Instrumental tactile diagnostics in robot-assisted surgery

Rozalia F Solodova,1,2 Vladimir V Galatenko,1,2 Eldar R Nakashidze,3 Igor L Andreytsev,3 Alexey V Galatenko,1 Dmitriy K Senchik,2 Vladimir M Staroverov,1 Vladimir E Podolskii,1,2 Mikhail E Sokolov,1,2 Victor A Sadovnichy1,2 1Faculty of Mechanics and Mathematics, 2Institute of Mathematical Studies of Complex Systems, Lomonosov Moscow State University, 31st Surgery Department, Clinical Hospital 31, Moscow, Russia Background: Robotic surgery has gained wide acceptance due to minimizing trauma in patients. However, the lack of tactile feedback is an essential limiting factor for the further expansion. In robotic surgery, feedback related to touch is currently kinesthetic, and it is mainly aimed at the minimization of force applied to tissues and organs. Design and implementation of diagnostic tactile feedback is still an open problem. We hypothesized that a sufficient tactile feedback in robot-assisted surgery can be provided by utilization of Medical Tactile Endosurgical Complex (MTEC), which is a novel specialized tool that is already commercially available in the Russian Federation. MTEC allows registration of tactile images by a mechanoreceptor, real-time visualization of these images, and reproduction of images via a tactile display. Materials and methods: Nine elective surgeries were performed with da Vinci™ robotic system. An assistant performed tactile examination through an additional port under the guidance of a surgeon during revision of tissues. The operating surgeon sensed registered tactile data using a tactile display, and the assistant inspected the visualization of tactile data. First, surgeries where lesion boundaries were visually detectable were performed. The goal was to promote cooperation between the surgeon and the assistant and to train them in perception of the tactile feedback. Then, instrumental tactile diagnostics was utilized in case of visually undetectable boundaries. Results: In robot-assisted surgeries where lesion boundaries were not visually detectable, instrumental tactile diagnostics performed using MTEC provided valid identification and localization of lesions. The results of instrumental tactile diagnostics were concordant with the results of intraoperative ultrasound examination. However, in certain cases, for example, thoracoscopy, ultrasound examination is inapplicable, while MTEC-based tactile diagnostics can be efficiently utilized. Conclusion: The study proved that MTEC can be efficiently used in robot-assisted surgery allowing correct localization of visually undetectable lesions and visually undetectable boundaries of pathological changes of tissues. Keywords: tactile feedback, instrumental palpation, Medical Tactile Endosurgical Complex, tactile lesion localizatio

Integrative topological analysis of mass spectrometry data reveals molecular features with clinical relevance in esophageal squamous cell carcinoma

Combining MS-based proteomic data with network and topological features of such network would identify more clinically relevant molecules and meaningfully expand the repertoire of proteins derived from MS analysis. The integrative topological indexes representing 95.96% information of seven individual topological measures of node proteins were calculated within a protein-protein interaction (PPI) network, built using 244 differentially expressed proteins (DEPs) identified by iTRAQ 2D-LC-MS/MS. Compared with DEPs, differentially expressed genes (DEGs) and comprehensive features (CFs), structurally dominant nodes (SDNs) based on integrative topological index distribution produced comparable classification performance in three different clinical settings using five independent gene expression data sets. The signature molecules of SDN-based classifier for distinction of early from late clinical TNM stages were enriched in biological traits of protein synthesis, intracellular localization and ribosome biogenesis, which suggests that ribosome biogenesis represents a promising therapeutic target for treating ESCC. In addition, ITGB1 expression selected exclusively by integrative topological measures correlated with clinical stages and prognosis, which was further validated with two independent cohorts of ESCC samples. Thus the integrative topological analysis of PPI networks proposed in this study provides an alternative approach to identify potential biomarkers and therapeutic targets from MS/MS data with functional insights in ESCC