Background: The optimal therapy for deep burn wounds is based on the early debridement of necrotic tissue followed by wound coverage to avoid a systemic inflammatory response and optimize scar-free healing. The outcomes are affected by available resources and underlying patient factors, which represent challenges in burn care and suboptimal outcomes. In this study, we aimed to determine optimal burn-wound management using enzymatic debridement (NexoBrid™, MediWound Germany GmbH, Rüsselsheim, Germany) and intact fish skin (Kerecis^® Omega3 Wound, Isafjordur, Iceland). Methods: In this retrospective case series, 12 patients with superficial or deep dermal burn wounds were treated with enzymatic debridement followed by fish skin, Suprathel^® (PolyMedics Innovations GmbH, Denkendorf, Germany), or a split-thickness skin graft (STSG). Patients’ outcomes regarding healing and scar quality were collected objectively and subjectively for 12 months after the burn injury. Results: Wounds treated with fish skin demonstrated accelerated wound healing, a significantly higher water-storage capacity, and better pain relief. Furthermore, improved functional and cosmetic outcomes, such as elasticity, skin thickness, and pigmentation, were demonstrated. The pain and itch expressed as POSAS scores (Patient and Observer Scar Assessment Scale) for fish skin decreased compared to those for wounds managed with an STSG or Suprathel. Importantly, fish skin-treated wounds had significantly improved sebum production and skin elasticity than those treated with Suprathel but showed no significant superiority compared to STSG-treated wounds. Conclusions: Enzymatic debridement in combination with intact fish skin grafts resulted in the faster healing of burn wounds and better functional and aesthetic outcomes than split-thickness skin grafts and Suprathel treatment.

The diagnosis and prognostic outcome of STS pose a therapeutic challenge in an interdisciplinary setting. The treatment protocols are still discussed controversially. This systematic meta-analysis aimed to determine prognostic factors leading to the development and recurrence of STS. Eligible studies that investigated potential risk factors such as smoking, genetic dispositions, toxins, chronic inflammation as well as prognostic relapse factors including radiation, chemotherapy and margins of resection were identified. Data from 24 studies published between 1993 and 2019 that comprised 6452 patients were pooled. A statistically significant effect developing STS was found in overall studies stating a causality between risk factors and the development of STS (p < 0.01). Although subgroup analysis did not meet statistical significances, it revealed a greater magnitude with smoking (p = 0.23), genetic predisposition (p = 0.13) chronic inflammation, (p = 0.20), and toxins (p = 0.14). Secondly, pooled analyses demonstrated a higher risk of relapse for margin of resection (p = 0.78), chemotherapy (p = 0.20) and radiation (p = 0.16); after 3 years of follow-up. Therefore, we were able to identify risk and relapse prognostic factors for STS, helping to diagnose and treat this low incidental cancer properly.

Sepsis is the leading cause of mortality in burn patients, yet early identification remains difficult due to persistent hyperinflammatory responses and altered baseline physiology. We developed a streamlined machine learning model for early sepsis risk prediction in burn patients using data from 6629 patients across 11 centers participating in the German Burn Registry. The model was trained using only six admission-level variables (age, burned body surface area, deep partial-thickness burns, full-thickness burns, inhalation injury, and hypertension), selected through multiple feature selection methods and evaluated using cross-validated machine learning pipelines. The final Random Forest model achieved an AUROC of 0.91, sensitivity of 0.81, specificity of 0.85, and a negative predictive value of 0.98, enabling reliable early risk stratification immediately upon ICU admission. By relying solely on admission-level variables, this model offers a reliable and interpretable solution for early sepsis risk detection in burn patients, supporting timely interventions and potentially improving critical care outcomes.

