Introduction: Many studies have started to search for the perfect aesthetic breast in order to create a pars-pro-toto for reconstruction, but especially for aesthetic surgery. To date, no representative study with anatomically accurate models was performed. Methods: In an online based United-States-census-representative survey with 1049 participants, questions regarding the preferred breast were asked utilizing lifelike morphed 3D-generated female models for the first time. Attributes such as breast pole ratio, areola size, breast direction and projection were asked. Results: The results show that, contrary to what has been claimed in previous studies, an upper-pole-to-lower-pole ratio of 55:45 is preferred by both female and male participants. When it comes to breast size, on the other hand, there are clear gender-specific differences. While women opted for a cup size around B, the men preferred larger cup sizes. Moreover, the smallest depicted areola size of 30 mm was favored among all groups in the survey. Discussion: Most publications used rather detrimental models for their surveys. We therefore opted for computer-generated 3D models and varied their naturalness. This enabled us to ensure a more aesthetic and accurate illustration and thus obtained more comparable and reliable results paired with the representation of the US-population. Taken together this study unveiled unexpected insights into the population favored breast attributes that might change operative planning in breast surgery. Level of Evidence V: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors http://www.springer.com/00266.

Ischemia reperfusion (IR) injury remains an important topic in clinical medicine. While a multitude of prophylactic and therapeutic strategies have been proposed, recent studies have illuminated protective effects of myostatin inhibition. This study aims to elaborate on the intracellular pathways involved in myostatin signaling and to explore key proteins that convey protective effects in IR injury. We used CRISPR/Cas9 gene editing to introduce a myostatin (Mstn) deletion into a C2C12 cell line. In subsequent experiments, we evaluated overall cell death, activation of apoptotic pathways, ROS generation, lipid peroxidation, intracellular signaling via mitogen-activated protein kinases (MAPKs), cell migration, and cell proliferation under hypoxic conditions followed by reoxygenation to simulate an IR situation in vitro (hypoxia reoxygenation). It was found that mitogen-activated protein kinase kinase 3/6, also known as MAPK/ERK Kinase 3/6 (MEK3/6), and subsequent p38 MAPK activation were blunted in C2C12-Mstn^−/− cells in response to hypoxia reoxygenation (HR). Similarly, c-Jun N-terminal kinase (JNK) activation was negated. We also found the intrinsic activation of apoptosis to be more important in comparison with the extrinsic activation. Additionally, intercepting myostatin signaling mitigated apoptosis activation. Ultimately, this research validated protective effects of myostatin inhibition in HR and identified potential mediators worth further investigation. Intercepting myostatin signaling did not inhibit ROS generation overall but mitigated cellular injury. In particular, intrinsic activation of apoptosis origination from mitochondria was alleviated. This was presumably mediated by decreased activation of p38 caused by the diminished kinase activity increase of MEK3/6. Overall, this work provides important insights into HR signaling in C2C12-Mstn^−/− cells and could serve as basis for further research.

The mortality of severely burned patients can be predicted by multiple scores which have been created over the last decades. As the treatment of burn injuries and intensive care management have improved immensely over the last years, former prediction scores seem to be losing accuracy in predicting survival. Therefore, various modifications of existing scores have been established and innovative scores have been introduced. In this study, we used data from the German Burn Registry and analyzed them regarding patient mortality using different methods of machine learning. We used Classification and Regression Trees (CARTs), random forests, XGBoost, and logistic regression regarding predictive features for patient mortality. Analyzing the data of 1401 patients via machine learning, the factors of full-thickness burns, patient’s age, and total burned surface area could be identified as the most important features regarding the prediction of patient mortality following burn trauma. Although the different methods identified similar aspects, application of machine learning shows that more data are necessary for a valid analysis. In the future, the usage of machine learning can contribute to the development of an innovative and precise predictive score in burn medicine and even to further interpretations of relevant data regarding different forms of outcome from the German Burn registry.

Burn mortality prediction remains a critical aspect in burn medicine. Established scores, such as the ABSI or Baux score, experience continuous revision and improvement due to advances in critical care and surgical procedures. However, these scores often rely on predefined variables and limited statistical models. This study aimed to create a new prediction score that is based solely on machine learning techniques and to assess its performance against established traditional scoring systems. Using different advanced machine learning methods, data from the German burn registry, encompassing over 10,000 cases, were analyzed regarding the most relevant factors concerning mortality and a new prediction score was created. A new prediction model was constructed, employing algorithms such as random forests and gradient boosting. Internal validation was conducted using cross-validation to ensure robustness and reproducibility. The Bochum Burn Survival (BoBS) score demonstrates strong predictive performance with an accuracy of 93.1 % and ROC AUC of 92.4 %, therefore surpassing traditional scores in predictive performance. Factors such as TBSA and age showed the strongest correlation with mortality, while comorbidities and treatment-specific variables contributed to model refinement. However, further adjustments and external validation beyond the German Burn Registry are crucial in the future. The BoBS score represents a paradigm shift in burn mortality prediction, leveraging the potential of machine learning to analyze complex, high-dimensional datasets. Compared to traditional models, the BoBS score offers improved accuracy while providing insights into underexplored variables that might impact patient outcomes. But challenges remain in integrating such models into clinical workflows and validating them across diverse populations. This score represents a significant advancement in burn mortality prediction by providing an interpretable, machine learning-based scoring system developed using multicenter data from the German Burn Registry. Its application has the potential to enhance decision-making in burn care, marking a significant step forward in personalized medicine for critically injured burn patients.

Introduction: Secondary lymphedema is a very common clinical issue with millions of patients suffering from pain, recurrent skin infections, and the constant need for a decongestive therapy. Well- established because of oncologic procedures, secondary lymphedema is also a well-known phenomenon after trauma. However, precise epidemiological data of posttraumatic lymphedema upon severe extremity injuries are rare. Methods: In the present study, we analyzed a patient cohort of 223 individuals who suffered closed fractures of the upper and lower extremity between 2016 and 2020. All of them simultaneously had a soft tissue injury, of 2nd and 3rd grade according to Tscherne classification. Typical symptoms of lymphedema were recorded in a retrospective cohort analysis through patient anamnesis and compared with documented clinical examination findings. Previous illnesses and trauma-specific characteristics were determined from patient files. Results: Of all patients, 36% showed symptoms of secondary lymphedema and 8,5% reported recurrent skin infections, indicating severe lymphedema. Furthermore, comparing patients with and without lymphedema, additional trauma-associated parameters, such as total number of surgeries, degree of soft tissue damage, localization of the fracture in lower extremity, related to lymphedema progress could be identified. Conclusion: According to these data, posttraumatic secondary lymphedema has even in closed fractures, especially of the lower extremity, a highly underestimated clinical prevalence. Further prospective studies are required to validate these findings, identify high-risk groups, and guide early prophylactic and therapeutic interventions.