoid failure (adjusted odds ratio 2.81 per +0.10; p = 0.011); a cut-off ≥ 0.69 yielded an AUC of 0.72 (63% sensitivity, 72% specificity). Inter-observer reliability was substantial for collision (κ 0.86) and excellent for morphometrics (ICC ≥ 0.90). Conclusion CT-based simulation demonstrates that the anterolateral approach markedly reduces the risk of implant-acromion collision in SAHN and should therefore be considered the preferred option. However, if the transdeltoid approach is selected, preoperative AI screening with a threshold of ≥0.69 is essential, as larger acromia increase collision risk and must be factored into surgical planning. Level of evidence Level IV – paired in-silico simulation study.

Background: As demonstrated by the PACIFIC trial, biomarker-driven patient selection is crucial. While treatment based on programmed death ligand-1 (PD-L1) and mutational status have become routine, tests for biomarkers available from pretherapeutic blood samples are currently a topic of scientific interest. Methods: This analysis was conducted on patients from the ALLSTAR RWD study, which is a nationwide, prospective registry for inoperable non-small cell lung cancer (NSCLC) stage III. Patients were amenable if they had a full routine pre-treatment blood sample, from which the following biomarkers were extracted: neutrophil-to-lymphocyte ratio (NLR), derived neutrophil-to-lymphocyte ratio (dNLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), derived monocyte-to-lymphocyte ratio (dMLR) and lactate dehydrogenase (LDH) levels. The intention was to find a cutoff for each of these biomarkers to predict locoregional control (LRC), progression-free survival (PFS) and overall survival (OS). Results: MLR and dMLR demonstrated their predictive potential with cutoff values of 0.665 and 0.945, respectively. Stratifying the whole cohort by means of these cutoffs demonstrated significantly better locoregional control for patients below the threshold, both in the whole cohort (N = 175; 55.7% vs. 75.5%; p-value = 0.018) and in the Durvalumab subgroup (N = 106; 57.5% vs. 77.3%; p-value = 0.030). Similar findings were observed for PFS in the whole cohort (N = 175; 20.5% vs. 56.1%; p-value p < 0.001) and in the Durvalumab subgroup (N = 106; 31.2% vs. 64.6%, p-value < 0.001). dMLR could also significantly predict PFS (N = 173; 17.4% vs. 56.3%; p-value < 0.001), which was corroborated in the Durvalumab subgroup (N = 108; 23.1% vs. 64.1%; p-value = 0.003). Conclusions: This explorative analysis demonstrates the predictive potential of MLR and dMLR for LRC and PFS. These blood biomarkers can be readily integrated into clinical routines since they are easily available.

Transimpedance matrix (TIM) measurements can not only detect abnormal electrode positioning in patients with cochlear implants (CI) but also estimate the angular insertion depth (AID). This study aims to assess the feasibility of using TIM to measure cochlear morphology-specifically its dimensions and electrode position-and to identify electrode pairs best associated with these dimensions. Retrospective cohort study. University Medical Center, tertiary academic referral center. Patients undergoing CI (CI622) surgery between January 2020 and April 2024. Cochlear size parameters, including diameter, width, height, and cochlear duct length (CDL), were obtained preoperatively from CT imaging using OTOPLAN. The maximal angular insertion depth (maxAID) was determined from x-ray imaging based on Stenvers' projection. Gradient phases between electrode pairs and heatmaps were extracted from TIM data. Pearson correlation analysis was conducted to examine the relationships between cochlear size parameters and TIM data. Out of 41 patients, 39 were included in this study. A significant positive correlation was found between the maxAID and the gradient phase of electrode pairs E20-E10 (R = 0.798, P < 0.0001). Negative correlations between cochlear size and gradient phase were identified: (1) diameter: R = -0.59, P < 0.05 (E18-E11), (2) width: R = -0.609, P < 0.05 (E16-E4), (3) height: R = -0.634, P < 0.01 (E8-E22), (4) cochlear duct length (CDL) R = -0.634, P < 0.01 (E16-E12). TIM measurements are useful for assessing cochlear dimensions.