Introduction: soft tissue sarcomas are a subset of malignant tumors that are relatively rare and make up 1% of all malignant tumors in adulthood. Due to the rarity of these tumors, there are significant differences in quality in the diagnosis and treatment of these tumors. One paramount aspect is the diagnosis of hematogenous metastases in the lungs. Guidelines recommend routine lung imaging by means of X-rays. With the ever advancing AI-based diagnostic support, there has so far been no implementation for sarcomas. The aim of the study was to utilize AI to obtain analyzes regarding metastasis on lung X-rays in the most possible sensitive and specific manner in sarcoma patients. Methods: a Python script was created and trained using a set of lung X-rays with sarcoma metastases from a high-volume German-speaking sarcoma center. 26 patients with lung metastasis were included. For all patients chest X-ray with corresponding lung CT scans, and histological biopsies were available. The number of trainable images were expanded to 600. In order to evaluate the biological sensitivity and specificity, the script was tested on lung X-rays with a lung CT as control. Results: in this study we present a new type of convolutional neural network-based system with a precision of 71.2%, specificity of 90.5%, sensitivity of 94%, recall of 94% and accuracy of 91.2%. A good detection of even small findings was determined. Discussion: the created script establishes the option to check lung X-rays for metastases at a safe level, especially given this rare tumor entity.

Background and Objectives: Excessive social media use is repeatedly linked to negative body image outcomes, yet its association with muscle dysmorphia, especially in athletic youth, remains underexplored. We investigated how social media exposure, comparison behavior, and platform engagement relate to muscle dysmorphia symptomatology in young German athletes. Materials and Methods: An anonymous, web-based cross-sectional survey was conducted (July–October 2024) of 540 individuals (45% female; mean age = 24.6 ± 5.3 years; 79% ≥ 3 h sport/week) recruited via Instagram. The questionnaire comprised demographics, sport type, detailed social media usage metrics, and the validated German Muscle Dysmorphic Disorder Inventory (MDDI-1, 15 items). Correlations (Spearman’s ρ, Kendall’s τ) were calculated; multivariate importance was probed with classification-and-regression trees and CatBoost gradient boosting, interpreted via SHAP values. Results: Median daily social media time was 76 min (IQR 55–110). Participants who spent ≥ 60 min per day on social media showed higher MDDI scores (mean 38 ± 7 vs. 35 ± 6; p = 0.010). The strongest bivariate link emerged between perceived social media-induced body dissatisfaction and felt pressure to attain a specific body composition (Spearman ρ = 0.748, Kendall τ = 0.672, p < 0.001). A CatBoost gradient-boosting model out-performed linear regression in predicting elevated MDDI. The three most influential features (via SHAP values) were daily social media time, frequency of comparison with fitness influencers, and frequency of “likes”-seeking behavior. Conclusions: Intensive social media exposure substantially heightens muscle dysmorphia risk in young German athletes. Machine-learning interpretation corroborates time on social media and influencer comparisons as primary drivers. Interventions should combine social media literacy training with sport-specific psychoeducation to mitigate maladaptive comparison cycles and prevent downstream eating disorder pathology. Longitudinal research is warranted to clarify causal pathways and to test targeted digital media interventions.

Firework-related injuries remain a serious public health issue in Germany, especially during New Year’s Eve. While many injuries are minor, the misuse of illegal or homemade fireworks can cause severe trauma resembling military combat injuries and can heavily burden emergency services. Notably, injury rates declined during the COVID-19 firework bans, underscoring the impact of preventive measures. We report two cases of young males with severe injuries from illicit fireworks. The first is a case of a 16-year-old that detonated an illegal Polish firework ball bomb, sustaining 9% total body surface area (TBSA) burns (second- to third-degree), hand fractures, compartment syndrome of the hand, and soft-tissue trauma. He underwent multiple surgeries, including fasciotomy, osteosynthesis, and skin grafting. The other case presented is a 19-year-old man who was injured by a homemade device made of bundled firecrackers, suffering deep facial and bilateral hand burns. He required prolonged ventilation, surgical debridement, and treatment with Kerecis^® fish skin and Epicite^® dressings. Both required intensive ICU care, interdisciplinary management, and lengthy rehabilitation. Total hospital costs amounted to €58,459.52 and €94,230.23, respectively, as calculated according to the standardized German DRG. These cases illustrate the devastating impact of illegal fireworks. The devastating consequences of explosive trauma are often difficult to treat and may lead to long-term functional and psychological impairments. Prevention through public education, stricter regulations, and preparedness is essential. Pandemic-era injury reductions support sustained policy efforts.