Long non-coding RNA VPS9D1-AS1 enhances proliferation, invasion, and epithelial-mesenchymal transition in endometrial cancer via miR-377-3p/SGK1.

Endometrial cancer (EC) is a kind of gynecologic malignancy with a rising incidence rate. This study aimed to explore the role of VPS9D1 antisense RNA1 (VPS9D1-AS1) in EC. The expression of VPS9D1-AS1, microRNA (miR)-377-3p, and serum and glucocorticoid-regulated kinase 1 (SGK1) was detected by Quantitative Real-Time PCR (qRT-PCR). Cell proliferation, invasion and epithelial-mesenchymal transition (EMT) were determined by cell counting kit-8 (CCK-8), 5-Ethynyl-2'-Deoxyuridine (EdU) transwell, and western bolt. VPS9D1-AS1 was predicted to sponge miR-377-3p via Starbase, and verified by luciferase reporter, RNA binding protein immunoprecipitation (RIP), and RNA pull-down experiments. The clinical characteristics of VPS9D1-AS1, miR-377-3p, and SGK1 were analyzed. The role of VPS9D1-AS1 on EC tumorigenesis was assessed in xenografted nude mice. VPS9D1-AS1 was upregulated in EC cells and tissues. Interference of VPS9D1-AS1 inhibited growth, invasion, and EMT of EC cells. Mechanically, VPS9D1-AS1 was a molecular sponge of miR-377-3p, and overexpression of miR-377-3p reversed VPS9D1-AS1-induced EC cells proliferation, invasion, and EMT. Moreover, SGK1 was confirmed to bind with miR-377-3p. Furthermore, overexpression of SGK1 alleviated sh-VPS9D1-AS1-caused effects on EC cells. High level of VPS9D1-AS1 and SGK1, or low miR-377-3p expression predicted a poor prognosis. The expression of the three genes was correlated with lymph node metastasis, pathological stage, and International Federation of Gynecology and Obstetrics (FIGO) stage, but not associated with age, ER, and PR expression. Interestingly, knockdown of VPS9D1-AS1 suppressed EC tumor growth in mice. VPS9D1-AS1 promoted cell invasion, proliferation, and EMT via modulating miR-377-3p/SGK1 axis, which provided new options for therapeutic strategies of EC.

Design and Performance Evaluation of a Novel Vascular Robotic System for Complex Percutaneous Coronary Interventions.

The robotic-assisted percutaneous coronary intervention is an emerging technology with great potential to solve the shortcomings of existing treatments. However, the current robotic systems can not manipulate two guidewires or ballons/stents simultaneously for coronary bifurcation lesions. This paper presents VasCure, a novel bio-inspired vascular robotic system, to deliver two guidewires and stents into the main branch and side branch of bifurcation lesions in sequence. The system is designed in master-slave architecture to reduce occupational hazards of radiation exposure and orthopedic injury to interventional surgeons. The slave delivery device has one active roller and two passive rollers to manipulate two interventional devices. The performance of the VasCure was verified by in vitro and in vivo animal experiments. In vitro results showed the robotic system has good accuracy to deliver guidewires and the maximum error is 0.38mm. In an animal experiment, the interventional surgeon delivered two guidewires and balloons to the left circumflex branch and the left anterior descending branch of the pig, which confirmed the feasibility of the vascular robotic system.

Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge.

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization; algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts).

Pyramid attention recurrent networks for real-time guidewire segmentation and tracking in intraoperative X-ray fluoroscopy.

In endovascular and cardiovascular surgery, real-time and accurate segmentation and tracking of interventional instruments can aid in reducing radiation exposure, contrast agent and processing time. Nevertheless, this task often comes with the challenges of the elongated deformable structures with low contrast in noisy X-ray fluoroscopy. To address these issues, a novel efficient network architecture, termed pyramid attention recurrent networks (PAR-Net), is proposed for real-time guidewire segmentation and tracking. The proposed PAR-Net contains three major modules, namely pyramid attention module, recurrent residual module and pre-trained MobileNetV2 encoder. Specifically, a hybrid loss function of both reinforced focal loss and dice loss is proposed to better address the issues of class imbalance and misclassified examples. Quantitative and qualitative evaluations on clinical intraoperative images demonstrate that the proposed approach significantly outperforms simpler baselines as well as the best previously published result for this task, achieving the state-of-the-art performance.

RASNet: Segmentation for Tracking Surgical Instruments in Surgical Videos Using Refined Attention Segmentation Network.

Segmentation for tracking surgical instruments plays an important role in robot-assisted surgery. Segmentation of surgical instruments contributes to capturing accurate spatial information for tracking. In this paper, a novel network, Refined Attention Segmentation Network, is proposed to simultaneously segment surgical instruments and identify their categories. The U-shape network which is popular in segmentation is used. Different from previous work, an attention module is adopted to help the network focus on key regions, which can improve the segmentation accuracy. To solve the class imbalance problem, the weighted sum of the cross entropy loss and the logarithm of the Jaccard index is used as loss function. Furthermore, transfer learning is adopted in our network. The encoder is pre-trained on ImageNet. The dataset from the MICCAI EndoVis Challenge 2017 is used to evaluate our network. Based on this dataset, our network achieves state-of-the-art performance 94.65% mean Dice and 90.33% mean IOU.