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Introduction: A 2D point cloud registration method was developed to generate digital twins of different tissue shapes and resection cavities by applying a machine learning (ML) approach. This demonstrates the feasibility of quantifying soft tissue shifts. Methods: An ML model was trained using simulated surface scan data obtained from tumor resections in a pig head cadaver model. It hereby uses 438 2D scans of the tissue surfaces. tissue shift was induced by a temperature change from 7,91 ± 4,1°C to 36,37 ± 1,28 °C. Results: Digital twins were generated from various branched and compact resection cavities (RC) and cut tissues (CT). Temperature increase induced a tissue shift with a significant volume increase of 6 ml and 2 ml in branched and compact RC, respectively (p=0.0443; 0.0157). The volumes of branched and compact CT decreased by 3 and 4 ml (p Conclusions: The simulation experiment of induced soft tissue deformation using digital twins based on 2point cloud models proved that our method helps to quantify shape-dependent tissue shifts.

Purpose Augmented Reality can improve surgical planning and performance in parotid surgery. For easier application we implemented a voice control manual for our augmented reality system. The aim of the study was to evaluate the feasibility of the voice control in real-life situations. Methods We used the HoloLens 1® (Microsoft Corporation) with a special speech recognition software for parotid surgery. The evaluation took place in a audiometry cubicle and during real surgical procedures. Voice commands were used to display various 3D structures of the patient with the HoloLens 1®. Commands had different variations (male/female, 65 dB SPL)/louder, various structures). Results In silence, 100% of commands were recognized. If the volume of the operation room (OR) background noise exceeds 42 dB, the recognition rate decreases significantly, and it drops below 40% at > 60 dB SPL. With constant speech volume at 65 dB SPL male speakers had a significant better recognition rate than female speakers (p = 0.046). Higher speech volumes can compensate this effect. The recognition rate depends on the type of background noise. Mixed OR noise (52 dB(A)) reduced the detection rate significantly compared to single suction noise at 52 dB(A) (p ≤ 0.00001). The recognition rate was significantly better in the OR than in the audio cubicle (p = 0.00013 both genders, 0.0086 female, and 0.0036 male). Conclusions The recognition rate of voice commands can be enhanced by increasing the speech volume and by singularizing ambient noises. The detection rate depends on the loudness of the OR noise. Male voices are understood significantly better than female voices.

// Verena M. Throm 1 , David Mannle 1 , Thomas Giese 2 , Andrea S. Bauer 3 , Matthias M. Gaida 4 , Juergen Kopitz 4 , Thomas Bruckner 5 , Konstanze Plaschke 1 , Svetlana P. Grekova 1 , Klaus Felix 1 , Thilo Hackert 1 , Nathalia A. Giese 1 and Oliver Strobel 1 1 European Pancreas Centre/EPZ, Department of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, Heidelberg, Germany 2 Institute of Immunology, University Hospital Heidelberg, Heidelberg, Germany 3 Department of Functional Genomics, DKFZ, Heidelberg, Germany 4 Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany 5 Institute of Medical Biometry and Informatics/IMBI, University Hospital Heidelberg, Heidelberg, Germany Correspondence to: Nathalia A. Giese, email: nathalia.giese@med.uni-heidelberg.de Keywords: SLURP1; CHRNA7; pancreatic cancer; nicotine Received: March 13, 2017 Accepted: December 05, 2017 Published: January 24, 2018 ABSTRACT Smoking is associated with increased risk and poorer prognosis of pancreatic ductal adenocarcinoma (PDAC). Nicotine acts through cholinergic nicotinic receptors, preferentially α7 (CHRNA7) that also binds the endogenous ligand SLURP1 (Secreted Ly-6/uPAR-Related Protein 1). The clinical significance of SLURP1 and its interaction with nicotine in PDAC are unclear. We detected similar levels of SLURP1 in sera from healthy donors and patients with chronic pancreatitis or PDAC; higher preoperative values were associated with significantly better survival in patients with resected tumors. Pancreatic tissue was not a source of circulating SLURP1 but contained diverse CHRNA7-expressing cells, preferentially epithelial and immune, whereas stromal stellate cells and a quarter of the tumor cells lacked CHRNA7. The CHRNA7 mRNA levels were decreased in PDAC, and CHRNA7 high -PDAC patients lived longer. In CHRNA7 high COLO357 and PANC-1 cultures, opposing activities of SLURP1 (anti-malignant/CHRNA7-dependent) and nicotine (pro-malignant/CHRNA7-infidel) were exerted without reciprocally interfering with receptor binding or downstream signaling. These data suggested that the ligands act independently and abolish each other’s effects through a mechanism resembling functional antagonism. Thus, SLURP1 might represent an inborn anti-PDAC defense being sensitive to and counteracting nicotine. Boosting SLURP1-CHRNA7 interaction might represent a novel strategy for treatment in high-risk individuals, i.e., smokers with pancreatic cancer.

Background/objectives Meaningful profiling of pancreatic cancer samples is particularly challenging due to their complex cellular composition. Beyond tumor cells, surgical biopsies contain desmoplastic stroma with infiltrating inflammatory cells, adjacent normal parenchyma, and "non-pancreatic tissues". The risk of misinterpretation rises when the heterogeneous cancer tissues are sub-divided into smaller fragments for multiple analytic procedures. Pre-analytic histological evaluation is the best option to characterize pancreatic tissue samples. Our aim was to develop a complement or alternative procedure to determine the cellular composition of pancreatic cancerous biopsies, basing on intra-analytic molecular annotation. A standard process for sample stratification at a molecular level does not yet exist. Particularly in the case of retrospective or data depository-based studies, when hematoxylin-eosin stained sections are not available, it supports the correct interpretation of expression profiles. Methods A five-gene transcriptional signature ( RNA CellStrat) was defined that allows cell type-specific stratification of pancreatic tissues. Testing biopsy material from biobanks with this procedure demonstrated high correspondence of molecular (qRT-PCR and microarray) and histologic (hematoxylin-eosin stain) evaluations. Results Notably, about a quarter of randomly selected samples (tissue fragments) were exposed as inappropriate for subsequent clinico-pathological interpretation. Conclusions Via immediate intra-analytical procedure, our RNA-based stratification RNA CellStrat increases the accuracy and reliability of the conclusions drawn from diagnostic and prognostic molecular information.