ABSTRACT Classification of spectra (1) and anomaly detection (2) are fundamental steps to guarantee the highest accuracy in redshift measurements (3) in modern all-sky spectroscopic surveys. We introduce a new Galaxy Spectra Neural Network (GaSNet-III) model that utilizes neural networks to perform these three tasks simultaneously with high efficiency. Two different reconstruction networks – an autoencoder-like network and a U-Net – are used to reconstruct the rest-frame spectrum, which is then compared with the observed spectrum via a $\chi ^2$ metric across the entire type and redshift spaces to find the best-fitting solution. SDSS DR16 spectra are used as a reference data set to provide a fully self-consistent science test to show that our model achieves accuracy comparable to that of classical principal component analysis-based methods, and even better in some specific metrics, while maintaining significantly higher efficiency. In particular, the model achieves an average of $&amp;gt;98~{{\ \rm per\ cent}}$ classification accuracy across all classes, and redshift accuracies of over 99 per cent for stars, over 98 per cent for galaxies with errors of the order of $\mathcal {O}(10^{-4})$, and over 93 per cent for quasars with errors of the order of $\mathcal {O}(10^{-3})$. Tests on DESI spectra demonstrate that the model can generalize well to other surveys without retraining, with only a small degradation in performance. Furthermore, by comparing different peaks of $\chi ^2$ curves, we define a robustness measure that enables the identification of anomalous spectra. The GaSNet-III provides accurate and high-efficiency spectrum modelling to perform accurate redshift estimates and anomaly detection in vast data volumes from future spectroscopic sky surveys.

Developing a modern video player presents unique challenges, especially in the context of handling parallel code execution. In this paper, we will present the solution we have developed for this specific but widely applicable set of problems in modern software engineering and how we applied it to greatly improve on an already existing product, a video player, that was subject to the difficulties mentioned above.

The aim of this part is to show the difference between normal and abnormal findings in the angle. In addition grading systems and their therapeutic consequences will be discussed.Congenital disorders and acquired diseases affecting the eye and angle will be shown. The clinical findings will be discussed as well its context of recent advances in glaucoma surgery with microsurgical techniques requiring intraoperative gonioscopy for implants.

Land and real estate have long been regarded as stable investments, with property prices steadily rising, underscoring the need for accurate predictive models to capture the varying rates of price growth across different locations. This study leverages a decade-long dataset of 83,527 apartment transactions in Vienna, Austria, to train machine learning models using XGBoost. Unlike most prior research, the extended time span of the dataset enables predictions for multiple future years, providing a more robust long-term prediction. The primary objective is to examine how spatial factors can enhance real estate price predictions. In addition to transaction data, socio-demographic and geographic variables were collected to characterize the neighborhoods surrounding each apartment. Ten models, each varying in the number of input years, were trained to predict the price per square meter. The model performance was assessed using the mean absolute percentage error (MAPE), offering insights into their predictive accuracy for both short-term and long-term predictions. This study underscores the importance of distinguishing between newly built and existing apartments in real estate price modeling. By splitting the dataset prior to training, predictive models focusing solely on newly built properties achieved an average reduction of about 6% in MAPE. The best-performing models achieved an average MAPE of 15% for one-year-ahead predictions and maintained a MAPE below 20% for predictions up to three years ahead, demonstrating the effectiveness of leveraging spatial features to enhance real estate price prediction accuracy.

Background: Bipolar disorder (BD) is a severe psychiatric illness characterized by a chronic course and recurrent episodes, including residual depressive symptoms even during euthymic phases. These symptoms, although not meeting criteria for a depressive episode, are linked to relapse risk and impaired social functioning. This study aims to assess whether Virtual Reality Cognitive Remediation Training reduces depressive symptoms below the clinical threshold in individuals with BD. Methods: This post hoc analysis focuses on the secondary outcome (PHQ9) of a randomized-controlled trial. Participants were recruited from the Center of Liaison Psychiatry and Psychosomatics in Italy. The experimental group received Virtual Reality Cognitive Remediation, while the control group received standard treatment Results: Data from 39 individuals in the experimental group and 25 in the control group were analyzed. A greater reduction in PHQ-9 scores (>9) was observed in the experimental group (71.8% to 48.7%) compared to the control group. Significant improvements in total PHQ-9 scores and specific symptoms were noted in the experimental group compared to the control group. Conclusions: The study highlights the significant impact of virtual reality intervention on reducing depressive symptoms in bipolar disorder. This promising outcome underscores the potential preventive role of cognitive stimulation in relapse prevention. The intervention could offer valuable benefits for both treatment and prevention strategies in bipolar disorder.

Beside the examination of optic nerve head and retinal nerve fibre bundles, gonioscopy is the most important examinations we have to perform when performing structure analysis.The differentiation of angle closure, angle closure suspect and open angle is essential not only for the correct diagnosis, but as well to draw the best therapeutic consequences out of these findings.In addition to grading systems that help us to classify the types of glaucoma, we can find pathological changes like abnormal vessels, inflammatory induced changes, pigmentations, clefts etc. that help us to make the right diagnosis and therapy.Nowadays As‐OCT is a big help and makes it a lot easier, but there are still essential deficits, that do not allow to replace gonioscopy by this new examination.In the presentation we will show, what we can see in normal and pathological eyes and which findings can be seen in AS‐OCT and which ones cannot been seen.