Open Access

BACKGROUND Autologous fat grafting is widely used in reconstructive, aesthetic and regenerative surgery and often requires repeated applications. Cryopreservation of lipoaspirate enables autologous fat banking and off-the-shelf availability; however, its clinical implementation is limited by freezing-induced tissue injury, regulatory requirements and uncertainties regarding the optimal preservation protocol. Glycerol is a biocompatible cryoprotective agent with promising preliminary data. Nevertheless, the optimal concentration for lipoaspirate cryopreservation remains unknown. The aim of this study was to determine the optimal glycerol concentration for preservation of adipose tissue processed according to the Cell-Enriched Lipotransfer (CELT) protocol in clinically relevant volumes under GMP-compatible conditions. METHODS Lipoaspirates from 10 patients were processed by centrifugation according to the CELT protocol and allocated into experimental groups: fresh unfrozen control, frozen samples without cryoprotectant, frozen samples with PBS, and frozen samples supplemented with glycerol in concentrations ranging from 10% to 60%. Samples were cryopreserved using a controlled freezing rate at a temperature of -80 °C for 24 h. Large-volume cryopreservation was additionally performed with the best concentration of glycerol. Post-thaw tissue quality was assessed by resazurin assay of whole tissue, stromal vascular fraction (SVF) cell live/dead counting, and resazurin assay after short-term cell culture. RESULTS Glycerol supplementation improved post-thaw tissue viability compared with cryopreservation without cryoprotectant or with PBS alone. An optimal concentration range between 10% and 30% glycerol was identified, with highest preservation of metabolic activity and surviving cell yield observed at 20%. Higher glycerol concentrations resulted in a marked decline in tissue quality. Cryopreservation in large volume was feasible and did not impair post-thaw viability compared with small-volume samples. CONCLUSIONS Glycerol-based cryopreservation allows effective and GMP-compatible preservation of human lipoaspirate. An optimal glycerol concentration range was identified, enabling large-volume fat banking without compromising tissue quality. This protocol provides a clinically applicable strategy for autologous fat storage and may facilitate repeated reconstructive and regenerative treatments.

OBJECTIVE Since the first use of silicone implants by Cronin in 1962, there have been several attempts to introduce alternative filling materials for breast implants on the market. A promising new development are lightweight implants, whose filler material is one third lighter than conventional silicone gel. While these implants have been used primarily for aesthetic augmentation, a benefit could be expected particularly in post-mastectomy reconstruction. MATERIALS AND METHODS Since 2019, 92 operations using lightweight implants have been performed at our clinic, 61 of them for breast reconstruction after mastectomy. These have been compared to 92 breast reconstructions using conventional silicone implants. RESULTS The average volume of the lightweight implants was 30% higher than of the conventional implants (452 ml resp. 347 ml), whereas the implant weight was comparable in both groups (317 g resp. 347 g). Grade 3-4 capsular fibrosis was seen in 6 cases in both groups; revision was required 9 times (lightweight implants) and 7 times (conventional silicone implants) during the follow-up period. DISCUSSION To our knowledge, this is the first study to investigate the use of lightweight implants in breast reconstruction. With exception of the filler material, the implants used in the two groups were comparable in shape and surface. The inserted lightweight implants had a greater volume but nearly the same weight as the conventional implants and were used in patients with a higher body mass index. Thus, lightweight implants were preferred in patients whose reconstruction required a larger implant volume. CONCLUSION Lightweight implants are a new alternative for breast reconstruction especially in case that larger implant volume is demanded. The increased complication rate has to be verified in further studies.

Manual processing of lipoaspirate can enhance stem cell concentration, thereby improving the take rate, which still represents a major challenge in autologous fat transfer. However, since the preparation consists of many manual steps that are difficult to standardize, we investigated the influence of residual tumescent solution on the macroscopic and microscopic outcome of the mechanically processed lipoaspirate. Additionally, we investigated whether sedimentation followed by vacuum filtration of the aqueous phase could accelerate processing by replacing the initial centrifugation step. Samples with more than 5% remaining aqueous phase show no clearly defined oil phase, preventing any volume reduction. In contrast, all centrifuged samples produced a clear oil phase. The remaining tissue, as confirmed by both histology and viability assays, was superior to nanofat. Although sedimentation and filtration in the LipoCollector did not sufficiently separate enough aqueous phase from the lipoaspirate, tissue viability was significantly higher compared to our control container. Our findings indicate that centrifugation remains essential for effective aqueous phase separation and further mechanical processing, while the automatic filtration may enhance processing efficiency. These results indicate that further work is needed to simplify mechanical processing, as the outcome can be significantly influenced by parameters such as tumescent impurities.

Aims  Systematic visualization of anatomical landmarks is critical to prevent "blind spots" during esophagogastroduodenoscopy (EGD), which are a primary cause of missed upper gastrointestinal cancers. Standardized documentation serves as a secondary but essential tool for quality assurance. We aimed to develop a deep learning approach for the automated classification of 28 anatomical categories, including a 22-segment mapping of the stomach based on the Japanese Systemic Stomach Screening (SSS) protocol. The primary goal was to provide a technical foundation for real-time monitoring of mucosal coverage to ensure examination completeness. Primary outcomes were macro F1-score and overall accuracy for this 28-class classification task. Methods  We used a multi-center dataset of 9.012 endoscopic images from 1.553 patients, merging the public GastroHUN database (5.202 images; 385 patients) [1] and an internal dataset (3.810 images; 1.168 patients) from a European tertiary referral centre (Augsburg, Germany). All images were obtained during routine EGD using Olympus endoscopes and anonymized prior to analysis. Each image was labelled at image level into one of 28 anatomical classes: oropharynx, esophagus, esophagogastric junction, duodenal bulb, second part of the duodenum, papilla and 22 gastric segments based on the SSS classification using a consensus-based multi-annotator process (n=4). A trained and supervised specialist annotated additional data from Augsburg. To handle this complexity, we employed a Vision Transformer (ViT-L/14) foundation model pre-trained via DINO-v2. Instead of full-parameter fine-tuning, which risks overfitting on medical datasets, we implemented Low-Rank Adaptation (LoRA) [2]. By targeting only the Q- and V-projection layers in the final six transformer blocks, we reduced the trainable parameters to 312.000 (0.36% of the total model). Evaluation was performed using a strict patient-wise 5-fold cross-validation. Results  The model achieved a mean macro F1-score of 0.88±0.01 and an overall accuracy of 0.88±0.01. Beyond robustly identifying major regions like the esophagus and duodenum, the model showed high precision in the more challenging differentiation of fine-grained SSS segments. The use of LoRA enabled stable training and strong generalization across the combined international and local datasets ([Abb. 1]). Conclusions  Integrating transformer-based foundation models with LoRA-based fine-tuning allows for highly accurate UGIT anatomical mapping. An F1-score of 0.88 across 28 classes suggests that this approach is robust enough for automated documentation and real-time quality monitoring. Future work will focus on expanding the multi-center data volume and implementing expert consensus-labeling across all subsets to fürther refine model reliability, alongside developing a real-time visualization tool that provides an intuitive overview of the explored segments. To our knowledge, this is among the first studies to leverage a public endoscopy dataset together with institutional data for upper GI anatomical site classification.

This data article describes the Surgical Procedure Phase, Keypoint, and Instrument Recognition (PhaKIR) dataset, a collection of eight complete laparoscopic cholecystectomy videos acquired during routine minimally invasive procedures at three German medical centers. The recordings were captured with different monocular endoscopic camera systems at 25 frames per second and a resolution of 1920 × 1080 pixels, with durations ranging from 28 to 58 minutes. Segments showing regions outside the abdominal cavity were removed for anonymization, and the corresponding cut indices are provided. The dataset offers unified annotations for three interrelated tasks on the same complete surgical sequences: surgical phase recognition, annotated for every frame at 25 fps (485,875 frames), instrument keypoint estimation (19,435 frames), and instrument instance segmentation (19,435 frames), each annotated at one frame per second (every 25th frame). Surgical phases follow the seven-phase Cholec80 scheme, extended by an undefined label for transitional frames. The instrument annotations distinguish 19 instrument classes as well as individual instances of the same class, with keypoint annotations comprising two to four class-dependent points labeled with COCO-style visibility states. Segmentation and keypoint labels were created manually using the Computer Vision Annotation Tool (CVAT), whereas phase labels were derived from documented phase-transition timestamps, and all annotations underwent a multi-stage review by a medically trained team. The data are provided in open formats (MP4, CSV, JSON, PNG) together with a frame-extraction script under the CC BY-NC-SA license and are available through controlled access upon request via the Zenodo platform. Because the dataset combines procedural context, instrument pose, and pixel-accurate instance segmentations within full-length recordings from multiple institutions, it can be reused for single-task or multi-task model development, for temporally aware approaches that exploit motion continuity, and for cross-institutional generalization protocols such as leave-one-hospital-out evaluation. The dataset served as the training resource for the PhaKIR Challenge at the Endoscopic Vision (EndoVis) Challenge at MICCAI 2024.